+
+
+ 1# mypy: disable-error-code="call-arg" + 2# Copyright (c) Shipt, Inc. + 3# This source code is licensed under the MIT license found in the + 4# LICENSE file in the root directory of this source tree. + 5 + 6import os + 7from typing import Any, Dict, List, Optional, cast + 8 + 9import yaml + 10from pydantic import BaseModel, ConfigDict, SerializeAsAny, model_validator + 11from rich.console import Console + 12from rich.table import Table + 13 + 14from opsml.cards.base import ArtifactCard + 15from opsml.helpers.logging import ArtifactLogger + 16from opsml.types import ( + 17 AuditCardMetadata, + 18 AuditSectionType, + 19 CardType, + 20 CardVersion, + 21 Comment, + 22) + 23 + 24logger = ArtifactLogger.get_logger() + 25DIR_PATH = os.path.dirname(__file__) + 26AUDIT_TEMPLATE_PATH = os.path.join(DIR_PATH, "templates/audit_card.yaml") + 27 + 28 + 29# create new python class that inherits from ArtifactCard and is called AuditCard + 30class Question(BaseModel): + 31 question: str + 32 purpose: str + 33 response: Optional[str] = None + 34 + 35 model_config = ConfigDict(frozen=False) + 36 + 37 + 38class AuditSections(BaseModel): + 39 business_understanding: Dict[int, SerializeAsAny[Question]] + 40 data_understanding: Dict[int, SerializeAsAny[Question]] + 41 data_preparation: Dict[int, SerializeAsAny[Question]] + 42 modeling: Dict[int, SerializeAsAny[Question]] + 43 evaluation: Dict[int, SerializeAsAny[Question]] + 44 deployment_ops: Dict[int, SerializeAsAny[Question]] + 45 misc: Dict[int, SerializeAsAny[Question]] + 46 + 47 @model_validator(mode="before") + 48 @classmethod + 49 def load_sections(cls, values: Dict[str, Any]) -> Dict[str, Any]: + 50 """Loads audit sections from template if no values are provided""" + 51 + 52 if any(values): + 53 return values + 54 return cls.load_yaml_template() + 55 + 56 @staticmethod + 57 def load_yaml_template() -> AuditSectionType: + 58 with open(AUDIT_TEMPLATE_PATH, "r", encoding="utf-8") as stream: + 59 try: + 60 audit_sections = cast(AuditSectionType, yaml.safe_load(stream)) + 61 except yaml.YAMLError as exc: + 62 raise exc + 63 return audit_sections + 64 + 65 + 66class AuditQuestionTable: + 67 """Helper class for creating a rich table to be used with an AuditCard""" + 68 + 69 def __init__(self) -> None: + 70 self.table = self.create_table() + 71 + 72 def create_table(self) -> Table: + 73 """Create Rich table of Audit""" + 74 table = Table(title="Audit Questions") + 75 table.add_column("Section", no_wrap=True) + 76 table.add_column("Number") + 77 table.add_column("Question") + 78 table.add_column("Answered", justify="right") + 79 return table + 80 + 81 def add_row(self, section_name: str, nbr: int, question: Question) -> None: + 82 """Add row to table""" + 83 self.table.add_row( + 84 section_name, + 85 str(nbr), + 86 question.question, + 87 "Yes" if question.response else "No", + 88 ) + 89 + 90 def add_section(self) -> None: + 91 """Add section""" + 92 self.table.add_section() + 93 + 94 def print_table(self) -> None: + 95 """Print table""" + 96 console = Console() + 97 console.print(self.table) + 98 + 99 +100class AuditCard(ArtifactCard): +101 """ +102 Creates an AuditCard for storing audit-related information about a +103 machine learning project. +104 +105 Args: +106 name: +107 What to name the AuditCard +108 repository: +109 Repository that this card is associated with +110 contact: +111 Contact to associate with the AuditCard +112 info: +113 `CardInfo` object containing additional metadata. If provided, it will override any +114 values provided for `name`, `repository`, `contact`, and `version`. +115 +116 Name, repository, and contact are required arguments for all cards. They can be provided +117 directly or through a `CardInfo` object. +118 +119 audit: +120 AuditSections object containing the audit questions and responses +121 approved: +122 Whether the audit has been approved +123 """ +124 +125 audit: AuditSections = AuditSections() +126 approved: bool = False +127 comments: List[SerializeAsAny[Comment]] = [] +128 metadata: AuditCardMetadata = AuditCardMetadata() +129 +130 def add_comment(self, name: str, comment: str) -> None: +131 """Adds comment to AuditCard +132 +133 Args: +134 name: +135 Name of person making comment +136 comment: +137 Comment to add +138 +139 """ +140 comment_model = Comment(name=name, comment=comment) +141 +142 if any(comment_model == _comment for _comment in self.comments): +143 return # Exit early if comment already exists +144 +145 self.comments.insert(0, comment_model) +146 +147 def create_registry_record(self) -> Dict[str, Any]: +148 """Creates a registry record for a audit""" +149 +150 return self.model_dump() +151 +152 def add_card(self, card: ArtifactCard) -> None: +153 """ +154 Adds a card uid to the appropriate card uid list for tracking +155 +156 Args: +157 card: +158 Card to add to AuditCard +159 """ +160 if card.uid is None: +161 raise ValueError( +162 f"""Card uid must be provided for {card.card_type}. +163 Uid must be registered prior to adding to AuditCard.""" +164 ) +165 +166 if card.card_type.lower() not in [ +167 CardType.DATACARD.value, +168 CardType.MODELCARD.value, +169 CardType.RUNCARD.value, +170 ]: +171 raise ValueError(f"Invalid card type {card.card_type}. Valid card types are: data, model or run") +172 +173 card_list = getattr(self.metadata, f"{card.card_type.lower()}cards") +174 card_list.append(CardVersion(name=card.name, version=card.version, card_type=card.card_type)) +175 +176 @property +177 def business(self) -> Dict[int, Question]: +178 return self.audit.business_understanding +179 +180 @property +181 def data_understanding(self) -> Dict[int, Question]: +182 return self.audit.data_understanding +183 +184 @property +185 def data_preparation(self) -> Dict[int, Question]: +186 return self.audit.data_preparation +187 +188 @property +189 def modeling(self) -> Dict[int, Question]: +190 return self.audit.modeling +191 +192 @property +193 def evaluation(self) -> Dict[int, Question]: +194 return self.audit.evaluation +195 +196 @property +197 def deployment(self) -> Dict[int, Question]: +198 return self.audit.deployment_ops +199 +200 @property +201 def misc(self) -> Dict[int, Question]: +202 return self.audit.misc +203 +204 def list_questions(self, section: Optional[str] = None) -> None: +205 """Lists all Audit Card questions in a rich table +206 +207 Args: +208 section: +209 Section name. Can be one of: business, data_understanding, data_preparation, modeling, +210 evaluation or misc +211 """ +212 +213 table = AuditQuestionTable() +214 +215 if section is not None: +216 questions = self._get_section(section) +217 for nbr, question in questions.items(): +218 table.add_row(section_name=section, nbr=nbr, question=question) +219 +220 else: +221 for _section in self.audit: +222 section_name, questions = _section +223 for nbr, question in questions.items(): +224 table.add_row(section_name=section_name, nbr=nbr, question=question) +225 +226 table.add_section() +227 +228 table.print_table() +229 +230 def _get_section(self, section: str) -> Dict[int, Question]: +231 """Gets a section from the audit card +232 +233 Args: +234 section: +235 Section name. Can be one of: business, data_understanding, data_preparation, modeling, +236 evaluation or misc +237 Returns: +238 Dict[int, Question]: A dictionary of questions +239 """ +240 +241 if not hasattr(self, section): +242 raise ValueError( +243 f"""Section {section} not found. Accepted values are: business, data_understanding, +244 data_preparation, modeling, evaluation, deployment or misc""" +245 ) +246 _section: Dict[int, Question] = getattr(self, section) +247 return _section +248 +249 def answer_question(self, section: str, question_nbr: int, response: str) -> None: +250 """Answers a question in a section +251 +252 Args: +253 section: +254 Section name. Can be one of: business, data_understanding, data_preparation, modeling, evaluation, +255 deployment or misc +256 question_nbr: +257 Question number +258 response: +259 Response to the question +260 +261 """ +262 +263 _section: Dict[int, Question] = self._get_section(section) +264 +265 try: +266 _section[question_nbr].response = response +267 except KeyError as exc: +268 logger.error("Question {} not found in section {}", question_nbr, section) +269 raise exc +270 +271 @property +272 def card_type(self) -> str: +273 return CardType.AUDITCARD.value +
31class Question(BaseModel): +32 question: str +33 purpose: str +34 response: Optional[str] = None +35 +36 model_config = ConfigDict(frozen=False) +
Usage docs: https://docs.pydantic.dev/2.6/concepts/models/
+ +A base class for creating Pydantic models.
+ +__init__ function.Model.__validators__ and Model.__root_validators__ from Pydantic V1.RootModel.model_config['extra'] == 'allow'.39class AuditSections(BaseModel): +40 business_understanding: Dict[int, SerializeAsAny[Question]] +41 data_understanding: Dict[int, SerializeAsAny[Question]] +42 data_preparation: Dict[int, SerializeAsAny[Question]] +43 modeling: Dict[int, SerializeAsAny[Question]] +44 evaluation: Dict[int, SerializeAsAny[Question]] +45 deployment_ops: Dict[int, SerializeAsAny[Question]] +46 misc: Dict[int, SerializeAsAny[Question]] +47 +48 @model_validator(mode="before") +49 @classmethod +50 def load_sections(cls, values: Dict[str, Any]) -> Dict[str, Any]: +51 """Loads audit sections from template if no values are provided""" +52 +53 if any(values): +54 return values +55 return cls.load_yaml_template() +56 +57 @staticmethod +58 def load_yaml_template() -> AuditSectionType: +59 with open(AUDIT_TEMPLATE_PATH, "r", encoding="utf-8") as stream: +60 try: +61 audit_sections = cast(AuditSectionType, yaml.safe_load(stream)) +62 except yaml.YAMLError as exc: +63 raise exc +64 return audit_sections +
Usage docs: https://docs.pydantic.dev/2.6/concepts/models/
+ +A base class for creating Pydantic models.
+ +__init__ function.Model.__validators__ and Model.__root_validators__ from Pydantic V1.RootModel.model_config['extra'] == 'allow'.48 @model_validator(mode="before") +49 @classmethod +50 def load_sections(cls, values: Dict[str, Any]) -> Dict[str, Any]: +51 """Loads audit sections from template if no values are provided""" +52 +53 if any(values): +54 return values +55 return cls.load_yaml_template() +
Loads audit sections from template if no values are provided
+67class AuditQuestionTable: +68 """Helper class for creating a rich table to be used with an AuditCard""" +69 +70 def __init__(self) -> None: +71 self.table = self.create_table() +72 +73 def create_table(self) -> Table: +74 """Create Rich table of Audit""" +75 table = Table(title="Audit Questions") +76 table.add_column("Section", no_wrap=True) +77 table.add_column("Number") +78 table.add_column("Question") +79 table.add_column("Answered", justify="right") +80 return table +81 +82 def add_row(self, section_name: str, nbr: int, question: Question) -> None: +83 """Add row to table""" +84 self.table.add_row( +85 section_name, +86 str(nbr), +87 question.question, +88 "Yes" if question.response else "No", +89 ) +90 +91 def add_section(self) -> None: +92 """Add section""" +93 self.table.add_section() +94 +95 def print_table(self) -> None: +96 """Print table""" +97 console = Console() +98 console.print(self.table) +
Helper class for creating a rich table to be used with an AuditCard
+73 def create_table(self) -> Table: +74 """Create Rich table of Audit""" +75 table = Table(title="Audit Questions") +76 table.add_column("Section", no_wrap=True) +77 table.add_column("Number") +78 table.add_column("Question") +79 table.add_column("Answered", justify="right") +80 return table +
Create Rich table of Audit
+82 def add_row(self, section_name: str, nbr: int, question: Question) -> None: +83 """Add row to table""" +84 self.table.add_row( +85 section_name, +86 str(nbr), +87 question.question, +88 "Yes" if question.response else "No", +89 ) +
Add row to table
+101class AuditCard(ArtifactCard): +102 """ +103 Creates an AuditCard for storing audit-related information about a +104 machine learning project. +105 +106 Args: +107 name: +108 What to name the AuditCard +109 repository: +110 Repository that this card is associated with +111 contact: +112 Contact to associate with the AuditCard +113 info: +114 `CardInfo` object containing additional metadata. If provided, it will override any +115 values provided for `name`, `repository`, `contact`, and `version`. +116 +117 Name, repository, and contact are required arguments for all cards. They can be provided +118 directly or through a `CardInfo` object. +119 +120 audit: +121 AuditSections object containing the audit questions and responses +122 approved: +123 Whether the audit has been approved +124 """ +125 +126 audit: AuditSections = AuditSections() +127 approved: bool = False +128 comments: List[SerializeAsAny[Comment]] = [] +129 metadata: AuditCardMetadata = AuditCardMetadata() +130 +131 def add_comment(self, name: str, comment: str) -> None: +132 """Adds comment to AuditCard +133 +134 Args: +135 name: +136 Name of person making comment +137 comment: +138 Comment to add +139 +140 """ +141 comment_model = Comment(name=name, comment=comment) +142 +143 if any(comment_model == _comment for _comment in self.comments): +144 return # Exit early if comment already exists +145 +146 self.comments.insert(0, comment_model) +147 +148 def create_registry_record(self) -> Dict[str, Any]: +149 """Creates a registry record for a audit""" +150 +151 return self.model_dump() +152 +153 def add_card(self, card: ArtifactCard) -> None: +154 """ +155 Adds a card uid to the appropriate card uid list for tracking +156 +157 Args: +158 card: +159 Card to add to AuditCard +160 """ +161 if card.uid is None: +162 raise ValueError( +163 f"""Card uid must be provided for {card.card_type}. +164 Uid must be registered prior to adding to AuditCard.""" +165 ) +166 +167 if card.card_type.lower() not in [ +168 CardType.DATACARD.value, +169 CardType.MODELCARD.value, +170 CardType.RUNCARD.value, +171 ]: +172 raise ValueError(f"Invalid card type {card.card_type}. Valid card types are: data, model or run") +173 +174 card_list = getattr(self.metadata, f"{card.card_type.lower()}cards") +175 card_list.append(CardVersion(name=card.name, version=card.version, card_type=card.card_type)) +176 +177 @property +178 def business(self) -> Dict[int, Question]: +179 return self.audit.business_understanding +180 +181 @property +182 def data_understanding(self) -> Dict[int, Question]: +183 return self.audit.data_understanding +184 +185 @property +186 def data_preparation(self) -> Dict[int, Question]: +187 return self.audit.data_preparation +188 +189 @property +190 def modeling(self) -> Dict[int, Question]: +191 return self.audit.modeling +192 +193 @property +194 def evaluation(self) -> Dict[int, Question]: +195 return self.audit.evaluation +196 +197 @property +198 def deployment(self) -> Dict[int, Question]: +199 return self.audit.deployment_ops +200 +201 @property +202 def misc(self) -> Dict[int, Question]: +203 return self.audit.misc +204 +205 def list_questions(self, section: Optional[str] = None) -> None: +206 """Lists all Audit Card questions in a rich table +207 +208 Args: +209 section: +210 Section name. Can be one of: business, data_understanding, data_preparation, modeling, +211 evaluation or misc +212 """ +213 +214 table = AuditQuestionTable() +215 +216 if section is not None: +217 questions = self._get_section(section) +218 for nbr, question in questions.items(): +219 table.add_row(section_name=section, nbr=nbr, question=question) +220 +221 else: +222 for _section in self.audit: +223 section_name, questions = _section +224 for nbr, question in questions.items(): +225 table.add_row(section_name=section_name, nbr=nbr, question=question) +226 +227 table.add_section() +228 +229 table.print_table() +230 +231 def _get_section(self, section: str) -> Dict[int, Question]: +232 """Gets a section from the audit card +233 +234 Args: +235 section: +236 Section name. Can be one of: business, data_understanding, data_preparation, modeling, +237 evaluation or misc +238 Returns: +239 Dict[int, Question]: A dictionary of questions +240 """ +241 +242 if not hasattr(self, section): +243 raise ValueError( +244 f"""Section {section} not found. Accepted values are: business, data_understanding, +245 data_preparation, modeling, evaluation, deployment or misc""" +246 ) +247 _section: Dict[int, Question] = getattr(self, section) +248 return _section +249 +250 def answer_question(self, section: str, question_nbr: int, response: str) -> None: +251 """Answers a question in a section +252 +253 Args: +254 section: +255 Section name. Can be one of: business, data_understanding, data_preparation, modeling, evaluation, +256 deployment or misc +257 question_nbr: +258 Question number +259 response: +260 Response to the question +261 +262 """ +263 +264 _section: Dict[int, Question] = self._get_section(section) +265 +266 try: +267 _section[question_nbr].response = response +268 except KeyError as exc: +269 logger.error("Question {} not found in section {}", question_nbr, section) +270 raise exc +271 +272 @property +273 def card_type(self) -> str: +274 return CardType.AUDITCARD.value +
Creates an AuditCard for storing audit-related information about a +machine learning project.
+ +info: CardInfo object containing additional metadata. If provided, it will override any
+values provided for name, repository, contact, and version.
Name, repository, and contact are required arguments for all cards. They can be provided
+directly or through a CardInfo object.
131 def add_comment(self, name: str, comment: str) -> None: +132 """Adds comment to AuditCard +133 +134 Args: +135 name: +136 Name of person making comment +137 comment: +138 Comment to add +139 +140 """ +141 comment_model = Comment(name=name, comment=comment) +142 +143 if any(comment_model == _comment for _comment in self.comments): +144 return # Exit early if comment already exists +145 +146 self.comments.insert(0, comment_model) +
Adds comment to AuditCard
+ +148 def create_registry_record(self) -> Dict[str, Any]: +149 """Creates a registry record for a audit""" +150 +151 return self.model_dump() +
Creates a registry record for a audit
+153 def add_card(self, card: ArtifactCard) -> None: +154 """ +155 Adds a card uid to the appropriate card uid list for tracking +156 +157 Args: +158 card: +159 Card to add to AuditCard +160 """ +161 if card.uid is None: +162 raise ValueError( +163 f"""Card uid must be provided for {card.card_type}. +164 Uid must be registered prior to adding to AuditCard.""" +165 ) +166 +167 if card.card_type.lower() not in [ +168 CardType.DATACARD.value, +169 CardType.MODELCARD.value, +170 CardType.RUNCARD.value, +171 ]: +172 raise ValueError(f"Invalid card type {card.card_type}. Valid card types are: data, model or run") +173 +174 card_list = getattr(self.metadata, f"{card.card_type.lower()}cards") +175 card_list.append(CardVersion(name=card.name, version=card.version, card_type=card.card_type)) +
Adds a card uid to the appropriate card uid list for tracking
+ +205 def list_questions(self, section: Optional[str] = None) -> None: +206 """Lists all Audit Card questions in a rich table +207 +208 Args: +209 section: +210 Section name. Can be one of: business, data_understanding, data_preparation, modeling, +211 evaluation or misc +212 """ +213 +214 table = AuditQuestionTable() +215 +216 if section is not None: +217 questions = self._get_section(section) +218 for nbr, question in questions.items(): +219 table.add_row(section_name=section, nbr=nbr, question=question) +220 +221 else: +222 for _section in self.audit: +223 section_name, questions = _section +224 for nbr, question in questions.items(): +225 table.add_row(section_name=section_name, nbr=nbr, question=question) +226 +227 table.add_section() +228 +229 table.print_table() +
Lists all Audit Card questions in a rich table
+ +250 def answer_question(self, section: str, question_nbr: int, response: str) -> None: +251 """Answers a question in a section +252 +253 Args: +254 section: +255 Section name. Can be one of: business, data_understanding, data_preparation, modeling, evaluation, +256 deployment or misc +257 question_nbr: +258 Question number +259 response: +260 Response to the question +261 +262 """ +263 +264 _section: Dict[int, Question] = self._get_section(section) +265 +266 try: +267 _section[question_nbr].response = response +268 except KeyError as exc: +269 logger.error("Question {} not found in section {}", question_nbr, section) +270 raise exc +
Answers a question in a section
+ +1# Copyright (c) Shipt, Inc. + 2# This source code is licensed under the MIT license found in the + 3# LICENSE file in the root directory of this source tree. + 4import os + 5from pathlib import Path + 6from typing import Any, Dict, Optional + 7 + 8from pydantic import BaseModel, ConfigDict, model_validator + 9 + 10from opsml.helpers.logging import ArtifactLogger + 11from opsml.helpers.utils import clean_string, validate_name_repository_pattern + 12from opsml.settings.config import config + 13from opsml.types import CardInfo, CommonKwargs, RegistryTableNames + 14 + 15logger = ArtifactLogger.get_logger() + 16 + 17 + 18class ArtifactCard(BaseModel): + 19 """Base pydantic class for artifact cards""" + 20 + 21 model_config = ConfigDict( + 22 arbitrary_types_allowed=True, + 23 validate_assignment=False, + 24 validate_default=True, + 25 ) + 26 + 27 name: str = CommonKwargs.UNDEFINED.value + 28 repository: str = CommonKwargs.UNDEFINED.value + 29 contact: str = CommonKwargs.UNDEFINED.value + 30 version: str = CommonKwargs.BASE_VERSION.value + 31 uid: Optional[str] = None + 32 info: Optional[CardInfo] = None + 33 tags: Dict[str, str] = {} + 34 + 35 @model_validator(mode="before") + 36 @classmethod + 37 def validate_args(cls, card_args: Dict[str, Any]) -> Dict[str, Any]: # pylint: disable=arguments-renamed + 38 """Validate base args and Lowercase name and repository + 39 + 40 Args: + 41 card_args: + 42 named args passed to card + 43 + 44 Returns: + 45 validated card_args + 46 """ + 47 card_info = card_args.get("info") + 48 + 49 for key in ["name", "repository", "contact", "version", "uid"]: + 50 # check card args + 51 val = card_args.get(key) + 52 + 53 # check card info + 54 if card_info is not None: + 55 val = val or getattr(card_info, key) + 56 + 57 # check runtime env vars + 58 if val is None: + 59 val = os.environ.get(f"OPSML_RUNTIME_{key.upper()}") + 60 + 61 if key in ["name", "repository"]: + 62 if val is not None: + 63 val = clean_string(val) + 64 + 65 if key == "version" and val is None: + 66 val = CommonKwargs.BASE_VERSION.value + 67 + 68 card_args[key] = val + 69 + 70 # need to check that name, repository and contact are set + 71 if not all(card_args[key] for key in ["name", "repository", "contact"]): + 72 raise ValueError("name, repository and contact must be set either as named arguments or through CardInfo") + 73 + 74 # validate name and repository for pattern + 75 validate_name_repository_pattern(name=card_args["name"], repository=card_args["repository"]) + 76 + 77 return card_args + 78 + 79 def create_registry_record(self) -> Dict[str, Any]: + 80 """Creates a registry record from self attributes""" + 81 raise NotImplementedError + 82 + 83 def add_tag(self, key: str, value: str) -> None: + 84 self.tags[key] = str(value) + 85 + 86 @property + 87 def uri(self) -> Path: + 88 """The base URI to use for the card and it's artifacts.""" + 89 if self.version == CommonKwargs.BASE_VERSION.value: + 90 raise ValueError("Could not create card uri - version is not set") + 91 + 92 assert self.repository is not None, "Repository must be set" + 93 assert self.name is not None, "Name must be set" + 94 + 95 return Path( + 96 config.storage_root, + 97 RegistryTableNames.from_str(self.card_type).value, + 98 self.repository, + 99 self.name, +100 f"v{self.version}", +101 ) +102 +103 @property +104 def artifact_uri(self) -> Path: +105 """Returns the root URI to which artifacts associated with this card should be saved.""" +106 return self.uri / "artifacts" +107 +108 @property +109 def card_type(self) -> str: +110 raise NotImplementedError +
19class ArtifactCard(BaseModel): + 20 """Base pydantic class for artifact cards""" + 21 + 22 model_config = ConfigDict( + 23 arbitrary_types_allowed=True, + 24 validate_assignment=False, + 25 validate_default=True, + 26 ) + 27 + 28 name: str = CommonKwargs.UNDEFINED.value + 29 repository: str = CommonKwargs.UNDEFINED.value + 30 contact: str = CommonKwargs.UNDEFINED.value + 31 version: str = CommonKwargs.BASE_VERSION.value + 32 uid: Optional[str] = None + 33 info: Optional[CardInfo] = None + 34 tags: Dict[str, str] = {} + 35 + 36 @model_validator(mode="before") + 37 @classmethod + 38 def validate_args(cls, card_args: Dict[str, Any]) -> Dict[str, Any]: # pylint: disable=arguments-renamed + 39 """Validate base args and Lowercase name and repository + 40 + 41 Args: + 42 card_args: + 43 named args passed to card + 44 + 45 Returns: + 46 validated card_args + 47 """ + 48 card_info = card_args.get("info") + 49 + 50 for key in ["name", "repository", "contact", "version", "uid"]: + 51 # check card args + 52 val = card_args.get(key) + 53 + 54 # check card info + 55 if card_info is not None: + 56 val = val or getattr(card_info, key) + 57 + 58 # check runtime env vars + 59 if val is None: + 60 val = os.environ.get(f"OPSML_RUNTIME_{key.upper()}") + 61 + 62 if key in ["name", "repository"]: + 63 if val is not None: + 64 val = clean_string(val) + 65 + 66 if key == "version" and val is None: + 67 val = CommonKwargs.BASE_VERSION.value + 68 + 69 card_args[key] = val + 70 + 71 # need to check that name, repository and contact are set + 72 if not all(card_args[key] for key in ["name", "repository", "contact"]): + 73 raise ValueError("name, repository and contact must be set either as named arguments or through CardInfo") + 74 + 75 # validate name and repository for pattern + 76 validate_name_repository_pattern(name=card_args["name"], repository=card_args["repository"]) + 77 + 78 return card_args + 79 + 80 def create_registry_record(self) -> Dict[str, Any]: + 81 """Creates a registry record from self attributes""" + 82 raise NotImplementedError + 83 + 84 def add_tag(self, key: str, value: str) -> None: + 85 self.tags[key] = str(value) + 86 + 87 @property + 88 def uri(self) -> Path: + 89 """The base URI to use for the card and it's artifacts.""" + 90 if self.version == CommonKwargs.BASE_VERSION.value: + 91 raise ValueError("Could not create card uri - version is not set") + 92 + 93 assert self.repository is not None, "Repository must be set" + 94 assert self.name is not None, "Name must be set" + 95 + 96 return Path( + 97 config.storage_root, + 98 RegistryTableNames.from_str(self.card_type).value, + 99 self.repository, +100 self.name, +101 f"v{self.version}", +102 ) +103 +104 @property +105 def artifact_uri(self) -> Path: +106 """Returns the root URI to which artifacts associated with this card should be saved.""" +107 return self.uri / "artifacts" +108 +109 @property +110 def card_type(self) -> str: +111 raise NotImplementedError +
Base pydantic class for artifact cards
+36 @model_validator(mode="before") +37 @classmethod +38 def validate_args(cls, card_args: Dict[str, Any]) -> Dict[str, Any]: # pylint: disable=arguments-renamed +39 """Validate base args and Lowercase name and repository +40 +41 Args: +42 card_args: +43 named args passed to card +44 +45 Returns: +46 validated card_args +47 """ +48 card_info = card_args.get("info") +49 +50 for key in ["name", "repository", "contact", "version", "uid"]: +51 # check card args +52 val = card_args.get(key) +53 +54 # check card info +55 if card_info is not None: +56 val = val or getattr(card_info, key) +57 +58 # check runtime env vars +59 if val is None: +60 val = os.environ.get(f"OPSML_RUNTIME_{key.upper()}") +61 +62 if key in ["name", "repository"]: +63 if val is not None: +64 val = clean_string(val) +65 +66 if key == "version" and val is None: +67 val = CommonKwargs.BASE_VERSION.value +68 +69 card_args[key] = val +70 +71 # need to check that name, repository and contact are set +72 if not all(card_args[key] for key in ["name", "repository", "contact"]): +73 raise ValueError("name, repository and contact must be set either as named arguments or through CardInfo") +74 +75 # validate name and repository for pattern +76 validate_name_repository_pattern(name=card_args["name"], repository=card_args["repository"]) +77 +78 return card_args +
Validate base args and Lowercase name and repository
+ +++validated card_args
+
80 def create_registry_record(self) -> Dict[str, Any]: +81 """Creates a registry record from self attributes""" +82 raise NotImplementedError +
Creates a registry record from self attributes
+87 @property + 88 def uri(self) -> Path: + 89 """The base URI to use for the card and it's artifacts.""" + 90 if self.version == CommonKwargs.BASE_VERSION.value: + 91 raise ValueError("Could not create card uri - version is not set") + 92 + 93 assert self.repository is not None, "Repository must be set" + 94 assert self.name is not None, "Name must be set" + 95 + 96 return Path( + 97 config.storage_root, + 98 RegistryTableNames.from_str(self.card_type).value, + 99 self.repository, +100 self.name, +101 f"v{self.version}", +102 ) +
The base URI to use for the card and it's artifacts.
+104 @property +105 def artifact_uri(self) -> Path: +106 """Returns the root URI to which artifacts associated with this card should be saved.""" +107 return self.uri / "artifacts" +
Returns the root URI to which artifacts associated with this card should be saved.
+1# Copyright (c) Shipt, Inc. + 2# This source code is licensed under the MIT license found in the + 3# LICENSE file in the root directory of this source tree. + 4 + 5# IMPORTANT: We need `Optional` imported here in order for Pydantic to be able to + 6# deserialize DataCard. + 7# + 8from typing import ( # noqa # pylint: disable=unused-import + 9 Any, + 10 Dict, + 11 List, + 12 Optional, + 13 Union, + 14) + 15 + 16from pydantic import SerializeAsAny + 17 + 18from opsml.cards.base import ArtifactCard + 19from opsml.data import Dataset + 20from opsml.data.interfaces._base import DataInterface + 21from opsml.data.splitter import Data, DataSplit + 22from opsml.helpers.logging import ArtifactLogger + 23from opsml.types import CardType, DataCardMetadata + 24 + 25try: + 26 from ydata_profiling import ProfileReport + 27except ModuleNotFoundError: + 28 ProfileReport = Any + 29 + 30logger = ArtifactLogger.get_logger() + 31 + 32 + 33class DataCard(ArtifactCard): + 34 """Create a DataCard from your data. + 35 + 36 Args: + 37 interface: + 38 Instance of `DataInterface` that contains data + 39 name: + 40 What to name the data + 41 repository: + 42 Repository that this data is associated with + 43 contact: + 44 Contact to associate with data card + 45 info: + 46 `CardInfo` object containing additional metadata. If provided, it will override any + 47 values provided for `name`, `repository`, `contact`, and `version`. + 48 + 49 Name, repository, and contact are required arguments for all cards. They can be provided + 50 directly or through a `CardInfo` object. + 51 + 52 version: + 53 DataCard version + 54 uid: + 55 Unique id assigned to the DataCard + 56 + 57 Returns: + 58 DataCard + 59 + 60 """ + 61 + 62 interface: SerializeAsAny[Union[DataInterface, Dataset]] + 63 metadata: DataCardMetadata = DataCardMetadata() + 64 + 65 def load_data(self, **kwargs: Union[str, int]) -> None: # pylint: disable=differing-param-doc + 66 """ + 67 Load data to interface + 68 + 69 Args: + 70 kwargs: + 71 Keyword arguments to pass to the data loader + 72 + 73 ---- Supported kwargs for ImageData and TextDataset ---- + 74 + 75 split: + 76 Split to use for data. If not provided, then all data will be loaded. + 77 Only used for subclasses of `Dataset`. + 78 + 79 batch_size: + 80 What batch size to use when loading data. Only used for subclasses of `Dataset`. + 81 Defaults to 1000. + 82 + 83 chunk_size: + 84 How many files per batch to use when writing arrow back to local file. + 85 Defaults to 1000. + 86 + 87 Example: + 88 + 89 - If batch_size=1000 and chunk_size=100, then the loaded batch will be split into + 90 10 chunks to write in parallel. This is useful for large datasets. + 91 + 92 """ + 93 from opsml.storage.card_loader import DataCardLoader + 94 + 95 DataCardLoader(self).load_data(**kwargs) + 96 + 97 def load_data_profile(self) -> None: + 98 """ + 99 Load data to interface +100 """ +101 from opsml.storage.card_loader import DataCardLoader +102 +103 DataCardLoader(self).load_data_profile() +104 +105 def create_registry_record(self) -> Dict[str, Any]: +106 """ +107 Creates required metadata for registering the current data card. +108 Implemented with a DataRegistry object. +109 Returns: +110 Registry metadata +111 """ +112 exclude_attr = {"data"} +113 return self.model_dump(exclude=exclude_attr) +114 +115 def add_info(self, info: Dict[str, Union[float, int, str]]) -> None: +116 """ +117 Adds metadata to the existing DataCard metadata dictionary +118 +119 Args: +120 info: +121 Dictionary containing name (str) and value (float, int, str) pairs +122 to add to the current metadata set +123 """ +124 +125 self.metadata.additional_info = {**info, **self.metadata.additional_info} +126 +127 def create_data_profile(self, sample_perc: float = 1) -> ProfileReport: +128 """Creates a data profile report +129 +130 Args: +131 sample_perc: +132 Percentage of data to use when creating a profile. Sampling is recommended for large dataframes. +133 Percentage is expressed as a decimal (e.g. 1 = 100%, 0.5 = 50%, etc.) +134 +135 """ +136 assert isinstance( +137 self.interface, DataInterface +138 ), "Data profile can only be created for a DataInterface subclasses" +139 self.interface.create_data_profile(sample_perc, str(self.name)) +140 +141 def split_data(self) -> Dict[str, Data]: +142 """Splits data interface according to data split logic""" +143 +144 assert isinstance(self.interface, DataInterface), "Splitting is only support for DataInterface subclasses" +145 if self.data is None: +146 self.load_data() +147 +148 return self.interface.split_data() +149 +150 @property +151 def data_splits(self) -> List[DataSplit]: +152 """Returns data splits""" +153 assert isinstance(self.interface, DataInterface), "Data splits are only supported for DataInterface subclasses" +154 return self.interface.data_splits +155 +156 @property +157 def data(self) -> Any: +158 """Returns data""" +159 assert isinstance( +160 self.interface, DataInterface +161 ), "Data attribute is only supported for DataInterface subclasses" +162 return self.interface.data +163 +164 @property +165 def data_profile(self) -> Any: +166 """Returns data profile""" +167 assert isinstance(self.interface, DataInterface), "Data profile is only supported for DataInterface subclasses" +168 return self.interface.data_profile +169 +170 @property +171 def card_type(self) -> str: +172 return CardType.DATACARD.value +
34class DataCard(ArtifactCard): + 35 """Create a DataCard from your data. + 36 + 37 Args: + 38 interface: + 39 Instance of `DataInterface` that contains data + 40 name: + 41 What to name the data + 42 repository: + 43 Repository that this data is associated with + 44 contact: + 45 Contact to associate with data card + 46 info: + 47 `CardInfo` object containing additional metadata. If provided, it will override any + 48 values provided for `name`, `repository`, `contact`, and `version`. + 49 + 50 Name, repository, and contact are required arguments for all cards. They can be provided + 51 directly or through a `CardInfo` object. + 52 + 53 version: + 54 DataCard version + 55 uid: + 56 Unique id assigned to the DataCard + 57 + 58 Returns: + 59 DataCard + 60 + 61 """ + 62 + 63 interface: SerializeAsAny[Union[DataInterface, Dataset]] + 64 metadata: DataCardMetadata = DataCardMetadata() + 65 + 66 def load_data(self, **kwargs: Union[str, int]) -> None: # pylint: disable=differing-param-doc + 67 """ + 68 Load data to interface + 69 + 70 Args: + 71 kwargs: + 72 Keyword arguments to pass to the data loader + 73 + 74 ---- Supported kwargs for ImageData and TextDataset ---- + 75 + 76 split: + 77 Split to use for data. If not provided, then all data will be loaded. + 78 Only used for subclasses of `Dataset`. + 79 + 80 batch_size: + 81 What batch size to use when loading data. Only used for subclasses of `Dataset`. + 82 Defaults to 1000. + 83 + 84 chunk_size: + 85 How many files per batch to use when writing arrow back to local file. + 86 Defaults to 1000. + 87 + 88 Example: + 89 + 90 - If batch_size=1000 and chunk_size=100, then the loaded batch will be split into + 91 10 chunks to write in parallel. This is useful for large datasets. + 92 + 93 """ + 94 from opsml.storage.card_loader import DataCardLoader + 95 + 96 DataCardLoader(self).load_data(**kwargs) + 97 + 98 def load_data_profile(self) -> None: + 99 """ +100 Load data to interface +101 """ +102 from opsml.storage.card_loader import DataCardLoader +103 +104 DataCardLoader(self).load_data_profile() +105 +106 def create_registry_record(self) -> Dict[str, Any]: +107 """ +108 Creates required metadata for registering the current data card. +109 Implemented with a DataRegistry object. +110 Returns: +111 Registry metadata +112 """ +113 exclude_attr = {"data"} +114 return self.model_dump(exclude=exclude_attr) +115 +116 def add_info(self, info: Dict[str, Union[float, int, str]]) -> None: +117 """ +118 Adds metadata to the existing DataCard metadata dictionary +119 +120 Args: +121 info: +122 Dictionary containing name (str) and value (float, int, str) pairs +123 to add to the current metadata set +124 """ +125 +126 self.metadata.additional_info = {**info, **self.metadata.additional_info} +127 +128 def create_data_profile(self, sample_perc: float = 1) -> ProfileReport: +129 """Creates a data profile report +130 +131 Args: +132 sample_perc: +133 Percentage of data to use when creating a profile. Sampling is recommended for large dataframes. +134 Percentage is expressed as a decimal (e.g. 1 = 100%, 0.5 = 50%, etc.) +135 +136 """ +137 assert isinstance( +138 self.interface, DataInterface +139 ), "Data profile can only be created for a DataInterface subclasses" +140 self.interface.create_data_profile(sample_perc, str(self.name)) +141 +142 def split_data(self) -> Dict[str, Data]: +143 """Splits data interface according to data split logic""" +144 +145 assert isinstance(self.interface, DataInterface), "Splitting is only support for DataInterface subclasses" +146 if self.data is None: +147 self.load_data() +148 +149 return self.interface.split_data() +150 +151 @property +152 def data_splits(self) -> List[DataSplit]: +153 """Returns data splits""" +154 assert isinstance(self.interface, DataInterface), "Data splits are only supported for DataInterface subclasses" +155 return self.interface.data_splits +156 +157 @property +158 def data(self) -> Any: +159 """Returns data""" +160 assert isinstance( +161 self.interface, DataInterface +162 ), "Data attribute is only supported for DataInterface subclasses" +163 return self.interface.data +164 +165 @property +166 def data_profile(self) -> Any: +167 """Returns data profile""" +168 assert isinstance(self.interface, DataInterface), "Data profile is only supported for DataInterface subclasses" +169 return self.interface.data_profile +170 +171 @property +172 def card_type(self) -> str: +173 return CardType.DATACARD.value +
Create a DataCard from your data.
+ +DataInterface that contains datainfo: CardInfo object containing additional metadata. If provided, it will override any
+values provided for name, repository, contact, and version.
Name, repository, and contact are required arguments for all cards. They can be provided
+directly or through a CardInfo object.
++DataCard
+
66 def load_data(self, **kwargs: Union[str, int]) -> None: # pylint: disable=differing-param-doc +67 """ +68 Load data to interface +69 +70 Args: +71 kwargs: +72 Keyword arguments to pass to the data loader +73 +74 ---- Supported kwargs for ImageData and TextDataset ---- +75 +76 split: +77 Split to use for data. If not provided, then all data will be loaded. +78 Only used for subclasses of `Dataset`. +79 +80 batch_size: +81 What batch size to use when loading data. Only used for subclasses of `Dataset`. +82 Defaults to 1000. +83 +84 chunk_size: +85 How many files per batch to use when writing arrow back to local file. +86 Defaults to 1000. +87 +88 Example: +89 +90 - If batch_size=1000 and chunk_size=100, then the loaded batch will be split into +91 10 chunks to write in parallel. This is useful for large datasets. +92 +93 """ +94 from opsml.storage.card_loader import DataCardLoader +95 +96 DataCardLoader(self).load_data(**kwargs) +
Load data to interface
+ +Dataset.Dataset.
+Defaults to 1000.chunk_size: How many files per batch to use when writing arrow back to local file. +Defaults to 1000.
+ +Example:
+ +98 def load_data_profile(self) -> None: + 99 """ +100 Load data to interface +101 """ +102 from opsml.storage.card_loader import DataCardLoader +103 +104 DataCardLoader(self).load_data_profile() +
Load data to interface
+106 def create_registry_record(self) -> Dict[str, Any]: +107 """ +108 Creates required metadata for registering the current data card. +109 Implemented with a DataRegistry object. +110 Returns: +111 Registry metadata +112 """ +113 exclude_attr = {"data"} +114 return self.model_dump(exclude=exclude_attr) +
Creates required metadata for registering the current data card. +Implemented with a DataRegistry object. + Returns: + Registry metadata
+116 def add_info(self, info: Dict[str, Union[float, int, str]]) -> None: +117 """ +118 Adds metadata to the existing DataCard metadata dictionary +119 +120 Args: +121 info: +122 Dictionary containing name (str) and value (float, int, str) pairs +123 to add to the current metadata set +124 """ +125 +126 self.metadata.additional_info = {**info, **self.metadata.additional_info} +
Adds metadata to the existing DataCard metadata dictionary
+ +128 def create_data_profile(self, sample_perc: float = 1) -> ProfileReport: +129 """Creates a data profile report +130 +131 Args: +132 sample_perc: +133 Percentage of data to use when creating a profile. Sampling is recommended for large dataframes. +134 Percentage is expressed as a decimal (e.g. 1 = 100%, 0.5 = 50%, etc.) +135 +136 """ +137 assert isinstance( +138 self.interface, DataInterface +139 ), "Data profile can only be created for a DataInterface subclasses" +140 self.interface.create_data_profile(sample_perc, str(self.name)) +
Creates a data profile report
+ +142 def split_data(self) -> Dict[str, Data]: +143 """Splits data interface according to data split logic""" +144 +145 assert isinstance(self.interface, DataInterface), "Splitting is only support for DataInterface subclasses" +146 if self.data is None: +147 self.load_data() +148 +149 return self.interface.split_data() +
Splits data interface according to data split logic
+151 @property +152 def data_splits(self) -> List[DataSplit]: +153 """Returns data splits""" +154 assert isinstance(self.interface, DataInterface), "Data splits are only supported for DataInterface subclasses" +155 return self.interface.data_splits +
Returns data splits
+157 @property +158 def data(self) -> Any: +159 """Returns data""" +160 assert isinstance( +161 self.interface, DataInterface +162 ), "Data attribute is only supported for DataInterface subclasses" +163 return self.interface.data +
Returns data
+165 @property +166 def data_profile(self) -> Any: +167 """Returns data profile""" +168 assert isinstance(self.interface, DataInterface), "Data profile is only supported for DataInterface subclasses" +169 return self.interface.data_profile +
Returns data profile
+1# Copyright (c) Shipt, Inc. + 2# This source code is licensed under the MIT license found in the + 3# LICENSE file in the root directory of this source tree. + 4from pathlib import Path + 5from typing import Any, Dict, Optional + 6from uuid import UUID + 7 + 8from pydantic import ConfigDict, SerializeAsAny, field_validator + 9 + 10from opsml.cards.base import ArtifactCard + 11from opsml.helpers.logging import ArtifactLogger + 12from opsml.model.interfaces.base import ModelInterface + 13from opsml.types import CardType, ModelCardMetadata, ModelMetadata, OnnxModel + 14 + 15logger = ArtifactLogger.get_logger() + 16 + 17 + 18class ModelCard(ArtifactCard): + 19 """Create a ModelCard from your trained machine learning model. + 20 This Card is used in conjunction with the ModelCardCreator class. + 21 + 22 Args: + 23 interface: + 24 Trained model interface. + 25 name: + 26 Name for the model specific to your current project + 27 repository: + 28 Repository that this model is associated with + 29 contact: + 30 Contact to associate with card + 31 info: + 32 `CardInfo` object containing additional metadata. If provided, it will override any + 33 values provided for `name`, `repository`, `contact`, and `version`. + 34 + 35 Name, repository, and contact are required arguments for all cards. They can be provided + 36 directly or through a `CardInfo` object. + 37 + 38 uid: + 39 Unique id (assigned if card has been registered) + 40 version: + 41 Current version (assigned if card has been registered) + 42 datacard_uid: + 43 Uid of the DataCard associated with training the model + 44 to_onnx: + 45 Whether to convert the model to onnx or not + 46 metadata: + 47 `ModelCardMetadata` associated with the model + 48 """ + 49 + 50 model_config = ConfigDict( + 51 arbitrary_types_allowed=True, + 52 protected_namespaces=("protect_",), + 53 validate_assignment=True, + 54 ) + 55 + 56 interface: SerializeAsAny[ModelInterface] + 57 datacard_uid: Optional[str] = None + 58 to_onnx: bool = False + 59 metadata: ModelCardMetadata = ModelCardMetadata() + 60 + 61 @field_validator("datacard_uid", mode="before") + 62 @classmethod + 63 def check_uid(cls, datacard_uid: Optional[str] = None) -> Optional[str]: + 64 if datacard_uid is None: + 65 return datacard_uid + 66 + 67 try: + 68 UUID(datacard_uid, version=4) # we use uuid4 + 69 return datacard_uid + 70 + 71 except ValueError as exc: + 72 raise ValueError("Datacard uid is not a valid uuid") from exc + 73 + 74 def load_model(self, **kwargs: Any) -> None: + 75 """Loads model, preprocessor and sample data to interface""" + 76 + 77 from opsml.storage.card_loader import ModelCardLoader + 78 + 79 ModelCardLoader(self).load_model(**kwargs) + 80 + 81 def download_model(self, path: Path, **kwargs: Any) -> None: + 82 """Downloads model, preprocessor and metadata to path + 83 + 84 Args: + 85 path: + 86 Path to download model + 87 + 88 kwargs: + 89 load_preprocessor: + 90 Whether to load preprocessor or not. Default is True + 91 load_onnx: + 92 Whether to load onnx model or not. Default is False + 93 quantize: + 94 Whether to quantize onnx model or not. Default is False + 95 """ + 96 + 97 from opsml.storage.card_loader import ModelCardLoader + 98 + 99 # set path to download model +100 kwargs["lpath"] = path +101 +102 ModelCardLoader(self).download_model(**kwargs) +103 +104 def load_onnx_model(self, **kwargs: Any) -> None: +105 """Loads onnx model to interface""" +106 +107 from opsml.storage.card_loader import ModelCardLoader +108 +109 ModelCardLoader(self).load_onnx_model(**kwargs) +110 +111 def load_preprocessor(self, **kwargs: Any) -> None: +112 """Loads onnx model to interface""" +113 +114 if self.preprocessor is not None: +115 return +116 +117 from opsml.storage.card_loader import ModelCardLoader +118 +119 ModelCardLoader(self).load_preprocessor(**kwargs) +120 +121 def create_registry_record(self) -> Dict[str, Any]: +122 """Creates a registry record from the current ModelCard""" +123 +124 exclude_vars = {"interface": {"model", "preprocessor", "sample_data", "onnx_model"}} +125 dumped_model = self.model_dump(exclude=exclude_vars) +126 +127 return dumped_model +128 +129 @property +130 def model(self) -> Any: +131 """Quick access to model from interface""" +132 return self.interface.model +133 +134 @property +135 def sample_data(self) -> Any: +136 """Quick access to sample data from interface""" +137 return self.interface.sample_data +138 +139 @property +140 def preprocessor(self) -> Any: +141 """Quick access to preprocessor from interface""" +142 +143 if hasattr(self.interface, "preprocessor"): +144 return self.interface.preprocessor +145 +146 if hasattr(self.interface, "tokenizer"): +147 if self.interface.tokenizer is not None: +148 return self.interface.tokenizer +149 +150 if hasattr(self.interface, "feature_extractor"): +151 if self.interface.feature_extractor is not None: +152 return self.interface.feature_extractor +153 +154 return None +155 +156 @property +157 def onnx_model(self) -> Optional[OnnxModel]: +158 """Quick access to onnx model from interface""" +159 return self.interface.onnx_model +160 +161 @property +162 def model_metadata(self) -> ModelMetadata: +163 """Loads `ModelMetadata` class""" +164 +165 from opsml.storage.card_loader import ModelCardLoader +166 +167 return ModelCardLoader(self).load_model_metadata() +168 +169 @property +170 def card_type(self) -> str: +171 return CardType.MODELCARD.value +
19class ModelCard(ArtifactCard): + 20 """Create a ModelCard from your trained machine learning model. + 21 This Card is used in conjunction with the ModelCardCreator class. + 22 + 23 Args: + 24 interface: + 25 Trained model interface. + 26 name: + 27 Name for the model specific to your current project + 28 repository: + 29 Repository that this model is associated with + 30 contact: + 31 Contact to associate with card + 32 info: + 33 `CardInfo` object containing additional metadata. If provided, it will override any + 34 values provided for `name`, `repository`, `contact`, and `version`. + 35 + 36 Name, repository, and contact are required arguments for all cards. They can be provided + 37 directly or through a `CardInfo` object. + 38 + 39 uid: + 40 Unique id (assigned if card has been registered) + 41 version: + 42 Current version (assigned if card has been registered) + 43 datacard_uid: + 44 Uid of the DataCard associated with training the model + 45 to_onnx: + 46 Whether to convert the model to onnx or not + 47 metadata: + 48 `ModelCardMetadata` associated with the model + 49 """ + 50 + 51 model_config = ConfigDict( + 52 arbitrary_types_allowed=True, + 53 protected_namespaces=("protect_",), + 54 validate_assignment=True, + 55 ) + 56 + 57 interface: SerializeAsAny[ModelInterface] + 58 datacard_uid: Optional[str] = None + 59 to_onnx: bool = False + 60 metadata: ModelCardMetadata = ModelCardMetadata() + 61 + 62 @field_validator("datacard_uid", mode="before") + 63 @classmethod + 64 def check_uid(cls, datacard_uid: Optional[str] = None) -> Optional[str]: + 65 if datacard_uid is None: + 66 return datacard_uid + 67 + 68 try: + 69 UUID(datacard_uid, version=4) # we use uuid4 + 70 return datacard_uid + 71 + 72 except ValueError as exc: + 73 raise ValueError("Datacard uid is not a valid uuid") from exc + 74 + 75 def load_model(self, **kwargs: Any) -> None: + 76 """Loads model, preprocessor and sample data to interface""" + 77 + 78 from opsml.storage.card_loader import ModelCardLoader + 79 + 80 ModelCardLoader(self).load_model(**kwargs) + 81 + 82 def download_model(self, path: Path, **kwargs: Any) -> None: + 83 """Downloads model, preprocessor and metadata to path + 84 + 85 Args: + 86 path: + 87 Path to download model + 88 + 89 kwargs: + 90 load_preprocessor: + 91 Whether to load preprocessor or not. Default is True + 92 load_onnx: + 93 Whether to load onnx model or not. Default is False + 94 quantize: + 95 Whether to quantize onnx model or not. Default is False + 96 """ + 97 + 98 from opsml.storage.card_loader import ModelCardLoader + 99 +100 # set path to download model +101 kwargs["lpath"] = path +102 +103 ModelCardLoader(self).download_model(**kwargs) +104 +105 def load_onnx_model(self, **kwargs: Any) -> None: +106 """Loads onnx model to interface""" +107 +108 from opsml.storage.card_loader import ModelCardLoader +109 +110 ModelCardLoader(self).load_onnx_model(**kwargs) +111 +112 def load_preprocessor(self, **kwargs: Any) -> None: +113 """Loads onnx model to interface""" +114 +115 if self.preprocessor is not None: +116 return +117 +118 from opsml.storage.card_loader import ModelCardLoader +119 +120 ModelCardLoader(self).load_preprocessor(**kwargs) +121 +122 def create_registry_record(self) -> Dict[str, Any]: +123 """Creates a registry record from the current ModelCard""" +124 +125 exclude_vars = {"interface": {"model", "preprocessor", "sample_data", "onnx_model"}} +126 dumped_model = self.model_dump(exclude=exclude_vars) +127 +128 return dumped_model +129 +130 @property +131 def model(self) -> Any: +132 """Quick access to model from interface""" +133 return self.interface.model +134 +135 @property +136 def sample_data(self) -> Any: +137 """Quick access to sample data from interface""" +138 return self.interface.sample_data +139 +140 @property +141 def preprocessor(self) -> Any: +142 """Quick access to preprocessor from interface""" +143 +144 if hasattr(self.interface, "preprocessor"): +145 return self.interface.preprocessor +146 +147 if hasattr(self.interface, "tokenizer"): +148 if self.interface.tokenizer is not None: +149 return self.interface.tokenizer +150 +151 if hasattr(self.interface, "feature_extractor"): +152 if self.interface.feature_extractor is not None: +153 return self.interface.feature_extractor +154 +155 return None +156 +157 @property +158 def onnx_model(self) -> Optional[OnnxModel]: +159 """Quick access to onnx model from interface""" +160 return self.interface.onnx_model +161 +162 @property +163 def model_metadata(self) -> ModelMetadata: +164 """Loads `ModelMetadata` class""" +165 +166 from opsml.storage.card_loader import ModelCardLoader +167 +168 return ModelCardLoader(self).load_model_metadata() +169 +170 @property +171 def card_type(self) -> str: +172 return CardType.MODELCARD.value +
Create a ModelCard from your trained machine learning model. +This Card is used in conjunction with the ModelCardCreator class.
+ +info: CardInfo object containing additional metadata. If provided, it will override any
+values provided for name, repository, contact, and version.
Name, repository, and contact are required arguments for all cards. They can be provided
+directly or through a CardInfo object.
ModelCardMetadata associated with the model62 @field_validator("datacard_uid", mode="before") +63 @classmethod +64 def check_uid(cls, datacard_uid: Optional[str] = None) -> Optional[str]: +65 if datacard_uid is None: +66 return datacard_uid +67 +68 try: +69 UUID(datacard_uid, version=4) # we use uuid4 +70 return datacard_uid +71 +72 except ValueError as exc: +73 raise ValueError("Datacard uid is not a valid uuid") from exc +
75 def load_model(self, **kwargs: Any) -> None: +76 """Loads model, preprocessor and sample data to interface""" +77 +78 from opsml.storage.card_loader import ModelCardLoader +79 +80 ModelCardLoader(self).load_model(**kwargs) +
Loads model, preprocessor and sample data to interface
+82 def download_model(self, path: Path, **kwargs: Any) -> None: + 83 """Downloads model, preprocessor and metadata to path + 84 + 85 Args: + 86 path: + 87 Path to download model + 88 + 89 kwargs: + 90 load_preprocessor: + 91 Whether to load preprocessor or not. Default is True + 92 load_onnx: + 93 Whether to load onnx model or not. Default is False + 94 quantize: + 95 Whether to quantize onnx model or not. Default is False + 96 """ + 97 + 98 from opsml.storage.card_loader import ModelCardLoader + 99 +100 # set path to download model +101 kwargs["lpath"] = path +102 +103 ModelCardLoader(self).download_model(**kwargs) +
Downloads model, preprocessor and metadata to path
+ +105 def load_onnx_model(self, **kwargs: Any) -> None: +106 """Loads onnx model to interface""" +107 +108 from opsml.storage.card_loader import ModelCardLoader +109 +110 ModelCardLoader(self).load_onnx_model(**kwargs) +
Loads onnx model to interface
+112 def load_preprocessor(self, **kwargs: Any) -> None: +113 """Loads onnx model to interface""" +114 +115 if self.preprocessor is not None: +116 return +117 +118 from opsml.storage.card_loader import ModelCardLoader +119 +120 ModelCardLoader(self).load_preprocessor(**kwargs) +
Loads onnx model to interface
+122 def create_registry_record(self) -> Dict[str, Any]: +123 """Creates a registry record from the current ModelCard""" +124 +125 exclude_vars = {"interface": {"model", "preprocessor", "sample_data", "onnx_model"}} +126 dumped_model = self.model_dump(exclude=exclude_vars) +127 +128 return dumped_model +
Creates a registry record from the current ModelCard
+130 @property +131 def model(self) -> Any: +132 """Quick access to model from interface""" +133 return self.interface.model +
Quick access to model from interface
+135 @property +136 def sample_data(self) -> Any: +137 """Quick access to sample data from interface""" +138 return self.interface.sample_data +
Quick access to sample data from interface
+140 @property +141 def preprocessor(self) -> Any: +142 """Quick access to preprocessor from interface""" +143 +144 if hasattr(self.interface, "preprocessor"): +145 return self.interface.preprocessor +146 +147 if hasattr(self.interface, "tokenizer"): +148 if self.interface.tokenizer is not None: +149 return self.interface.tokenizer +150 +151 if hasattr(self.interface, "feature_extractor"): +152 if self.interface.feature_extractor is not None: +153 return self.interface.feature_extractor +154 +155 return None +
Quick access to preprocessor from interface
+157 @property +158 def onnx_model(self) -> Optional[OnnxModel]: +159 """Quick access to onnx model from interface""" +160 return self.interface.onnx_model +
Quick access to onnx model from interface
+162 @property +163 def model_metadata(self) -> ModelMetadata: +164 """Loads `ModelMetadata` class""" +165 +166 from opsml.storage.card_loader import ModelCardLoader +167 +168 return ModelCardLoader(self).load_model_metadata() +
Loads ModelMetadata class
1# Copyright (c) Shipt, Inc. + 2# This source code is licensed under the MIT license found in the + 3# LICENSE file in the root directory of this source tree. + 4 + 5# + 6# IMPORTANT: We need `Dict`, `List`, and `Optional` imported here in order for Pydantic to be able to + 7# deserialize ProjectCard. + 8# + 9from typing import Any, Dict, List, Optional # noqa # pylint: disable=unused-import +10 +11from opsml.cards.base import ArtifactCard +12from opsml.types import CardType +13 +14 +15class ProjectCard(ArtifactCard): +16 """ +17 Card containing project information +18 """ +19 +20 project_id: int = 0 # placeholder +21 +22 def create_registry_record(self) -> Dict[str, Any]: +23 """Creates a registry record for a project""" +24 +25 return self.model_dump() +26 +27 @property +28 def card_type(self) -> str: +29 return CardType.PROJECTCARD.value +
16class ProjectCard(ArtifactCard): +17 """ +18 Card containing project information +19 """ +20 +21 project_id: int = 0 # placeholder +22 +23 def create_registry_record(self) -> Dict[str, Any]: +24 """Creates a registry record for a project""" +25 +26 return self.model_dump() +27 +28 @property +29 def card_type(self) -> str: +30 return CardType.PROJECTCARD.value +
Card containing project information
+23 def create_registry_record(self) -> Dict[str, Any]: +24 """Creates a registry record for a project""" +25 +26 return self.model_dump() +
Creates a registry record for a project
+1# Copyright (c) Shipt, Inc. + 2# This source code is licensed under the MIT license found in the + 3# LICENSE file in the root directory of this source tree. + 4 + 5# pylint: disable=invalid-name + 6 + 7 + 8import tempfile + 9import uuid + 10from functools import cached_property + 11from pathlib import Path + 12from typing import Any, Dict, List, Optional, Tuple, Union, cast + 13 + 14import joblib + 15import numpy as np + 16from numpy.typing import NDArray + 17from pydantic import model_validator + 18 + 19from opsml.cards.base import ArtifactCard + 20from opsml.helpers.logging import ArtifactLogger + 21from opsml.helpers.utils import TypeChecker + 22from opsml.settings.config import config + 23from opsml.storage import client + 24from opsml.types import ( + 25 Artifact, + 26 ArtifactUris, + 27 CardType, + 28 CommonKwargs, + 29 GraphStyle, + 30 Metric, + 31 Metrics, + 32 Param, + 33 Params, + 34 RegistryTableNames, + 35 RegistryType, + 36 RunCardRegistry, + 37 RunGraph, + 38 SaveName, + 39) + 40 + 41logger = ArtifactLogger.get_logger() + 42 + 43_List = List[Union[float, int]] + 44_Dict = Dict[str, List[Union[float, int]]] + 45_YReturn = Union[_List, _Dict] + 46_ParseReturn = Tuple[_YReturn, str] + 47 + 48 + 49def _dump_graph_artifact(graph: RunGraph, name: str, uri: Path) -> Tuple[Path, Path]: + 50 """Helper method for saving graph artifacts to storage + 51 + 52 Args: + 53 graph: + 54 RunGraph object + 55 name: + 56 Name of graph + 57 uri: + 58 Uri to store graph artifact + 59 """ + 60 with tempfile.TemporaryDirectory() as tempdir: + 61 lpath = Path(tempdir) / f"{name}.joblib" + 62 rpath = (uri / SaveName.GRAPHS.value) / lpath.name + 63 joblib.dump(graph.model_dump(), lpath) + 64 + 65 client.storage_client.put(lpath, rpath) + 66 + 67 return lpath, rpath + 68 + 69 + 70def _decimate_list(array: List[Union[float, int]]) -> List[Union[float, int]]: + 71 """Decimates array to no more than 200,000 points + 72 + 73 Args: + 74 array: + 75 List of floats or ints + 76 + 77 Returns: + 78 Decimated array + 79 """ + 80 length = len(array) + 81 if len(array) > 200_000: + 82 step = round(length / 200_000) + 83 return array[::step] + 84 + 85 return array + 86 + 87 + 88def _parse_y_to_list( + 89 x_length: int, + 90 y: Union[List[Union[float, int]], NDArray[Any], Dict[str, Union[List[Union[float, int]], NDArray[Any]]]], + 91) -> _ParseReturn: + 92 """Helper method for parsing y to list when logging a graph + 93 + 94 Args: + 95 x_length: + 96 Length of x + 97 y: + 98 Y values to parse. Can be a list, dictionary or numpy array + 99 +100 Returns: +101 List or dictionary of y values +102 +103 """ +104 # if y is dictionary +105 if isinstance(y, dict): +106 _y: Dict[str, List[Union[float, int]]] = {} +107 +108 # common sense constraint +109 if len(y.keys()) > 50: +110 raise ValueError("Too many keys in dictionary. A maximum of 50 keys for y is allowed.") +111 +112 for k, v in y.items(): +113 if isinstance(v, np.ndarray): +114 v = v.flatten().tolist() +115 assert isinstance(v, list), "y must be a list or dictionary" +116 v = _decimate_list(v) +117 +118 assert x_length == len(v), "x and y must be the same length" +119 _y[k] = v +120 +121 return _y, "multi" +122 +123 # if y is ndarray +124 if isinstance(y, np.ndarray): +125 y = y.flatten().tolist() +126 assert isinstance(y, list), "y must be a list or dictionary" +127 +128 y = _decimate_list(y) +129 assert x_length == len(y), "x and y must be the same length" +130 +131 return y, "single" +132 +133 # if y is list +134 assert isinstance(y, list), "y must be a list or dictionary" +135 y = _decimate_list(y) +136 assert x_length == len(y), "x and y must be the same length" +137 return y, "single" +138 +139 +140class RunCard(ArtifactCard): +141 +142 """ +143 Create a RunCard from specified arguments. +144 +145 Apart from required args, a RunCard must be associated with one of +146 datacard_uid, modelcard_uids or pipelinecard_uid +147 +148 Args: +149 name: +150 Run name +151 repository: +152 Repository that this card is associated with +153 contact: +154 Contact to associate with card +155 info: +156 `CardInfo` object containing additional metadata. If provided, it will override any +157 values provided for `name`, `repository`, `contact`, and `version`. +158 +159 Name, repository, and contact are required arguments for all cards. They can be provided +160 directly or through a `CardInfo` object. +161 +162 datacard_uids: +163 Optional DataCard uids associated with this run +164 modelcard_uids: +165 Optional List of ModelCard uids to associate with this run +166 pipelinecard_uid: +167 Optional PipelineCard uid to associate with this experiment +168 metrics: +169 Optional dictionary of key (str), value (int, float) metric paris. +170 Metrics can also be added via class methods. +171 parameters: +172 Parameters associated with a RunCard +173 artifact_uris: +174 Optional dictionary of artifact uris associated with artifacts. +175 uid: +176 Unique id (assigned if card has been registered) +177 version: +178 Current version (assigned if card has been registered) +179 +180 """ +181 +182 datacard_uids: List[str] = [] +183 modelcard_uids: List[str] = [] +184 pipelinecard_uid: Optional[str] = None +185 metrics: Metrics = {} +186 parameters: Params = {} +187 artifact_uris: ArtifactUris = {} +188 tags: Dict[str, Union[str, int]] = {} +189 project: Optional[str] = None +190 +191 @model_validator(mode="before") +192 @classmethod +193 def validate_defaults_args(cls, card_args: Dict[str, Any]) -> Dict[str, Any]: +194 # add default +195 contact = card_args.get("contact") +196 +197 if contact is None: +198 card_args["contact"] = CommonKwargs.UNDEFINED.value +199 +200 repository = card_args.get("repository") +201 +202 if repository is None: +203 card_args["repository"] = "opsml" +204 +205 return card_args +206 +207 def add_tag(self, key: str, value: str) -> None: +208 """ +209 Logs tags to current RunCard +210 +211 Args: +212 key: +213 Key for tag +214 value: +215 value for tag +216 """ +217 self.tags = {**{key: value}, **self.tags} +218 +219 def add_tags(self, tags: Dict[str, str]) -> None: +220 """ +221 Logs tags to current RunCard +222 +223 Args: +224 tags: +225 Dictionary of tags +226 """ +227 self.tags = {**tags, **self.tags} +228 +229 def log_graph( +230 self, +231 name: str, +232 x: Union[List[Union[float, int]], NDArray[Any]], +233 y: Union[List[Union[float, int]], NDArray[Any], Dict[str, Union[List[Union[float, int]], NDArray[Any]]]], +234 y_label: str, +235 x_label: str, +236 graph_style: str, +237 ) -> None: +238 """Logs a graph to the RunCard, which will be rendered in the UI as a line graph +239 +240 Args: +241 name: +242 Name of graph +243 x: +244 List or numpy array of x values +245 +246 x_label: +247 Label for x axis +248 y: +249 Either a list or numpy array of y values or a dictionary of y values where key is the group label and +250 value is a list or numpy array of y values +251 y_label: +252 Label for y axis +253 graph_style: +254 Style of graph. Options are "line" or "scatter" +255 +256 example: +257 +258 ### single line graph +259 x = np.arange(1, 400, 0.5) +260 y = x * x +261 run.log_graph(name="graph1", x=x, y=y, x_label="x", y_label="y", graph_style="line") +262 +263 ### multi line graph +264 x = np.arange(1, 1000, 0.5) +265 y1 = x * x +266 y2 = y1 * 1.1 +267 y3 = y2 * 3 +268 run.log_graph( +269 name="multiline", +270 x=x, +271 y={"y1": y1, "y2": y2, "y3": y3}, +272 x_label="x", +273 y_label="y", +274 graph_style="line", +275 ) +276 +277 """ +278 +279 if isinstance(x, np.ndarray): +280 x = x.flatten().tolist() +281 assert isinstance(x, list), "x must be a list or dictionary" +282 +283 x = _decimate_list(x) +284 +285 parsed_y, graph_type = _parse_y_to_list(len(x), y) +286 +287 logger.info(f"Logging graph {name} to RunCard") +288 graph = RunGraph( +289 name=name, +290 x=x, +291 x_label=x_label, +292 y=parsed_y, +293 y_label=y_label, +294 graph_type=graph_type, +295 graph_style=GraphStyle.from_str(graph_style).value, # validate graph style +296 ) +297 +298 # save graph to storage so we can view in ui while run is active +299 lpath, rpath = _dump_graph_artifact(graph, name, self.uri) +300 +301 self._add_artifact_uri( +302 name=name, +303 local_path=lpath.as_posix(), +304 remote_path=rpath.as_posix(), +305 ) +306 +307 def log_parameters(self, parameters: Dict[str, Union[float, int, str]]) -> None: +308 """ +309 Logs parameters to current RunCard +310 +311 Args: +312 parameters: +313 Dictionary of parameters +314 """ +315 +316 for key, value in parameters.items(): +317 # check key +318 self.log_parameter(key, value) +319 +320 def log_parameter(self, key: str, value: Union[int, float, str]) -> None: +321 """ +322 Logs parameter to current RunCard +323 +324 Args: +325 key: +326 Param name +327 value: +328 Param value +329 """ +330 +331 TypeChecker.check_param_type(param=value) +332 _key = TypeChecker.replace_spaces(key) +333 +334 param = Param(name=key, value=value) +335 +336 if self.parameters.get(_key) is not None: +337 self.parameters[_key].append(param) +338 +339 else: +340 self.parameters[_key] = [param] +341 +342 def log_metric( +343 self, +344 key: str, +345 value: Union[int, float], +346 timestamp: Optional[int] = None, +347 step: Optional[int] = None, +348 ) -> None: +349 """ +350 Logs metric to the existing RunCard metric dictionary +351 +352 Args: +353 key: +354 Metric name +355 value: +356 Metric value +357 timestamp: +358 Optional timestamp +359 step: +360 Optional step associated with name and value +361 """ +362 +363 TypeChecker.check_metric_type(metric=value) +364 _key = TypeChecker.replace_spaces(key) +365 +366 metric = Metric(name=_key, value=value, timestamp=timestamp, step=step) +367 +368 self._registry.insert_metric([{**metric.model_dump(), **{"run_uid": self.uid}}]) +369 +370 if self.metrics.get(_key) is not None: +371 self.metrics[_key].append(metric) +372 else: +373 self.metrics[_key] = [metric] +374 +375 def log_metrics(self, metrics: Dict[str, Union[float, int]], step: Optional[int] = None) -> None: +376 """ +377 Log metrics to the existing RunCard metric dictionary +378 +379 Args: +380 metrics: +381 Dictionary containing key (str) and value (float or int) pairs +382 to add to the current metric set +383 step: +384 Optional step associated with metrics +385 """ +386 +387 for key, value in metrics.items(): +388 self.log_metric(key, value, step) +389 +390 def log_artifact_from_file( +391 self, +392 name: str, +393 local_path: Union[str, Path], +394 artifact_path: Optional[Union[str, Path]] = None, +395 ) -> None: +396 """ +397 Log a local file or directory to the opsml server and associate with the current run. +398 +399 Args: +400 name: +401 Name to assign to artifact(s) +402 local_path: +403 Local path to file or directory. Can be string or pathlike object +404 artifact_path: +405 Optional path to store artifact in opsml server. If not provided, 'artifacts' will be used +406 """ +407 +408 lpath = Path(local_path) +409 rpath = self.uri / (artifact_path or SaveName.ARTIFACTS.value) +410 +411 if lpath.is_file(): +412 rpath = rpath / lpath.name +413 +414 client.storage_client.put(lpath, rpath) +415 self._add_artifact_uri( +416 name=name, +417 local_path=lpath.as_posix(), +418 remote_path=rpath.as_posix(), +419 ) +420 +421 def create_registry_record(self) -> Dict[str, Any]: +422 """Creates a registry record from the current RunCard""" +423 +424 exclude_attr = {"parameters", "metrics"} +425 +426 return self.model_dump(exclude=exclude_attr) +427 +428 def _add_artifact_uri(self, name: str, local_path: str, remote_path: str) -> None: +429 """ +430 Adds an artifact_uri to the runcard +431 +432 Args: +433 name: +434 Name to associate with artifact +435 uri: +436 Uri where artifact is stored +437 """ +438 +439 self.artifact_uris[name] = Artifact( +440 name=name, +441 local_path=local_path, +442 remote_path=remote_path, +443 ) +444 +445 def add_card_uid(self, card_type: str, uid: str) -> None: +446 """ +447 Adds a card uid to the appropriate card uid list for tracking +448 +449 Args: +450 card_type: +451 ArtifactCard class name +452 uid: +453 Uid of registered ArtifactCard +454 """ +455 +456 if card_type == CardType.DATACARD: +457 self.datacard_uids = [uid, *self.datacard_uids] +458 elif card_type == CardType.MODELCARD: +459 self.modelcard_uids = [uid, *self.modelcard_uids] +460 +461 def get_metric(self, name: str) -> Union[List[Metric], Metric]: +462 """ +463 Gets a metric by name +464 +465 Args: +466 name: +467 Name of metric +468 +469 Returns: +470 List of dictionaries or dictionary containing value +471 +472 """ +473 _key = TypeChecker.replace_spaces(name) +474 +475 metric = self.metrics.get(_key) +476 +477 if metric is None: +478 # try to get metric from registry +479 assert self.uid is not None, "RunCard must be registered to get metric" +480 _metric = self._registry.get_metric(run_uid=self.uid, name=[_key]) +481 +482 if _metric is not None: +483 metric = [Metric(**i) for i in _metric] +484 +485 else: +486 raise ValueError(f"Metric {metric} was not defined") +487 +488 if len(metric) > 1: +489 return metric +490 if len(metric) == 1: +491 return metric[0] +492 return metric +493 +494 def load_metrics(self) -> None: +495 """Reloads metrics from registry""" +496 assert self.uid is not None, "RunCard must be registered to load metrics" +497 +498 metrics = self._registry.get_metric(run_uid=self.uid) +499 +500 if metrics is None: +501 logger.info("No metrics found for RunCard") +502 return None +503 +504 # reset metrics +505 self.metrics = {} +506 for metric in metrics: +507 _metric = Metric(**metric) +508 if _metric.name not in self.metrics: +509 self.metrics[_metric.name] = [_metric] +510 else: +511 self.metrics[_metric.name].append(_metric) +512 return None +513 +514 def get_parameter(self, name: str) -> Union[List[Param], Param]: +515 """ +516 Gets a parameter by name +517 +518 Args: +519 name: +520 Name of parameter +521 +522 Returns: +523 List of dictionaries or dictionary containing value +524 +525 """ +526 _key = TypeChecker.replace_spaces(name) +527 param = self.parameters.get(_key) +528 if param is not None: +529 if len(param) > 1: +530 return param +531 if len(param) == 1: +532 return param[0] +533 return param +534 +535 raise ValueError(f"Param {param} is not defined") +536 +537 def load_artifacts(self, name: Optional[str] = None) -> None: +538 """Loads artifacts from artifact_uris""" +539 if bool(self.artifact_uris) is False: +540 logger.info("No artifact uris associated with RunCard") +541 return None +542 +543 if name is not None: +544 artifact = self.artifact_uris.get(name) +545 assert artifact is not None, f"Artifact {name} not found" +546 client.storage_client.get( +547 Path(artifact.remote_path), +548 Path(artifact.local_path), +549 ) +550 +551 else: +552 for _, artifact in self.artifact_uris.items(): +553 client.storage_client.get( +554 Path(artifact.remote_path), +555 Path(artifact.local_path), +556 ) +557 return None +558 +559 @property +560 def uri(self) -> Path: +561 """The base URI to use for the card and it's artifacts.""" +562 +563 # when using runcard outside of run context +564 if self.version == CommonKwargs.BASE_VERSION.value: +565 if self.uid is None: +566 self.uid = uuid.uuid4().hex +567 +568 end_path = self.uid +569 else: +570 end_path = f"v{self.version}" +571 +572 return Path( +573 config.storage_root, +574 RegistryTableNames.from_str(self.card_type).value, +575 str(self.repository), +576 str(self.name), +577 end_path, +578 ) +579 +580 @cached_property +581 def _registry(self) -> RunCardRegistry: +582 from opsml.registry.backend import _set_registry +583 +584 return cast(RunCardRegistry, _set_registry(RegistryType.RUN)) +585 +586 @property +587 def card_type(self) -> str: +588 return CardType.RUNCARD.value +
141class RunCard(ArtifactCard): +142 +143 """ +144 Create a RunCard from specified arguments. +145 +146 Apart from required args, a RunCard must be associated with one of +147 datacard_uid, modelcard_uids or pipelinecard_uid +148 +149 Args: +150 name: +151 Run name +152 repository: +153 Repository that this card is associated with +154 contact: +155 Contact to associate with card +156 info: +157 `CardInfo` object containing additional metadata. If provided, it will override any +158 values provided for `name`, `repository`, `contact`, and `version`. +159 +160 Name, repository, and contact are required arguments for all cards. They can be provided +161 directly or through a `CardInfo` object. +162 +163 datacard_uids: +164 Optional DataCard uids associated with this run +165 modelcard_uids: +166 Optional List of ModelCard uids to associate with this run +167 pipelinecard_uid: +168 Optional PipelineCard uid to associate with this experiment +169 metrics: +170 Optional dictionary of key (str), value (int, float) metric paris. +171 Metrics can also be added via class methods. +172 parameters: +173 Parameters associated with a RunCard +174 artifact_uris: +175 Optional dictionary of artifact uris associated with artifacts. +176 uid: +177 Unique id (assigned if card has been registered) +178 version: +179 Current version (assigned if card has been registered) +180 +181 """ +182 +183 datacard_uids: List[str] = [] +184 modelcard_uids: List[str] = [] +185 pipelinecard_uid: Optional[str] = None +186 metrics: Metrics = {} +187 parameters: Params = {} +188 artifact_uris: ArtifactUris = {} +189 tags: Dict[str, Union[str, int]] = {} +190 project: Optional[str] = None +191 +192 @model_validator(mode="before") +193 @classmethod +194 def validate_defaults_args(cls, card_args: Dict[str, Any]) -> Dict[str, Any]: +195 # add default +196 contact = card_args.get("contact") +197 +198 if contact is None: +199 card_args["contact"] = CommonKwargs.UNDEFINED.value +200 +201 repository = card_args.get("repository") +202 +203 if repository is None: +204 card_args["repository"] = "opsml" +205 +206 return card_args +207 +208 def add_tag(self, key: str, value: str) -> None: +209 """ +210 Logs tags to current RunCard +211 +212 Args: +213 key: +214 Key for tag +215 value: +216 value for tag +217 """ +218 self.tags = {**{key: value}, **self.tags} +219 +220 def add_tags(self, tags: Dict[str, str]) -> None: +221 """ +222 Logs tags to current RunCard +223 +224 Args: +225 tags: +226 Dictionary of tags +227 """ +228 self.tags = {**tags, **self.tags} +229 +230 def log_graph( +231 self, +232 name: str, +233 x: Union[List[Union[float, int]], NDArray[Any]], +234 y: Union[List[Union[float, int]], NDArray[Any], Dict[str, Union[List[Union[float, int]], NDArray[Any]]]], +235 y_label: str, +236 x_label: str, +237 graph_style: str, +238 ) -> None: +239 """Logs a graph to the RunCard, which will be rendered in the UI as a line graph +240 +241 Args: +242 name: +243 Name of graph +244 x: +245 List or numpy array of x values +246 +247 x_label: +248 Label for x axis +249 y: +250 Either a list or numpy array of y values or a dictionary of y values where key is the group label and +251 value is a list or numpy array of y values +252 y_label: +253 Label for y axis +254 graph_style: +255 Style of graph. Options are "line" or "scatter" +256 +257 example: +258 +259 ### single line graph +260 x = np.arange(1, 400, 0.5) +261 y = x * x +262 run.log_graph(name="graph1", x=x, y=y, x_label="x", y_label="y", graph_style="line") +263 +264 ### multi line graph +265 x = np.arange(1, 1000, 0.5) +266 y1 = x * x +267 y2 = y1 * 1.1 +268 y3 = y2 * 3 +269 run.log_graph( +270 name="multiline", +271 x=x, +272 y={"y1": y1, "y2": y2, "y3": y3}, +273 x_label="x", +274 y_label="y", +275 graph_style="line", +276 ) +277 +278 """ +279 +280 if isinstance(x, np.ndarray): +281 x = x.flatten().tolist() +282 assert isinstance(x, list), "x must be a list or dictionary" +283 +284 x = _decimate_list(x) +285 +286 parsed_y, graph_type = _parse_y_to_list(len(x), y) +287 +288 logger.info(f"Logging graph {name} to RunCard") +289 graph = RunGraph( +290 name=name, +291 x=x, +292 x_label=x_label, +293 y=parsed_y, +294 y_label=y_label, +295 graph_type=graph_type, +296 graph_style=GraphStyle.from_str(graph_style).value, # validate graph style +297 ) +298 +299 # save graph to storage so we can view in ui while run is active +300 lpath, rpath = _dump_graph_artifact(graph, name, self.uri) +301 +302 self._add_artifact_uri( +303 name=name, +304 local_path=lpath.as_posix(), +305 remote_path=rpath.as_posix(), +306 ) +307 +308 def log_parameters(self, parameters: Dict[str, Union[float, int, str]]) -> None: +309 """ +310 Logs parameters to current RunCard +311 +312 Args: +313 parameters: +314 Dictionary of parameters +315 """ +316 +317 for key, value in parameters.items(): +318 # check key +319 self.log_parameter(key, value) +320 +321 def log_parameter(self, key: str, value: Union[int, float, str]) -> None: +322 """ +323 Logs parameter to current RunCard +324 +325 Args: +326 key: +327 Param name +328 value: +329 Param value +330 """ +331 +332 TypeChecker.check_param_type(param=value) +333 _key = TypeChecker.replace_spaces(key) +334 +335 param = Param(name=key, value=value) +336 +337 if self.parameters.get(_key) is not None: +338 self.parameters[_key].append(param) +339 +340 else: +341 self.parameters[_key] = [param] +342 +343 def log_metric( +344 self, +345 key: str, +346 value: Union[int, float], +347 timestamp: Optional[int] = None, +348 step: Optional[int] = None, +349 ) -> None: +350 """ +351 Logs metric to the existing RunCard metric dictionary +352 +353 Args: +354 key: +355 Metric name +356 value: +357 Metric value +358 timestamp: +359 Optional timestamp +360 step: +361 Optional step associated with name and value +362 """ +363 +364 TypeChecker.check_metric_type(metric=value) +365 _key = TypeChecker.replace_spaces(key) +366 +367 metric = Metric(name=_key, value=value, timestamp=timestamp, step=step) +368 +369 self._registry.insert_metric([{**metric.model_dump(), **{"run_uid": self.uid}}]) +370 +371 if self.metrics.get(_key) is not None: +372 self.metrics[_key].append(metric) +373 else: +374 self.metrics[_key] = [metric] +375 +376 def log_metrics(self, metrics: Dict[str, Union[float, int]], step: Optional[int] = None) -> None: +377 """ +378 Log metrics to the existing RunCard metric dictionary +379 +380 Args: +381 metrics: +382 Dictionary containing key (str) and value (float or int) pairs +383 to add to the current metric set +384 step: +385 Optional step associated with metrics +386 """ +387 +388 for key, value in metrics.items(): +389 self.log_metric(key, value, step) +390 +391 def log_artifact_from_file( +392 self, +393 name: str, +394 local_path: Union[str, Path], +395 artifact_path: Optional[Union[str, Path]] = None, +396 ) -> None: +397 """ +398 Log a local file or directory to the opsml server and associate with the current run. +399 +400 Args: +401 name: +402 Name to assign to artifact(s) +403 local_path: +404 Local path to file or directory. Can be string or pathlike object +405 artifact_path: +406 Optional path to store artifact in opsml server. If not provided, 'artifacts' will be used +407 """ +408 +409 lpath = Path(local_path) +410 rpath = self.uri / (artifact_path or SaveName.ARTIFACTS.value) +411 +412 if lpath.is_file(): +413 rpath = rpath / lpath.name +414 +415 client.storage_client.put(lpath, rpath) +416 self._add_artifact_uri( +417 name=name, +418 local_path=lpath.as_posix(), +419 remote_path=rpath.as_posix(), +420 ) +421 +422 def create_registry_record(self) -> Dict[str, Any]: +423 """Creates a registry record from the current RunCard""" +424 +425 exclude_attr = {"parameters", "metrics"} +426 +427 return self.model_dump(exclude=exclude_attr) +428 +429 def _add_artifact_uri(self, name: str, local_path: str, remote_path: str) -> None: +430 """ +431 Adds an artifact_uri to the runcard +432 +433 Args: +434 name: +435 Name to associate with artifact +436 uri: +437 Uri where artifact is stored +438 """ +439 +440 self.artifact_uris[name] = Artifact( +441 name=name, +442 local_path=local_path, +443 remote_path=remote_path, +444 ) +445 +446 def add_card_uid(self, card_type: str, uid: str) -> None: +447 """ +448 Adds a card uid to the appropriate card uid list for tracking +449 +450 Args: +451 card_type: +452 ArtifactCard class name +453 uid: +454 Uid of registered ArtifactCard +455 """ +456 +457 if card_type == CardType.DATACARD: +458 self.datacard_uids = [uid, *self.datacard_uids] +459 elif card_type == CardType.MODELCARD: +460 self.modelcard_uids = [uid, *self.modelcard_uids] +461 +462 def get_metric(self, name: str) -> Union[List[Metric], Metric]: +463 """ +464 Gets a metric by name +465 +466 Args: +467 name: +468 Name of metric +469 +470 Returns: +471 List of dictionaries or dictionary containing value +472 +473 """ +474 _key = TypeChecker.replace_spaces(name) +475 +476 metric = self.metrics.get(_key) +477 +478 if metric is None: +479 # try to get metric from registry +480 assert self.uid is not None, "RunCard must be registered to get metric" +481 _metric = self._registry.get_metric(run_uid=self.uid, name=[_key]) +482 +483 if _metric is not None: +484 metric = [Metric(**i) for i in _metric] +485 +486 else: +487 raise ValueError(f"Metric {metric} was not defined") +488 +489 if len(metric) > 1: +490 return metric +491 if len(metric) == 1: +492 return metric[0] +493 return metric +494 +495 def load_metrics(self) -> None: +496 """Reloads metrics from registry""" +497 assert self.uid is not None, "RunCard must be registered to load metrics" +498 +499 metrics = self._registry.get_metric(run_uid=self.uid) +500 +501 if metrics is None: +502 logger.info("No metrics found for RunCard") +503 return None +504 +505 # reset metrics +506 self.metrics = {} +507 for metric in metrics: +508 _metric = Metric(**metric) +509 if _metric.name not in self.metrics: +510 self.metrics[_metric.name] = [_metric] +511 else: +512 self.metrics[_metric.name].append(_metric) +513 return None +514 +515 def get_parameter(self, name: str) -> Union[List[Param], Param]: +516 """ +517 Gets a parameter by name +518 +519 Args: +520 name: +521 Name of parameter +522 +523 Returns: +524 List of dictionaries or dictionary containing value +525 +526 """ +527 _key = TypeChecker.replace_spaces(name) +528 param = self.parameters.get(_key) +529 if param is not None: +530 if len(param) > 1: +531 return param +532 if len(param) == 1: +533 return param[0] +534 return param +535 +536 raise ValueError(f"Param {param} is not defined") +537 +538 def load_artifacts(self, name: Optional[str] = None) -> None: +539 """Loads artifacts from artifact_uris""" +540 if bool(self.artifact_uris) is False: +541 logger.info("No artifact uris associated with RunCard") +542 return None +543 +544 if name is not None: +545 artifact = self.artifact_uris.get(name) +546 assert artifact is not None, f"Artifact {name} not found" +547 client.storage_client.get( +548 Path(artifact.remote_path), +549 Path(artifact.local_path), +550 ) +551 +552 else: +553 for _, artifact in self.artifact_uris.items(): +554 client.storage_client.get( +555 Path(artifact.remote_path), +556 Path(artifact.local_path), +557 ) +558 return None +559 +560 @property +561 def uri(self) -> Path: +562 """The base URI to use for the card and it's artifacts.""" +563 +564 # when using runcard outside of run context +565 if self.version == CommonKwargs.BASE_VERSION.value: +566 if self.uid is None: +567 self.uid = uuid.uuid4().hex +568 +569 end_path = self.uid +570 else: +571 end_path = f"v{self.version}" +572 +573 return Path( +574 config.storage_root, +575 RegistryTableNames.from_str(self.card_type).value, +576 str(self.repository), +577 str(self.name), +578 end_path, +579 ) +580 +581 @cached_property +582 def _registry(self) -> RunCardRegistry: +583 from opsml.registry.backend import _set_registry +584 +585 return cast(RunCardRegistry, _set_registry(RegistryType.RUN)) +586 +587 @property +588 def card_type(self) -> str: +589 return CardType.RUNCARD.value +
Create a RunCard from specified arguments.
+ +Apart from required args, a RunCard must be associated with one of +datacard_uid, modelcard_uids or pipelinecard_uid
+ +info: CardInfo object containing additional metadata. If provided, it will override any
+values provided for name, repository, contact, and version.
Name, repository, and contact are required arguments for all cards. They can be provided
+directly or through a CardInfo object.
192 @model_validator(mode="before") +193 @classmethod +194 def validate_defaults_args(cls, card_args: Dict[str, Any]) -> Dict[str, Any]: +195 # add default +196 contact = card_args.get("contact") +197 +198 if contact is None: +199 card_args["contact"] = CommonKwargs.UNDEFINED.value +200 +201 repository = card_args.get("repository") +202 +203 if repository is None: +204 card_args["repository"] = "opsml" +205 +206 return card_args +
208 def add_tag(self, key: str, value: str) -> None: +209 """ +210 Logs tags to current RunCard +211 +212 Args: +213 key: +214 Key for tag +215 value: +216 value for tag +217 """ +218 self.tags = {**{key: value}, **self.tags} +
Logs tags to current RunCard
+ +230 def log_graph( +231 self, +232 name: str, +233 x: Union[List[Union[float, int]], NDArray[Any]], +234 y: Union[List[Union[float, int]], NDArray[Any], Dict[str, Union[List[Union[float, int]], NDArray[Any]]]], +235 y_label: str, +236 x_label: str, +237 graph_style: str, +238 ) -> None: +239 """Logs a graph to the RunCard, which will be rendered in the UI as a line graph +240 +241 Args: +242 name: +243 Name of graph +244 x: +245 List or numpy array of x values +246 +247 x_label: +248 Label for x axis +249 y: +250 Either a list or numpy array of y values or a dictionary of y values where key is the group label and +251 value is a list or numpy array of y values +252 y_label: +253 Label for y axis +254 graph_style: +255 Style of graph. Options are "line" or "scatter" +256 +257 example: +258 +259 ### single line graph +260 x = np.arange(1, 400, 0.5) +261 y = x * x +262 run.log_graph(name="graph1", x=x, y=y, x_label="x", y_label="y", graph_style="line") +263 +264 ### multi line graph +265 x = np.arange(1, 1000, 0.5) +266 y1 = x * x +267 y2 = y1 * 1.1 +268 y3 = y2 * 3 +269 run.log_graph( +270 name="multiline", +271 x=x, +272 y={"y1": y1, "y2": y2, "y3": y3}, +273 x_label="x", +274 y_label="y", +275 graph_style="line", +276 ) +277 +278 """ +279 +280 if isinstance(x, np.ndarray): +281 x = x.flatten().tolist() +282 assert isinstance(x, list), "x must be a list or dictionary" +283 +284 x = _decimate_list(x) +285 +286 parsed_y, graph_type = _parse_y_to_list(len(x), y) +287 +288 logger.info(f"Logging graph {name} to RunCard") +289 graph = RunGraph( +290 name=name, +291 x=x, +292 x_label=x_label, +293 y=parsed_y, +294 y_label=y_label, +295 graph_type=graph_type, +296 graph_style=GraphStyle.from_str(graph_style).value, # validate graph style +297 ) +298 +299 # save graph to storage so we can view in ui while run is active +300 lpath, rpath = _dump_graph_artifact(graph, name, self.uri) +301 +302 self._add_artifact_uri( +303 name=name, +304 local_path=lpath.as_posix(), +305 remote_path=rpath.as_posix(), +306 ) +
Logs a graph to the RunCard, which will be rendered in the UI as a line graph
+ +example:
+ +### single line graph
+x = np.arange(1, 400, 0.5)
+y = x * x
+run.log_graph(name="graph1", x=x, y=y, x_label="x", y_label="y", graph_style="line")
+
+### multi line graph
+x = np.arange(1, 1000, 0.5)
+y1 = x * x
+y2 = y1 * 1.1
+y3 = y2 * 3
+run.log_graph(
+ name="multiline",
+ x=x,
+ y={"y1": y1, "y2": y2, "y3": y3},
+ x_label="x",
+ y_label="y",
+ graph_style="line",
+)
+308 def log_parameters(self, parameters: Dict[str, Union[float, int, str]]) -> None: +309 """ +310 Logs parameters to current RunCard +311 +312 Args: +313 parameters: +314 Dictionary of parameters +315 """ +316 +317 for key, value in parameters.items(): +318 # check key +319 self.log_parameter(key, value) +
Logs parameters to current RunCard
+ +321 def log_parameter(self, key: str, value: Union[int, float, str]) -> None: +322 """ +323 Logs parameter to current RunCard +324 +325 Args: +326 key: +327 Param name +328 value: +329 Param value +330 """ +331 +332 TypeChecker.check_param_type(param=value) +333 _key = TypeChecker.replace_spaces(key) +334 +335 param = Param(name=key, value=value) +336 +337 if self.parameters.get(_key) is not None: +338 self.parameters[_key].append(param) +339 +340 else: +341 self.parameters[_key] = [param] +
Logs parameter to current RunCard
+ +343 def log_metric( +344 self, +345 key: str, +346 value: Union[int, float], +347 timestamp: Optional[int] = None, +348 step: Optional[int] = None, +349 ) -> None: +350 """ +351 Logs metric to the existing RunCard metric dictionary +352 +353 Args: +354 key: +355 Metric name +356 value: +357 Metric value +358 timestamp: +359 Optional timestamp +360 step: +361 Optional step associated with name and value +362 """ +363 +364 TypeChecker.check_metric_type(metric=value) +365 _key = TypeChecker.replace_spaces(key) +366 +367 metric = Metric(name=_key, value=value, timestamp=timestamp, step=step) +368 +369 self._registry.insert_metric([{**metric.model_dump(), **{"run_uid": self.uid}}]) +370 +371 if self.metrics.get(_key) is not None: +372 self.metrics[_key].append(metric) +373 else: +374 self.metrics[_key] = [metric] +
Logs metric to the existing RunCard metric dictionary
+ +376 def log_metrics(self, metrics: Dict[str, Union[float, int]], step: Optional[int] = None) -> None: +377 """ +378 Log metrics to the existing RunCard metric dictionary +379 +380 Args: +381 metrics: +382 Dictionary containing key (str) and value (float or int) pairs +383 to add to the current metric set +384 step: +385 Optional step associated with metrics +386 """ +387 +388 for key, value in metrics.items(): +389 self.log_metric(key, value, step) +
Log metrics to the existing RunCard metric dictionary
+ +391 def log_artifact_from_file( +392 self, +393 name: str, +394 local_path: Union[str, Path], +395 artifact_path: Optional[Union[str, Path]] = None, +396 ) -> None: +397 """ +398 Log a local file or directory to the opsml server and associate with the current run. +399 +400 Args: +401 name: +402 Name to assign to artifact(s) +403 local_path: +404 Local path to file or directory. Can be string or pathlike object +405 artifact_path: +406 Optional path to store artifact in opsml server. If not provided, 'artifacts' will be used +407 """ +408 +409 lpath = Path(local_path) +410 rpath = self.uri / (artifact_path or SaveName.ARTIFACTS.value) +411 +412 if lpath.is_file(): +413 rpath = rpath / lpath.name +414 +415 client.storage_client.put(lpath, rpath) +416 self._add_artifact_uri( +417 name=name, +418 local_path=lpath.as_posix(), +419 remote_path=rpath.as_posix(), +420 ) +
Log a local file or directory to the opsml server and associate with the current run.
+ +422 def create_registry_record(self) -> Dict[str, Any]: +423 """Creates a registry record from the current RunCard""" +424 +425 exclude_attr = {"parameters", "metrics"} +426 +427 return self.model_dump(exclude=exclude_attr) +
Creates a registry record from the current RunCard
+446 def add_card_uid(self, card_type: str, uid: str) -> None: +447 """ +448 Adds a card uid to the appropriate card uid list for tracking +449 +450 Args: +451 card_type: +452 ArtifactCard class name +453 uid: +454 Uid of registered ArtifactCard +455 """ +456 +457 if card_type == CardType.DATACARD: +458 self.datacard_uids = [uid, *self.datacard_uids] +459 elif card_type == CardType.MODELCARD: +460 self.modelcard_uids = [uid, *self.modelcard_uids] +
Adds a card uid to the appropriate card uid list for tracking
+ +462 def get_metric(self, name: str) -> Union[List[Metric], Metric]: +463 """ +464 Gets a metric by name +465 +466 Args: +467 name: +468 Name of metric +469 +470 Returns: +471 List of dictionaries or dictionary containing value +472 +473 """ +474 _key = TypeChecker.replace_spaces(name) +475 +476 metric = self.metrics.get(_key) +477 +478 if metric is None: +479 # try to get metric from registry +480 assert self.uid is not None, "RunCard must be registered to get metric" +481 _metric = self._registry.get_metric(run_uid=self.uid, name=[_key]) +482 +483 if _metric is not None: +484 metric = [Metric(**i) for i in _metric] +485 +486 else: +487 raise ValueError(f"Metric {metric} was not defined") +488 +489 if len(metric) > 1: +490 return metric +491 if len(metric) == 1: +492 return metric[0] +493 return metric +
Gets a metric by name
+ +++List of dictionaries or dictionary containing value
+
495 def load_metrics(self) -> None: +496 """Reloads metrics from registry""" +497 assert self.uid is not None, "RunCard must be registered to load metrics" +498 +499 metrics = self._registry.get_metric(run_uid=self.uid) +500 +501 if metrics is None: +502 logger.info("No metrics found for RunCard") +503 return None +504 +505 # reset metrics +506 self.metrics = {} +507 for metric in metrics: +508 _metric = Metric(**metric) +509 if _metric.name not in self.metrics: +510 self.metrics[_metric.name] = [_metric] +511 else: +512 self.metrics[_metric.name].append(_metric) +513 return None +
Reloads metrics from registry
+515 def get_parameter(self, name: str) -> Union[List[Param], Param]: +516 """ +517 Gets a parameter by name +518 +519 Args: +520 name: +521 Name of parameter +522 +523 Returns: +524 List of dictionaries or dictionary containing value +525 +526 """ +527 _key = TypeChecker.replace_spaces(name) +528 param = self.parameters.get(_key) +529 if param is not None: +530 if len(param) > 1: +531 return param +532 if len(param) == 1: +533 return param[0] +534 return param +535 +536 raise ValueError(f"Param {param} is not defined") +
Gets a parameter by name
+ +++List of dictionaries or dictionary containing value
+
538 def load_artifacts(self, name: Optional[str] = None) -> None: +539 """Loads artifacts from artifact_uris""" +540 if bool(self.artifact_uris) is False: +541 logger.info("No artifact uris associated with RunCard") +542 return None +543 +544 if name is not None: +545 artifact = self.artifact_uris.get(name) +546 assert artifact is not None, f"Artifact {name} not found" +547 client.storage_client.get( +548 Path(artifact.remote_path), +549 Path(artifact.local_path), +550 ) +551 +552 else: +553 for _, artifact in self.artifact_uris.items(): +554 client.storage_client.get( +555 Path(artifact.remote_path), +556 Path(artifact.local_path), +557 ) +558 return None +
Loads artifacts from artifact_uris
+560 @property +561 def uri(self) -> Path: +562 """The base URI to use for the card and it's artifacts.""" +563 +564 # when using runcard outside of run context +565 if self.version == CommonKwargs.BASE_VERSION.value: +566 if self.uid is None: +567 self.uid = uuid.uuid4().hex +568 +569 end_path = self.uid +570 else: +571 end_path = f"v{self.version}" +572 +573 return Path( +574 config.storage_root, +575 RegistryTableNames.from_str(self.card_type).value, +576 str(self.repository), +577 str(self.name), +578 end_path, +579 ) +
The base URI to use for the card and it's artifacts.
+1from pathlib import Path + 2from typing import Optional + 3 + 4import pyarrow as pa + 5import pyarrow.parquet as pq + 6 + 7from opsml.data.interfaces._base import DataInterface + 8from opsml.types import AllowedDataType, Feature, Suffix + 9 +10 +11class ArrowData(DataInterface): +12 +13 """Arrow Table data interface +14 +15 Args: +16 data: +17 Pyarrow Table +18 dependent_vars: +19 List of dependent variables. Can be string or index if using numpy +20 data_splits: +21 Optional list of `DataSplit` +22 data_profile: +23 Optional ydata-profiling `ProfileReport` +24 feature_map: +25 Dictionary of features -> automatically generated +26 feature_descriptions: +27 Dictionary or feature descriptions +28 sql_logic: +29 Sql logic used to generate data +30 +31 """ +32 +33 data: Optional[pa.Table] = None +34 +35 def save_data(self, path: Path) -> None: +36 """Saves pandas dataframe to parquet""" +37 +38 assert self.data is not None, "No data detected in interface" +39 schema = self.data.schema +40 self.feature_map = { +41 feature: Feature( +42 feature_type=str(type_), +43 shape=(1,), +44 ) +45 for feature, type_ in zip(schema.names, schema.types) +46 } +47 +48 pq.write_table(self.data, path) +49 +50 def load_data(self, path: Path) -> None: +51 """Load parquet dataset to pandas dataframe""" +52 +53 load_path = path.with_suffix(self.data_suffix) +54 pa_table: pa.Table = pq.ParquetDataset(path_or_paths=load_path).read() +55 +56 self.data = pa_table +57 +58 @property +59 def data_type(self) -> str: +60 return AllowedDataType.PYARROW.value +61 +62 @property +63 def data_suffix(self) -> str: +64 """Returns suffix for storage""" +65 return Suffix.PARQUET.value +66 +67 @staticmethod +68 def name() -> str: +69 return ArrowData.__name__ +
12class ArrowData(DataInterface): +13 +14 """Arrow Table data interface +15 +16 Args: +17 data: +18 Pyarrow Table +19 dependent_vars: +20 List of dependent variables. Can be string or index if using numpy +21 data_splits: +22 Optional list of `DataSplit` +23 data_profile: +24 Optional ydata-profiling `ProfileReport` +25 feature_map: +26 Dictionary of features -> automatically generated +27 feature_descriptions: +28 Dictionary or feature descriptions +29 sql_logic: +30 Sql logic used to generate data +31 +32 """ +33 +34 data: Optional[pa.Table] = None +35 +36 def save_data(self, path: Path) -> None: +37 """Saves pandas dataframe to parquet""" +38 +39 assert self.data is not None, "No data detected in interface" +40 schema = self.data.schema +41 self.feature_map = { +42 feature: Feature( +43 feature_type=str(type_), +44 shape=(1,), +45 ) +46 for feature, type_ in zip(schema.names, schema.types) +47 } +48 +49 pq.write_table(self.data, path) +50 +51 def load_data(self, path: Path) -> None: +52 """Load parquet dataset to pandas dataframe""" +53 +54 load_path = path.with_suffix(self.data_suffix) +55 pa_table: pa.Table = pq.ParquetDataset(path_or_paths=load_path).read() +56 +57 self.data = pa_table +58 +59 @property +60 def data_type(self) -> str: +61 return AllowedDataType.PYARROW.value +62 +63 @property +64 def data_suffix(self) -> str: +65 """Returns suffix for storage""" +66 return Suffix.PARQUET.value +67 +68 @staticmethod +69 def name() -> str: +70 return ArrowData.__name__ +
Arrow Table data interface
+ +DataSplitProfileReport36 def save_data(self, path: Path) -> None: +37 """Saves pandas dataframe to parquet""" +38 +39 assert self.data is not None, "No data detected in interface" +40 schema = self.data.schema +41 self.feature_map = { +42 feature: Feature( +43 feature_type=str(type_), +44 shape=(1,), +45 ) +46 for feature, type_ in zip(schema.names, schema.types) +47 } +48 +49 pq.write_table(self.data, path) +
Saves pandas dataframe to parquet
+51 def load_data(self, path: Path) -> None: +52 """Load parquet dataset to pandas dataframe""" +53 +54 load_path = path.with_suffix(self.data_suffix) +55 pa_table: pa.Table = pq.ParquetDataset(path_or_paths=load_path).read() +56 +57 self.data = pa_table +
Load parquet dataset to pandas dataframe
+63 @property +64 def data_suffix(self) -> str: +65 """Returns suffix for storage""" +66 return Suffix.PARQUET.value +
Returns suffix for storage
+1from pathlib import Path + 2from typing import Any, Dict, List, Optional, Union + 3 + 4import joblib + 5import pandas as pd + 6import polars as pl + 7from pydantic import BaseModel, ConfigDict, field_validator + 8 + 9from opsml.data.splitter import Data, DataSplit, DataSplitter + 10from opsml.helpers.logging import ArtifactLogger + 11from opsml.helpers.utils import FileUtils + 12from opsml.types import CommonKwargs, Feature, Suffix + 13 + 14logger = ArtifactLogger.get_logger() + 15 + 16try: + 17 from ydata_profiling import ProfileReport + 18except ModuleNotFoundError: + 19 ProfileReport = Any + 20 + 21 + 22class DataInterface(BaseModel): + 23 """Base data interface for all data types + 24 + 25 Args: + 26 data: + 27 Data. Can be a pyarrow table, pandas dataframe, polars dataframe + 28 or numpy array + 29 dependent_vars: + 30 List of dependent variables. Can be string or index if using numpy + 31 data_splits: + 32 Optional list of `DataSplit` + 33 data_profile: + 34 Optional ydata-profiling `ProfileReport` + 35 feature_map: + 36 Dictionary of features -> automatically generated + 37 feature_descriptions: + 38 Dictionary or feature descriptions + 39 sql_logic: + 40 Sql logic used to generate data + 41 + 42 """ + 43 + 44 data: Optional[Any] = None + 45 data_splits: List[DataSplit] = [] + 46 dependent_vars: List[Union[int, str]] = [] + 47 data_profile: Optional[ProfileReport] = None + 48 feature_map: Dict[str, Feature] = {} + 49 feature_descriptions: Dict[str, str] = {} + 50 sql_logic: Dict[str, str] = {} + 51 + 52 model_config = ConfigDict( + 53 arbitrary_types_allowed=True, + 54 validate_assignment=False, + 55 validate_default=True, + 56 ) + 57 + 58 @property + 59 def data_type(self) -> str: + 60 return CommonKwargs.UNDEFINED.value + 61 + 62 @field_validator("sql_logic", mode="before") + 63 @classmethod + 64 def _load_sql(cls, sql_logic: Dict[str, str]) -> Dict[str, str]: + 65 if not bool(sql_logic): + 66 return sql_logic + 67 + 68 for name, query in sql_logic.items(): + 69 if ".sql" in query: + 70 try: + 71 sql_path = FileUtils.find_filepath(name=query) + 72 with open(sql_path, "r", encoding="utf-8") as file_: + 73 query_ = file_.read() + 74 sql_logic[name] = query_ + 75 + 76 except Exception as error: + 77 raise ValueError(f"Could not load sql file {query}. {error}") from error + 78 + 79 return sql_logic + 80 + 81 def add_sql( + 82 self, + 83 name: str, + 84 query: Optional[str] = None, + 85 filename: Optional[str] = None, + 86 ) -> None: + 87 """ + 88 Adds a query or query from file to the sql_logic dictionary. Either a query or + 89 a filename pointing to a sql file are required in addition to a name. + 90 + 91 Args: + 92 name: + 93 Name for sql query + 94 query: + 95 SQL query + 96 filename: Filename of sql query + 97 """ + 98 if query is not None: + 99 self.sql_logic[name] = query +100 +101 elif filename is not None: +102 sql_path = str(FileUtils.find_filepath(name=filename)) +103 with open(sql_path, "r", encoding="utf-8") as file_: +104 query = file_.read() +105 self.sql_logic[name] = query +106 +107 else: +108 raise ValueError("SQL Query or Filename must be provided") +109 +110 @field_validator("data_profile", mode="before") +111 @classmethod +112 def _check_profile(cls, profile: Optional[ProfileReport]) -> Optional[ProfileReport]: +113 if profile is not None: +114 from ydata_profiling import ProfileReport as ydata_profile +115 +116 assert isinstance(profile, ydata_profile) +117 return profile +118 +119 def save_data(self, path: Path) -> None: +120 """Saves data to path. Base implementation use Joblib +121 +122 Args: +123 path: +124 Pathlib object +125 """ +126 assert self.data is not None, "No data detected in interface" +127 joblib.dump(self.data, path) +128 +129 self.feature_map = { +130 "features": Feature( +131 feature_type=str(type(self.data)), +132 shape=CommonKwargs.UNDEFINED.value, +133 ) +134 } +135 +136 def load_data(self, path: Path) -> None: +137 """Load data from pathlib object +138 +139 Args: +140 path: +141 Pathlib object +142 """ +143 +144 self.data = joblib.load(path) +145 +146 def load_data_profile(self, path: Path) -> None: +147 """Load data profile from pathlib object +148 +149 Args: +150 path: +151 Pathlib object +152 """ +153 self.data_profile = ProfileReport().loads( +154 joblib.load(path), +155 ) +156 +157 def save_data_profile(self, path: Path) -> None: +158 """Saves data profile to path. Data profiles are saved as joblib +159 joblib +160 +161 Args: +162 path: +163 Pathlib object +164 """ +165 assert self.data_profile is not None, "No data profile detected in interface" +166 +167 if path.suffix == Suffix.HTML.value: +168 profile_artifact = self.data_profile.to_html() +169 path.write_text(profile_artifact, encoding="utf-8") +170 else: +171 profile_artifact = self.data_profile.dumps() +172 joblib.dump(profile_artifact, path) +173 +174 def create_data_profile(self, sample_perc: float = 1, name: str = "data_profile") -> ProfileReport: +175 """Creates a data profile report +176 +177 Args: +178 sample_perc: +179 Percentage of data to use when creating a profile. Sampling is recommended for large dataframes. +180 Percentage is expressed as a decimal (e.g. 1 = 100%, 0.5 = 50%, etc.) +181 name: +182 Name of data profile +183 +184 """ +185 from opsml.profile.profile_data import DataProfiler +186 +187 if isinstance(self.data, (pl.DataFrame, pd.DataFrame)): +188 if self.data_profile is None: +189 self.data_profile = DataProfiler.create_profile_report( +190 data=self.data, +191 name=name, +192 sample_perc=min(sample_perc, 1), # max of 1 +193 ) +194 return self.data_profile +195 +196 logger.info("Data profile already exists") +197 return self.data_profile +198 +199 raise ValueError("A pandas dataframe type is required to create a data profile") +200 +201 def split_data(self) -> Dict[str, Data]: +202 """ +203 Loops through data splits and splits data either by indexing or +204 column values +205 +206 Example: +207 +208 ```python +209 card_info = CardInfo(name="linnerrud", repository="tutorial", contact="user@email.com") +210 data_card = DataCard( +211 info=card_info, +212 data=data, +213 dependent_vars=["Pulse"], +214 # define splits +215 data_splits=[ +216 DataSplit(label="train", indices=train_idx), +217 DataSplit(label="test", indices=test_idx), +218 ], +219 +220 ) +221 +222 splits = data_card.split_data() +223 print(splits["train"].X.head()) +224 +225 Chins Situps Jumps +226 0 5.0 162.0 60.0 +227 1 2.0 110.0 60.0 +228 2 12.0 101.0 101.0 +229 3 12.0 105.0 37.0 +230 4 13.0 155.0 58.0 +231 ``` +232 +233 Returns +234 Class containing data splits +235 """ +236 if self.data is None: +237 raise ValueError("Data must not be None. Either supply data or load data") +238 +239 if len(self.data_splits) > 0: +240 data_holder: Dict[str, Data] = {} +241 for data_split in self.data_splits: +242 label, data = DataSplitter.split( +243 split=data_split, +244 dependent_vars=self.dependent_vars, +245 data=self.data, +246 data_type=self.data_type, +247 ) +248 data_holder[label] = data +249 +250 return data_holder +251 raise ValueError("No data splits provided") +252 +253 @property +254 def data_suffix(self) -> str: +255 """Returns suffix for storage""" +256 return Suffix.JOBLIB.value +257 +258 @staticmethod +259 def name() -> str: +260 raise NotImplementedError +
23class DataInterface(BaseModel): + 24 """Base data interface for all data types + 25 + 26 Args: + 27 data: + 28 Data. Can be a pyarrow table, pandas dataframe, polars dataframe + 29 or numpy array + 30 dependent_vars: + 31 List of dependent variables. Can be string or index if using numpy + 32 data_splits: + 33 Optional list of `DataSplit` + 34 data_profile: + 35 Optional ydata-profiling `ProfileReport` + 36 feature_map: + 37 Dictionary of features -> automatically generated + 38 feature_descriptions: + 39 Dictionary or feature descriptions + 40 sql_logic: + 41 Sql logic used to generate data + 42 + 43 """ + 44 + 45 data: Optional[Any] = None + 46 data_splits: List[DataSplit] = [] + 47 dependent_vars: List[Union[int, str]] = [] + 48 data_profile: Optional[ProfileReport] = None + 49 feature_map: Dict[str, Feature] = {} + 50 feature_descriptions: Dict[str, str] = {} + 51 sql_logic: Dict[str, str] = {} + 52 + 53 model_config = ConfigDict( + 54 arbitrary_types_allowed=True, + 55 validate_assignment=False, + 56 validate_default=True, + 57 ) + 58 + 59 @property + 60 def data_type(self) -> str: + 61 return CommonKwargs.UNDEFINED.value + 62 + 63 @field_validator("sql_logic", mode="before") + 64 @classmethod + 65 def _load_sql(cls, sql_logic: Dict[str, str]) -> Dict[str, str]: + 66 if not bool(sql_logic): + 67 return sql_logic + 68 + 69 for name, query in sql_logic.items(): + 70 if ".sql" in query: + 71 try: + 72 sql_path = FileUtils.find_filepath(name=query) + 73 with open(sql_path, "r", encoding="utf-8") as file_: + 74 query_ = file_.read() + 75 sql_logic[name] = query_ + 76 + 77 except Exception as error: + 78 raise ValueError(f"Could not load sql file {query}. {error}") from error + 79 + 80 return sql_logic + 81 + 82 def add_sql( + 83 self, + 84 name: str, + 85 query: Optional[str] = None, + 86 filename: Optional[str] = None, + 87 ) -> None: + 88 """ + 89 Adds a query or query from file to the sql_logic dictionary. Either a query or + 90 a filename pointing to a sql file are required in addition to a name. + 91 + 92 Args: + 93 name: + 94 Name for sql query + 95 query: + 96 SQL query + 97 filename: Filename of sql query + 98 """ + 99 if query is not None: +100 self.sql_logic[name] = query +101 +102 elif filename is not None: +103 sql_path = str(FileUtils.find_filepath(name=filename)) +104 with open(sql_path, "r", encoding="utf-8") as file_: +105 query = file_.read() +106 self.sql_logic[name] = query +107 +108 else: +109 raise ValueError("SQL Query or Filename must be provided") +110 +111 @field_validator("data_profile", mode="before") +112 @classmethod +113 def _check_profile(cls, profile: Optional[ProfileReport]) -> Optional[ProfileReport]: +114 if profile is not None: +115 from ydata_profiling import ProfileReport as ydata_profile +116 +117 assert isinstance(profile, ydata_profile) +118 return profile +119 +120 def save_data(self, path: Path) -> None: +121 """Saves data to path. Base implementation use Joblib +122 +123 Args: +124 path: +125 Pathlib object +126 """ +127 assert self.data is not None, "No data detected in interface" +128 joblib.dump(self.data, path) +129 +130 self.feature_map = { +131 "features": Feature( +132 feature_type=str(type(self.data)), +133 shape=CommonKwargs.UNDEFINED.value, +134 ) +135 } +136 +137 def load_data(self, path: Path) -> None: +138 """Load data from pathlib object +139 +140 Args: +141 path: +142 Pathlib object +143 """ +144 +145 self.data = joblib.load(path) +146 +147 def load_data_profile(self, path: Path) -> None: +148 """Load data profile from pathlib object +149 +150 Args: +151 path: +152 Pathlib object +153 """ +154 self.data_profile = ProfileReport().loads( +155 joblib.load(path), +156 ) +157 +158 def save_data_profile(self, path: Path) -> None: +159 """Saves data profile to path. Data profiles are saved as joblib +160 joblib +161 +162 Args: +163 path: +164 Pathlib object +165 """ +166 assert self.data_profile is not None, "No data profile detected in interface" +167 +168 if path.suffix == Suffix.HTML.value: +169 profile_artifact = self.data_profile.to_html() +170 path.write_text(profile_artifact, encoding="utf-8") +171 else: +172 profile_artifact = self.data_profile.dumps() +173 joblib.dump(profile_artifact, path) +174 +175 def create_data_profile(self, sample_perc: float = 1, name: str = "data_profile") -> ProfileReport: +176 """Creates a data profile report +177 +178 Args: +179 sample_perc: +180 Percentage of data to use when creating a profile. Sampling is recommended for large dataframes. +181 Percentage is expressed as a decimal (e.g. 1 = 100%, 0.5 = 50%, etc.) +182 name: +183 Name of data profile +184 +185 """ +186 from opsml.profile.profile_data import DataProfiler +187 +188 if isinstance(self.data, (pl.DataFrame, pd.DataFrame)): +189 if self.data_profile is None: +190 self.data_profile = DataProfiler.create_profile_report( +191 data=self.data, +192 name=name, +193 sample_perc=min(sample_perc, 1), # max of 1 +194 ) +195 return self.data_profile +196 +197 logger.info("Data profile already exists") +198 return self.data_profile +199 +200 raise ValueError("A pandas dataframe type is required to create a data profile") +201 +202 def split_data(self) -> Dict[str, Data]: +203 """ +204 Loops through data splits and splits data either by indexing or +205 column values +206 +207 Example: +208 +209 ```python +210 card_info = CardInfo(name="linnerrud", repository="tutorial", contact="user@email.com") +211 data_card = DataCard( +212 info=card_info, +213 data=data, +214 dependent_vars=["Pulse"], +215 # define splits +216 data_splits=[ +217 DataSplit(label="train", indices=train_idx), +218 DataSplit(label="test", indices=test_idx), +219 ], +220 +221 ) +222 +223 splits = data_card.split_data() +224 print(splits["train"].X.head()) +225 +226 Chins Situps Jumps +227 0 5.0 162.0 60.0 +228 1 2.0 110.0 60.0 +229 2 12.0 101.0 101.0 +230 3 12.0 105.0 37.0 +231 4 13.0 155.0 58.0 +232 ``` +233 +234 Returns +235 Class containing data splits +236 """ +237 if self.data is None: +238 raise ValueError("Data must not be None. Either supply data or load data") +239 +240 if len(self.data_splits) > 0: +241 data_holder: Dict[str, Data] = {} +242 for data_split in self.data_splits: +243 label, data = DataSplitter.split( +244 split=data_split, +245 dependent_vars=self.dependent_vars, +246 data=self.data, +247 data_type=self.data_type, +248 ) +249 data_holder[label] = data +250 +251 return data_holder +252 raise ValueError("No data splits provided") +253 +254 @property +255 def data_suffix(self) -> str: +256 """Returns suffix for storage""" +257 return Suffix.JOBLIB.value +258 +259 @staticmethod +260 def name() -> str: +261 raise NotImplementedError +
Base data interface for all data types
+ +DataSplitProfileReport82 def add_sql( + 83 self, + 84 name: str, + 85 query: Optional[str] = None, + 86 filename: Optional[str] = None, + 87 ) -> None: + 88 """ + 89 Adds a query or query from file to the sql_logic dictionary. Either a query or + 90 a filename pointing to a sql file are required in addition to a name. + 91 + 92 Args: + 93 name: + 94 Name for sql query + 95 query: + 96 SQL query + 97 filename: Filename of sql query + 98 """ + 99 if query is not None: +100 self.sql_logic[name] = query +101 +102 elif filename is not None: +103 sql_path = str(FileUtils.find_filepath(name=filename)) +104 with open(sql_path, "r", encoding="utf-8") as file_: +105 query = file_.read() +106 self.sql_logic[name] = query +107 +108 else: +109 raise ValueError("SQL Query or Filename must be provided") +
Adds a query or query from file to the sql_logic dictionary. Either a query or +a filename pointing to a sql file are required in addition to a name.
+ +120 def save_data(self, path: Path) -> None: +121 """Saves data to path. Base implementation use Joblib +122 +123 Args: +124 path: +125 Pathlib object +126 """ +127 assert self.data is not None, "No data detected in interface" +128 joblib.dump(self.data, path) +129 +130 self.feature_map = { +131 "features": Feature( +132 feature_type=str(type(self.data)), +133 shape=CommonKwargs.UNDEFINED.value, +134 ) +135 } +
Saves data to path. Base implementation use Joblib
+ +137 def load_data(self, path: Path) -> None: +138 """Load data from pathlib object +139 +140 Args: +141 path: +142 Pathlib object +143 """ +144 +145 self.data = joblib.load(path) +
Load data from pathlib object
+ +147 def load_data_profile(self, path: Path) -> None: +148 """Load data profile from pathlib object +149 +150 Args: +151 path: +152 Pathlib object +153 """ +154 self.data_profile = ProfileReport().loads( +155 joblib.load(path), +156 ) +
Load data profile from pathlib object
+ +158 def save_data_profile(self, path: Path) -> None: +159 """Saves data profile to path. Data profiles are saved as joblib +160 joblib +161 +162 Args: +163 path: +164 Pathlib object +165 """ +166 assert self.data_profile is not None, "No data profile detected in interface" +167 +168 if path.suffix == Suffix.HTML.value: +169 profile_artifact = self.data_profile.to_html() +170 path.write_text(profile_artifact, encoding="utf-8") +171 else: +172 profile_artifact = self.data_profile.dumps() +173 joblib.dump(profile_artifact, path) +
Saves data profile to path. Data profiles are saved as joblib +joblib
+ +175 def create_data_profile(self, sample_perc: float = 1, name: str = "data_profile") -> ProfileReport: +176 """Creates a data profile report +177 +178 Args: +179 sample_perc: +180 Percentage of data to use when creating a profile. Sampling is recommended for large dataframes. +181 Percentage is expressed as a decimal (e.g. 1 = 100%, 0.5 = 50%, etc.) +182 name: +183 Name of data profile +184 +185 """ +186 from opsml.profile.profile_data import DataProfiler +187 +188 if isinstance(self.data, (pl.DataFrame, pd.DataFrame)): +189 if self.data_profile is None: +190 self.data_profile = DataProfiler.create_profile_report( +191 data=self.data, +192 name=name, +193 sample_perc=min(sample_perc, 1), # max of 1 +194 ) +195 return self.data_profile +196 +197 logger.info("Data profile already exists") +198 return self.data_profile +199 +200 raise ValueError("A pandas dataframe type is required to create a data profile") +
Creates a data profile report
+ +202 def split_data(self) -> Dict[str, Data]: +203 """ +204 Loops through data splits and splits data either by indexing or +205 column values +206 +207 Example: +208 +209 ```python +210 card_info = CardInfo(name="linnerrud", repository="tutorial", contact="user@email.com") +211 data_card = DataCard( +212 info=card_info, +213 data=data, +214 dependent_vars=["Pulse"], +215 # define splits +216 data_splits=[ +217 DataSplit(label="train", indices=train_idx), +218 DataSplit(label="test", indices=test_idx), +219 ], +220 +221 ) +222 +223 splits = data_card.split_data() +224 print(splits["train"].X.head()) +225 +226 Chins Situps Jumps +227 0 5.0 162.0 60.0 +228 1 2.0 110.0 60.0 +229 2 12.0 101.0 101.0 +230 3 12.0 105.0 37.0 +231 4 13.0 155.0 58.0 +232 ``` +233 +234 Returns +235 Class containing data splits +236 """ +237 if self.data is None: +238 raise ValueError("Data must not be None. Either supply data or load data") +239 +240 if len(self.data_splits) > 0: +241 data_holder: Dict[str, Data] = {} +242 for data_split in self.data_splits: +243 label, data = DataSplitter.split( +244 split=data_split, +245 dependent_vars=self.dependent_vars, +246 data=self.data, +247 data_type=self.data_type, +248 ) +249 data_holder[label] = data +250 +251 return data_holder +252 raise ValueError("No data splits provided") +
Loops through data splits and splits data either by indexing or +column values
+ +++ ++++card_info = CardInfo(name="linnerrud", repository="tutorial", contact="user@email.com") +data_card = DataCard( + info=card_info, + data=data, + dependent_vars=["Pulse"], + # define splits + data_splits=[ + DataSplit(label="train", indices=train_idx), + DataSplit(label="test", indices=test_idx), + ], + +) + +splits = data_card.split_data() +print(splits["train"].X.head()) + + Chins Situps Jumps +0 5.0 162.0 60.0 +1 2.0 110.0 60.0 +2 12.0 101.0 101.0 +3 12.0 105.0 37.0 +4 13.0 155.0 58.0 +
Returns + Class containing data splits
+254 @property +255 def data_suffix(self) -> str: +256 """Returns suffix for storage""" +257 return Suffix.JOBLIB.value +
Returns suffix for storage
+1# Copyright (c) Shipt, Inc. + 2# This source code is licensed under the MIT license found in the + 3# LICENSE file in the root directory of this source tree. + 4from pathlib import Path + 5from typing import Dict, Optional, Union + 6 + 7import pyarrow as pa + 8 + 9from opsml.data.interfaces.custom_data.arrow_reader import PyarrowDatasetReader + 10from opsml.data.interfaces.custom_data.arrow_writer import PyarrowDatasetWriter + 11from opsml.data.interfaces.custom_data.base import ( + 12 Dataset, + 13 check_for_dirs, + 14 get_metadata_filepath, + 15) + 16from opsml.helpers.logging import ArtifactLogger + 17from opsml.types import CommonKwargs + 18 + 19logger = ArtifactLogger.get_logger() + 20 + 21try: + 22 from opsml.data.interfaces.custom_data.image import ImageMetadata + 23 + 24 class ImageDataset(Dataset): + 25 """Create an image dataset from a directory of images. + 26 User can also provide a split that indicates the subdirectory of images to use. + 27 It is expected that each split contains a metadata.jsonl built from the ImageMetadata class. + 28 ImageDataset was built to have parity with HuggingFace. + 29 + 30 Args: + 31 data_dir: + 32 Root directory for images. + 33 + 34 For example, you the image file is located at either: + 35 + 36 - "images/train/my_image.png" + 37 - "images/my_image.png" + 38 + 39 Then the data_dir should be `images/` + 40 + 41 shard_size: + 42 Size of shards to use for dataset. Default is 512MB. + 43 + 44 splits: + 45 Dictionary of splits to use for dataset. If no splits are provided, then the + 46 data_dir or subdirs will be used as the split. It is expected that each split contains a + 47 metadata.jsonl built from the ImageMetadata class. It is recommended to allow opsml + 48 to create the splits for you. + 49 """ + 50 + 51 splits: Dict[Optional[str], ImageMetadata] = {} + 52 + 53 def save_data(self, path: Path) -> None: + 54 """Saves data to path. Base implementation use Joblib + 55 + 56 Args: + 57 path: + 58 Pathlib object + 59 """ + 60 PyarrowDatasetWriter(self, path, self.arrow_schema).write_dataset_to_table() + 61 + 62 def load_data(self, path: Path, **kwargs: Union[str, int]) -> None: + 63 """Saves data to data_dir + 64 + 65 Args: + 66 path: + 67 Path to load_data + 68 + 69 kwargs: + 70 + 71 Keyword arguments to pass to the data loader + 72 + 73 ---- Supported kwargs for ImageData and TextDataset ---- + 74 + 75 split: + 76 Split to use for data. If not provided, then all data will be loaded. + 77 Only used for subclasses of `Dataset`. + 78 + 79 batch_size: + 80 What batch size to use when loading data in memory. Only used for subclasses of `Dataset`. + 81 Defaults to 1000. + 82 + 83 chunk_size: + 84 How many files per batch to use when writing arrow back to local file. + 85 Defaults to 1000. + 86 + 87 Example: + 88 + 89 - If batch_size=1000 and chunk_size=100, then the loaded batch will be split into + 90 10 chunks to write in parallel. This is useful for large datasets. + 91 """ + 92 PyarrowDatasetReader(dataset=self, lpath=path, **kwargs).load_dataset() # type: ignore[arg-type] + 93 + 94 def split_data(self) -> None: + 95 """Creates data splits based on subdirectories of data_dir and supplied split value""" + 96 if bool(self.splits): + 97 return + 98 + 99 splits = check_for_dirs(self.data_dir) +100 +101 if bool(splits): +102 for split in splits: +103 self.splits[split] = ImageMetadata.load_from_file(get_metadata_filepath(self.data_dir, split)[0]) +104 else: +105 self.splits[None] = ImageMetadata.load_from_file(get_metadata_filepath(self.data_dir)[0]) +106 +107 @property +108 def arrow_schema(self) -> pa.Schema: +109 """Returns schema for ImageData records +110 +111 Returns: +112 pyarrow.Schema +113 """ +114 +115 return pa.schema( +116 [ +117 pa.field("split_label", pa.string()), +118 pa.field("path", pa.string()), +119 pa.field("height", pa.int32()), +120 pa.field("width", pa.int32()), +121 pa.field("bytes", pa.binary()), +122 pa.field("mode", pa.string()), +123 ], +124 metadata={ +125 "split_label": "label assigned to image", +126 "path": "path to image", +127 "height": "image height", +128 "width": "image width", +129 "bytes": "image bytes", +130 "mode": "image mode", +131 }, +132 ) +133 +134 @staticmethod +135 def name() -> str: +136 return ImageDataset.__name__ +137 +138 @property +139 def data_type(self) -> str: +140 return CommonKwargs.IMAGE.value +141 +142except ModuleNotFoundError: +143 from opsml.data.interfaces.backups import ImageDatasetNoModule as ImageDataset +
25 class ImageDataset(Dataset): + 26 """Create an image dataset from a directory of images. + 27 User can also provide a split that indicates the subdirectory of images to use. + 28 It is expected that each split contains a metadata.jsonl built from the ImageMetadata class. + 29 ImageDataset was built to have parity with HuggingFace. + 30 + 31 Args: + 32 data_dir: + 33 Root directory for images. + 34 + 35 For example, you the image file is located at either: + 36 + 37 - "images/train/my_image.png" + 38 - "images/my_image.png" + 39 + 40 Then the data_dir should be `images/` + 41 + 42 shard_size: + 43 Size of shards to use for dataset. Default is 512MB. + 44 + 45 splits: + 46 Dictionary of splits to use for dataset. If no splits are provided, then the + 47 data_dir or subdirs will be used as the split. It is expected that each split contains a + 48 metadata.jsonl built from the ImageMetadata class. It is recommended to allow opsml + 49 to create the splits for you. + 50 """ + 51 + 52 splits: Dict[Optional[str], ImageMetadata] = {} + 53 + 54 def save_data(self, path: Path) -> None: + 55 """Saves data to path. Base implementation use Joblib + 56 + 57 Args: + 58 path: + 59 Pathlib object + 60 """ + 61 PyarrowDatasetWriter(self, path, self.arrow_schema).write_dataset_to_table() + 62 + 63 def load_data(self, path: Path, **kwargs: Union[str, int]) -> None: + 64 """Saves data to data_dir + 65 + 66 Args: + 67 path: + 68 Path to load_data + 69 + 70 kwargs: + 71 + 72 Keyword arguments to pass to the data loader + 73 + 74 ---- Supported kwargs for ImageData and TextDataset ---- + 75 + 76 split: + 77 Split to use for data. If not provided, then all data will be loaded. + 78 Only used for subclasses of `Dataset`. + 79 + 80 batch_size: + 81 What batch size to use when loading data in memory. Only used for subclasses of `Dataset`. + 82 Defaults to 1000. + 83 + 84 chunk_size: + 85 How many files per batch to use when writing arrow back to local file. + 86 Defaults to 1000. + 87 + 88 Example: + 89 + 90 - If batch_size=1000 and chunk_size=100, then the loaded batch will be split into + 91 10 chunks to write in parallel. This is useful for large datasets. + 92 """ + 93 PyarrowDatasetReader(dataset=self, lpath=path, **kwargs).load_dataset() # type: ignore[arg-type] + 94 + 95 def split_data(self) -> None: + 96 """Creates data splits based on subdirectories of data_dir and supplied split value""" + 97 if bool(self.splits): + 98 return + 99 +100 splits = check_for_dirs(self.data_dir) +101 +102 if bool(splits): +103 for split in splits: +104 self.splits[split] = ImageMetadata.load_from_file(get_metadata_filepath(self.data_dir, split)[0]) +105 else: +106 self.splits[None] = ImageMetadata.load_from_file(get_metadata_filepath(self.data_dir)[0]) +107 +108 @property +109 def arrow_schema(self) -> pa.Schema: +110 """Returns schema for ImageData records +111 +112 Returns: +113 pyarrow.Schema +114 """ +115 +116 return pa.schema( +117 [ +118 pa.field("split_label", pa.string()), +119 pa.field("path", pa.string()), +120 pa.field("height", pa.int32()), +121 pa.field("width", pa.int32()), +122 pa.field("bytes", pa.binary()), +123 pa.field("mode", pa.string()), +124 ], +125 metadata={ +126 "split_label": "label assigned to image", +127 "path": "path to image", +128 "height": "image height", +129 "width": "image width", +130 "bytes": "image bytes", +131 "mode": "image mode", +132 }, +133 ) +134 +135 @staticmethod +136 def name() -> str: +137 return ImageDataset.__name__ +138 +139 @property +140 def data_type(self) -> str: +141 return CommonKwargs.IMAGE.value +
Create an image dataset from a directory of images. +User can also provide a split that indicates the subdirectory of images to use. +It is expected that each split contains a metadata.jsonl built from the ImageMetadata class. +ImageDataset was built to have parity with HuggingFace.
+ +data_dir: Root directory for images.
+ +For example, you the image file is located at either:
+ +Then the data_dir should be images/
54 def save_data(self, path: Path) -> None: +55 """Saves data to path. Base implementation use Joblib +56 +57 Args: +58 path: +59 Pathlib object +60 """ +61 PyarrowDatasetWriter(self, path, self.arrow_schema).write_dataset_to_table() +
Saves data to path. Base implementation use Joblib
+ +63 def load_data(self, path: Path, **kwargs: Union[str, int]) -> None: +64 """Saves data to data_dir +65 +66 Args: +67 path: +68 Path to load_data +69 +70 kwargs: +71 +72 Keyword arguments to pass to the data loader +73 +74 ---- Supported kwargs for ImageData and TextDataset ---- +75 +76 split: +77 Split to use for data. If not provided, then all data will be loaded. +78 Only used for subclasses of `Dataset`. +79 +80 batch_size: +81 What batch size to use when loading data in memory. Only used for subclasses of `Dataset`. +82 Defaults to 1000. +83 +84 chunk_size: +85 How many files per batch to use when writing arrow back to local file. +86 Defaults to 1000. +87 +88 Example: +89 +90 - If batch_size=1000 and chunk_size=100, then the loaded batch will be split into +91 10 chunks to write in parallel. This is useful for large datasets. +92 """ +93 PyarrowDatasetReader(dataset=self, lpath=path, **kwargs).load_dataset() # type: ignore[arg-type] +
Saves data to data_dir
+ +kwargs: Keyword arguments to pass to the data loader
+ +---- Supported kwargs for ImageData and TextDataset ----
+ +split:
+ Split to use for data. If not provided, then all data will be loaded.
+ Only used for subclasses of Dataset.
batch_size:
+ What batch size to use when loading data in memory. Only used for subclasses of Dataset.
+ Defaults to 1000.
chunk_size: + How many files per batch to use when writing arrow back to local file. + Defaults to 1000.
+ +Example:
+ + - If batch_size=1000 and chunk_size=100, then the loaded batch will be split into
+ 10 chunks to write in parallel. This is useful for large datasets.
+95 def split_data(self) -> None: + 96 """Creates data splits based on subdirectories of data_dir and supplied split value""" + 97 if bool(self.splits): + 98 return + 99 +100 splits = check_for_dirs(self.data_dir) +101 +102 if bool(splits): +103 for split in splits: +104 self.splits[split] = ImageMetadata.load_from_file(get_metadata_filepath(self.data_dir, split)[0]) +105 else: +106 self.splits[None] = ImageMetadata.load_from_file(get_metadata_filepath(self.data_dir)[0]) +
Creates data splits based on subdirectories of data_dir and supplied split value
+108 @property +109 def arrow_schema(self) -> pa.Schema: +110 """Returns schema for ImageData records +111 +112 Returns: +113 pyarrow.Schema +114 """ +115 +116 return pa.schema( +117 [ +118 pa.field("split_label", pa.string()), +119 pa.field("path", pa.string()), +120 pa.field("height", pa.int32()), +121 pa.field("width", pa.int32()), +122 pa.field("bytes", pa.binary()), +123 pa.field("mode", pa.string()), +124 ], +125 metadata={ +126 "split_label": "label assigned to image", +127 "path": "path to image", +128 "height": "image height", +129 "width": "image width", +130 "bytes": "image bytes", +131 "mode": "image mode", +132 }, +133 ) +
Returns schema for ImageData records
+ +++pyarrow.Schema
+
1from pathlib import Path + 2from typing import Any, Optional + 3 + 4import numpy as np + 5import zarr + 6 + 7from opsml.data.interfaces._base import DataInterface + 8from opsml.types import AllowedDataType, Feature, Suffix + 9 +10 +11class NumpyData(DataInterface): +12 """Numpy data interface +13 +14 Args: +15 data: +16 Numpy array +17 dependent_vars: +18 List of dependent variables. Can be string or index if using numpy +19 data_splits: +20 Optional list of `DataSplit` +21 data_profile: +22 Optional ydata-profiling `ProfileReport` +23 feature_map: +24 Dictionary of features -> automatically generated +25 feature_descriptions: +26 Dictionary or feature descriptions +27 sql_logic: +28 Sql logic used to generate data +29 """ +30 +31 data: Optional[np.ndarray[Any, Any]] = None +32 +33 def save_data(self, path: Path) -> None: +34 """Saves numpy array as a zarr file""" +35 +36 assert self.data is not None, "No data detected in interface" +37 +38 zarr.save(path, self.data) +39 +40 self.feature_map = { +41 "features": Feature( +42 feature_type=str(self.data.dtype), +43 shape=self.data.shape, +44 ) +45 } +46 +47 def load_data(self, path: Path) -> None: +48 """Load numpy array from zarr file""" +49 +50 self.data = zarr.load(path) +51 +52 @property +53 def data_type(self) -> str: +54 return AllowedDataType.NUMPY.value +55 +56 @property +57 def data_suffix(self) -> str: +58 """Returns suffix for storage""" +59 return Suffix.ZARR.value +60 +61 @staticmethod +62 def name() -> str: +63 return NumpyData.__name__ +
12class NumpyData(DataInterface): +13 """Numpy data interface +14 +15 Args: +16 data: +17 Numpy array +18 dependent_vars: +19 List of dependent variables. Can be string or index if using numpy +20 data_splits: +21 Optional list of `DataSplit` +22 data_profile: +23 Optional ydata-profiling `ProfileReport` +24 feature_map: +25 Dictionary of features -> automatically generated +26 feature_descriptions: +27 Dictionary or feature descriptions +28 sql_logic: +29 Sql logic used to generate data +30 """ +31 +32 data: Optional[np.ndarray[Any, Any]] = None +33 +34 def save_data(self, path: Path) -> None: +35 """Saves numpy array as a zarr file""" +36 +37 assert self.data is not None, "No data detected in interface" +38 +39 zarr.save(path, self.data) +40 +41 self.feature_map = { +42 "features": Feature( +43 feature_type=str(self.data.dtype), +44 shape=self.data.shape, +45 ) +46 } +47 +48 def load_data(self, path: Path) -> None: +49 """Load numpy array from zarr file""" +50 +51 self.data = zarr.load(path) +52 +53 @property +54 def data_type(self) -> str: +55 return AllowedDataType.NUMPY.value +56 +57 @property +58 def data_suffix(self) -> str: +59 """Returns suffix for storage""" +60 return Suffix.ZARR.value +61 +62 @staticmethod +63 def name() -> str: +64 return NumpyData.__name__ +
Numpy data interface
+ +DataSplitProfileReport34 def save_data(self, path: Path) -> None: +35 """Saves numpy array as a zarr file""" +36 +37 assert self.data is not None, "No data detected in interface" +38 +39 zarr.save(path, self.data) +40 +41 self.feature_map = { +42 "features": Feature( +43 feature_type=str(self.data.dtype), +44 shape=self.data.shape, +45 ) +46 } +
Saves numpy array as a zarr file
+48 def load_data(self, path: Path) -> None: +49 """Load numpy array from zarr file""" +50 +51 self.data = zarr.load(path) +
Load numpy array from zarr file
+57 @property +58 def data_suffix(self) -> str: +59 """Returns suffix for storage""" +60 return Suffix.ZARR.value +
Returns suffix for storage
+1from pathlib import Path + 2from typing import Optional, cast + 3 + 4import pandas as pd + 5import pyarrow as pa + 6import pyarrow.parquet as pq + 7 + 8from opsml.data.formatter import check_data_schema + 9from opsml.data.interfaces._base import DataInterface +10from opsml.types import AllowedDataType, Feature, Suffix +11 +12 +13class PandasData(DataInterface): +14 """Pandas interface +15 +16 Args: +17 data: +18 Pandas DataFrame +19 dependent_vars: +20 List of dependent variables. Can be string or index if using numpy +21 data_splits: +22 Optional list of `DataSplit` +23 data_profile: +24 Optional ydata-profiling `ProfileReport` +25 feature_map: +26 Dictionary of features -> automatically generated +27 feature_descriptions: +28 Dictionary or feature descriptions +29 sql_logic: +30 Sql logic used to generate data +31 """ +32 +33 data: Optional[pd.DataFrame] = None +34 +35 def save_data(self, path: Path) -> None: +36 """Saves pandas dataframe to parquet""" +37 +38 assert self.data is not None, "No data detected in interface" +39 arrow_table = pa.Table.from_pandas(self.data, preserve_index=False) +40 self.feature_map = { +41 key: Feature( +42 feature_type=str(value), +43 shape=(1,), +44 ) +45 for key, value in self.data.dtypes.to_dict().items() +46 } +47 pq.write_table(arrow_table, path) +48 +49 def load_data(self, path: Path) -> None: +50 """Load parquet dataset to pandas dataframe""" +51 +52 pa_table: pa.Table = pq.ParquetDataset(path_or_paths=path).read() +53 +54 data = check_data_schema( +55 pa_table.to_pandas(), +56 self.feature_map, +57 self.data_type, +58 ) +59 +60 self.data = cast(pd.DataFrame, data) +61 +62 @property +63 def data_type(self) -> str: +64 return AllowedDataType.PANDAS.value +65 +66 @property +67 def data_suffix(self) -> str: +68 """Returns suffix for storage""" +69 return Suffix.PARQUET.value +70 +71 @staticmethod +72 def name() -> str: +73 return PandasData.__name__ +
14class PandasData(DataInterface): +15 """Pandas interface +16 +17 Args: +18 data: +19 Pandas DataFrame +20 dependent_vars: +21 List of dependent variables. Can be string or index if using numpy +22 data_splits: +23 Optional list of `DataSplit` +24 data_profile: +25 Optional ydata-profiling `ProfileReport` +26 feature_map: +27 Dictionary of features -> automatically generated +28 feature_descriptions: +29 Dictionary or feature descriptions +30 sql_logic: +31 Sql logic used to generate data +32 """ +33 +34 data: Optional[pd.DataFrame] = None +35 +36 def save_data(self, path: Path) -> None: +37 """Saves pandas dataframe to parquet""" +38 +39 assert self.data is not None, "No data detected in interface" +40 arrow_table = pa.Table.from_pandas(self.data, preserve_index=False) +41 self.feature_map = { +42 key: Feature( +43 feature_type=str(value), +44 shape=(1,), +45 ) +46 for key, value in self.data.dtypes.to_dict().items() +47 } +48 pq.write_table(arrow_table, path) +49 +50 def load_data(self, path: Path) -> None: +51 """Load parquet dataset to pandas dataframe""" +52 +53 pa_table: pa.Table = pq.ParquetDataset(path_or_paths=path).read() +54 +55 data = check_data_schema( +56 pa_table.to_pandas(), +57 self.feature_map, +58 self.data_type, +59 ) +60 +61 self.data = cast(pd.DataFrame, data) +62 +63 @property +64 def data_type(self) -> str: +65 return AllowedDataType.PANDAS.value +66 +67 @property +68 def data_suffix(self) -> str: +69 """Returns suffix for storage""" +70 return Suffix.PARQUET.value +71 +72 @staticmethod +73 def name() -> str: +74 return PandasData.__name__ +
Pandas interface
+ +DataSplitProfileReport36 def save_data(self, path: Path) -> None: +37 """Saves pandas dataframe to parquet""" +38 +39 assert self.data is not None, "No data detected in interface" +40 arrow_table = pa.Table.from_pandas(self.data, preserve_index=False) +41 self.feature_map = { +42 key: Feature( +43 feature_type=str(value), +44 shape=(1,), +45 ) +46 for key, value in self.data.dtypes.to_dict().items() +47 } +48 pq.write_table(arrow_table, path) +
Saves pandas dataframe to parquet
+50 def load_data(self, path: Path) -> None: +51 """Load parquet dataset to pandas dataframe""" +52 +53 pa_table: pa.Table = pq.ParquetDataset(path_or_paths=path).read() +54 +55 data = check_data_schema( +56 pa_table.to_pandas(), +57 self.feature_map, +58 self.data_type, +59 ) +60 +61 self.data = cast(pd.DataFrame, data) +
Load parquet dataset to pandas dataframe
+67 @property +68 def data_suffix(self) -> str: +69 """Returns suffix for storage""" +70 return Suffix.PARQUET.value +
Returns suffix for storage
+1from pathlib import Path + 2from typing import Optional, cast + 3 + 4import polars as pl + 5import pyarrow as pa + 6import pyarrow.parquet as pq + 7 + 8from opsml.data.formatter import check_data_schema + 9from opsml.data.interfaces._base import DataInterface +10from opsml.types import AllowedDataType, Feature, Suffix +11 +12 +13class PolarsData(DataInterface): +14 """Polars data interface +15 +16 Args: +17 data: +18 Polars DataFrame +19 dependent_vars: +20 List of dependent variables. Can be string or index if using numpy +21 data_splits: +22 Optional list of `DataSplit` +23 data_profile: +24 Optional ydata-profiling `ProfileReport` +25 feature_map: +26 Dictionary of features -> automatically generated +27 feature_descriptions: +28 Dictionary or feature descriptions +29 sql_logic: +30 Sql logic used to generate data +31 """ +32 +33 data: Optional[pl.DataFrame] = None +34 +35 def save_data(self, path: Path) -> None: +36 """Saves pandas dataframe to parquet""" +37 +38 assert self.data is not None, "No data detected in interface" +39 self.feature_map = { +40 key: Feature( +41 feature_type=str(value), +42 shape=(1,), +43 ) +44 for key, value in self.data.schema.items() +45 } +46 +47 pq.write_table(self.data.to_arrow(), path) +48 +49 def load_data(self, path: Path) -> None: +50 """Load parquet dataset to pandas dataframe""" +51 +52 load_path = path.with_suffix(self.data_suffix) +53 pa_table: pa.Table = pq.ParquetDataset(path_or_paths=load_path).read() +54 data = check_data_schema( +55 pl.from_arrow(data=pa_table), +56 self.feature_map, +57 self.data_type, +58 ) +59 +60 self.data = cast(pl.DataFrame, data) +61 +62 @property +63 def data_type(self) -> str: +64 return AllowedDataType.POLARS.value +65 +66 @property +67 def data_suffix(self) -> str: +68 """Returns suffix for storage""" +69 return Suffix.PARQUET.value +70 +71 @staticmethod +72 def name() -> str: +73 return PolarsData.__name__ +
14class PolarsData(DataInterface): +15 """Polars data interface +16 +17 Args: +18 data: +19 Polars DataFrame +20 dependent_vars: +21 List of dependent variables. Can be string or index if using numpy +22 data_splits: +23 Optional list of `DataSplit` +24 data_profile: +25 Optional ydata-profiling `ProfileReport` +26 feature_map: +27 Dictionary of features -> automatically generated +28 feature_descriptions: +29 Dictionary or feature descriptions +30 sql_logic: +31 Sql logic used to generate data +32 """ +33 +34 data: Optional[pl.DataFrame] = None +35 +36 def save_data(self, path: Path) -> None: +37 """Saves pandas dataframe to parquet""" +38 +39 assert self.data is not None, "No data detected in interface" +40 self.feature_map = { +41 key: Feature( +42 feature_type=str(value), +43 shape=(1,), +44 ) +45 for key, value in self.data.schema.items() +46 } +47 +48 pq.write_table(self.data.to_arrow(), path) +49 +50 def load_data(self, path: Path) -> None: +51 """Load parquet dataset to pandas dataframe""" +52 +53 load_path = path.with_suffix(self.data_suffix) +54 pa_table: pa.Table = pq.ParquetDataset(path_or_paths=load_path).read() +55 data = check_data_schema( +56 pl.from_arrow(data=pa_table), +57 self.feature_map, +58 self.data_type, +59 ) +60 +61 self.data = cast(pl.DataFrame, data) +62 +63 @property +64 def data_type(self) -> str: +65 return AllowedDataType.POLARS.value +66 +67 @property +68 def data_suffix(self) -> str: +69 """Returns suffix for storage""" +70 return Suffix.PARQUET.value +71 +72 @staticmethod +73 def name() -> str: +74 return PolarsData.__name__ +
Polars data interface
+ +DataSplitProfileReport36 def save_data(self, path: Path) -> None: +37 """Saves pandas dataframe to parquet""" +38 +39 assert self.data is not None, "No data detected in interface" +40 self.feature_map = { +41 key: Feature( +42 feature_type=str(value), +43 shape=(1,), +44 ) +45 for key, value in self.data.schema.items() +46 } +47 +48 pq.write_table(self.data.to_arrow(), path) +
Saves pandas dataframe to parquet
+50 def load_data(self, path: Path) -> None: +51 """Load parquet dataset to pandas dataframe""" +52 +53 load_path = path.with_suffix(self.data_suffix) +54 pa_table: pa.Table = pq.ParquetDataset(path_or_paths=load_path).read() +55 data = check_data_schema( +56 pl.from_arrow(data=pa_table), +57 self.feature_map, +58 self.data_type, +59 ) +60 +61 self.data = cast(pl.DataFrame, data) +
Load parquet dataset to pandas dataframe
+67 @property +68 def data_suffix(self) -> str: +69 """Returns suffix for storage""" +70 return Suffix.PARQUET.value +
Returns suffix for storage
+1from opsml.data.interfaces._base import DataInterface + 2from opsml.types import AllowedDataType + 3 + 4 + 5class SqlData(DataInterface): + 6 + 7 """Arrow Table data interface + 8 + 9 Args: +10 +11 sql_logic: +12 Dictionary of strings containing sql logic or sql files used to create the data +13 feature_descriptions: +14 Dictionary or feature descriptions +15 """ +16 +17 @property +18 def data_type(self) -> str: +19 return AllowedDataType.SQL.value +20 +21 @staticmethod +22 def name() -> str: +23 return SqlData.__name__ +
6class SqlData(DataInterface): + 7 + 8 """Arrow Table data interface + 9 +10 Args: +11 +12 sql_logic: +13 Dictionary of strings containing sql logic or sql files used to create the data +14 feature_descriptions: +15 Dictionary or feature descriptions +16 """ +17 +18 @property +19 def data_type(self) -> str: +20 return AllowedDataType.SQL.value +21 +22 @staticmethod +23 def name() -> str: +24 return SqlData.__name__ +
Arrow Table data interface
+ +1# Copyright (c) Shipt, Inc. + 2# This source code is licensed under the MIT license found in the + 3# LICENSE file in the root directory of this source tree. + 4from pathlib import Path + 5from typing import Dict, Optional, Union + 6 + 7import pyarrow as pa + 8 + 9from opsml.data.interfaces.custom_data.arrow_reader import PyarrowDatasetReader + 10from opsml.data.interfaces.custom_data.arrow_writer import PyarrowDatasetWriter + 11from opsml.data.interfaces.custom_data.base import ( + 12 Dataset, + 13 check_for_dirs, + 14 get_metadata_filepath, + 15) + 16from opsml.data.interfaces.custom_data.text import TextMetadata + 17from opsml.helpers.logging import ArtifactLogger + 18from opsml.types import CommonKwargs + 19 + 20logger = ArtifactLogger.get_logger() + 21 + 22 + 23class TextDataset(Dataset): + 24 """Create a text dataset from a directory of files + 25 User can also provide a split that indicates the subdirectory of files to use. + 26 It is expected that each split contains a metadata.jsonl built from the TextMetadata class. + 27 TextDataset was built to have parity with HuggingFace. + 28 + 29 Args: + 30 data_dir: + 31 Root directory for text data. + 32 + 33 For example, the file is located at either: + 34 + 35 - "text/train/example.txt" + 36 - "text/example.txt" + 37 + 38 Then the data_dir should be `text/` + 39 + 40 shard_size: + 41 Size of shards to use for dataset. Default is 512MB. + 42 + 43 splits: + 44 Dictionary of splits to use for dataset. If no splits are provided, then the + 45 data_dir or subdirs will be used as the split. It is expected that each split contains a + 46 metadata.jsonl built from the TextMetadata class. It is recommended to allow opsml + 47 to create the splits for you. + 48 """ + 49 + 50 splits: Dict[Optional[str], TextMetadata] = {} + 51 + 52 def save_data(self, path: Path) -> None: + 53 """Saves data to path. Base implementation use Joblib + 54 + 55 Args: + 56 path: + 57 Pathlib object + 58 """ + 59 PyarrowDatasetWriter(self, path, self.arrow_schema).write_dataset_to_table() + 60 + 61 def load_data(self, path: Path, **kwargs: Union[str, int]) -> None: + 62 """Saves data to data_dir + 63 + 64 Args: + 65 path: + 66 Path to load_data + 67 + 68 kwargs: + 69 + 70 Keyword arguments to pass to the data loader + 71 + 72 ---- Supported kwargs for ImageDataset and TextDataset ---- + 73 + 74 split: + 75 Split to use for data. If not provided, then all data will be loaded. + 76 Only used for subclasses of `Dataset`. + 77 + 78 batch_size: + 79 What batch size to use when loading data. Only used for subclasses of `Dataset`. + 80 Defaults to 1000. + 81 + 82 chunk_size: + 83 How many files per batch to use when writing arrow back to local file. + 84 Defaults to 1000. + 85 + 86 Example: + 87 + 88 - If batch_size=1000 and chunk_size=100, then the loaded batch will be split into + 89 10 chunks to write in parallel. This is useful for large datasets. + 90 """ + 91 PyarrowDatasetReader(dataset=self, lpath=path, **kwargs).load_dataset() # type: ignore[arg-type] + 92 + 93 def split_data(self) -> None: + 94 """Creates data splits based on subdirectories of data_dir and supplied split value""" + 95 if bool(self.splits): + 96 return + 97 + 98 splits = check_for_dirs(self.data_dir) + 99 +100 if bool(splits): +101 for split in splits: +102 self.splits[split] = TextMetadata.load_from_file(get_metadata_filepath(self.data_dir, split)[0]) +103 else: +104 self.splits[None] = TextMetadata.load_from_file(get_metadata_filepath(self.data_dir)[0]) +105 +106 @property +107 def arrow_schema(self) -> pa.Schema: +108 """Returns schema for TextData records +109 +110 Returns: +111 pyarrow.Schema +112 """ +113 +114 return pa.schema( +115 [ +116 pa.field("split_label", pa.string()), +117 pa.field("path", pa.string()), +118 pa.field("bytes", pa.binary()), +119 ], +120 metadata={ +121 "split_label": "label assigned to text", +122 "path": "path to text", +123 "bytes": "text bytes", +124 }, +125 ) +126 +127 @staticmethod +128 def name() -> str: +129 return TextDataset.__name__ +130 +131 @property +132 def data_type(self) -> str: +133 return CommonKwargs.TEXT.value +
24class TextDataset(Dataset): + 25 """Create a text dataset from a directory of files + 26 User can also provide a split that indicates the subdirectory of files to use. + 27 It is expected that each split contains a metadata.jsonl built from the TextMetadata class. + 28 TextDataset was built to have parity with HuggingFace. + 29 + 30 Args: + 31 data_dir: + 32 Root directory for text data. + 33 + 34 For example, the file is located at either: + 35 + 36 - "text/train/example.txt" + 37 - "text/example.txt" + 38 + 39 Then the data_dir should be `text/` + 40 + 41 shard_size: + 42 Size of shards to use for dataset. Default is 512MB. + 43 + 44 splits: + 45 Dictionary of splits to use for dataset. If no splits are provided, then the + 46 data_dir or subdirs will be used as the split. It is expected that each split contains a + 47 metadata.jsonl built from the TextMetadata class. It is recommended to allow opsml + 48 to create the splits for you. + 49 """ + 50 + 51 splits: Dict[Optional[str], TextMetadata] = {} + 52 + 53 def save_data(self, path: Path) -> None: + 54 """Saves data to path. Base implementation use Joblib + 55 + 56 Args: + 57 path: + 58 Pathlib object + 59 """ + 60 PyarrowDatasetWriter(self, path, self.arrow_schema).write_dataset_to_table() + 61 + 62 def load_data(self, path: Path, **kwargs: Union[str, int]) -> None: + 63 """Saves data to data_dir + 64 + 65 Args: + 66 path: + 67 Path to load_data + 68 + 69 kwargs: + 70 + 71 Keyword arguments to pass to the data loader + 72 + 73 ---- Supported kwargs for ImageDataset and TextDataset ---- + 74 + 75 split: + 76 Split to use for data. If not provided, then all data will be loaded. + 77 Only used for subclasses of `Dataset`. + 78 + 79 batch_size: + 80 What batch size to use when loading data. Only used for subclasses of `Dataset`. + 81 Defaults to 1000. + 82 + 83 chunk_size: + 84 How many files per batch to use when writing arrow back to local file. + 85 Defaults to 1000. + 86 + 87 Example: + 88 + 89 - If batch_size=1000 and chunk_size=100, then the loaded batch will be split into + 90 10 chunks to write in parallel. This is useful for large datasets. + 91 """ + 92 PyarrowDatasetReader(dataset=self, lpath=path, **kwargs).load_dataset() # type: ignore[arg-type] + 93 + 94 def split_data(self) -> None: + 95 """Creates data splits based on subdirectories of data_dir and supplied split value""" + 96 if bool(self.splits): + 97 return + 98 + 99 splits = check_for_dirs(self.data_dir) +100 +101 if bool(splits): +102 for split in splits: +103 self.splits[split] = TextMetadata.load_from_file(get_metadata_filepath(self.data_dir, split)[0]) +104 else: +105 self.splits[None] = TextMetadata.load_from_file(get_metadata_filepath(self.data_dir)[0]) +106 +107 @property +108 def arrow_schema(self) -> pa.Schema: +109 """Returns schema for TextData records +110 +111 Returns: +112 pyarrow.Schema +113 """ +114 +115 return pa.schema( +116 [ +117 pa.field("split_label", pa.string()), +118 pa.field("path", pa.string()), +119 pa.field("bytes", pa.binary()), +120 ], +121 metadata={ +122 "split_label": "label assigned to text", +123 "path": "path to text", +124 "bytes": "text bytes", +125 }, +126 ) +127 +128 @staticmethod +129 def name() -> str: +130 return TextDataset.__name__ +131 +132 @property +133 def data_type(self) -> str: +134 return CommonKwargs.TEXT.value +
Create a text dataset from a directory of files +User can also provide a split that indicates the subdirectory of files to use. +It is expected that each split contains a metadata.jsonl built from the TextMetadata class. +TextDataset was built to have parity with HuggingFace.
+ +data_dir: Root directory for text data.
+ +For example, the file is located at either:
+ +Then the data_dir should be text/
53 def save_data(self, path: Path) -> None: +54 """Saves data to path. Base implementation use Joblib +55 +56 Args: +57 path: +58 Pathlib object +59 """ +60 PyarrowDatasetWriter(self, path, self.arrow_schema).write_dataset_to_table() +
Saves data to path. Base implementation use Joblib
+ +62 def load_data(self, path: Path, **kwargs: Union[str, int]) -> None: +63 """Saves data to data_dir +64 +65 Args: +66 path: +67 Path to load_data +68 +69 kwargs: +70 +71 Keyword arguments to pass to the data loader +72 +73 ---- Supported kwargs for ImageDataset and TextDataset ---- +74 +75 split: +76 Split to use for data. If not provided, then all data will be loaded. +77 Only used for subclasses of `Dataset`. +78 +79 batch_size: +80 What batch size to use when loading data. Only used for subclasses of `Dataset`. +81 Defaults to 1000. +82 +83 chunk_size: +84 How many files per batch to use when writing arrow back to local file. +85 Defaults to 1000. +86 +87 Example: +88 +89 - If batch_size=1000 and chunk_size=100, then the loaded batch will be split into +90 10 chunks to write in parallel. This is useful for large datasets. +91 """ +92 PyarrowDatasetReader(dataset=self, lpath=path, **kwargs).load_dataset() # type: ignore[arg-type] +
Saves data to data_dir
+ +kwargs: Keyword arguments to pass to the data loader
+ +---- Supported kwargs for ImageDataset and TextDataset ----
+ +split:
+ Split to use for data. If not provided, then all data will be loaded.
+ Only used for subclasses of Dataset.
batch_size:
+ What batch size to use when loading data. Only used for subclasses of Dataset.
+ Defaults to 1000.
chunk_size: + How many files per batch to use when writing arrow back to local file. + Defaults to 1000.
+ +Example:
+ + - If batch_size=1000 and chunk_size=100, then the loaded batch will be split into
+ 10 chunks to write in parallel. This is useful for large datasets.
+94 def split_data(self) -> None: + 95 """Creates data splits based on subdirectories of data_dir and supplied split value""" + 96 if bool(self.splits): + 97 return + 98 + 99 splits = check_for_dirs(self.data_dir) +100 +101 if bool(splits): +102 for split in splits: +103 self.splits[split] = TextMetadata.load_from_file(get_metadata_filepath(self.data_dir, split)[0]) +104 else: +105 self.splits[None] = TextMetadata.load_from_file(get_metadata_filepath(self.data_dir)[0]) +
Creates data splits based on subdirectories of data_dir and supplied split value
+107 @property +108 def arrow_schema(self) -> pa.Schema: +109 """Returns schema for TextData records +110 +111 Returns: +112 pyarrow.Schema +113 """ +114 +115 return pa.schema( +116 [ +117 pa.field("split_label", pa.string()), +118 pa.field("path", pa.string()), +119 pa.field("bytes", pa.binary()), +120 ], +121 metadata={ +122 "split_label": "label assigned to text", +123 "path": "path to text", +124 "bytes": "text bytes", +125 }, +126 ) +
Returns schema for TextData records
+ +++pyarrow.Schema
+
1from pathlib import Path + 2from typing import Optional + 3 + 4from opsml.data.interfaces._base import DataInterface + 5from opsml.types import AllowedDataType, Feature, Suffix + 6 + 7try: + 8 import torch + 9 +10 class TorchData(DataInterface): +11 """Torch dataset interface +12 +13 Args: +14 data: +15 Torch dataset or torch tensors +16 dependent_vars: +17 List of dependent variables. Can be string or index if using numpy +18 data_splits: +19 Optional list of `DataSplit` +20 feature_map: +21 Dictionary of features -> automatically generated +22 feature_descriptions: +23 Dictionary or feature descriptions +24 sql_logic: +25 Sql logic used to generate data +26 is_dataset: +27 Whether data is a torch dataset or not +28 """ +29 +30 data: Optional[torch.Tensor] = None +31 +32 def save_data(self, path: Path) -> None: +33 """Saves torch dataset or tensor(s)""" +34 +35 assert self.data is not None, "No data detected in interface" +36 +37 torch.save(self.data, path) +38 self.feature_map["features"] = Feature(feature_type=str(self.data.dtype), shape=self.data.shape) +39 +40 def load_data(self, path: Path) -> None: +41 """Load torch tensors or torch datasets""" +42 self.data = torch.load(path) +43 +44 @property +45 def data_type(self) -> str: +46 return AllowedDataType.TORCH_TENSOR.value +47 +48 @property +49 def data_suffix(self) -> str: +50 """Returns suffix for storage""" +51 return Suffix.PT.value +52 +53 @staticmethod +54 def name() -> str: +55 return TorchData.__name__ +56 +57except ModuleNotFoundError: +58 from opsml.data.interfaces.backups import TorchDataNoModule as TorchData +
11 class TorchData(DataInterface): +12 """Torch dataset interface +13 +14 Args: +15 data: +16 Torch dataset or torch tensors +17 dependent_vars: +18 List of dependent variables. Can be string or index if using numpy +19 data_splits: +20 Optional list of `DataSplit` +21 feature_map: +22 Dictionary of features -> automatically generated +23 feature_descriptions: +24 Dictionary or feature descriptions +25 sql_logic: +26 Sql logic used to generate data +27 is_dataset: +28 Whether data is a torch dataset or not +29 """ +30 +31 data: Optional[torch.Tensor] = None +32 +33 def save_data(self, path: Path) -> None: +34 """Saves torch dataset or tensor(s)""" +35 +36 assert self.data is not None, "No data detected in interface" +37 +38 torch.save(self.data, path) +39 self.feature_map["features"] = Feature(feature_type=str(self.data.dtype), shape=self.data.shape) +40 +41 def load_data(self, path: Path) -> None: +42 """Load torch tensors or torch datasets""" +43 self.data = torch.load(path) +44 +45 @property +46 def data_type(self) -> str: +47 return AllowedDataType.TORCH_TENSOR.value +48 +49 @property +50 def data_suffix(self) -> str: +51 """Returns suffix for storage""" +52 return Suffix.PT.value +53 +54 @staticmethod +55 def name() -> str: +56 return TorchData.__name__ +
Torch dataset interface
+ +DataSplit33 def save_data(self, path: Path) -> None: +34 """Saves torch dataset or tensor(s)""" +35 +36 assert self.data is not None, "No data detected in interface" +37 +38 torch.save(self.data, path) +39 self.feature_map["features"] = Feature(feature_type=str(self.data.dtype), shape=self.data.shape) +
Saves torch dataset or tensor(s)
+41 def load_data(self, path: Path) -> None: +42 """Load torch tensors or torch datasets""" +43 self.data = torch.load(path) +
Load torch tensors or torch datasets
+49 @property +50 def data_suffix(self) -> str: +51 """Returns suffix for storage""" +52 return Suffix.PT.value +
Returns suffix for storage
+1# pylint: disable=invalid-name + 2# Copyright (c) Shipt, Inc. + 3# This source code is licensed under the MIT license found in the + 4# LICENSE file in the root directory of this source tree. + 5from dataclasses import dataclass + 6from typing import Any, List, Optional, Tuple, Union + 7 + 8import pandas as pd + 9import polars as pl + 10import pyarrow as pa + 11from numpy.typing import NDArray + 12from pydantic import BaseModel, ConfigDict, field_validator + 13 + 14from opsml.types import AllowedDataType + 15 + 16 + 17@dataclass + 18class Data: + 19 X: Any + 20 y: Optional[Any] = None + 21 + 22 + 23class DataSplit(BaseModel): + 24 model_config = ConfigDict(arbitrary_types_allowed=True) + 25 + 26 label: str + 27 column_name: Optional[str] = None + 28 column_value: Optional[Union[str, float, int, pd.Timestamp]] = None + 29 inequality: Optional[str] = None + 30 start: Optional[int] = None + 31 stop: Optional[int] = None + 32 indices: Optional[List[int]] = None + 33 + 34 @field_validator("indices", mode="before") + 35 @classmethod + 36 def convert_to_list(cls, value: Optional[List[int]]) -> Optional[List[int]]: + 37 """Pre to convert indices to list if not None""" + 38 + 39 if value is not None and not isinstance(value, list): + 40 value = list(value) + 41 + 42 return value + 43 + 44 @field_validator("inequality", mode="before") + 45 @classmethod + 46 def trim_whitespace(cls, value: str) -> str: + 47 """Trims whitespace from inequality signs""" + 48 + 49 if value is not None: + 50 value = value.strip() + 51 + 52 return value + 53 + 54 + 55class DataSplitterBase: + 56 def __init__( + 57 self, + 58 split: DataSplit, + 59 dependent_vars: List[Union[int, str]], + 60 ): + 61 self.split = split + 62 self.dependent_vars = dependent_vars + 63 + 64 @property + 65 def column_name(self) -> str: + 66 if self.split.column_name is not None: + 67 return self.split.column_name + 68 + 69 raise ValueError("Column name was not provided") + 70 + 71 @property + 72 def column_value(self) -> Any: + 73 if self.split.column_value is not None: + 74 return self.split.column_value + 75 + 76 raise ValueError("Column value was not provided") + 77 + 78 @property + 79 def indices(self) -> List[int]: + 80 if self.split.indices is not None: + 81 return self.split.indices + 82 raise ValueError("List of indices was not provided") + 83 + 84 @property + 85 def start(self) -> int: + 86 if self.split.start is not None: + 87 return self.split.start + 88 raise ValueError("Start index was not provided") + 89 + 90 @property + 91 def stop(self) -> int: + 92 if self.split.stop is not None: + 93 return self.split.stop + 94 raise ValueError("Stop index was not provided") + 95 + 96 def get_x_cols(self, columns: List[str], dependent_vars: List[Union[str, int]]) -> List[str]: + 97 for var in dependent_vars: + 98 if isinstance(var, str): + 99 columns.remove(var) +100 +101 return columns +102 +103 def create_split(self, data: Any) -> Tuple[str, Data]: +104 raise NotImplementedError +105 +106 @staticmethod +107 def validate(data_type: str, split: DataSplit) -> bool: +108 raise NotImplementedError +109 +110 +111class PolarsColumnSplitter(DataSplitterBase): +112 """Column splitter for Polars dataframe""" +113 +114 def create_split(self, data: pl.DataFrame) -> Tuple[str, Data]: +115 if self.split.inequality is None: +116 data = data.filter(pl.col(self.column_name) == self.column_value) +117 +118 elif self.split.inequality == ">": +119 data = data.filter(pl.col(self.column_name) > self.column_value) +120 +121 elif self.split.inequality == ">=": +122 data = data.filter(pl.col(self.column_name) >= self.column_value) +123 +124 elif self.split.inequality == "<": +125 data = data.filter(pl.col(self.column_name) < self.column_value) +126 +127 else: +128 data = data.filter(pl.col(self.column_name) <= self.column_value) +129 +130 if bool(self.dependent_vars): +131 x_cols = self.get_x_cols(columns=data.columns, dependent_vars=self.dependent_vars) +132 +133 return self.split.label, Data( +134 X=data.select(x_cols), +135 y=data.select(self.dependent_vars), +136 ) +137 +138 return self.split.label, Data(X=data) +139 +140 @staticmethod +141 def validate(data_type: str, split: DataSplit) -> bool: +142 return data_type == AllowedDataType.POLARS and split.column_name is not None +143 +144 +145class PolarsIndexSplitter(DataSplitterBase): +146 """Split Polars DataFrame by rows index""" +147 +148 def create_split(self, data: pl.DataFrame) -> Tuple[str, Data]: +149 # slice +150 data = data[self.indices] +151 +152 if bool(self.dependent_vars): +153 x_cols = self.get_x_cols(columns=data.columns, dependent_vars=self.dependent_vars) +154 +155 return self.split.label, Data( +156 X=data.select(x_cols), +157 y=data.select(self.dependent_vars), +158 ) +159 +160 return self.split.label, Data(X=data) +161 +162 @staticmethod +163 def validate(data_type: str, split: DataSplit) -> bool: +164 return data_type == AllowedDataType.POLARS and split.indices is not None +165 +166 +167class PolarsRowsSplitter(DataSplitterBase): +168 """Split Polars DataFrame by rows slice""" +169 +170 def create_split(self, data: pl.DataFrame) -> Tuple[str, Data]: +171 # slice +172 data = data[self.start : self.stop] +173 +174 if bool(self.dependent_vars): +175 x_cols = self.get_x_cols(columns=data.columns, dependent_vars=self.dependent_vars) +176 +177 return self.split.label, Data( +178 X=data.select(x_cols), +179 y=data.select(self.dependent_vars), +180 ) +181 +182 return self.split.label, Data(X=data) +183 +184 @staticmethod +185 def validate(data_type: str, split: DataSplit) -> bool: +186 return data_type == AllowedDataType.POLARS and split.start is not None +187 +188 +189class PandasIndexSplitter(DataSplitterBase): +190 def create_split(self, data: pd.DataFrame) -> Tuple[str, Data]: +191 data = data.iloc[self.indices] +192 +193 if bool(self.dependent_vars): +194 x = data[data.columns[~data.columns.isin(self.dependent_vars)]] +195 y = data[data.columns[data.columns.isin(self.dependent_vars)]] +196 +197 return self.split.label, Data(X=x, y=y) +198 +199 return self.split.label, Data(X=data) +200 +201 @staticmethod +202 def validate(data_type: str, split: DataSplit) -> bool: +203 return data_type == AllowedDataType.PANDAS and split.indices is not None +204 +205 +206class PandasRowSplitter(DataSplitterBase): +207 def create_split(self, data: pd.DataFrame) -> Tuple[str, Data]: +208 # slice +209 data = data[self.start : self.stop] +210 +211 if bool(self.dependent_vars): +212 x = data[data.columns[~data.columns.isin(self.dependent_vars)]] +213 y = data[data.columns[data.columns.isin(self.dependent_vars)]] +214 +215 return self.split.label, Data(X=x, y=y) +216 +217 return self.split.label, Data(X=data) +218 +219 @staticmethod +220 def validate(data_type: str, split: DataSplit) -> bool: +221 return data_type == AllowedDataType.PANDAS and split.start is not None +222 +223 +224class PandasColumnSplitter(DataSplitterBase): +225 def create_split(self, data: pd.DataFrame) -> Tuple[str, Data]: +226 if self.split.inequality is None: +227 data = data[data[self.column_name] == self.column_value] +228 +229 elif self.split.inequality == ">": +230 data = data[data[self.column_name] > self.column_value] +231 +232 elif self.split.inequality == ">=": +233 data = data[data[self.column_name] >= self.column_value] +234 +235 elif self.split.inequality == "<": +236 data = data[data[self.column_name] < self.column_value] +237 +238 else: +239 data = data[data[self.column_name] <= self.column_value] +240 +241 if bool(self.dependent_vars): +242 return self.split.label, Data( +243 X=data[data.columns[~data.columns.isin(self.dependent_vars)]], +244 y=data[data.columns[data.columns.isin(self.dependent_vars)]], +245 ) +246 +247 data_split = Data(X=data) +248 return self.split.label, data_split +249 +250 @staticmethod +251 def validate(data_type: str, split: DataSplit) -> bool: +252 return data_type == AllowedDataType.PANDAS and split.column_name is not None +253 +254 +255class PyArrowIndexSplitter(DataSplitterBase): +256 def create_split(self, data: pa.Table) -> Tuple[str, Data]: +257 return self.split.label, Data(X=data.take(self.indices)) +258 +259 @staticmethod +260 def validate(data_type: str, split: DataSplit) -> bool: +261 return data_type == AllowedDataType.PYARROW and split.indices is not None +262 +263 +264class NumpyIndexSplitter(DataSplitterBase): +265 def create_split(self, data: NDArray[Any]) -> Tuple[str, Data]: +266 return self.split.label, Data(X=data[self.indices]) +267 +268 @staticmethod +269 def validate(data_type: str, split: DataSplit) -> bool: +270 return data_type == AllowedDataType.NUMPY and split.indices is not None +271 +272 +273class NumpyRowSplitter(DataSplitterBase): +274 def create_split(self, data: NDArray[Any]) -> Tuple[str, Data]: +275 data_split = data[self.start : self.stop] +276 return self.split.label, Data(X=data_split) +277 +278 @staticmethod +279 def validate(data_type: str, split: DataSplit) -> bool: +280 return data_type == AllowedDataType.NUMPY and split.start is not None +281 +282 +283class DataSplitter: +284 @staticmethod +285 def split( +286 split: DataSplit, +287 data: Union[pd.DataFrame, NDArray[Any], pl.DataFrame], +288 data_type: str, +289 dependent_vars: List[Union[int, str]], +290 ) -> Tuple[str, Data]: +291 data_splitter = next( +292 ( +293 data_splitter +294 for data_splitter in DataSplitterBase.__subclasses__() +295 if data_splitter.validate( +296 data_type=data_type, +297 split=split, +298 ) +299 ), +300 None, +301 ) +302 +303 if data_splitter is not None: +304 return data_splitter( +305 split=split, +306 dependent_vars=dependent_vars, +307 ).create_split(data=data) +308 +309 raise ValueError("Failed to find data supporter that supports provided logic") +
24class DataSplit(BaseModel): +25 model_config = ConfigDict(arbitrary_types_allowed=True) +26 +27 label: str +28 column_name: Optional[str] = None +29 column_value: Optional[Union[str, float, int, pd.Timestamp]] = None +30 inequality: Optional[str] = None +31 start: Optional[int] = None +32 stop: Optional[int] = None +33 indices: Optional[List[int]] = None +34 +35 @field_validator("indices", mode="before") +36 @classmethod +37 def convert_to_list(cls, value: Optional[List[int]]) -> Optional[List[int]]: +38 """Pre to convert indices to list if not None""" +39 +40 if value is not None and not isinstance(value, list): +41 value = list(value) +42 +43 return value +44 +45 @field_validator("inequality", mode="before") +46 @classmethod +47 def trim_whitespace(cls, value: str) -> str: +48 """Trims whitespace from inequality signs""" +49 +50 if value is not None: +51 value = value.strip() +52 +53 return value +
Usage docs: https://docs.pydantic.dev/2.6/concepts/models/
+ +A base class for creating Pydantic models.
+ +__init__ function.Model.__validators__ and Model.__root_validators__ from Pydantic V1.RootModel.model_config['extra'] == 'allow'.35 @field_validator("indices", mode="before") +36 @classmethod +37 def convert_to_list(cls, value: Optional[List[int]]) -> Optional[List[int]]: +38 """Pre to convert indices to list if not None""" +39 +40 if value is not None and not isinstance(value, list): +41 value = list(value) +42 +43 return value +
Pre to convert indices to list if not None
+45 @field_validator("inequality", mode="before") +46 @classmethod +47 def trim_whitespace(cls, value: str) -> str: +48 """Trims whitespace from inequality signs""" +49 +50 if value is not None: +51 value = value.strip() +52 +53 return value +
Trims whitespace from inequality signs
+56class DataSplitterBase: + 57 def __init__( + 58 self, + 59 split: DataSplit, + 60 dependent_vars: List[Union[int, str]], + 61 ): + 62 self.split = split + 63 self.dependent_vars = dependent_vars + 64 + 65 @property + 66 def column_name(self) -> str: + 67 if self.split.column_name is not None: + 68 return self.split.column_name + 69 + 70 raise ValueError("Column name was not provided") + 71 + 72 @property + 73 def column_value(self) -> Any: + 74 if self.split.column_value is not None: + 75 return self.split.column_value + 76 + 77 raise ValueError("Column value was not provided") + 78 + 79 @property + 80 def indices(self) -> List[int]: + 81 if self.split.indices is not None: + 82 return self.split.indices + 83 raise ValueError("List of indices was not provided") + 84 + 85 @property + 86 def start(self) -> int: + 87 if self.split.start is not None: + 88 return self.split.start + 89 raise ValueError("Start index was not provided") + 90 + 91 @property + 92 def stop(self) -> int: + 93 if self.split.stop is not None: + 94 return self.split.stop + 95 raise ValueError("Stop index was not provided") + 96 + 97 def get_x_cols(self, columns: List[str], dependent_vars: List[Union[str, int]]) -> List[str]: + 98 for var in dependent_vars: + 99 if isinstance(var, str): +100 columns.remove(var) +101 +102 return columns +103 +104 def create_split(self, data: Any) -> Tuple[str, Data]: +105 raise NotImplementedError +106 +107 @staticmethod +108 def validate(data_type: str, split: DataSplit) -> bool: +109 raise NotImplementedError +
112class PolarsColumnSplitter(DataSplitterBase): +113 """Column splitter for Polars dataframe""" +114 +115 def create_split(self, data: pl.DataFrame) -> Tuple[str, Data]: +116 if self.split.inequality is None: +117 data = data.filter(pl.col(self.column_name) == self.column_value) +118 +119 elif self.split.inequality == ">": +120 data = data.filter(pl.col(self.column_name) > self.column_value) +121 +122 elif self.split.inequality == ">=": +123 data = data.filter(pl.col(self.column_name) >= self.column_value) +124 +125 elif self.split.inequality == "<": +126 data = data.filter(pl.col(self.column_name) < self.column_value) +127 +128 else: +129 data = data.filter(pl.col(self.column_name) <= self.column_value) +130 +131 if bool(self.dependent_vars): +132 x_cols = self.get_x_cols(columns=data.columns, dependent_vars=self.dependent_vars) +133 +134 return self.split.label, Data( +135 X=data.select(x_cols), +136 y=data.select(self.dependent_vars), +137 ) +138 +139 return self.split.label, Data(X=data) +140 +141 @staticmethod +142 def validate(data_type: str, split: DataSplit) -> bool: +143 return data_type == AllowedDataType.POLARS and split.column_name is not None +
Column splitter for Polars dataframe
+115 def create_split(self, data: pl.DataFrame) -> Tuple[str, Data]: +116 if self.split.inequality is None: +117 data = data.filter(pl.col(self.column_name) == self.column_value) +118 +119 elif self.split.inequality == ">": +120 data = data.filter(pl.col(self.column_name) > self.column_value) +121 +122 elif self.split.inequality == ">=": +123 data = data.filter(pl.col(self.column_name) >= self.column_value) +124 +125 elif self.split.inequality == "<": +126 data = data.filter(pl.col(self.column_name) < self.column_value) +127 +128 else: +129 data = data.filter(pl.col(self.column_name) <= self.column_value) +130 +131 if bool(self.dependent_vars): +132 x_cols = self.get_x_cols(columns=data.columns, dependent_vars=self.dependent_vars) +133 +134 return self.split.label, Data( +135 X=data.select(x_cols), +136 y=data.select(self.dependent_vars), +137 ) +138 +139 return self.split.label, Data(X=data) +
146class PolarsIndexSplitter(DataSplitterBase): +147 """Split Polars DataFrame by rows index""" +148 +149 def create_split(self, data: pl.DataFrame) -> Tuple[str, Data]: +150 # slice +151 data = data[self.indices] +152 +153 if bool(self.dependent_vars): +154 x_cols = self.get_x_cols(columns=data.columns, dependent_vars=self.dependent_vars) +155 +156 return self.split.label, Data( +157 X=data.select(x_cols), +158 y=data.select(self.dependent_vars), +159 ) +160 +161 return self.split.label, Data(X=data) +162 +163 @staticmethod +164 def validate(data_type: str, split: DataSplit) -> bool: +165 return data_type == AllowedDataType.POLARS and split.indices is not None +
Split Polars DataFrame by rows index
+149 def create_split(self, data: pl.DataFrame) -> Tuple[str, Data]: +150 # slice +151 data = data[self.indices] +152 +153 if bool(self.dependent_vars): +154 x_cols = self.get_x_cols(columns=data.columns, dependent_vars=self.dependent_vars) +155 +156 return self.split.label, Data( +157 X=data.select(x_cols), +158 y=data.select(self.dependent_vars), +159 ) +160 +161 return self.split.label, Data(X=data) +
168class PolarsRowsSplitter(DataSplitterBase): +169 """Split Polars DataFrame by rows slice""" +170 +171 def create_split(self, data: pl.DataFrame) -> Tuple[str, Data]: +172 # slice +173 data = data[self.start : self.stop] +174 +175 if bool(self.dependent_vars): +176 x_cols = self.get_x_cols(columns=data.columns, dependent_vars=self.dependent_vars) +177 +178 return self.split.label, Data( +179 X=data.select(x_cols), +180 y=data.select(self.dependent_vars), +181 ) +182 +183 return self.split.label, Data(X=data) +184 +185 @staticmethod +186 def validate(data_type: str, split: DataSplit) -> bool: +187 return data_type == AllowedDataType.POLARS and split.start is not None +
Split Polars DataFrame by rows slice
+171 def create_split(self, data: pl.DataFrame) -> Tuple[str, Data]: +172 # slice +173 data = data[self.start : self.stop] +174 +175 if bool(self.dependent_vars): +176 x_cols = self.get_x_cols(columns=data.columns, dependent_vars=self.dependent_vars) +177 +178 return self.split.label, Data( +179 X=data.select(x_cols), +180 y=data.select(self.dependent_vars), +181 ) +182 +183 return self.split.label, Data(X=data) +
190class PandasIndexSplitter(DataSplitterBase): +191 def create_split(self, data: pd.DataFrame) -> Tuple[str, Data]: +192 data = data.iloc[self.indices] +193 +194 if bool(self.dependent_vars): +195 x = data[data.columns[~data.columns.isin(self.dependent_vars)]] +196 y = data[data.columns[data.columns.isin(self.dependent_vars)]] +197 +198 return self.split.label, Data(X=x, y=y) +199 +200 return self.split.label, Data(X=data) +201 +202 @staticmethod +203 def validate(data_type: str, split: DataSplit) -> bool: +204 return data_type == AllowedDataType.PANDAS and split.indices is not None +
191 def create_split(self, data: pd.DataFrame) -> Tuple[str, Data]: +192 data = data.iloc[self.indices] +193 +194 if bool(self.dependent_vars): +195 x = data[data.columns[~data.columns.isin(self.dependent_vars)]] +196 y = data[data.columns[data.columns.isin(self.dependent_vars)]] +197 +198 return self.split.label, Data(X=x, y=y) +199 +200 return self.split.label, Data(X=data) +
207class PandasRowSplitter(DataSplitterBase): +208 def create_split(self, data: pd.DataFrame) -> Tuple[str, Data]: +209 # slice +210 data = data[self.start : self.stop] +211 +212 if bool(self.dependent_vars): +213 x = data[data.columns[~data.columns.isin(self.dependent_vars)]] +214 y = data[data.columns[data.columns.isin(self.dependent_vars)]] +215 +216 return self.split.label, Data(X=x, y=y) +217 +218 return self.split.label, Data(X=data) +219 +220 @staticmethod +221 def validate(data_type: str, split: DataSplit) -> bool: +222 return data_type == AllowedDataType.PANDAS and split.start is not None +
208 def create_split(self, data: pd.DataFrame) -> Tuple[str, Data]: +209 # slice +210 data = data[self.start : self.stop] +211 +212 if bool(self.dependent_vars): +213 x = data[data.columns[~data.columns.isin(self.dependent_vars)]] +214 y = data[data.columns[data.columns.isin(self.dependent_vars)]] +215 +216 return self.split.label, Data(X=x, y=y) +217 +218 return self.split.label, Data(X=data) +
225class PandasColumnSplitter(DataSplitterBase): +226 def create_split(self, data: pd.DataFrame) -> Tuple[str, Data]: +227 if self.split.inequality is None: +228 data = data[data[self.column_name] == self.column_value] +229 +230 elif self.split.inequality == ">": +231 data = data[data[self.column_name] > self.column_value] +232 +233 elif self.split.inequality == ">=": +234 data = data[data[self.column_name] >= self.column_value] +235 +236 elif self.split.inequality == "<": +237 data = data[data[self.column_name] < self.column_value] +238 +239 else: +240 data = data[data[self.column_name] <= self.column_value] +241 +242 if bool(self.dependent_vars): +243 return self.split.label, Data( +244 X=data[data.columns[~data.columns.isin(self.dependent_vars)]], +245 y=data[data.columns[data.columns.isin(self.dependent_vars)]], +246 ) +247 +248 data_split = Data(X=data) +249 return self.split.label, data_split +250 +251 @staticmethod +252 def validate(data_type: str, split: DataSplit) -> bool: +253 return data_type == AllowedDataType.PANDAS and split.column_name is not None +
226 def create_split(self, data: pd.DataFrame) -> Tuple[str, Data]: +227 if self.split.inequality is None: +228 data = data[data[self.column_name] == self.column_value] +229 +230 elif self.split.inequality == ">": +231 data = data[data[self.column_name] > self.column_value] +232 +233 elif self.split.inequality == ">=": +234 data = data[data[self.column_name] >= self.column_value] +235 +236 elif self.split.inequality == "<": +237 data = data[data[self.column_name] < self.column_value] +238 +239 else: +240 data = data[data[self.column_name] <= self.column_value] +241 +242 if bool(self.dependent_vars): +243 return self.split.label, Data( +244 X=data[data.columns[~data.columns.isin(self.dependent_vars)]], +245 y=data[data.columns[data.columns.isin(self.dependent_vars)]], +246 ) +247 +248 data_split = Data(X=data) +249 return self.split.label, data_split +
256class PyArrowIndexSplitter(DataSplitterBase): +257 def create_split(self, data: pa.Table) -> Tuple[str, Data]: +258 return self.split.label, Data(X=data.take(self.indices)) +259 +260 @staticmethod +261 def validate(data_type: str, split: DataSplit) -> bool: +262 return data_type == AllowedDataType.PYARROW and split.indices is not None +
265class NumpyIndexSplitter(DataSplitterBase): +266 def create_split(self, data: NDArray[Any]) -> Tuple[str, Data]: +267 return self.split.label, Data(X=data[self.indices]) +268 +269 @staticmethod +270 def validate(data_type: str, split: DataSplit) -> bool: +271 return data_type == AllowedDataType.NUMPY and split.indices is not None +
274class NumpyRowSplitter(DataSplitterBase): +275 def create_split(self, data: NDArray[Any]) -> Tuple[str, Data]: +276 data_split = data[self.start : self.stop] +277 return self.split.label, Data(X=data_split) +278 +279 @staticmethod +280 def validate(data_type: str, split: DataSplit) -> bool: +281 return data_type == AllowedDataType.NUMPY and split.start is not None +
284class DataSplitter: +285 @staticmethod +286 def split( +287 split: DataSplit, +288 data: Union[pd.DataFrame, NDArray[Any], pl.DataFrame], +289 data_type: str, +290 dependent_vars: List[Union[int, str]], +291 ) -> Tuple[str, Data]: +292 data_splitter = next( +293 ( +294 data_splitter +295 for data_splitter in DataSplitterBase.__subclasses__() +296 if data_splitter.validate( +297 data_type=data_type, +298 split=split, +299 ) +300 ), +301 None, +302 ) +303 +304 if data_splitter is not None: +305 return data_splitter( +306 split=split, +307 dependent_vars=dependent_vars, +308 ).create_split(data=data) +309 +310 raise ValueError("Failed to find data supporter that supports provided logic") +
285 @staticmethod +286 def split( +287 split: DataSplit, +288 data: Union[pd.DataFrame, NDArray[Any], pl.DataFrame], +289 data_type: str, +290 dependent_vars: List[Union[int, str]], +291 ) -> Tuple[str, Data]: +292 data_splitter = next( +293 ( +294 data_splitter +295 for data_splitter in DataSplitterBase.__subclasses__() +296 if data_splitter.validate( +297 data_type=data_type, +298 split=split, +299 ) +300 ), +301 None, +302 ) +303 +304 if data_splitter is not None: +305 return data_splitter( +306 split=split, +307 dependent_vars=dependent_vars, +308 ).create_split(data=data) +309 +310 raise ValueError("Failed to find data supporter that supports provided logic") +
1# Copyright (c) Shipt, Inc. + 2# This source code is licensed under the MIT license found in the + 3# LICENSE file in the root directory of this source tree. + 4from typing import Any, Dict, List, Optional, Union, cast + 5 + 6from pydantic import BaseModel, ConfigDict, ValidationInfo, field_validator + 7 + 8from opsml.cards.model import ModelCard + 9from opsml.cards.run import RunCard + 10from opsml.helpers.logging import ArtifactLogger + 11from opsml.registry.registry import CardRegistries + 12from opsml.types import CardInfo, Metric + 13 + 14logger = ArtifactLogger.get_logger() + 15 + 16# User interfaces should primarily be checked at runtime + 17 + 18 + 19class BattleReport(BaseModel): + 20 model_config = ConfigDict(arbitrary_types_allowed=True) + 21 champion_name: str + 22 champion_version: str + 23 champion_metric: Optional[Metric] = None + 24 challenger_metric: Optional[Metric] = None + 25 challenger_win: bool + 26 + 27 + 28MetricName = Union[str, List[str]] + 29MetricValue = Union[int, float, List[Union[int, float]]] + 30 + 31 + 32class ChallengeInputs(BaseModel): + 33 metric_name: MetricName + 34 metric_value: Optional[MetricValue] = None + 35 lower_is_better: Union[bool, List[bool]] = True + 36 + 37 @property + 38 def metric_names(self) -> List[str]: + 39 return cast(List[str], self.metric_name) + 40 + 41 @property + 42 def metric_values(self) -> List[Optional[Union[int, float]]]: + 43 return cast(List[Optional[Union[int, float]]], self.metric_value) + 44 + 45 @property + 46 def thresholds(self) -> List[bool]: + 47 return cast(List[bool], self.lower_is_better) + 48 + 49 @field_validator("metric_name") + 50 @classmethod + 51 def convert_name(cls, name: Union[List[str], str]) -> List[str]: + 52 if not isinstance(name, list): + 53 return [name] + 54 return name + 55 + 56 @field_validator("metric_value") + 57 @classmethod + 58 def convert_value(cls, value: Optional[MetricValue], info: ValidationInfo) -> List[Any]: + 59 data = info.data + 60 metric = cast(MetricName, data["metric_name"]) + 61 nbr_metrics = len(metric) + 62 + 63 if value is not None: + 64 if not isinstance(value, list): + 65 metric_value = [value] + 66 else: + 67 metric_value = value + 68 else: + 69 metric_value = [None] * nbr_metrics # type: ignore + 70 + 71 if len(metric_value) != nbr_metrics: + 72 raise ValueError("List of metric values must be the same length as metric names") + 73 + 74 return metric_value + 75 + 76 @field_validator("lower_is_better") + 77 @classmethod + 78 def convert_threshold(cls, threshold: Union[bool, List[bool]], info: ValidationInfo) -> List[bool]: + 79 data = info.data + 80 metric = cast(MetricName, data["metric_name"]) + 81 nbr_metrics = len(metric) + 82 + 83 if not isinstance(threshold, list): + 84 _threshold = [threshold] * nbr_metrics + 85 else: + 86 _threshold = threshold + 87 + 88 if len(_threshold) != nbr_metrics: + 89 if len(_threshold) == 1: + 90 _threshold = _threshold * nbr_metrics + 91 else: + 92 raise ValueError("Length of lower_is_better must be the same length as number of metrics") + 93 + 94 return _threshold + 95 + 96 + 97class ModelChallenger: + 98 def __init__(self, challenger: ModelCard): + 99 """ +100 Instantiates ModelChallenger class +101 +102 Args: +103 challenger: +104 ModelCard of challenger +105 +106 """ +107 self._challenger = challenger +108 self._challenger_metric: Optional[Metric] = None +109 self._registries = CardRegistries() +110 +111 @property +112 def challenger_metric(self) -> Metric: +113 if self._challenger_metric is not None: +114 return self._challenger_metric +115 raise ValueError("Challenger metric not set") +116 +117 @challenger_metric.setter +118 def challenger_metric(self, metric: Metric) -> None: +119 self._challenger_metric = metric +120 +121 def _get_last_champion_record(self) -> Optional[Dict[str, Any]]: +122 """Gets the previous champion record""" +123 +124 champion_records = self._registries.model.list_cards( +125 name=self._challenger.name, +126 repository=self._challenger.repository, +127 ) +128 +129 if not bool(champion_records): +130 return None +131 +132 # indicates challenger has been registered +133 if self._challenger.version is not None and len(champion_records) > 1: +134 return champion_records[1] +135 +136 # account for cases where challenger is only model in registry +137 champion_record = champion_records[0] +138 if champion_record.get("version") == self._challenger.version: +139 return None +140 +141 return champion_record +142 +143 def _get_runcard_metric(self, runcard_uid: str, metric_name: str) -> Metric: +144 """ +145 Loads a RunCard from uid +146 +147 Args: +148 runcard_uid: +149 RunCard uid +150 metric_name: +151 Name of metric +152 +153 """ +154 runcard = cast(RunCard, self._registries.run.load_card(uid=runcard_uid)) +155 metric = runcard.get_metric(name=metric_name) +156 +157 if isinstance(metric, list): +158 metric = metric[0] +159 +160 return metric +161 +162 def _battle(self, champion: CardInfo, champion_metric: Metric, lower_is_better: bool) -> BattleReport: +163 """ +164 Runs a battle between champion and current challenger +165 +166 Args: +167 champion: +168 Champion record +169 champion_metric: +170 Champion metric from a runcard +171 lower_is_better: +172 Whether lower metric is preferred +173 +174 Returns: +175 `BattleReport` +176 +177 """ +178 if lower_is_better: +179 challenger_win = self.challenger_metric.value < champion_metric.value +180 else: +181 challenger_win = self.challenger_metric.value > champion_metric.value +182 return BattleReport.model_construct( +183 champion_name=str(champion.name), +184 champion_version=str(champion.version), +185 champion_metric=champion_metric, +186 challenger_metric=self.challenger_metric.model_copy(deep=True), +187 challenger_win=challenger_win, +188 ) +189 +190 def _battle_last_model_version(self, metric_name: str, lower_is_better: bool) -> BattleReport: +191 """Compares the last champion model to the current challenger""" +192 +193 champion_record = self._get_last_champion_record() +194 +195 if champion_record is None: +196 logger.info("No previous model found. Challenger wins") +197 +198 return BattleReport( +199 champion_name="No model", +200 champion_version="No version", +201 challenger_win=True, +202 ) +203 +204 runcard_id = champion_record.get("runcard_uid") +205 if runcard_id is None: +206 raise ValueError(f"No RunCard is associated with champion: {champion_record}") +207 +208 champion_metric = self._get_runcard_metric(runcard_uid=runcard_id, metric_name=metric_name) +209 +210 return self._battle( +211 champion=CardInfo( +212 name=champion_record.get("name"), +213 version=champion_record.get("version"), +214 ), +215 champion_metric=champion_metric, +216 lower_is_better=lower_is_better, +217 ) +218 +219 def _battle_champions( +220 self, +221 champions: List[CardInfo], +222 metric_name: str, +223 lower_is_better: bool, +224 ) -> List[BattleReport]: +225 """Loops through and creates a `BattleReport` for each champion""" +226 battle_reports = [] +227 +228 for champion in champions: +229 champion_record = self._registries.model.list_cards( +230 info=champion, +231 ) +232 +233 if not bool(champion_record): +234 raise ValueError(f"Champion model does not exist. {champion}") +235 +236 champion_card = champion_record[0] +237 runcard_uid = champion_card.get("runcard_uid") +238 if runcard_uid is None: +239 raise ValueError(f"No RunCard associated with champion: {champion}") +240 +241 champion_metric = self._get_runcard_metric( +242 runcard_uid=runcard_uid, +243 metric_name=metric_name, +244 ) +245 +246 # update name, repository and version in case of None +247 champion.name = champion.name or champion_card.get("name") +248 champion.repository = champion.repository or champion_card.get("repository") +249 champion.version = champion.version or champion_card.get("version") +250 +251 battle_reports.append( +252 self._battle( +253 champion=champion, +254 champion_metric=champion_metric, +255 lower_is_better=lower_is_better, +256 ) +257 ) +258 return battle_reports +259 +260 def challenge_champion( +261 self, +262 metric_name: MetricName, +263 metric_value: Optional[MetricValue] = None, +264 champions: Optional[List[CardInfo]] = None, +265 lower_is_better: Union[bool, List[bool]] = True, +266 ) -> Dict[str, List[BattleReport]]: +267 """ +268 Challenges n champion models against the challenger model. If no champion is provided, +269 the latest model version is used as a champion. +270 +271 Args: +272 champions: +273 Optional list of champion CardInfo +274 metric_name: +275 Name of metric to evaluate +276 metric_value: +277 Challenger metric value +278 lower_is_better: +279 Whether a lower metric value is better or not +280 +281 Returns +282 `BattleReport` +283 """ +284 +285 # validate inputs +286 inputs = ChallengeInputs( +287 metric_name=metric_name, +288 metric_value=metric_value, +289 lower_is_better=lower_is_better, +290 ) +291 +292 report_dict = {} +293 +294 for name, value, _lower_is_better in zip( +295 inputs.metric_names, +296 inputs.metric_values, +297 inputs.thresholds, +298 ): +299 # get challenger metric +300 if value is None: +301 if self._challenger.metadata.runcard_uid is not None: +302 self.challenger_metric = self._get_runcard_metric( +303 self._challenger.metadata.runcard_uid, metric_name=name +304 ) +305 else: +306 raise ValueError("Challenger and champions must be associated with a registered RunCard") +307 else: +308 self.challenger_metric = Metric(name=name, value=value) +309 +310 if champions is None: +311 report_dict[name] = [ +312 self._battle_last_model_version( +313 metric_name=name, +314 lower_is_better=_lower_is_better, +315 ) +316 ] +317 +318 else: +319 report_dict[name] = self._battle_champions( +320 champions=champions, +321 metric_name=name, +322 lower_is_better=_lower_is_better, +323 ) +324 +325 return report_dict +
20class BattleReport(BaseModel): +21 model_config = ConfigDict(arbitrary_types_allowed=True) +22 champion_name: str +23 champion_version: str +24 champion_metric: Optional[Metric] = None +25 challenger_metric: Optional[Metric] = None +26 challenger_win: bool +
Usage docs: https://docs.pydantic.dev/2.6/concepts/models/
+ +A base class for creating Pydantic models.
+ +__init__ function.Model.__validators__ and Model.__root_validators__ from Pydantic V1.RootModel.model_config['extra'] == 'allow'.33class ChallengeInputs(BaseModel): +34 metric_name: MetricName +35 metric_value: Optional[MetricValue] = None +36 lower_is_better: Union[bool, List[bool]] = True +37 +38 @property +39 def metric_names(self) -> List[str]: +40 return cast(List[str], self.metric_name) +41 +42 @property +43 def metric_values(self) -> List[Optional[Union[int, float]]]: +44 return cast(List[Optional[Union[int, float]]], self.metric_value) +45 +46 @property +47 def thresholds(self) -> List[bool]: +48 return cast(List[bool], self.lower_is_better) +49 +50 @field_validator("metric_name") +51 @classmethod +52 def convert_name(cls, name: Union[List[str], str]) -> List[str]: +53 if not isinstance(name, list): +54 return [name] +55 return name +56 +57 @field_validator("metric_value") +58 @classmethod +59 def convert_value(cls, value: Optional[MetricValue], info: ValidationInfo) -> List[Any]: +60 data = info.data +61 metric = cast(MetricName, data["metric_name"]) +62 nbr_metrics = len(metric) +63 +64 if value is not None: +65 if not isinstance(value, list): +66 metric_value = [value] +67 else: +68 metric_value = value +69 else: +70 metric_value = [None] * nbr_metrics # type: ignore +71 +72 if len(metric_value) != nbr_metrics: +73 raise ValueError("List of metric values must be the same length as metric names") +74 +75 return metric_value +76 +77 @field_validator("lower_is_better") +78 @classmethod +79 def convert_threshold(cls, threshold: Union[bool, List[bool]], info: ValidationInfo) -> List[bool]: +80 data = info.data +81 metric = cast(MetricName, data["metric_name"]) +82 nbr_metrics = len(metric) +83 +84 if not isinstance(threshold, list): +85 _threshold = [threshold] * nbr_metrics +86 else: +87 _threshold = threshold +88 +89 if len(_threshold) != nbr_metrics: +90 if len(_threshold) == 1: +91 _threshold = _threshold * nbr_metrics +92 else: +93 raise ValueError("Length of lower_is_better must be the same length as number of metrics") +94 +95 return _threshold +
Usage docs: https://docs.pydantic.dev/2.6/concepts/models/
+ +A base class for creating Pydantic models.
+ +__init__ function.Model.__validators__ and Model.__root_validators__ from Pydantic V1.RootModel.model_config['extra'] == 'allow'.57 @field_validator("metric_value") +58 @classmethod +59 def convert_value(cls, value: Optional[MetricValue], info: ValidationInfo) -> List[Any]: +60 data = info.data +61 metric = cast(MetricName, data["metric_name"]) +62 nbr_metrics = len(metric) +63 +64 if value is not None: +65 if not isinstance(value, list): +66 metric_value = [value] +67 else: +68 metric_value = value +69 else: +70 metric_value = [None] * nbr_metrics # type: ignore +71 +72 if len(metric_value) != nbr_metrics: +73 raise ValueError("List of metric values must be the same length as metric names") +74 +75 return metric_value +
77 @field_validator("lower_is_better") +78 @classmethod +79 def convert_threshold(cls, threshold: Union[bool, List[bool]], info: ValidationInfo) -> List[bool]: +80 data = info.data +81 metric = cast(MetricName, data["metric_name"]) +82 nbr_metrics = len(metric) +83 +84 if not isinstance(threshold, list): +85 _threshold = [threshold] * nbr_metrics +86 else: +87 _threshold = threshold +88 +89 if len(_threshold) != nbr_metrics: +90 if len(_threshold) == 1: +91 _threshold = _threshold * nbr_metrics +92 else: +93 raise ValueError("Length of lower_is_better must be the same length as number of metrics") +94 +95 return _threshold +
98class ModelChallenger: + 99 def __init__(self, challenger: ModelCard): +100 """ +101 Instantiates ModelChallenger class +102 +103 Args: +104 challenger: +105 ModelCard of challenger +106 +107 """ +108 self._challenger = challenger +109 self._challenger_metric: Optional[Metric] = None +110 self._registries = CardRegistries() +111 +112 @property +113 def challenger_metric(self) -> Metric: +114 if self._challenger_metric is not None: +115 return self._challenger_metric +116 raise ValueError("Challenger metric not set") +117 +118 @challenger_metric.setter +119 def challenger_metric(self, metric: Metric) -> None: +120 self._challenger_metric = metric +121 +122 def _get_last_champion_record(self) -> Optional[Dict[str, Any]]: +123 """Gets the previous champion record""" +124 +125 champion_records = self._registries.model.list_cards( +126 name=self._challenger.name, +127 repository=self._challenger.repository, +128 ) +129 +130 if not bool(champion_records): +131 return None +132 +133 # indicates challenger has been registered +134 if self._challenger.version is not None and len(champion_records) > 1: +135 return champion_records[1] +136 +137 # account for cases where challenger is only model in registry +138 champion_record = champion_records[0] +139 if champion_record.get("version") == self._challenger.version: +140 return None +141 +142 return champion_record +143 +144 def _get_runcard_metric(self, runcard_uid: str, metric_name: str) -> Metric: +145 """ +146 Loads a RunCard from uid +147 +148 Args: +149 runcard_uid: +150 RunCard uid +151 metric_name: +152 Name of metric +153 +154 """ +155 runcard = cast(RunCard, self._registries.run.load_card(uid=runcard_uid)) +156 metric = runcard.get_metric(name=metric_name) +157 +158 if isinstance(metric, list): +159 metric = metric[0] +160 +161 return metric +162 +163 def _battle(self, champion: CardInfo, champion_metric: Metric, lower_is_better: bool) -> BattleReport: +164 """ +165 Runs a battle between champion and current challenger +166 +167 Args: +168 champion: +169 Champion record +170 champion_metric: +171 Champion metric from a runcard +172 lower_is_better: +173 Whether lower metric is preferred +174 +175 Returns: +176 `BattleReport` +177 +178 """ +179 if lower_is_better: +180 challenger_win = self.challenger_metric.value < champion_metric.value +181 else: +182 challenger_win = self.challenger_metric.value > champion_metric.value +183 return BattleReport.model_construct( +184 champion_name=str(champion.name), +185 champion_version=str(champion.version), +186 champion_metric=champion_metric, +187 challenger_metric=self.challenger_metric.model_copy(deep=True), +188 challenger_win=challenger_win, +189 ) +190 +191 def _battle_last_model_version(self, metric_name: str, lower_is_better: bool) -> BattleReport: +192 """Compares the last champion model to the current challenger""" +193 +194 champion_record = self._get_last_champion_record() +195 +196 if champion_record is None: +197 logger.info("No previous model found. Challenger wins") +198 +199 return BattleReport( +200 champion_name="No model", +201 champion_version="No version", +202 challenger_win=True, +203 ) +204 +205 runcard_id = champion_record.get("runcard_uid") +206 if runcard_id is None: +207 raise ValueError(f"No RunCard is associated with champion: {champion_record}") +208 +209 champion_metric = self._get_runcard_metric(runcard_uid=runcard_id, metric_name=metric_name) +210 +211 return self._battle( +212 champion=CardInfo( +213 name=champion_record.get("name"), +214 version=champion_record.get("version"), +215 ), +216 champion_metric=champion_metric, +217 lower_is_better=lower_is_better, +218 ) +219 +220 def _battle_champions( +221 self, +222 champions: List[CardInfo], +223 metric_name: str, +224 lower_is_better: bool, +225 ) -> List[BattleReport]: +226 """Loops through and creates a `BattleReport` for each champion""" +227 battle_reports = [] +228 +229 for champion in champions: +230 champion_record = self._registries.model.list_cards( +231 info=champion, +232 ) +233 +234 if not bool(champion_record): +235 raise ValueError(f"Champion model does not exist. {champion}") +236 +237 champion_card = champion_record[0] +238 runcard_uid = champion_card.get("runcard_uid") +239 if runcard_uid is None: +240 raise ValueError(f"No RunCard associated with champion: {champion}") +241 +242 champion_metric = self._get_runcard_metric( +243 runcard_uid=runcard_uid, +244 metric_name=metric_name, +245 ) +246 +247 # update name, repository and version in case of None +248 champion.name = champion.name or champion_card.get("name") +249 champion.repository = champion.repository or champion_card.get("repository") +250 champion.version = champion.version or champion_card.get("version") +251 +252 battle_reports.append( +253 self._battle( +254 champion=champion, +255 champion_metric=champion_metric, +256 lower_is_better=lower_is_better, +257 ) +258 ) +259 return battle_reports +260 +261 def challenge_champion( +262 self, +263 metric_name: MetricName, +264 metric_value: Optional[MetricValue] = None, +265 champions: Optional[List[CardInfo]] = None, +266 lower_is_better: Union[bool, List[bool]] = True, +267 ) -> Dict[str, List[BattleReport]]: +268 """ +269 Challenges n champion models against the challenger model. If no champion is provided, +270 the latest model version is used as a champion. +271 +272 Args: +273 champions: +274 Optional list of champion CardInfo +275 metric_name: +276 Name of metric to evaluate +277 metric_value: +278 Challenger metric value +279 lower_is_better: +280 Whether a lower metric value is better or not +281 +282 Returns +283 `BattleReport` +284 """ +285 +286 # validate inputs +287 inputs = ChallengeInputs( +288 metric_name=metric_name, +289 metric_value=metric_value, +290 lower_is_better=lower_is_better, +291 ) +292 +293 report_dict = {} +294 +295 for name, value, _lower_is_better in zip( +296 inputs.metric_names, +297 inputs.metric_values, +298 inputs.thresholds, +299 ): +300 # get challenger metric +301 if value is None: +302 if self._challenger.metadata.runcard_uid is not None: +303 self.challenger_metric = self._get_runcard_metric( +304 self._challenger.metadata.runcard_uid, metric_name=name +305 ) +306 else: +307 raise ValueError("Challenger and champions must be associated with a registered RunCard") +308 else: +309 self.challenger_metric = Metric(name=name, value=value) +310 +311 if champions is None: +312 report_dict[name] = [ +313 self._battle_last_model_version( +314 metric_name=name, +315 lower_is_better=_lower_is_better, +316 ) +317 ] +318 +319 else: +320 report_dict[name] = self._battle_champions( +321 champions=champions, +322 metric_name=name, +323 lower_is_better=_lower_is_better, +324 ) +325 +326 return report_dict +
99 def __init__(self, challenger: ModelCard): +100 """ +101 Instantiates ModelChallenger class +102 +103 Args: +104 challenger: +105 ModelCard of challenger +106 +107 """ +108 self._challenger = challenger +109 self._challenger_metric: Optional[Metric] = None +110 self._registries = CardRegistries() +
Instantiates ModelChallenger class
+ +261 def challenge_champion( +262 self, +263 metric_name: MetricName, +264 metric_value: Optional[MetricValue] = None, +265 champions: Optional[List[CardInfo]] = None, +266 lower_is_better: Union[bool, List[bool]] = True, +267 ) -> Dict[str, List[BattleReport]]: +268 """ +269 Challenges n champion models against the challenger model. If no champion is provided, +270 the latest model version is used as a champion. +271 +272 Args: +273 champions: +274 Optional list of champion CardInfo +275 metric_name: +276 Name of metric to evaluate +277 metric_value: +278 Challenger metric value +279 lower_is_better: +280 Whether a lower metric value is better or not +281 +282 Returns +283 `BattleReport` +284 """ +285 +286 # validate inputs +287 inputs = ChallengeInputs( +288 metric_name=metric_name, +289 metric_value=metric_value, +290 lower_is_better=lower_is_better, +291 ) +292 +293 report_dict = {} +294 +295 for name, value, _lower_is_better in zip( +296 inputs.metric_names, +297 inputs.metric_values, +298 inputs.thresholds, +299 ): +300 # get challenger metric +301 if value is None: +302 if self._challenger.metadata.runcard_uid is not None: +303 self.challenger_metric = self._get_runcard_metric( +304 self._challenger.metadata.runcard_uid, metric_name=name +305 ) +306 else: +307 raise ValueError("Challenger and champions must be associated with a registered RunCard") +308 else: +309 self.challenger_metric = Metric(name=name, value=value) +310 +311 if champions is None: +312 report_dict[name] = [ +313 self._battle_last_model_version( +314 metric_name=name, +315 lower_is_better=_lower_is_better, +316 ) +317 ] +318 +319 else: +320 report_dict[name] = self._battle_champions( +321 champions=champions, +322 metric_name=name, +323 lower_is_better=_lower_is_better, +324 ) +325 +326 return report_dict +
Challenges n champion models against the challenger model. If no champion is provided, +the latest model version is used as a champion.
+ +Returns
+ BattleReport
1from dataclasses import dataclass + 2from pathlib import Path + 3from typing import Any, Dict, List, Optional, Tuple, cast + 4from uuid import UUID + 5 + 6import joblib + 7import numpy as np + 8import pandas as pd + 9from pydantic import BaseModel, ConfigDict, field_validator, model_validator + 10 + 11from opsml.helpers.utils import get_class_name + 12from opsml.types import CommonKwargs, ModelReturn, OnnxModel + 13from opsml.types.extra import Suffix + 14 + 15 + 16def get_processor_name(_class: Optional[Any] = None) -> str: + 17 if _class is not None: + 18 return str(_class.__class__.__name__) + 19 + 20 return CommonKwargs.UNDEFINED.value + 21 + 22 + 23def get_model_args(model: Any) -> Tuple[Any, str, List[str]]: + 24 assert model is not None, "Model must not be None" + 25 + 26 model_module = model.__module__ + 27 model_bases = [str(base) for base in model.__class__.__bases__] + 28 + 29 return model, model_module, model_bases + 30 + 31 + 32@dataclass + 33class SamplePrediction: + 34 """Dataclass that holds sample prediction information + 35 + 36 Args: + 37 prediction_type: + 38 Type of prediction + 39 prediction: + 40 Sample prediction + 41 """ + 42 + 43 prediction_type: str + 44 prediction: Any + 45 + 46 + 47class ModelInterface(BaseModel): + 48 model: Optional[Any] = None + 49 sample_data: Optional[Any] = None + 50 onnx_model: Optional[OnnxModel] = None + 51 task_type: str = CommonKwargs.UNDEFINED.value + 52 model_type: str = CommonKwargs.UNDEFINED.value + 53 data_type: str = CommonKwargs.UNDEFINED.value + 54 modelcard_uid: str = "" + 55 + 56 model_config = ConfigDict( + 57 protected_namespaces=("protect_",), + 58 arbitrary_types_allowed=True, + 59 validate_assignment=False, + 60 validate_default=True, + 61 extra="allow", + 62 ) + 63 + 64 @property + 65 def model_class(self) -> str: + 66 return CommonKwargs.UNDEFINED.value + 67 + 68 @model_validator(mode="before") + 69 @classmethod + 70 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 71 if model_args.get("modelcard_uid", False): + 72 return model_args + 73 + 74 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) + 75 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data + 76 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) + 77 + 78 return model_args + 79 + 80 @field_validator("modelcard_uid", mode="before") + 81 @classmethod + 82 def check_modelcard_uid(cls, modelcard_uid: str) -> str: + 83 # empty strings are falsey + 84 if not modelcard_uid: + 85 return modelcard_uid + 86 + 87 try: + 88 UUID(modelcard_uid, version=4) # we use uuid4 + 89 return modelcard_uid + 90 + 91 except ValueError as exc: + 92 raise ValueError("ModelCard uid is not a valid uuid") from exc + 93 + 94 def save_model(self, path: Path) -> None: + 95 """Saves model to path. Base implementation use Joblib + 96 + 97 Args: + 98 path: + 99 Pathlib object +100 """ +101 assert self.model is not None, "No model detected in interface" +102 joblib.dump(self.model, path) +103 +104 def load_model(self, path: Path, **kwargs: Any) -> None: +105 """Load model from pathlib object +106 +107 Args: +108 path: +109 Pathlib object +110 kwargs: +111 Additional kwargs +112 """ +113 self.model = joblib.load(path) +114 +115 def save_onnx(self, path: Path) -> ModelReturn: +116 """Saves the onnx model +117 +118 Args: +119 path: +120 Path to save +121 +122 Returns: +123 ModelReturn +124 """ +125 import onnxruntime as rt +126 +127 from opsml.model.onnx import _get_onnx_metadata +128 +129 if self.onnx_model is None: +130 self.convert_to_onnx() +131 sess: rt.InferenceSession = self.onnx_model.sess +132 path.write_bytes(sess._model_bytes) # pylint: disable=protected-access +133 +134 else: +135 self.onnx_model.sess_to_path(path.with_suffix(Suffix.ONNX.value)) +136 +137 assert self.onnx_model is not None, "No onnx model detected in interface" +138 metadata = _get_onnx_metadata(self, cast(rt.InferenceSession, self.onnx_model.sess)) +139 +140 return metadata +141 +142 def convert_to_onnx(self, **kwargs: Path) -> None: +143 """Converts model to onnx format""" +144 from opsml.model.onnx import _OnnxModelConverter +145 +146 if self.onnx_model is not None: +147 return None +148 +149 metadata = _OnnxModelConverter(self).convert_model() +150 self.onnx_model = metadata.onnx_model +151 +152 return None +153 +154 def load_onnx_model(self, path: Path) -> None: +155 """Load onnx model from pathlib object +156 +157 Args: +158 path: +159 Pathlib object +160 """ +161 from onnxruntime import InferenceSession +162 +163 assert self.onnx_model is not None, "No onnx model detected in interface" +164 self.onnx_model.sess = InferenceSession(path) +165 +166 def save_sample_data(self, path: Path) -> None: +167 """Serialized and save sample data to path. +168 +169 Args: +170 path: +171 Pathlib object +172 """ +173 joblib.dump(self.sample_data, path) +174 +175 def load_sample_data(self, path: Path) -> None: +176 """Serialized and save sample data to path. +177 +178 Args: +179 path: +180 Pathlib object +181 """ +182 +183 self.sample_data = joblib.load(path) +184 +185 @classmethod +186 def _get_sample_data(cls, sample_data: Any) -> Any: +187 """Check sample data and returns one record to be used +188 during type inference and ONNX conversion/validation. +189 +190 Returns: +191 Sample data with only one record +192 """ +193 +194 if isinstance(sample_data, list): +195 return [data[0:1] for data in sample_data] +196 +197 if isinstance(sample_data, tuple): +198 return (data[0:1] for data in sample_data) +199 +200 if isinstance(sample_data, dict): +201 return {key: data[0:1] for key, data in sample_data.items()} +202 +203 return sample_data[0:1] +204 +205 def get_sample_prediction(self) -> SamplePrediction: +206 assert self.model is not None, "Model is not defined" +207 assert self.sample_data is not None, "Sample data must be provided" +208 +209 if isinstance(self.sample_data, (pd.DataFrame, np.ndarray)): +210 prediction = self.model.predict(self.sample_data) +211 +212 elif isinstance(self.sample_data, dict): +213 try: +214 prediction = self.model.predict(**self.sample_data) +215 except Exception as _: # pylint: disable=broad-except +216 prediction = self.model.predict(self.sample_data) +217 +218 elif isinstance(self.sample_data, (list, tuple)): +219 try: +220 prediction = self.model.predict(*self.sample_data) +221 except Exception as _: # pylint: disable=broad-except +222 prediction = self.model.predict(self.sample_data) +223 +224 else: +225 prediction = self.model.predict(self.sample_data) +226 +227 prediction_type = get_class_name(prediction) +228 +229 return SamplePrediction( +230 prediction_type, +231 prediction, +232 ) +233 +234 @property +235 def model_suffix(self) -> str: +236 """Returns suffix for storage""" +237 return Suffix.JOBLIB.value +238 +239 @property +240 def data_suffix(self) -> str: +241 """Returns suffix for storage""" +242 return Suffix.JOBLIB.value +243 +244 @staticmethod +245 def name() -> str: +246 return ModelInterface.__name__ +
33@dataclass +34class SamplePrediction: +35 """Dataclass that holds sample prediction information +36 +37 Args: +38 prediction_type: +39 Type of prediction +40 prediction: +41 Sample prediction +42 """ +43 +44 prediction_type: str +45 prediction: Any +
Dataclass that holds sample prediction information
+ +48class ModelInterface(BaseModel): + 49 model: Optional[Any] = None + 50 sample_data: Optional[Any] = None + 51 onnx_model: Optional[OnnxModel] = None + 52 task_type: str = CommonKwargs.UNDEFINED.value + 53 model_type: str = CommonKwargs.UNDEFINED.value + 54 data_type: str = CommonKwargs.UNDEFINED.value + 55 modelcard_uid: str = "" + 56 + 57 model_config = ConfigDict( + 58 protected_namespaces=("protect_",), + 59 arbitrary_types_allowed=True, + 60 validate_assignment=False, + 61 validate_default=True, + 62 extra="allow", + 63 ) + 64 + 65 @property + 66 def model_class(self) -> str: + 67 return CommonKwargs.UNDEFINED.value + 68 + 69 @model_validator(mode="before") + 70 @classmethod + 71 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 72 if model_args.get("modelcard_uid", False): + 73 return model_args + 74 + 75 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) + 76 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data + 77 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) + 78 + 79 return model_args + 80 + 81 @field_validator("modelcard_uid", mode="before") + 82 @classmethod + 83 def check_modelcard_uid(cls, modelcard_uid: str) -> str: + 84 # empty strings are falsey + 85 if not modelcard_uid: + 86 return modelcard_uid + 87 + 88 try: + 89 UUID(modelcard_uid, version=4) # we use uuid4 + 90 return modelcard_uid + 91 + 92 except ValueError as exc: + 93 raise ValueError("ModelCard uid is not a valid uuid") from exc + 94 + 95 def save_model(self, path: Path) -> None: + 96 """Saves model to path. Base implementation use Joblib + 97 + 98 Args: + 99 path: +100 Pathlib object +101 """ +102 assert self.model is not None, "No model detected in interface" +103 joblib.dump(self.model, path) +104 +105 def load_model(self, path: Path, **kwargs: Any) -> None: +106 """Load model from pathlib object +107 +108 Args: +109 path: +110 Pathlib object +111 kwargs: +112 Additional kwargs +113 """ +114 self.model = joblib.load(path) +115 +116 def save_onnx(self, path: Path) -> ModelReturn: +117 """Saves the onnx model +118 +119 Args: +120 path: +121 Path to save +122 +123 Returns: +124 ModelReturn +125 """ +126 import onnxruntime as rt +127 +128 from opsml.model.onnx import _get_onnx_metadata +129 +130 if self.onnx_model is None: +131 self.convert_to_onnx() +132 sess: rt.InferenceSession = self.onnx_model.sess +133 path.write_bytes(sess._model_bytes) # pylint: disable=protected-access +134 +135 else: +136 self.onnx_model.sess_to_path(path.with_suffix(Suffix.ONNX.value)) +137 +138 assert self.onnx_model is not None, "No onnx model detected in interface" +139 metadata = _get_onnx_metadata(self, cast(rt.InferenceSession, self.onnx_model.sess)) +140 +141 return metadata +142 +143 def convert_to_onnx(self, **kwargs: Path) -> None: +144 """Converts model to onnx format""" +145 from opsml.model.onnx import _OnnxModelConverter +146 +147 if self.onnx_model is not None: +148 return None +149 +150 metadata = _OnnxModelConverter(self).convert_model() +151 self.onnx_model = metadata.onnx_model +152 +153 return None +154 +155 def load_onnx_model(self, path: Path) -> None: +156 """Load onnx model from pathlib object +157 +158 Args: +159 path: +160 Pathlib object +161 """ +162 from onnxruntime import InferenceSession +163 +164 assert self.onnx_model is not None, "No onnx model detected in interface" +165 self.onnx_model.sess = InferenceSession(path) +166 +167 def save_sample_data(self, path: Path) -> None: +168 """Serialized and save sample data to path. +169 +170 Args: +171 path: +172 Pathlib object +173 """ +174 joblib.dump(self.sample_data, path) +175 +176 def load_sample_data(self, path: Path) -> None: +177 """Serialized and save sample data to path. +178 +179 Args: +180 path: +181 Pathlib object +182 """ +183 +184 self.sample_data = joblib.load(path) +185 +186 @classmethod +187 def _get_sample_data(cls, sample_data: Any) -> Any: +188 """Check sample data and returns one record to be used +189 during type inference and ONNX conversion/validation. +190 +191 Returns: +192 Sample data with only one record +193 """ +194 +195 if isinstance(sample_data, list): +196 return [data[0:1] for data in sample_data] +197 +198 if isinstance(sample_data, tuple): +199 return (data[0:1] for data in sample_data) +200 +201 if isinstance(sample_data, dict): +202 return {key: data[0:1] for key, data in sample_data.items()} +203 +204 return sample_data[0:1] +205 +206 def get_sample_prediction(self) -> SamplePrediction: +207 assert self.model is not None, "Model is not defined" +208 assert self.sample_data is not None, "Sample data must be provided" +209 +210 if isinstance(self.sample_data, (pd.DataFrame, np.ndarray)): +211 prediction = self.model.predict(self.sample_data) +212 +213 elif isinstance(self.sample_data, dict): +214 try: +215 prediction = self.model.predict(**self.sample_data) +216 except Exception as _: # pylint: disable=broad-except +217 prediction = self.model.predict(self.sample_data) +218 +219 elif isinstance(self.sample_data, (list, tuple)): +220 try: +221 prediction = self.model.predict(*self.sample_data) +222 except Exception as _: # pylint: disable=broad-except +223 prediction = self.model.predict(self.sample_data) +224 +225 else: +226 prediction = self.model.predict(self.sample_data) +227 +228 prediction_type = get_class_name(prediction) +229 +230 return SamplePrediction( +231 prediction_type, +232 prediction, +233 ) +234 +235 @property +236 def model_suffix(self) -> str: +237 """Returns suffix for storage""" +238 return Suffix.JOBLIB.value +239 +240 @property +241 def data_suffix(self) -> str: +242 """Returns suffix for storage""" +243 return Suffix.JOBLIB.value +244 +245 @staticmethod +246 def name() -> str: +247 return ModelInterface.__name__ +
Usage docs: https://docs.pydantic.dev/2.6/concepts/models/
+ +A base class for creating Pydantic models.
+ +__init__ function.Model.__validators__ and Model.__root_validators__ from Pydantic V1.RootModel.model_config['extra'] == 'allow'.69 @model_validator(mode="before") +70 @classmethod +71 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: +72 if model_args.get("modelcard_uid", False): +73 return model_args +74 +75 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) +76 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +77 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +78 +79 return model_args +
81 @field_validator("modelcard_uid", mode="before") +82 @classmethod +83 def check_modelcard_uid(cls, modelcard_uid: str) -> str: +84 # empty strings are falsey +85 if not modelcard_uid: +86 return modelcard_uid +87 +88 try: +89 UUID(modelcard_uid, version=4) # we use uuid4 +90 return modelcard_uid +91 +92 except ValueError as exc: +93 raise ValueError("ModelCard uid is not a valid uuid") from exc +
95 def save_model(self, path: Path) -> None: + 96 """Saves model to path. Base implementation use Joblib + 97 + 98 Args: + 99 path: +100 Pathlib object +101 """ +102 assert self.model is not None, "No model detected in interface" +103 joblib.dump(self.model, path) +
Saves model to path. Base implementation use Joblib
+ +105 def load_model(self, path: Path, **kwargs: Any) -> None: +106 """Load model from pathlib object +107 +108 Args: +109 path: +110 Pathlib object +111 kwargs: +112 Additional kwargs +113 """ +114 self.model = joblib.load(path) +
Load model from pathlib object
+ +116 def save_onnx(self, path: Path) -> ModelReturn: +117 """Saves the onnx model +118 +119 Args: +120 path: +121 Path to save +122 +123 Returns: +124 ModelReturn +125 """ +126 import onnxruntime as rt +127 +128 from opsml.model.onnx import _get_onnx_metadata +129 +130 if self.onnx_model is None: +131 self.convert_to_onnx() +132 sess: rt.InferenceSession = self.onnx_model.sess +133 path.write_bytes(sess._model_bytes) # pylint: disable=protected-access +134 +135 else: +136 self.onnx_model.sess_to_path(path.with_suffix(Suffix.ONNX.value)) +137 +138 assert self.onnx_model is not None, "No onnx model detected in interface" +139 metadata = _get_onnx_metadata(self, cast(rt.InferenceSession, self.onnx_model.sess)) +140 +141 return metadata +
Saves the onnx model
+ +++ModelReturn
+
143 def convert_to_onnx(self, **kwargs: Path) -> None: +144 """Converts model to onnx format""" +145 from opsml.model.onnx import _OnnxModelConverter +146 +147 if self.onnx_model is not None: +148 return None +149 +150 metadata = _OnnxModelConverter(self).convert_model() +151 self.onnx_model = metadata.onnx_model +152 +153 return None +
Converts model to onnx format
+155 def load_onnx_model(self, path: Path) -> None: +156 """Load onnx model from pathlib object +157 +158 Args: +159 path: +160 Pathlib object +161 """ +162 from onnxruntime import InferenceSession +163 +164 assert self.onnx_model is not None, "No onnx model detected in interface" +165 self.onnx_model.sess = InferenceSession(path) +
Load onnx model from pathlib object
+ +167 def save_sample_data(self, path: Path) -> None: +168 """Serialized and save sample data to path. +169 +170 Args: +171 path: +172 Pathlib object +173 """ +174 joblib.dump(self.sample_data, path) +
Serialized and save sample data to path.
+ +176 def load_sample_data(self, path: Path) -> None: +177 """Serialized and save sample data to path. +178 +179 Args: +180 path: +181 Pathlib object +182 """ +183 +184 self.sample_data = joblib.load(path) +
Serialized and save sample data to path.
+ +206 def get_sample_prediction(self) -> SamplePrediction: +207 assert self.model is not None, "Model is not defined" +208 assert self.sample_data is not None, "Sample data must be provided" +209 +210 if isinstance(self.sample_data, (pd.DataFrame, np.ndarray)): +211 prediction = self.model.predict(self.sample_data) +212 +213 elif isinstance(self.sample_data, dict): +214 try: +215 prediction = self.model.predict(**self.sample_data) +216 except Exception as _: # pylint: disable=broad-except +217 prediction = self.model.predict(self.sample_data) +218 +219 elif isinstance(self.sample_data, (list, tuple)): +220 try: +221 prediction = self.model.predict(*self.sample_data) +222 except Exception as _: # pylint: disable=broad-except +223 prediction = self.model.predict(self.sample_data) +224 +225 else: +226 prediction = self.model.predict(self.sample_data) +227 +228 prediction_type = get_class_name(prediction) +229 +230 return SamplePrediction( +231 prediction_type, +232 prediction, +233 ) +
235 @property +236 def model_suffix(self) -> str: +237 """Returns suffix for storage""" +238 return Suffix.JOBLIB.value +
Returns suffix for storage
+240 @property +241 def data_suffix(self) -> str: +242 """Returns suffix for storage""" +243 return Suffix.JOBLIB.value +
Returns suffix for storage
+1import tempfile + 2from pathlib import Path + 3from typing import Any, Dict, List, Optional, Union, cast + 4 + 5import joblib + 6import numpy as np + 7from numpy.typing import NDArray + 8from pydantic import model_validator + 9 + 10from opsml.helpers.logging import ArtifactLogger + 11from opsml.helpers.utils import get_class_name + 12from opsml.model.interfaces.base import ( + 13 ModelInterface, + 14 SamplePrediction, + 15 get_model_args, + 16 get_processor_name, + 17) + 18from opsml.types import ( + 19 CommonKwargs, + 20 ModelReturn, + 21 OnnxModel, + 22 SaveName, + 23 Suffix, + 24 TrainedModelType, + 25) + 26 + 27logger = ArtifactLogger.get_logger() + 28 + 29try: + 30 from catboost import CatBoost + 31 + 32 class CatBoostModel(ModelInterface): + 33 """Model interface for CatBoost models. + 34 + 35 Args: + 36 model: + 37 CatBoost model (Classifier, Regressor, Ranker) + 38 preprocessor: + 39 Optional preprocessor + 40 sample_data: + 41 Sample data to be used for type inference and sample prediction. + 42 For catboost models this should be a numpy array (either 1d or 2d) or list of feature values. + 43 This should match exactly what the model expects as input. + 44 task_type: + 45 Task type for model. Defaults to undefined. + 46 model_type: + 47 Optional model type. This is inferred automatically. + 48 preprocessor_name: + 49 Optional preprocessor name. This is inferred automatically if a + 50 preprocessor is provided. + 51 + 52 Returns: + 53 CatBoostModel + 54 """ + 55 + 56 model: Optional[CatBoost] = None + 57 sample_data: Optional[Union[List[Any], NDArray[Any]]] = None + 58 preprocessor: Optional[Any] = None + 59 preprocessor_name: str = CommonKwargs.UNDEFINED.value + 60 + 61 @classmethod + 62 def _get_sample_data(cls, sample_data: NDArray[Any]) -> Union[List[Any], NDArray[Any]]: + 63 """Check sample data and returns one record to be used + 64 during type inference and sample prediction. + 65 + 66 Returns: + 67 Sample data with only one record + 68 """ + 69 + 70 if isinstance(sample_data, list): + 71 return sample_data + 72 + 73 if isinstance(sample_data, np.ndarray): + 74 if len(sample_data.shape) == 1: + 75 return sample_data.reshape(1, -1) + 76 return sample_data[0:1] + 77 + 78 raise ValueError("Sample data should be a list or numpy array") + 79 + 80 def get_sample_prediction(self) -> SamplePrediction: + 81 assert self.model is not None, "Model is not defined" + 82 assert self.sample_data is not None, "Sample data must be provided" + 83 + 84 prediction = self.model.predict(self.sample_data) + 85 + 86 prediction_type = get_class_name(prediction) + 87 + 88 return SamplePrediction(prediction_type, prediction) + 89 + 90 @property + 91 def model_class(self) -> str: + 92 return TrainedModelType.CATBOOST.value + 93 + 94 @model_validator(mode="before") + 95 @classmethod + 96 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 97 model = model_args.get("model") + 98 + 99 if model_args.get("modelcard_uid", False): +100 return model_args +101 +102 model, module, bases = get_model_args(model) +103 +104 if "catboost" in module: +105 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ +106 +107 else: +108 for base in bases: +109 if "catboost" in base: +110 model_args[CommonKwargs.MODEL_TYPE.value] = "subclass" +111 +112 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) +113 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +114 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +115 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( +116 model_args.get(CommonKwargs.PREPROCESSOR.value), +117 ) +118 +119 return model_args +120 +121 def save_model(self, path: Path) -> None: +122 """Saves model to path. Base implementation use Joblib +123 +124 Args: +125 path: +126 Pathlib object +127 """ +128 assert self.model is not None, "No model detected in interface" +129 self.model.save_model(path.as_posix()) +130 +131 def load_model(self, path: Path, **kwargs: Any) -> None: +132 """Load model from pathlib object +133 +134 Args: +135 path: +136 Pathlib object +137 kwargs: +138 Additional kwargs +139 """ +140 import catboost +141 +142 model = getattr(catboost, self.model_type, CatBoost)() +143 self.model = model.load_model(path.as_posix()) +144 +145 def _convert_to_onnx_inplace(self) -> None: +146 """Convert to onnx model using temp dir""" +147 with tempfile.TemporaryDirectory() as tmpdir: +148 lpath = Path(tmpdir) / SaveName.ONNX_MODEL.value +149 onnx_path = lpath.with_suffix(Suffix.ONNX.value) +150 self.convert_to_onnx(**{"path": onnx_path}) +151 +152 def convert_to_onnx(self, **kwargs: Path) -> None: +153 """Converts model to onnx format""" +154 +155 logger.info("Converting CatBoost model to onnx format") +156 +157 import onnx +158 import onnxruntime as rt +159 +160 if self.onnx_model is not None: +161 return None +162 +163 path: Optional[Path] = kwargs.get("path") +164 if path is None: +165 return self._convert_to_onnx_inplace() +166 +167 assert self.model is not None, "No model detected in interface" +168 self.model.save_model( +169 path.as_posix(), +170 format="onnx", +171 export_parameters={"onnx_domain": "ai.catboost"}, +172 ) +173 self.onnx_model = OnnxModel( +174 onnx_version=onnx.__version__, +175 sess=rt.InferenceSession(path.as_posix()), +176 ) +177 return None +178 +179 def save_onnx(self, path: Path) -> ModelReturn: +180 import onnxruntime as rt +181 +182 from opsml.model.onnx import _get_onnx_metadata +183 +184 if self.onnx_model is None: +185 self.convert_to_onnx(**{"path": path}) +186 +187 else: +188 self.onnx_model.sess_to_path(path) +189 +190 assert self.onnx_model is not None, "No onnx model detected in interface" +191 +192 # no need to save onnx to bytes since its done during onnx conversion +193 return _get_onnx_metadata(self, cast(rt.InferenceSession, self.onnx_model.sess)) +194 +195 def save_preprocessor(self, path: Path) -> None: +196 """Saves preprocessor to path if present. Base implementation use Joblib +197 +198 Args: +199 path: +200 Pathlib object +201 """ +202 assert self.preprocessor is not None, "No preprocessor detected in interface" +203 joblib.dump(self.preprocessor, path) +204 +205 def load_preprocessor(self, path: Path) -> None: +206 """Load preprocessor from pathlib object +207 +208 Args: +209 path: +210 Pathlib object +211 """ +212 self.preprocessor = joblib.load(path) +213 +214 @property +215 def preprocessor_suffix(self) -> str: +216 """Returns suffix for storage""" +217 return Suffix.JOBLIB.value +218 +219 @property +220 def model_suffix(self) -> str: +221 """Returns suffix for storage""" +222 return Suffix.CATBOOST.value +223 +224 @staticmethod +225 def name() -> str: +226 return CatBoostModel.__name__ +227 +228except ModuleNotFoundError: +229 from opsml.model.interfaces.backups import CatBoostModelNoModule as CatBoostModel +
33 class CatBoostModel(ModelInterface): + 34 """Model interface for CatBoost models. + 35 + 36 Args: + 37 model: + 38 CatBoost model (Classifier, Regressor, Ranker) + 39 preprocessor: + 40 Optional preprocessor + 41 sample_data: + 42 Sample data to be used for type inference and sample prediction. + 43 For catboost models this should be a numpy array (either 1d or 2d) or list of feature values. + 44 This should match exactly what the model expects as input. + 45 task_type: + 46 Task type for model. Defaults to undefined. + 47 model_type: + 48 Optional model type. This is inferred automatically. + 49 preprocessor_name: + 50 Optional preprocessor name. This is inferred automatically if a + 51 preprocessor is provided. + 52 + 53 Returns: + 54 CatBoostModel + 55 """ + 56 + 57 model: Optional[CatBoost] = None + 58 sample_data: Optional[Union[List[Any], NDArray[Any]]] = None + 59 preprocessor: Optional[Any] = None + 60 preprocessor_name: str = CommonKwargs.UNDEFINED.value + 61 + 62 @classmethod + 63 def _get_sample_data(cls, sample_data: NDArray[Any]) -> Union[List[Any], NDArray[Any]]: + 64 """Check sample data and returns one record to be used + 65 during type inference and sample prediction. + 66 + 67 Returns: + 68 Sample data with only one record + 69 """ + 70 + 71 if isinstance(sample_data, list): + 72 return sample_data + 73 + 74 if isinstance(sample_data, np.ndarray): + 75 if len(sample_data.shape) == 1: + 76 return sample_data.reshape(1, -1) + 77 return sample_data[0:1] + 78 + 79 raise ValueError("Sample data should be a list or numpy array") + 80 + 81 def get_sample_prediction(self) -> SamplePrediction: + 82 assert self.model is not None, "Model is not defined" + 83 assert self.sample_data is not None, "Sample data must be provided" + 84 + 85 prediction = self.model.predict(self.sample_data) + 86 + 87 prediction_type = get_class_name(prediction) + 88 + 89 return SamplePrediction(prediction_type, prediction) + 90 + 91 @property + 92 def model_class(self) -> str: + 93 return TrainedModelType.CATBOOST.value + 94 + 95 @model_validator(mode="before") + 96 @classmethod + 97 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 98 model = model_args.get("model") + 99 +100 if model_args.get("modelcard_uid", False): +101 return model_args +102 +103 model, module, bases = get_model_args(model) +104 +105 if "catboost" in module: +106 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ +107 +108 else: +109 for base in bases: +110 if "catboost" in base: +111 model_args[CommonKwargs.MODEL_TYPE.value] = "subclass" +112 +113 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) +114 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +115 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +116 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( +117 model_args.get(CommonKwargs.PREPROCESSOR.value), +118 ) +119 +120 return model_args +121 +122 def save_model(self, path: Path) -> None: +123 """Saves model to path. Base implementation use Joblib +124 +125 Args: +126 path: +127 Pathlib object +128 """ +129 assert self.model is not None, "No model detected in interface" +130 self.model.save_model(path.as_posix()) +131 +132 def load_model(self, path: Path, **kwargs: Any) -> None: +133 """Load model from pathlib object +134 +135 Args: +136 path: +137 Pathlib object +138 kwargs: +139 Additional kwargs +140 """ +141 import catboost +142 +143 model = getattr(catboost, self.model_type, CatBoost)() +144 self.model = model.load_model(path.as_posix()) +145 +146 def _convert_to_onnx_inplace(self) -> None: +147 """Convert to onnx model using temp dir""" +148 with tempfile.TemporaryDirectory() as tmpdir: +149 lpath = Path(tmpdir) / SaveName.ONNX_MODEL.value +150 onnx_path = lpath.with_suffix(Suffix.ONNX.value) +151 self.convert_to_onnx(**{"path": onnx_path}) +152 +153 def convert_to_onnx(self, **kwargs: Path) -> None: +154 """Converts model to onnx format""" +155 +156 logger.info("Converting CatBoost model to onnx format") +157 +158 import onnx +159 import onnxruntime as rt +160 +161 if self.onnx_model is not None: +162 return None +163 +164 path: Optional[Path] = kwargs.get("path") +165 if path is None: +166 return self._convert_to_onnx_inplace() +167 +168 assert self.model is not None, "No model detected in interface" +169 self.model.save_model( +170 path.as_posix(), +171 format="onnx", +172 export_parameters={"onnx_domain": "ai.catboost"}, +173 ) +174 self.onnx_model = OnnxModel( +175 onnx_version=onnx.__version__, +176 sess=rt.InferenceSession(path.as_posix()), +177 ) +178 return None +179 +180 def save_onnx(self, path: Path) -> ModelReturn: +181 import onnxruntime as rt +182 +183 from opsml.model.onnx import _get_onnx_metadata +184 +185 if self.onnx_model is None: +186 self.convert_to_onnx(**{"path": path}) +187 +188 else: +189 self.onnx_model.sess_to_path(path) +190 +191 assert self.onnx_model is not None, "No onnx model detected in interface" +192 +193 # no need to save onnx to bytes since its done during onnx conversion +194 return _get_onnx_metadata(self, cast(rt.InferenceSession, self.onnx_model.sess)) +195 +196 def save_preprocessor(self, path: Path) -> None: +197 """Saves preprocessor to path if present. Base implementation use Joblib +198 +199 Args: +200 path: +201 Pathlib object +202 """ +203 assert self.preprocessor is not None, "No preprocessor detected in interface" +204 joblib.dump(self.preprocessor, path) +205 +206 def load_preprocessor(self, path: Path) -> None: +207 """Load preprocessor from pathlib object +208 +209 Args: +210 path: +211 Pathlib object +212 """ +213 self.preprocessor = joblib.load(path) +214 +215 @property +216 def preprocessor_suffix(self) -> str: +217 """Returns suffix for storage""" +218 return Suffix.JOBLIB.value +219 +220 @property +221 def model_suffix(self) -> str: +222 """Returns suffix for storage""" +223 return Suffix.CATBOOST.value +224 +225 @staticmethod +226 def name() -> str: +227 return CatBoostModel.__name__ +
Model interface for CatBoost models.
+ +++CatBoostModel
+
81 def get_sample_prediction(self) -> SamplePrediction: +82 assert self.model is not None, "Model is not defined" +83 assert self.sample_data is not None, "Sample data must be provided" +84 +85 prediction = self.model.predict(self.sample_data) +86 +87 prediction_type = get_class_name(prediction) +88 +89 return SamplePrediction(prediction_type, prediction) +
95 @model_validator(mode="before") + 96 @classmethod + 97 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 98 model = model_args.get("model") + 99 +100 if model_args.get("modelcard_uid", False): +101 return model_args +102 +103 model, module, bases = get_model_args(model) +104 +105 if "catboost" in module: +106 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ +107 +108 else: +109 for base in bases: +110 if "catboost" in base: +111 model_args[CommonKwargs.MODEL_TYPE.value] = "subclass" +112 +113 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) +114 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +115 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +116 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( +117 model_args.get(CommonKwargs.PREPROCESSOR.value), +118 ) +119 +120 return model_args +
122 def save_model(self, path: Path) -> None: +123 """Saves model to path. Base implementation use Joblib +124 +125 Args: +126 path: +127 Pathlib object +128 """ +129 assert self.model is not None, "No model detected in interface" +130 self.model.save_model(path.as_posix()) +
Saves model to path. Base implementation use Joblib
+ +132 def load_model(self, path: Path, **kwargs: Any) -> None: +133 """Load model from pathlib object +134 +135 Args: +136 path: +137 Pathlib object +138 kwargs: +139 Additional kwargs +140 """ +141 import catboost +142 +143 model = getattr(catboost, self.model_type, CatBoost)() +144 self.model = model.load_model(path.as_posix()) +
Load model from pathlib object
+ +153 def convert_to_onnx(self, **kwargs: Path) -> None: +154 """Converts model to onnx format""" +155 +156 logger.info("Converting CatBoost model to onnx format") +157 +158 import onnx +159 import onnxruntime as rt +160 +161 if self.onnx_model is not None: +162 return None +163 +164 path: Optional[Path] = kwargs.get("path") +165 if path is None: +166 return self._convert_to_onnx_inplace() +167 +168 assert self.model is not None, "No model detected in interface" +169 self.model.save_model( +170 path.as_posix(), +171 format="onnx", +172 export_parameters={"onnx_domain": "ai.catboost"}, +173 ) +174 self.onnx_model = OnnxModel( +175 onnx_version=onnx.__version__, +176 sess=rt.InferenceSession(path.as_posix()), +177 ) +178 return None +
Converts model to onnx format
+180 def save_onnx(self, path: Path) -> ModelReturn: +181 import onnxruntime as rt +182 +183 from opsml.model.onnx import _get_onnx_metadata +184 +185 if self.onnx_model is None: +186 self.convert_to_onnx(**{"path": path}) +187 +188 else: +189 self.onnx_model.sess_to_path(path) +190 +191 assert self.onnx_model is not None, "No onnx model detected in interface" +192 +193 # no need to save onnx to bytes since its done during onnx conversion +194 return _get_onnx_metadata(self, cast(rt.InferenceSession, self.onnx_model.sess)) +
Saves the onnx model
+ +++ModelReturn
+
196 def save_preprocessor(self, path: Path) -> None: +197 """Saves preprocessor to path if present. Base implementation use Joblib +198 +199 Args: +200 path: +201 Pathlib object +202 """ +203 assert self.preprocessor is not None, "No preprocessor detected in interface" +204 joblib.dump(self.preprocessor, path) +
Saves preprocessor to path if present. Base implementation use Joblib
+ +206 def load_preprocessor(self, path: Path) -> None: +207 """Load preprocessor from pathlib object +208 +209 Args: +210 path: +211 Pathlib object +212 """ +213 self.preprocessor = joblib.load(path) +
Load preprocessor from pathlib object
+ +215 @property +216 def preprocessor_suffix(self) -> str: +217 """Returns suffix for storage""" +218 return Suffix.JOBLIB.value +
Returns suffix for storage
+220 @property +221 def model_suffix(self) -> str: +222 """Returns suffix for storage""" +223 return Suffix.CATBOOST.value +
Returns suffix for storage
+1import tempfile + 2from pathlib import Path + 3from typing import Any, Dict, Optional, Union, cast + 4 + 5from pydantic import field_validator, model_validator + 6 + 7from opsml.helpers.logging import ArtifactLogger + 8from opsml.helpers.utils import get_class_name + 9from opsml.model.interfaces.base import ( + 10 ModelInterface, + 11 SamplePrediction, + 12 get_processor_name, + 13) + 14from opsml.types import ( + 15 GENERATION_TYPES, + 16 CommonKwargs, + 17 HuggingFaceOnnxArgs, + 18 HuggingFaceTask, + 19 ModelReturn, + 20 OnnxModel, + 21 SaveName, + 22 TrainedModelType, + 23) + 24 + 25logger = ArtifactLogger.get_logger() + 26 + 27try: + 28 import transformers + 29 from PIL import ImageFile + 30 from transformers import ( + 31 BatchEncoding, + 32 BatchFeature, + 33 FeatureExtractionMixin, + 34 ImageProcessingMixin, + 35 Pipeline, + 36 PreTrainedModel, + 37 PreTrainedTokenizer, + 38 PreTrainedTokenizerFast, + 39 TFPreTrainedModel, + 40 pipeline, + 41 ) + 42 from transformers.utils import ModelOutput + 43 + 44 class HuggingFaceModel(ModelInterface): + 45 """Model interface for HuggingFace models + 46 + 47 Args: + 48 model: + 49 HuggingFace model or pipeline + 50 tokenizer: + 51 HuggingFace tokenizer. If passing pipeline, tokenizer will be extracted + 52 feature_extractor: + 53 HuggingFace feature extractor or image processor. If passing pipeline, feature extractor will be extracted + 54 sample_data: + 55 Sample data to be used for type inference. + 56 This should match exactly what the model expects as input. + 57 task_type: + 58 Task type for HuggingFace model. See `HuggingFaceTask` for supported tasks. + 59 model_type: + 60 Optional model type for HuggingFace model. This is inferred automatically. + 61 is_pipeline: + 62 If model is a pipeline. Defaults to False. + 63 backend: + 64 Backend for HuggingFace model. This is inferred from model + 65 onnx_args: + 66 Optional arguments for ONNX conversion. See `HuggingFaceOnnxArgs` for supported arguments. + 67 tokenizer_name: + 68 Optional tokenizer name for HuggingFace model. This is inferred automatically. + 69 feature_extractor_name: + 70 Optional feature_extractor name for HuggingFace model. This is inferred automatically. + 71 + 72 Returns: + 73 HuggingFaceModel + 74 + 75 Example:: + 76 + 77 from transformers import BartModel, BartTokenizer + 78 + 79 tokenizer = BartTokenizer.from_pretrained("facebook/bart-base") + 80 model = BartModel.from_pretrained("facebook/bart-base") + 81 inputs = tokenizer(["Hello. How are you"], return_tensors="pt") + 82 + 83 # this is fed to the ModelCard + 84 model = HuggingFaceModel( + 85 model=model, + 86 tokenizer=tokenizer, + 87 sample_data=inputs, + 88 task_type=HuggingFaceTask.TEXT_CLASSIFICATION.value, + 89 ) + 90 """ + 91 + 92 model: Optional[Union[Pipeline, PreTrainedModel, TFPreTrainedModel]] = None + 93 tokenizer: Optional[Union[PreTrainedTokenizer, PreTrainedTokenizerFast]] = None + 94 feature_extractor: Optional[Union[FeatureExtractionMixin, ImageProcessingMixin]] = None + 95 is_pipeline: bool = False + 96 backend: str = CommonKwargs.PYTORCH.value + 97 onnx_args: Optional[HuggingFaceOnnxArgs] = None + 98 tokenizer_name: str = CommonKwargs.UNDEFINED.value + 99 feature_extractor_name: str = CommonKwargs.UNDEFINED.value +100 +101 @classmethod +102 def _get_sample_data(cls, sample_data: Any) -> Any: +103 """Check sample data and returns one record to be used +104 during type inference and ONNX conversion/validation. +105 +106 Returns: +107 Sample data with only one record +108 """ +109 +110 if isinstance(sample_data, list): +111 return [data[0:1] for data in sample_data] +112 +113 if isinstance(sample_data, tuple): +114 return (data[0:1] for data in sample_data) +115 +116 if isinstance(sample_data, (BatchEncoding, BatchFeature, dict)): +117 sample_dict = {} +118 for key, value in sample_data.items(): +119 sample_dict[key] = value[0:1] +120 return sample_dict +121 +122 if isinstance(sample_data, str): +123 return sample_data +124 +125 if isinstance(sample_data, ImageFile.ImageFile): +126 return sample_data +127 +128 return sample_data[0:1] +129 +130 @classmethod +131 def _check_model_backend(cls, model: Any) -> str: +132 """Check model backend type for HuggingFace model +133 +134 Args: +135 model: +136 HuggingFace model +137 +138 Returns: +139 backend name +140 """ +141 +142 if isinstance(model, PreTrainedModel): +143 return CommonKwargs.PYTORCH.value +144 +145 if isinstance(model, TFPreTrainedModel): +146 return CommonKwargs.TENSORFLOW.value +147 +148 raise ValueError("Model must be a huggingface model") +149 +150 @model_validator(mode="before") +151 @classmethod +152 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: +153 if bool(model_args.get("modelcard_uid", False)): +154 return model_args +155 +156 hf_model = model_args.get("model") +157 assert hf_model is not None, "Model is not defined" +158 +159 if isinstance(hf_model, Pipeline): +160 model_args[CommonKwargs.BACKEND.value] = cls._check_model_backend(hf_model.model) +161 model_args[CommonKwargs.IS_PIPELINE.value] = True +162 model_args[CommonKwargs.MODEL_TYPE.value] = hf_model.model.__class__.__name__ +163 model_args[CommonKwargs.TOKENIZER.value] = hf_model.tokenizer +164 model_args[CommonKwargs.TOKENIZER_NAME.value] = get_processor_name(hf_model.tokenizer) +165 model_args[CommonKwargs.FEATURE_EXTRACTOR.value] = hf_model.feature_extractor +166 model_args[CommonKwargs.FEATURE_EXTRACTOR_NAME.value] = get_processor_name(hf_model.feature_extractor) +167 +168 else: +169 model_args[CommonKwargs.BACKEND.value] = cls._check_model_backend(hf_model) +170 model_args[CommonKwargs.IS_PIPELINE.value] = False +171 model_args[CommonKwargs.MODEL_TYPE.value] = hf_model.__class__.__name__ +172 model_args[CommonKwargs.TOKENIZER_NAME.value] = get_processor_name( +173 model_args.get(CommonKwargs.TOKENIZER.value), +174 ) +175 +176 model_args[CommonKwargs.FEATURE_EXTRACTOR_NAME.value] = get_processor_name( +177 model_args.get(CommonKwargs.FEATURE_EXTRACTOR.value) +178 ) +179 +180 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) +181 +182 # set args +183 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +184 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +185 +186 return model_args +187 +188 @field_validator("task_type", mode="before") +189 @classmethod +190 def check_task_type(cls, task_type: str) -> str: +191 """Check if task is a huggingface approved task""" +192 +193 task = task_type.lower() +194 if task not in list(HuggingFaceTask): +195 raise ValueError(f"Task type {task} is not supported") +196 return task +197 +198 # ------------ Model Interface Helper Methods ------------# +199 +200 def _generate_predictions(self) -> Any: +201 """Use model in generate mode if generate task""" +202 +203 assert self.sample_data is not None, "Sample data must be provided" +204 assert self.model is not None, "Model must be provided" +205 +206 try: # try generation first , then functional +207 if isinstance(self.sample_data, (BatchEncoding, dict)): +208 return self.model.generate(**self.sample_data) +209 +210 return self.model.generate(self.sample_data) +211 +212 except Exception as _: # pylint: disable=broad-except +213 return self._functional_predictions() +214 +215 def _functional_predictions(self) -> Any: +216 """Use model in functional mode if functional task""" +217 +218 assert self.sample_data is not None, "Sample data must be provided" +219 assert self.model is not None, "Model must be provided" +220 +221 if isinstance(self.sample_data, (BatchEncoding, dict)): +222 return self.model(**self.sample_data) +223 +224 return self.model(self.sample_data) +225 +226 def _get_pipeline_prediction(self) -> Any: +227 """Use model in pipeline mode if pipeline task""" +228 assert isinstance(self.model, Pipeline), "Model must be a pipeline" +229 +230 if isinstance(self.sample_data, dict): +231 prediction = self.model(**self.sample_data) +232 +233 else: +234 prediction = self.model(self.sample_data) +235 +236 if isinstance(prediction, dict): +237 return prediction +238 +239 if isinstance(prediction, list): +240 assert len(prediction) >= 1, "Pipeline model must return a prediction" +241 return prediction[0] +242 +243 raise ValueError("Pipeline model must return a prediction") +244 +245 def get_sample_prediction(self) -> SamplePrediction: +246 """Generates prediction from model and provided sample data""" +247 +248 if self.is_pipeline: +249 prediction = self._get_pipeline_prediction() +250 elif self.task_type in GENERATION_TYPES: +251 prediction = self._generate_predictions() +252 else: +253 prediction = self._functional_predictions() +254 +255 if isinstance(prediction, ModelOutput): +256 prediction = dict(prediction) +257 +258 prediction_type = get_class_name(prediction) +259 +260 return SamplePrediction(prediction_type, prediction) +261 +262 # ------------ Model Interface Onnx Methods ------------# +263 +264 def _quantize_model(self, path: Path, onnx_model: Any) -> None: +265 """Quantizes an huggingface model +266 +267 Args: +268 path: +269 parent path to save too +270 onnx_model: +271 onnx model to quantize +272 +273 Returns: +274 Path to quantized model +275 """ +276 assert self.onnx_args is not None, "No onnx args provided" +277 assert self.onnx_args.config is not None, "No quantization config provided" +278 +279 logger.info("Quantizing HuggingFace ONNX model") +280 +281 from optimum.onnxruntime import ORTQuantizer +282 +283 save_path = path / SaveName.QUANTIZED_MODEL.value +284 quantizer = ORTQuantizer.from_pretrained(onnx_model) +285 +286 quantizer.quantize(save_dir=save_path, quantization_config=self.onnx_args.config) +287 +288 def _convert_to_onnx_inplace(self) -> None: +289 """Converts model to onnx in place""" +290 +291 with tempfile.TemporaryDirectory() as tmpdirname: +292 lpath = Path(tmpdirname) +293 self.save_model((lpath / SaveName.TRAINED_MODEL.value)) +294 +295 if self.tokenizer is not None: +296 self.save_tokenizer((lpath / SaveName.TOKENIZER.value)) +297 +298 if self.feature_extractor is not None: +299 self.save_feature_extractor((lpath / SaveName.FEATURE_EXTRACTOR.value)) +300 +301 onnx_path = lpath / SaveName.ONNX_MODEL.value +302 return self.convert_to_onnx(**{"path": onnx_path}) +303 +304 def convert_to_onnx(self, **kwargs: Path) -> None: +305 """Converts a huggingface model or pipeline to onnx via optimum library. +306 Converted model or pipeline is accessible via the `onnx_model` attribute. +307 """ +308 +309 logger.info("Staring conversion of HuggingFace model to ONNX") +310 +311 assert ( +312 self.onnx_args is not None +313 ), "No onnx args provided. If converting to onnx, provide a HuggingFaceOnnxArgs instance" +314 +315 if self.onnx_model is not None: +316 return None +317 +318 import onnx +319 import optimum.onnxruntime as ort +320 +321 path: Optional[Path] = kwargs.get("path") +322 if path is None: +323 return self._convert_to_onnx_inplace() +324 +325 # ensure no suffix (this is an exception to the rule to all model interfaces) +326 # huggingface prefers to save onnx models in dirs instead of single model.onnx file +327 path = path.with_suffix("") +328 ort_model: ort.ORTModel = getattr(ort, self.onnx_args.ort_type) +329 model_path = path.parent / SaveName.TRAINED_MODEL.value +330 onnx_model = ort_model.from_pretrained(model_path, export=True, provider=self.onnx_args.provider) +331 onnx_model.save_pretrained(path) +332 +333 if self.is_pipeline: +334 self.onnx_model = OnnxModel( +335 onnx_version=onnx.__version__, +336 sess=pipeline( +337 self.task_type, +338 model=onnx_model, +339 tokenizer=self.tokenizer, +340 feature_extractor=self.feature_extractor, +341 ), +342 ) +343 else: +344 self.onnx_model = OnnxModel(onnx_version=onnx.__version__, sess=onnx_model) +345 +346 if self.onnx_args.quantize: +347 self._quantize_model(path.parent, onnx_model) +348 +349 return None +350 +351 # ------------ Model Interface Save Methods ------------# +352 +353 def save_model(self, path: Path) -> None: +354 assert self.model is not None, "No model detected in interface" +355 +356 if isinstance(self.model, Pipeline): +357 self.model.model.save_pretrained(path) +358 return None +359 +360 self.model.save_pretrained(path) +361 return None +362 +363 def save_tokenizer(self, path: Path) -> None: +364 if self.tokenizer is None: +365 return None +366 +367 if isinstance(self.model, Pipeline): +368 assert self.model.tokenizer is not None, "Tokenizer is missing" +369 self.model.tokenizer.save_pretrained(path) +370 return None +371 +372 self.tokenizer.save_pretrained(path) +373 return None +374 +375 def save_feature_extractor(self, path: Path) -> None: +376 if self.feature_extractor is None: +377 return None +378 +379 if isinstance(self.model, Pipeline): +380 assert self.model.feature_extractor is not None, "Feature extractor is missing" +381 self.model.feature_extractor.save_pretrained(path) +382 return None +383 +384 self.feature_extractor.save_pretrained(path) +385 return None +386 +387 def save_onnx(self, path: Path) -> ModelReturn: +388 import onnxruntime as rt +389 +390 from opsml.model.onnx import _get_onnx_metadata +391 +392 model_saved = False +393 if self.onnx_model is None: +394 # HF saves model during conversion +395 self.convert_to_onnx(**{"path": path}) +396 model_saved = True +397 +398 assert self.onnx_model is not None, "No onnx model detected in interface" +399 if self.is_pipeline: +400 if not model_saved: +401 self.onnx_model.sess.model.save_pretrained(path.with_suffix("")) +402 return _get_onnx_metadata(self, cast(rt.InferenceSession, self.onnx_model.sess.model.model)) +403 +404 if not model_saved: +405 self.onnx_model.sess.save_pretrained(path.with_suffix("")) +406 +407 return _get_onnx_metadata(self, cast(rt.InferenceSession, self.onnx_model.sess.model)) +408 +409 # ------------ Model Interface Load Methods ------------# +410 +411 def load_tokenizer(self, path: Path) -> None: +412 self.tokenizer = getattr(transformers, self.tokenizer_name).from_pretrained(path) +413 +414 def load_feature_extractor(self, path: Path) -> None: +415 self.feature_extractor = getattr(transformers, self.feature_extractor_name).from_pretrained(path) +416 +417 def load_model(self, path: Path, **kwargs: Any) -> None: +418 """Load huggingface model from path +419 +420 Args: +421 path: +422 Path to model +423 kwargs: +424 Additional kwargs to pass to transformers.load_pretrained +425 """ +426 custom_arch = kwargs.get("custom_architecture") +427 if custom_arch is not None: +428 assert isinstance( +429 custom_arch, (PreTrainedModel, TFPreTrainedModel) +430 ), "Custom architecture must be a huggingface model" +431 self.model = custom_arch.from_pretrained(path) +432 +433 else: +434 self.model = getattr(transformers, self.model_type).from_pretrained(path) +435 +436 def to_pipeline(self) -> None: +437 """Converts model to pipeline""" +438 +439 if isinstance(self.model, Pipeline): +440 return None +441 +442 pipe = pipeline( +443 task=self.task_type, +444 model=self.model, +445 tokenizer=self.tokenizer, +446 feature_extractor=self.feature_extractor, +447 ) +448 +449 self.model = pipe +450 self.is_pipeline = True +451 return None +452 +453 def load_onnx_model(self, path: Path) -> None: +454 """Load onnx model from path""" +455 import onnx +456 import optimum.onnxruntime as ort +457 +458 assert self.onnx_args is not None, "No onnx args provided" +459 ort_model = getattr(ort, self.onnx_args.ort_type) +460 onnx_model = ort_model.from_pretrained( +461 path, +462 config=self.onnx_args.config, +463 provider=self.onnx_args.provider, +464 ) +465 +466 if self.is_pipeline: +467 self.onnx_model = OnnxModel( +468 onnx_version=onnx.__version__, # type: ignore[attr-defined] +469 sess=pipeline( +470 self.task_type, +471 model=onnx_model, +472 tokenizer=self.tokenizer, +473 feature_extractor=self.feature_extractor, +474 ), +475 ) +476 else: +477 self.onnx_model = OnnxModel( +478 onnx_version=onnx.__version__, # type: ignore[attr-defined] +479 sess=onnx_model, +480 ) +481 +482 @property +483 def model_class(self) -> str: +484 return TrainedModelType.TRANSFORMERS.value +485 +486 @property +487 def model_suffix(self) -> str: +488 """Returns suffix for storage""" +489 return "" +490 +491 @staticmethod +492 def name() -> str: +493 return HuggingFaceModel.__name__ +494 +495except ModuleNotFoundError: +496 from opsml.model.interfaces.backups import ( +497 HuggingFaceModelNoModule as HuggingFaceModel, +498 ) +
45 class HuggingFaceModel(ModelInterface): + 46 """Model interface for HuggingFace models + 47 + 48 Args: + 49 model: + 50 HuggingFace model or pipeline + 51 tokenizer: + 52 HuggingFace tokenizer. If passing pipeline, tokenizer will be extracted + 53 feature_extractor: + 54 HuggingFace feature extractor or image processor. If passing pipeline, feature extractor will be extracted + 55 sample_data: + 56 Sample data to be used for type inference. + 57 This should match exactly what the model expects as input. + 58 task_type: + 59 Task type for HuggingFace model. See `HuggingFaceTask` for supported tasks. + 60 model_type: + 61 Optional model type for HuggingFace model. This is inferred automatically. + 62 is_pipeline: + 63 If model is a pipeline. Defaults to False. + 64 backend: + 65 Backend for HuggingFace model. This is inferred from model + 66 onnx_args: + 67 Optional arguments for ONNX conversion. See `HuggingFaceOnnxArgs` for supported arguments. + 68 tokenizer_name: + 69 Optional tokenizer name for HuggingFace model. This is inferred automatically. + 70 feature_extractor_name: + 71 Optional feature_extractor name for HuggingFace model. This is inferred automatically. + 72 + 73 Returns: + 74 HuggingFaceModel + 75 + 76 Example:: + 77 + 78 from transformers import BartModel, BartTokenizer + 79 + 80 tokenizer = BartTokenizer.from_pretrained("facebook/bart-base") + 81 model = BartModel.from_pretrained("facebook/bart-base") + 82 inputs = tokenizer(["Hello. How are you"], return_tensors="pt") + 83 + 84 # this is fed to the ModelCard + 85 model = HuggingFaceModel( + 86 model=model, + 87 tokenizer=tokenizer, + 88 sample_data=inputs, + 89 task_type=HuggingFaceTask.TEXT_CLASSIFICATION.value, + 90 ) + 91 """ + 92 + 93 model: Optional[Union[Pipeline, PreTrainedModel, TFPreTrainedModel]] = None + 94 tokenizer: Optional[Union[PreTrainedTokenizer, PreTrainedTokenizerFast]] = None + 95 feature_extractor: Optional[Union[FeatureExtractionMixin, ImageProcessingMixin]] = None + 96 is_pipeline: bool = False + 97 backend: str = CommonKwargs.PYTORCH.value + 98 onnx_args: Optional[HuggingFaceOnnxArgs] = None + 99 tokenizer_name: str = CommonKwargs.UNDEFINED.value +100 feature_extractor_name: str = CommonKwargs.UNDEFINED.value +101 +102 @classmethod +103 def _get_sample_data(cls, sample_data: Any) -> Any: +104 """Check sample data and returns one record to be used +105 during type inference and ONNX conversion/validation. +106 +107 Returns: +108 Sample data with only one record +109 """ +110 +111 if isinstance(sample_data, list): +112 return [data[0:1] for data in sample_data] +113 +114 if isinstance(sample_data, tuple): +115 return (data[0:1] for data in sample_data) +116 +117 if isinstance(sample_data, (BatchEncoding, BatchFeature, dict)): +118 sample_dict = {} +119 for key, value in sample_data.items(): +120 sample_dict[key] = value[0:1] +121 return sample_dict +122 +123 if isinstance(sample_data, str): +124 return sample_data +125 +126 if isinstance(sample_data, ImageFile.ImageFile): +127 return sample_data +128 +129 return sample_data[0:1] +130 +131 @classmethod +132 def _check_model_backend(cls, model: Any) -> str: +133 """Check model backend type for HuggingFace model +134 +135 Args: +136 model: +137 HuggingFace model +138 +139 Returns: +140 backend name +141 """ +142 +143 if isinstance(model, PreTrainedModel): +144 return CommonKwargs.PYTORCH.value +145 +146 if isinstance(model, TFPreTrainedModel): +147 return CommonKwargs.TENSORFLOW.value +148 +149 raise ValueError("Model must be a huggingface model") +150 +151 @model_validator(mode="before") +152 @classmethod +153 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: +154 if bool(model_args.get("modelcard_uid", False)): +155 return model_args +156 +157 hf_model = model_args.get("model") +158 assert hf_model is not None, "Model is not defined" +159 +160 if isinstance(hf_model, Pipeline): +161 model_args[CommonKwargs.BACKEND.value] = cls._check_model_backend(hf_model.model) +162 model_args[CommonKwargs.IS_PIPELINE.value] = True +163 model_args[CommonKwargs.MODEL_TYPE.value] = hf_model.model.__class__.__name__ +164 model_args[CommonKwargs.TOKENIZER.value] = hf_model.tokenizer +165 model_args[CommonKwargs.TOKENIZER_NAME.value] = get_processor_name(hf_model.tokenizer) +166 model_args[CommonKwargs.FEATURE_EXTRACTOR.value] = hf_model.feature_extractor +167 model_args[CommonKwargs.FEATURE_EXTRACTOR_NAME.value] = get_processor_name(hf_model.feature_extractor) +168 +169 else: +170 model_args[CommonKwargs.BACKEND.value] = cls._check_model_backend(hf_model) +171 model_args[CommonKwargs.IS_PIPELINE.value] = False +172 model_args[CommonKwargs.MODEL_TYPE.value] = hf_model.__class__.__name__ +173 model_args[CommonKwargs.TOKENIZER_NAME.value] = get_processor_name( +174 model_args.get(CommonKwargs.TOKENIZER.value), +175 ) +176 +177 model_args[CommonKwargs.FEATURE_EXTRACTOR_NAME.value] = get_processor_name( +178 model_args.get(CommonKwargs.FEATURE_EXTRACTOR.value) +179 ) +180 +181 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) +182 +183 # set args +184 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +185 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +186 +187 return model_args +188 +189 @field_validator("task_type", mode="before") +190 @classmethod +191 def check_task_type(cls, task_type: str) -> str: +192 """Check if task is a huggingface approved task""" +193 +194 task = task_type.lower() +195 if task not in list(HuggingFaceTask): +196 raise ValueError(f"Task type {task} is not supported") +197 return task +198 +199 # ------------ Model Interface Helper Methods ------------# +200 +201 def _generate_predictions(self) -> Any: +202 """Use model in generate mode if generate task""" +203 +204 assert self.sample_data is not None, "Sample data must be provided" +205 assert self.model is not None, "Model must be provided" +206 +207 try: # try generation first , then functional +208 if isinstance(self.sample_data, (BatchEncoding, dict)): +209 return self.model.generate(**self.sample_data) +210 +211 return self.model.generate(self.sample_data) +212 +213 except Exception as _: # pylint: disable=broad-except +214 return self._functional_predictions() +215 +216 def _functional_predictions(self) -> Any: +217 """Use model in functional mode if functional task""" +218 +219 assert self.sample_data is not None, "Sample data must be provided" +220 assert self.model is not None, "Model must be provided" +221 +222 if isinstance(self.sample_data, (BatchEncoding, dict)): +223 return self.model(**self.sample_data) +224 +225 return self.model(self.sample_data) +226 +227 def _get_pipeline_prediction(self) -> Any: +228 """Use model in pipeline mode if pipeline task""" +229 assert isinstance(self.model, Pipeline), "Model must be a pipeline" +230 +231 if isinstance(self.sample_data, dict): +232 prediction = self.model(**self.sample_data) +233 +234 else: +235 prediction = self.model(self.sample_data) +236 +237 if isinstance(prediction, dict): +238 return prediction +239 +240 if isinstance(prediction, list): +241 assert len(prediction) >= 1, "Pipeline model must return a prediction" +242 return prediction[0] +243 +244 raise ValueError("Pipeline model must return a prediction") +245 +246 def get_sample_prediction(self) -> SamplePrediction: +247 """Generates prediction from model and provided sample data""" +248 +249 if self.is_pipeline: +250 prediction = self._get_pipeline_prediction() +251 elif self.task_type in GENERATION_TYPES: +252 prediction = self._generate_predictions() +253 else: +254 prediction = self._functional_predictions() +255 +256 if isinstance(prediction, ModelOutput): +257 prediction = dict(prediction) +258 +259 prediction_type = get_class_name(prediction) +260 +261 return SamplePrediction(prediction_type, prediction) +262 +263 # ------------ Model Interface Onnx Methods ------------# +264 +265 def _quantize_model(self, path: Path, onnx_model: Any) -> None: +266 """Quantizes an huggingface model +267 +268 Args: +269 path: +270 parent path to save too +271 onnx_model: +272 onnx model to quantize +273 +274 Returns: +275 Path to quantized model +276 """ +277 assert self.onnx_args is not None, "No onnx args provided" +278 assert self.onnx_args.config is not None, "No quantization config provided" +279 +280 logger.info("Quantizing HuggingFace ONNX model") +281 +282 from optimum.onnxruntime import ORTQuantizer +283 +284 save_path = path / SaveName.QUANTIZED_MODEL.value +285 quantizer = ORTQuantizer.from_pretrained(onnx_model) +286 +287 quantizer.quantize(save_dir=save_path, quantization_config=self.onnx_args.config) +288 +289 def _convert_to_onnx_inplace(self) -> None: +290 """Converts model to onnx in place""" +291 +292 with tempfile.TemporaryDirectory() as tmpdirname: +293 lpath = Path(tmpdirname) +294 self.save_model((lpath / SaveName.TRAINED_MODEL.value)) +295 +296 if self.tokenizer is not None: +297 self.save_tokenizer((lpath / SaveName.TOKENIZER.value)) +298 +299 if self.feature_extractor is not None: +300 self.save_feature_extractor((lpath / SaveName.FEATURE_EXTRACTOR.value)) +301 +302 onnx_path = lpath / SaveName.ONNX_MODEL.value +303 return self.convert_to_onnx(**{"path": onnx_path}) +304 +305 def convert_to_onnx(self, **kwargs: Path) -> None: +306 """Converts a huggingface model or pipeline to onnx via optimum library. +307 Converted model or pipeline is accessible via the `onnx_model` attribute. +308 """ +309 +310 logger.info("Staring conversion of HuggingFace model to ONNX") +311 +312 assert ( +313 self.onnx_args is not None +314 ), "No onnx args provided. If converting to onnx, provide a HuggingFaceOnnxArgs instance" +315 +316 if self.onnx_model is not None: +317 return None +318 +319 import onnx +320 import optimum.onnxruntime as ort +321 +322 path: Optional[Path] = kwargs.get("path") +323 if path is None: +324 return self._convert_to_onnx_inplace() +325 +326 # ensure no suffix (this is an exception to the rule to all model interfaces) +327 # huggingface prefers to save onnx models in dirs instead of single model.onnx file +328 path = path.with_suffix("") +329 ort_model: ort.ORTModel = getattr(ort, self.onnx_args.ort_type) +330 model_path = path.parent / SaveName.TRAINED_MODEL.value +331 onnx_model = ort_model.from_pretrained(model_path, export=True, provider=self.onnx_args.provider) +332 onnx_model.save_pretrained(path) +333 +334 if self.is_pipeline: +335 self.onnx_model = OnnxModel( +336 onnx_version=onnx.__version__, +337 sess=pipeline( +338 self.task_type, +339 model=onnx_model, +340 tokenizer=self.tokenizer, +341 feature_extractor=self.feature_extractor, +342 ), +343 ) +344 else: +345 self.onnx_model = OnnxModel(onnx_version=onnx.__version__, sess=onnx_model) +346 +347 if self.onnx_args.quantize: +348 self._quantize_model(path.parent, onnx_model) +349 +350 return None +351 +352 # ------------ Model Interface Save Methods ------------# +353 +354 def save_model(self, path: Path) -> None: +355 assert self.model is not None, "No model detected in interface" +356 +357 if isinstance(self.model, Pipeline): +358 self.model.model.save_pretrained(path) +359 return None +360 +361 self.model.save_pretrained(path) +362 return None +363 +364 def save_tokenizer(self, path: Path) -> None: +365 if self.tokenizer is None: +366 return None +367 +368 if isinstance(self.model, Pipeline): +369 assert self.model.tokenizer is not None, "Tokenizer is missing" +370 self.model.tokenizer.save_pretrained(path) +371 return None +372 +373 self.tokenizer.save_pretrained(path) +374 return None +375 +376 def save_feature_extractor(self, path: Path) -> None: +377 if self.feature_extractor is None: +378 return None +379 +380 if isinstance(self.model, Pipeline): +381 assert self.model.feature_extractor is not None, "Feature extractor is missing" +382 self.model.feature_extractor.save_pretrained(path) +383 return None +384 +385 self.feature_extractor.save_pretrained(path) +386 return None +387 +388 def save_onnx(self, path: Path) -> ModelReturn: +389 import onnxruntime as rt +390 +391 from opsml.model.onnx import _get_onnx_metadata +392 +393 model_saved = False +394 if self.onnx_model is None: +395 # HF saves model during conversion +396 self.convert_to_onnx(**{"path": path}) +397 model_saved = True +398 +399 assert self.onnx_model is not None, "No onnx model detected in interface" +400 if self.is_pipeline: +401 if not model_saved: +402 self.onnx_model.sess.model.save_pretrained(path.with_suffix("")) +403 return _get_onnx_metadata(self, cast(rt.InferenceSession, self.onnx_model.sess.model.model)) +404 +405 if not model_saved: +406 self.onnx_model.sess.save_pretrained(path.with_suffix("")) +407 +408 return _get_onnx_metadata(self, cast(rt.InferenceSession, self.onnx_model.sess.model)) +409 +410 # ------------ Model Interface Load Methods ------------# +411 +412 def load_tokenizer(self, path: Path) -> None: +413 self.tokenizer = getattr(transformers, self.tokenizer_name).from_pretrained(path) +414 +415 def load_feature_extractor(self, path: Path) -> None: +416 self.feature_extractor = getattr(transformers, self.feature_extractor_name).from_pretrained(path) +417 +418 def load_model(self, path: Path, **kwargs: Any) -> None: +419 """Load huggingface model from path +420 +421 Args: +422 path: +423 Path to model +424 kwargs: +425 Additional kwargs to pass to transformers.load_pretrained +426 """ +427 custom_arch = kwargs.get("custom_architecture") +428 if custom_arch is not None: +429 assert isinstance( +430 custom_arch, (PreTrainedModel, TFPreTrainedModel) +431 ), "Custom architecture must be a huggingface model" +432 self.model = custom_arch.from_pretrained(path) +433 +434 else: +435 self.model = getattr(transformers, self.model_type).from_pretrained(path) +436 +437 def to_pipeline(self) -> None: +438 """Converts model to pipeline""" +439 +440 if isinstance(self.model, Pipeline): +441 return None +442 +443 pipe = pipeline( +444 task=self.task_type, +445 model=self.model, +446 tokenizer=self.tokenizer, +447 feature_extractor=self.feature_extractor, +448 ) +449 +450 self.model = pipe +451 self.is_pipeline = True +452 return None +453 +454 def load_onnx_model(self, path: Path) -> None: +455 """Load onnx model from path""" +456 import onnx +457 import optimum.onnxruntime as ort +458 +459 assert self.onnx_args is not None, "No onnx args provided" +460 ort_model = getattr(ort, self.onnx_args.ort_type) +461 onnx_model = ort_model.from_pretrained( +462 path, +463 config=self.onnx_args.config, +464 provider=self.onnx_args.provider, +465 ) +466 +467 if self.is_pipeline: +468 self.onnx_model = OnnxModel( +469 onnx_version=onnx.__version__, # type: ignore[attr-defined] +470 sess=pipeline( +471 self.task_type, +472 model=onnx_model, +473 tokenizer=self.tokenizer, +474 feature_extractor=self.feature_extractor, +475 ), +476 ) +477 else: +478 self.onnx_model = OnnxModel( +479 onnx_version=onnx.__version__, # type: ignore[attr-defined] +480 sess=onnx_model, +481 ) +482 +483 @property +484 def model_class(self) -> str: +485 return TrainedModelType.TRANSFORMERS.value +486 +487 @property +488 def model_suffix(self) -> str: +489 """Returns suffix for storage""" +490 return "" +491 +492 @staticmethod +493 def name() -> str: +494 return HuggingFaceModel.__name__ +
Model interface for HuggingFace models
+ +HuggingFaceTask for supported tasks.HuggingFaceOnnxArgs for supported arguments.++ +HuggingFaceModel
+
Example::
+ +from transformers import BartModel, BartTokenizer
+
+tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
+model = BartModel.from_pretrained("facebook/bart-base")
+inputs = tokenizer(["Hello. How are you"], return_tensors="pt")
+
+# this is fed to the ModelCard
+model = HuggingFaceModel(
+ model=model,
+ tokenizer=tokenizer,
+ sample_data=inputs,
+ task_type=HuggingFaceTask.TEXT_CLASSIFICATION.value,
+)
+151 @model_validator(mode="before") +152 @classmethod +153 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: +154 if bool(model_args.get("modelcard_uid", False)): +155 return model_args +156 +157 hf_model = model_args.get("model") +158 assert hf_model is not None, "Model is not defined" +159 +160 if isinstance(hf_model, Pipeline): +161 model_args[CommonKwargs.BACKEND.value] = cls._check_model_backend(hf_model.model) +162 model_args[CommonKwargs.IS_PIPELINE.value] = True +163 model_args[CommonKwargs.MODEL_TYPE.value] = hf_model.model.__class__.__name__ +164 model_args[CommonKwargs.TOKENIZER.value] = hf_model.tokenizer +165 model_args[CommonKwargs.TOKENIZER_NAME.value] = get_processor_name(hf_model.tokenizer) +166 model_args[CommonKwargs.FEATURE_EXTRACTOR.value] = hf_model.feature_extractor +167 model_args[CommonKwargs.FEATURE_EXTRACTOR_NAME.value] = get_processor_name(hf_model.feature_extractor) +168 +169 else: +170 model_args[CommonKwargs.BACKEND.value] = cls._check_model_backend(hf_model) +171 model_args[CommonKwargs.IS_PIPELINE.value] = False +172 model_args[CommonKwargs.MODEL_TYPE.value] = hf_model.__class__.__name__ +173 model_args[CommonKwargs.TOKENIZER_NAME.value] = get_processor_name( +174 model_args.get(CommonKwargs.TOKENIZER.value), +175 ) +176 +177 model_args[CommonKwargs.FEATURE_EXTRACTOR_NAME.value] = get_processor_name( +178 model_args.get(CommonKwargs.FEATURE_EXTRACTOR.value) +179 ) +180 +181 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) +182 +183 # set args +184 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +185 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +186 +187 return model_args +
189 @field_validator("task_type", mode="before") +190 @classmethod +191 def check_task_type(cls, task_type: str) -> str: +192 """Check if task is a huggingface approved task""" +193 +194 task = task_type.lower() +195 if task not in list(HuggingFaceTask): +196 raise ValueError(f"Task type {task} is not supported") +197 return task +
Check if task is a huggingface approved task
+246 def get_sample_prediction(self) -> SamplePrediction: +247 """Generates prediction from model and provided sample data""" +248 +249 if self.is_pipeline: +250 prediction = self._get_pipeline_prediction() +251 elif self.task_type in GENERATION_TYPES: +252 prediction = self._generate_predictions() +253 else: +254 prediction = self._functional_predictions() +255 +256 if isinstance(prediction, ModelOutput): +257 prediction = dict(prediction) +258 +259 prediction_type = get_class_name(prediction) +260 +261 return SamplePrediction(prediction_type, prediction) +
Generates prediction from model and provided sample data
+305 def convert_to_onnx(self, **kwargs: Path) -> None: +306 """Converts a huggingface model or pipeline to onnx via optimum library. +307 Converted model or pipeline is accessible via the `onnx_model` attribute. +308 """ +309 +310 logger.info("Staring conversion of HuggingFace model to ONNX") +311 +312 assert ( +313 self.onnx_args is not None +314 ), "No onnx args provided. If converting to onnx, provide a HuggingFaceOnnxArgs instance" +315 +316 if self.onnx_model is not None: +317 return None +318 +319 import onnx +320 import optimum.onnxruntime as ort +321 +322 path: Optional[Path] = kwargs.get("path") +323 if path is None: +324 return self._convert_to_onnx_inplace() +325 +326 # ensure no suffix (this is an exception to the rule to all model interfaces) +327 # huggingface prefers to save onnx models in dirs instead of single model.onnx file +328 path = path.with_suffix("") +329 ort_model: ort.ORTModel = getattr(ort, self.onnx_args.ort_type) +330 model_path = path.parent / SaveName.TRAINED_MODEL.value +331 onnx_model = ort_model.from_pretrained(model_path, export=True, provider=self.onnx_args.provider) +332 onnx_model.save_pretrained(path) +333 +334 if self.is_pipeline: +335 self.onnx_model = OnnxModel( +336 onnx_version=onnx.__version__, +337 sess=pipeline( +338 self.task_type, +339 model=onnx_model, +340 tokenizer=self.tokenizer, +341 feature_extractor=self.feature_extractor, +342 ), +343 ) +344 else: +345 self.onnx_model = OnnxModel(onnx_version=onnx.__version__, sess=onnx_model) +346 +347 if self.onnx_args.quantize: +348 self._quantize_model(path.parent, onnx_model) +349 +350 return None +
Converts a huggingface model or pipeline to onnx via optimum library.
+Converted model or pipeline is accessible via the onnx_model attribute.
354 def save_model(self, path: Path) -> None: +355 assert self.model is not None, "No model detected in interface" +356 +357 if isinstance(self.model, Pipeline): +358 self.model.model.save_pretrained(path) +359 return None +360 +361 self.model.save_pretrained(path) +362 return None +
Saves model to path. Base implementation use Joblib
+ +364 def save_tokenizer(self, path: Path) -> None: +365 if self.tokenizer is None: +366 return None +367 +368 if isinstance(self.model, Pipeline): +369 assert self.model.tokenizer is not None, "Tokenizer is missing" +370 self.model.tokenizer.save_pretrained(path) +371 return None +372 +373 self.tokenizer.save_pretrained(path) +374 return None +
376 def save_feature_extractor(self, path: Path) -> None: +377 if self.feature_extractor is None: +378 return None +379 +380 if isinstance(self.model, Pipeline): +381 assert self.model.feature_extractor is not None, "Feature extractor is missing" +382 self.model.feature_extractor.save_pretrained(path) +383 return None +384 +385 self.feature_extractor.save_pretrained(path) +386 return None +
388 def save_onnx(self, path: Path) -> ModelReturn: +389 import onnxruntime as rt +390 +391 from opsml.model.onnx import _get_onnx_metadata +392 +393 model_saved = False +394 if self.onnx_model is None: +395 # HF saves model during conversion +396 self.convert_to_onnx(**{"path": path}) +397 model_saved = True +398 +399 assert self.onnx_model is not None, "No onnx model detected in interface" +400 if self.is_pipeline: +401 if not model_saved: +402 self.onnx_model.sess.model.save_pretrained(path.with_suffix("")) +403 return _get_onnx_metadata(self, cast(rt.InferenceSession, self.onnx_model.sess.model.model)) +404 +405 if not model_saved: +406 self.onnx_model.sess.save_pretrained(path.with_suffix("")) +407 +408 return _get_onnx_metadata(self, cast(rt.InferenceSession, self.onnx_model.sess.model)) +
Saves the onnx model
+ +++ModelReturn
+
418 def load_model(self, path: Path, **kwargs: Any) -> None: +419 """Load huggingface model from path +420 +421 Args: +422 path: +423 Path to model +424 kwargs: +425 Additional kwargs to pass to transformers.load_pretrained +426 """ +427 custom_arch = kwargs.get("custom_architecture") +428 if custom_arch is not None: +429 assert isinstance( +430 custom_arch, (PreTrainedModel, TFPreTrainedModel) +431 ), "Custom architecture must be a huggingface model" +432 self.model = custom_arch.from_pretrained(path) +433 +434 else: +435 self.model = getattr(transformers, self.model_type).from_pretrained(path) +
Load huggingface model from path
+ +437 def to_pipeline(self) -> None: +438 """Converts model to pipeline""" +439 +440 if isinstance(self.model, Pipeline): +441 return None +442 +443 pipe = pipeline( +444 task=self.task_type, +445 model=self.model, +446 tokenizer=self.tokenizer, +447 feature_extractor=self.feature_extractor, +448 ) +449 +450 self.model = pipe +451 self.is_pipeline = True +452 return None +
Converts model to pipeline
+454 def load_onnx_model(self, path: Path) -> None: +455 """Load onnx model from path""" +456 import onnx +457 import optimum.onnxruntime as ort +458 +459 assert self.onnx_args is not None, "No onnx args provided" +460 ort_model = getattr(ort, self.onnx_args.ort_type) +461 onnx_model = ort_model.from_pretrained( +462 path, +463 config=self.onnx_args.config, +464 provider=self.onnx_args.provider, +465 ) +466 +467 if self.is_pipeline: +468 self.onnx_model = OnnxModel( +469 onnx_version=onnx.__version__, # type: ignore[attr-defined] +470 sess=pipeline( +471 self.task_type, +472 model=onnx_model, +473 tokenizer=self.tokenizer, +474 feature_extractor=self.feature_extractor, +475 ), +476 ) +477 else: +478 self.onnx_model = OnnxModel( +479 onnx_version=onnx.__version__, # type: ignore[attr-defined] +480 sess=onnx_model, +481 ) +
Load onnx model from path
+487 @property +488 def model_suffix(self) -> str: +489 """Returns suffix for storage""" +490 return "" +
Returns suffix for storage
+1from pathlib import Path + 2from typing import Any, Dict, Optional, Union + 3 + 4import joblib + 5import pandas as pd + 6from numpy.typing import NDArray + 7from pydantic import model_validator + 8 + 9from opsml.helpers.utils import get_class_name + 10from opsml.model.interfaces.base import ( + 11 ModelInterface, + 12 get_model_args, + 13 get_processor_name, + 14) + 15from opsml.types import CommonKwargs, TrainedModelType + 16from opsml.types.extra import Suffix + 17 + 18try: + 19 import lightgbm as lgb + 20 from lightgbm import Booster, LGBMModel + 21 + 22 class LightGBMModel(ModelInterface): + 23 """Model interface for LightGBM Booster model class. If using the sklearn API, use SklearnModel instead. + 24 + 25 Args: + 26 model: + 27 LightGBM booster model + 28 preprocessor: + 29 Optional preprocessor + 30 sample_data: + 31 Sample data to be used for type inference. + 32 For lightgbm models this should be a pandas DataFrame or numpy array. + 33 This should match exactly what the model expects as input. + 34 task_type: + 35 Task type for model. Defaults to undefined. + 36 model_type: + 37 Optional model type. This is inferred automatically. + 38 preprocessor_name: + 39 Optional preprocessor. This is inferred automatically if a + 40 preprocessor is provided. + 41 onnx_args: + 42 Optional arguments for ONNX conversion. See `TorchOnnxArgs` for supported arguments. + 43 + 44 Returns: + 45 LightGBMModel + 46 """ + 47 + 48 model: Optional[Union[Booster, LGBMModel]] = None + 49 sample_data: Optional[Union[pd.DataFrame, NDArray[Any]]] = None + 50 preprocessor: Optional[Any] = None + 51 preprocessor_name: str = CommonKwargs.UNDEFINED.value + 52 + 53 @property + 54 def model_class(self) -> str: + 55 if "Booster" in self.model_type: + 56 return TrainedModelType.LGBM_BOOSTER.value + 57 return TrainedModelType.SKLEARN_ESTIMATOR.value + 58 + 59 @model_validator(mode="before") + 60 @classmethod + 61 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 62 model = model_args.get("model") + 63 + 64 if model_args.get("modelcard_uid", False): + 65 return model_args + 66 + 67 model, module, _ = get_model_args(model) + 68 + 69 if "lightgbm" in module or isinstance(model, LGBMModel): + 70 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ + 71 + 72 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) + 73 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data + 74 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) + 75 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( + 76 model_args.get(CommonKwargs.PREPROCESSOR.value), + 77 ) + 78 + 79 return model_args + 80 + 81 def save_model(self, path: Path) -> None: + 82 """Saves lgb model according to model format. Booster models are saved to text. + 83 Sklearn models are saved via joblib. + 84 + 85 Args: + 86 path: + 87 base path to save model to + 88 """ + 89 assert self.model is not None, "No model found" + 90 if isinstance(self.model, Booster): + 91 self.model.save_model(filename=path) + 92 + 93 else: + 94 super().save_model(path) + 95 + 96 def load_model(self, path: Path, **kwargs: Any) -> None: + 97 """Loads lightgbm booster or sklearn model + 98 + 99 +100 Args: +101 path: +102 base path to load from +103 **kwargs: +104 Additional keyword arguments +105 """ +106 +107 if self.model_type == TrainedModelType.LGBM_BOOSTER.value: +108 self.model = lgb.Booster(model_file=path) +109 else: +110 super().load_model(path) +111 +112 def save_preprocessor(self, path: Path) -> None: +113 """Saves preprocessor to path if present. Base implementation use Joblib +114 +115 Args: +116 path: +117 Pathlib object +118 """ +119 assert self.preprocessor is not None, "No preprocessor detected in interface" +120 joblib.dump(self.preprocessor, path) +121 +122 def load_preprocessor(self, path: Path) -> None: +123 """Load preprocessor from pathlib object +124 +125 Args: +126 path: +127 Pathlib object +128 """ +129 self.preprocessor = joblib.load(path) +130 +131 @property +132 def model_suffix(self) -> str: +133 if self.model_type == TrainedModelType.LGBM_BOOSTER.value: +134 return Suffix.TEXT.value +135 +136 return super().model_suffix +137 +138 @property +139 def preprocessor_suffix(self) -> str: +140 """Returns suffix for storage""" +141 return Suffix.JOBLIB.value +142 +143 @staticmethod +144 def name() -> str: +145 return LightGBMModel.__name__ +146 +147except ModuleNotFoundError: +148 from opsml.model.interfaces.backups import LightGBMModelNoModule as LightGBMModel +
23 class LightGBMModel(ModelInterface): + 24 """Model interface for LightGBM Booster model class. If using the sklearn API, use SklearnModel instead. + 25 + 26 Args: + 27 model: + 28 LightGBM booster model + 29 preprocessor: + 30 Optional preprocessor + 31 sample_data: + 32 Sample data to be used for type inference. + 33 For lightgbm models this should be a pandas DataFrame or numpy array. + 34 This should match exactly what the model expects as input. + 35 task_type: + 36 Task type for model. Defaults to undefined. + 37 model_type: + 38 Optional model type. This is inferred automatically. + 39 preprocessor_name: + 40 Optional preprocessor. This is inferred automatically if a + 41 preprocessor is provided. + 42 onnx_args: + 43 Optional arguments for ONNX conversion. See `TorchOnnxArgs` for supported arguments. + 44 + 45 Returns: + 46 LightGBMModel + 47 """ + 48 + 49 model: Optional[Union[Booster, LGBMModel]] = None + 50 sample_data: Optional[Union[pd.DataFrame, NDArray[Any]]] = None + 51 preprocessor: Optional[Any] = None + 52 preprocessor_name: str = CommonKwargs.UNDEFINED.value + 53 + 54 @property + 55 def model_class(self) -> str: + 56 if "Booster" in self.model_type: + 57 return TrainedModelType.LGBM_BOOSTER.value + 58 return TrainedModelType.SKLEARN_ESTIMATOR.value + 59 + 60 @model_validator(mode="before") + 61 @classmethod + 62 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 63 model = model_args.get("model") + 64 + 65 if model_args.get("modelcard_uid", False): + 66 return model_args + 67 + 68 model, module, _ = get_model_args(model) + 69 + 70 if "lightgbm" in module or isinstance(model, LGBMModel): + 71 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ + 72 + 73 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) + 74 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data + 75 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) + 76 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( + 77 model_args.get(CommonKwargs.PREPROCESSOR.value), + 78 ) + 79 + 80 return model_args + 81 + 82 def save_model(self, path: Path) -> None: + 83 """Saves lgb model according to model format. Booster models are saved to text. + 84 Sklearn models are saved via joblib. + 85 + 86 Args: + 87 path: + 88 base path to save model to + 89 """ + 90 assert self.model is not None, "No model found" + 91 if isinstance(self.model, Booster): + 92 self.model.save_model(filename=path) + 93 + 94 else: + 95 super().save_model(path) + 96 + 97 def load_model(self, path: Path, **kwargs: Any) -> None: + 98 """Loads lightgbm booster or sklearn model + 99 +100 +101 Args: +102 path: +103 base path to load from +104 **kwargs: +105 Additional keyword arguments +106 """ +107 +108 if self.model_type == TrainedModelType.LGBM_BOOSTER.value: +109 self.model = lgb.Booster(model_file=path) +110 else: +111 super().load_model(path) +112 +113 def save_preprocessor(self, path: Path) -> None: +114 """Saves preprocessor to path if present. Base implementation use Joblib +115 +116 Args: +117 path: +118 Pathlib object +119 """ +120 assert self.preprocessor is not None, "No preprocessor detected in interface" +121 joblib.dump(self.preprocessor, path) +122 +123 def load_preprocessor(self, path: Path) -> None: +124 """Load preprocessor from pathlib object +125 +126 Args: +127 path: +128 Pathlib object +129 """ +130 self.preprocessor = joblib.load(path) +131 +132 @property +133 def model_suffix(self) -> str: +134 if self.model_type == TrainedModelType.LGBM_BOOSTER.value: +135 return Suffix.TEXT.value +136 +137 return super().model_suffix +138 +139 @property +140 def preprocessor_suffix(self) -> str: +141 """Returns suffix for storage""" +142 return Suffix.JOBLIB.value +143 +144 @staticmethod +145 def name() -> str: +146 return LightGBMModel.__name__ +
Model interface for LightGBM Booster model class. If using the sklearn API, use SklearnModel instead.
+ +TorchOnnxArgs for supported arguments.++LightGBMModel
+
60 @model_validator(mode="before") +61 @classmethod +62 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: +63 model = model_args.get("model") +64 +65 if model_args.get("modelcard_uid", False): +66 return model_args +67 +68 model, module, _ = get_model_args(model) +69 +70 if "lightgbm" in module or isinstance(model, LGBMModel): +71 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ +72 +73 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) +74 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +75 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +76 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( +77 model_args.get(CommonKwargs.PREPROCESSOR.value), +78 ) +79 +80 return model_args +
82 def save_model(self, path: Path) -> None: +83 """Saves lgb model according to model format. Booster models are saved to text. +84 Sklearn models are saved via joblib. +85 +86 Args: +87 path: +88 base path to save model to +89 """ +90 assert self.model is not None, "No model found" +91 if isinstance(self.model, Booster): +92 self.model.save_model(filename=path) +93 +94 else: +95 super().save_model(path) +
Saves lgb model according to model format. Booster models are saved to text. +Sklearn models are saved via joblib.
+ +97 def load_model(self, path: Path, **kwargs: Any) -> None: + 98 """Loads lightgbm booster or sklearn model + 99 +100 +101 Args: +102 path: +103 base path to load from +104 **kwargs: +105 Additional keyword arguments +106 """ +107 +108 if self.model_type == TrainedModelType.LGBM_BOOSTER.value: +109 self.model = lgb.Booster(model_file=path) +110 else: +111 super().load_model(path) +
Loads lightgbm booster or sklearn model
+ +113 def save_preprocessor(self, path: Path) -> None: +114 """Saves preprocessor to path if present. Base implementation use Joblib +115 +116 Args: +117 path: +118 Pathlib object +119 """ +120 assert self.preprocessor is not None, "No preprocessor detected in interface" +121 joblib.dump(self.preprocessor, path) +
Saves preprocessor to path if present. Base implementation use Joblib
+ +123 def load_preprocessor(self, path: Path) -> None: +124 """Load preprocessor from pathlib object +125 +126 Args: +127 path: +128 Pathlib object +129 """ +130 self.preprocessor = joblib.load(path) +
Load preprocessor from pathlib object
+ +132 @property +133 def model_suffix(self) -> str: +134 if self.model_type == TrainedModelType.LGBM_BOOSTER.value: +135 return Suffix.TEXT.value +136 +137 return super().model_suffix +
Returns suffix for storage
+139 @property +140 def preprocessor_suffix(self) -> str: +141 """Returns suffix for storage""" +142 return Suffix.JOBLIB.value +
Returns suffix for storage
+1import tempfile + 2from pathlib import Path + 3from typing import Any, Dict, List, Optional, Tuple, Union, cast + 4 + 5import joblib + 6from pydantic import model_validator + 7 + 8from opsml.helpers.utils import OpsmlImportExceptions, get_class_name + 9from opsml.model.interfaces.base import ( + 10 ModelInterface, + 11 SamplePrediction, + 12 get_model_args, + 13 get_processor_name, + 14) + 15from opsml.types import ( + 16 CommonKwargs, + 17 ModelReturn, + 18 SaveName, + 19 Suffix, + 20 TorchOnnxArgs, + 21 TorchSaveArgs, + 22 TrainedModelType, + 23) + 24 + 25try: + 26 import torch + 27 + 28 ValidData = Union[torch.Tensor, Dict[str, torch.Tensor], List[torch.Tensor], Tuple[torch.Tensor]] + 29 + 30 class TorchModel(ModelInterface): + 31 """Model interface for Pytorch models. + 32 + 33 Args: + 34 model: + 35 Torch model + 36 preprocessor: + 37 Optional preprocessor + 38 sample_data: + 39 Sample data to be used for type inference and ONNX conversion/validation. + 40 This should match exactly what the model expects as input. + 41 save_args: + 42 Optional arguments for saving model. See `TorchSaveArgs` for supported arguments. + 43 task_type: + 44 Task type for model. Defaults to undefined. + 45 model_type: + 46 Optional model type. This is inferred automatically. + 47 preprocessor_name: + 48 Optional preprocessor. This is inferred automatically if a + 49 preprocessor is provided. + 50 onnx_args: + 51 Optional arguments for ONNX conversion. See `TorchOnnxArgs` for supported arguments. + 52 + 53 Returns: + 54 TorchModel + 55 """ + 56 + 57 model: Optional[torch.nn.Module] = None + 58 sample_data: Optional[ + 59 Union[torch.Tensor, Dict[str, torch.Tensor], List[torch.Tensor], Tuple[torch.Tensor]] + 60 ] = None + 61 onnx_args: Optional[TorchOnnxArgs] = None + 62 save_args: TorchSaveArgs = TorchSaveArgs() + 63 preprocessor: Optional[Any] = None + 64 preprocessor_name: str = CommonKwargs.UNDEFINED.value + 65 + 66 @property + 67 def model_class(self) -> str: + 68 return TrainedModelType.PYTORCH.value + 69 + 70 @classmethod + 71 def _get_sample_data(cls, sample_data: Any) -> Any: + 72 """Check sample data and returns one record to be used + 73 during type inference and ONNX conversion/validation. + 74 + 75 Returns: + 76 Sample data with only one record + 77 """ + 78 if isinstance(sample_data, torch.Tensor): + 79 return sample_data[0:1] + 80 + 81 if isinstance(sample_data, list): + 82 return [data[0:1] for data in sample_data] + 83 + 84 if isinstance(sample_data, tuple): + 85 return tuple(data[0:1] for data in sample_data) + 86 + 87 if isinstance(sample_data, dict): + 88 sample_dict = {} + 89 for key, value in sample_data.items(): + 90 sample_dict[key] = value[0:1] + 91 return sample_dict + 92 + 93 raise ValueError("Provided sample data is not a valid type") + 94 + 95 @model_validator(mode="before") + 96 @classmethod + 97 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 98 model = model_args.get("model") + 99 +100 if model_args.get("modelcard_uid", False): +101 return model_args +102 +103 model, _, bases = get_model_args(model) +104 +105 for base in bases: +106 if "torch" in base: +107 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ +108 +109 sample_data = cls._get_sample_data(model_args[CommonKwargs.SAMPLE_DATA.value]) +110 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +111 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +112 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( +113 model_args.get(CommonKwargs.PREPROCESSOR.value), +114 ) +115 +116 return model_args +117 +118 def get_sample_prediction(self) -> SamplePrediction: +119 assert self.model is not None, "Model is not defined" +120 assert self.sample_data is not None, "Sample data must be provided" +121 +122 # test dict input +123 if isinstance(self.sample_data, dict): +124 try: +125 prediction = self.model(**self.sample_data) +126 except Exception as _: # pylint: disable=broad-except +127 prediction = self.model(self.sample_data) +128 +129 # test list and tuple inputs +130 elif isinstance(self.sample_data, (list, tuple)): +131 try: +132 prediction = self.model(*self.sample_data) +133 except Exception as _: # pylint: disable=broad-except +134 prediction = self.model(self.sample_data) +135 +136 # all others +137 else: +138 prediction = self.model(self.sample_data) +139 +140 prediction_type = get_class_name(prediction) +141 +142 return SamplePrediction(prediction_type, prediction) +143 +144 def save_model(self, path: Path) -> None: +145 """Save pytorch model to path +146 +147 Args: +148 path: +149 pathlib object +150 """ +151 assert self.model is not None, "No model found" +152 +153 if self.save_args.as_state_dict: +154 torch.save(self.model.state_dict(), path) +155 else: +156 torch.save(self.model, path) +157 +158 def load_model(self, path: Path, **kwargs: Any) -> None: +159 """Load pytorch model from path +160 +161 Args: +162 path: +163 pathlib object +164 kwargs: +165 Additional arguments to be passed to torch.load +166 """ +167 model_arch = kwargs.get(CommonKwargs.MODEL_ARCH.value) +168 +169 if model_arch is not None: +170 model_arch.load_state_dict(torch.load(path)) +171 model_arch.eval() +172 self.model = model_arch +173 +174 else: +175 self.model = torch.load(path) +176 +177 def save_onnx(self, path: Path) -> ModelReturn: +178 """Saves an onnx model +179 +180 Args: +181 path: +182 Path to save model to +183 +184 Returns: +185 ModelReturn +186 """ +187 import onnxruntime as rt +188 +189 from opsml.model.onnx import _get_onnx_metadata +190 +191 if self.onnx_model is None: +192 self.convert_to_onnx(**{"path": path}) +193 +194 else: +195 # save onnx model +196 self.onnx_model.sess_to_path(path) +197 +198 # no need to save onnx to bytes since its done during onnx conversion +199 assert self.onnx_model is not None, "No onnx model detected in interface" +200 return _get_onnx_metadata(self, cast(rt.InferenceSession, self.onnx_model.sess)) +201 +202 def _convert_to_onnx_inplace(self) -> None: +203 """Convert to onnx model using temp dir""" +204 with tempfile.TemporaryDirectory() as tmpdir: +205 lpath = Path(tmpdir) / SaveName.ONNX_MODEL.value +206 onnx_path = lpath.with_suffix(Suffix.ONNX.value) +207 self.convert_to_onnx(**{"path": onnx_path}) +208 +209 def convert_to_onnx(self, **kwargs: Path) -> None: +210 # import packages for onnx conversion +211 OpsmlImportExceptions.try_torchonnx_imports() +212 if self.onnx_model is not None: +213 return None +214 +215 from opsml.model.onnx.torch_converter import _PyTorchOnnxModel +216 +217 path: Optional[Path] = kwargs.get("path") +218 +219 if path is None: +220 return self._convert_to_onnx_inplace() +221 +222 self.onnx_model = _PyTorchOnnxModel(self).convert_to_onnx(path=path) +223 return None +224 +225 def save_preprocessor(self, path: Path) -> None: +226 """Saves preprocessor to path if present. Base implementation use Joblib +227 +228 Args: +229 path: +230 Pathlib object +231 """ +232 assert self.preprocessor is not None, "No preprocessor detected in interface" +233 joblib.dump(self.preprocessor, path) +234 +235 def load_preprocessor(self, path: Path) -> None: +236 """Load preprocessor from pathlib object +237 +238 Args: +239 path: +240 Pathlib object +241 """ +242 self.preprocessor = joblib.load(path) +243 +244 @property +245 def preprocessor_suffix(self) -> str: +246 """Returns suffix for storage""" +247 return Suffix.JOBLIB.value +248 +249 @property +250 def model_suffix(self) -> str: +251 """Returns suffix for storage""" +252 return Suffix.PT.value +253 +254 @staticmethod +255 def name() -> str: +256 return TorchModel.__name__ +257 +258except ModuleNotFoundError: +259 from opsml.model.interfaces.backups import TorchModelNoModule as TorchModel +
31 class TorchModel(ModelInterface): + 32 """Model interface for Pytorch models. + 33 + 34 Args: + 35 model: + 36 Torch model + 37 preprocessor: + 38 Optional preprocessor + 39 sample_data: + 40 Sample data to be used for type inference and ONNX conversion/validation. + 41 This should match exactly what the model expects as input. + 42 save_args: + 43 Optional arguments for saving model. See `TorchSaveArgs` for supported arguments. + 44 task_type: + 45 Task type for model. Defaults to undefined. + 46 model_type: + 47 Optional model type. This is inferred automatically. + 48 preprocessor_name: + 49 Optional preprocessor. This is inferred automatically if a + 50 preprocessor is provided. + 51 onnx_args: + 52 Optional arguments for ONNX conversion. See `TorchOnnxArgs` for supported arguments. + 53 + 54 Returns: + 55 TorchModel + 56 """ + 57 + 58 model: Optional[torch.nn.Module] = None + 59 sample_data: Optional[ + 60 Union[torch.Tensor, Dict[str, torch.Tensor], List[torch.Tensor], Tuple[torch.Tensor]] + 61 ] = None + 62 onnx_args: Optional[TorchOnnxArgs] = None + 63 save_args: TorchSaveArgs = TorchSaveArgs() + 64 preprocessor: Optional[Any] = None + 65 preprocessor_name: str = CommonKwargs.UNDEFINED.value + 66 + 67 @property + 68 def model_class(self) -> str: + 69 return TrainedModelType.PYTORCH.value + 70 + 71 @classmethod + 72 def _get_sample_data(cls, sample_data: Any) -> Any: + 73 """Check sample data and returns one record to be used + 74 during type inference and ONNX conversion/validation. + 75 + 76 Returns: + 77 Sample data with only one record + 78 """ + 79 if isinstance(sample_data, torch.Tensor): + 80 return sample_data[0:1] + 81 + 82 if isinstance(sample_data, list): + 83 return [data[0:1] for data in sample_data] + 84 + 85 if isinstance(sample_data, tuple): + 86 return tuple(data[0:1] for data in sample_data) + 87 + 88 if isinstance(sample_data, dict): + 89 sample_dict = {} + 90 for key, value in sample_data.items(): + 91 sample_dict[key] = value[0:1] + 92 return sample_dict + 93 + 94 raise ValueError("Provided sample data is not a valid type") + 95 + 96 @model_validator(mode="before") + 97 @classmethod + 98 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 99 model = model_args.get("model") +100 +101 if model_args.get("modelcard_uid", False): +102 return model_args +103 +104 model, _, bases = get_model_args(model) +105 +106 for base in bases: +107 if "torch" in base: +108 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ +109 +110 sample_data = cls._get_sample_data(model_args[CommonKwargs.SAMPLE_DATA.value]) +111 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +112 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +113 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( +114 model_args.get(CommonKwargs.PREPROCESSOR.value), +115 ) +116 +117 return model_args +118 +119 def get_sample_prediction(self) -> SamplePrediction: +120 assert self.model is not None, "Model is not defined" +121 assert self.sample_data is not None, "Sample data must be provided" +122 +123 # test dict input +124 if isinstance(self.sample_data, dict): +125 try: +126 prediction = self.model(**self.sample_data) +127 except Exception as _: # pylint: disable=broad-except +128 prediction = self.model(self.sample_data) +129 +130 # test list and tuple inputs +131 elif isinstance(self.sample_data, (list, tuple)): +132 try: +133 prediction = self.model(*self.sample_data) +134 except Exception as _: # pylint: disable=broad-except +135 prediction = self.model(self.sample_data) +136 +137 # all others +138 else: +139 prediction = self.model(self.sample_data) +140 +141 prediction_type = get_class_name(prediction) +142 +143 return SamplePrediction(prediction_type, prediction) +144 +145 def save_model(self, path: Path) -> None: +146 """Save pytorch model to path +147 +148 Args: +149 path: +150 pathlib object +151 """ +152 assert self.model is not None, "No model found" +153 +154 if self.save_args.as_state_dict: +155 torch.save(self.model.state_dict(), path) +156 else: +157 torch.save(self.model, path) +158 +159 def load_model(self, path: Path, **kwargs: Any) -> None: +160 """Load pytorch model from path +161 +162 Args: +163 path: +164 pathlib object +165 kwargs: +166 Additional arguments to be passed to torch.load +167 """ +168 model_arch = kwargs.get(CommonKwargs.MODEL_ARCH.value) +169 +170 if model_arch is not None: +171 model_arch.load_state_dict(torch.load(path)) +172 model_arch.eval() +173 self.model = model_arch +174 +175 else: +176 self.model = torch.load(path) +177 +178 def save_onnx(self, path: Path) -> ModelReturn: +179 """Saves an onnx model +180 +181 Args: +182 path: +183 Path to save model to +184 +185 Returns: +186 ModelReturn +187 """ +188 import onnxruntime as rt +189 +190 from opsml.model.onnx import _get_onnx_metadata +191 +192 if self.onnx_model is None: +193 self.convert_to_onnx(**{"path": path}) +194 +195 else: +196 # save onnx model +197 self.onnx_model.sess_to_path(path) +198 +199 # no need to save onnx to bytes since its done during onnx conversion +200 assert self.onnx_model is not None, "No onnx model detected in interface" +201 return _get_onnx_metadata(self, cast(rt.InferenceSession, self.onnx_model.sess)) +202 +203 def _convert_to_onnx_inplace(self) -> None: +204 """Convert to onnx model using temp dir""" +205 with tempfile.TemporaryDirectory() as tmpdir: +206 lpath = Path(tmpdir) / SaveName.ONNX_MODEL.value +207 onnx_path = lpath.with_suffix(Suffix.ONNX.value) +208 self.convert_to_onnx(**{"path": onnx_path}) +209 +210 def convert_to_onnx(self, **kwargs: Path) -> None: +211 # import packages for onnx conversion +212 OpsmlImportExceptions.try_torchonnx_imports() +213 if self.onnx_model is not None: +214 return None +215 +216 from opsml.model.onnx.torch_converter import _PyTorchOnnxModel +217 +218 path: Optional[Path] = kwargs.get("path") +219 +220 if path is None: +221 return self._convert_to_onnx_inplace() +222 +223 self.onnx_model = _PyTorchOnnxModel(self).convert_to_onnx(path=path) +224 return None +225 +226 def save_preprocessor(self, path: Path) -> None: +227 """Saves preprocessor to path if present. Base implementation use Joblib +228 +229 Args: +230 path: +231 Pathlib object +232 """ +233 assert self.preprocessor is not None, "No preprocessor detected in interface" +234 joblib.dump(self.preprocessor, path) +235 +236 def load_preprocessor(self, path: Path) -> None: +237 """Load preprocessor from pathlib object +238 +239 Args: +240 path: +241 Pathlib object +242 """ +243 self.preprocessor = joblib.load(path) +244 +245 @property +246 def preprocessor_suffix(self) -> str: +247 """Returns suffix for storage""" +248 return Suffix.JOBLIB.value +249 +250 @property +251 def model_suffix(self) -> str: +252 """Returns suffix for storage""" +253 return Suffix.PT.value +254 +255 @staticmethod +256 def name() -> str: +257 return TorchModel.__name__ +
Model interface for Pytorch models.
+ +TorchSaveArgs for supported arguments.TorchOnnxArgs for supported arguments.Returns: +TorchModel
+96 @model_validator(mode="before") + 97 @classmethod + 98 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 99 model = model_args.get("model") +100 +101 if model_args.get("modelcard_uid", False): +102 return model_args +103 +104 model, _, bases = get_model_args(model) +105 +106 for base in bases: +107 if "torch" in base: +108 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ +109 +110 sample_data = cls._get_sample_data(model_args[CommonKwargs.SAMPLE_DATA.value]) +111 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +112 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +113 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( +114 model_args.get(CommonKwargs.PREPROCESSOR.value), +115 ) +116 +117 return model_args +
119 def get_sample_prediction(self) -> SamplePrediction: +120 assert self.model is not None, "Model is not defined" +121 assert self.sample_data is not None, "Sample data must be provided" +122 +123 # test dict input +124 if isinstance(self.sample_data, dict): +125 try: +126 prediction = self.model(**self.sample_data) +127 except Exception as _: # pylint: disable=broad-except +128 prediction = self.model(self.sample_data) +129 +130 # test list and tuple inputs +131 elif isinstance(self.sample_data, (list, tuple)): +132 try: +133 prediction = self.model(*self.sample_data) +134 except Exception as _: # pylint: disable=broad-except +135 prediction = self.model(self.sample_data) +136 +137 # all others +138 else: +139 prediction = self.model(self.sample_data) +140 +141 prediction_type = get_class_name(prediction) +142 +143 return SamplePrediction(prediction_type, prediction) +
145 def save_model(self, path: Path) -> None: +146 """Save pytorch model to path +147 +148 Args: +149 path: +150 pathlib object +151 """ +152 assert self.model is not None, "No model found" +153 +154 if self.save_args.as_state_dict: +155 torch.save(self.model.state_dict(), path) +156 else: +157 torch.save(self.model, path) +
Save pytorch model to path
+ +159 def load_model(self, path: Path, **kwargs: Any) -> None: +160 """Load pytorch model from path +161 +162 Args: +163 path: +164 pathlib object +165 kwargs: +166 Additional arguments to be passed to torch.load +167 """ +168 model_arch = kwargs.get(CommonKwargs.MODEL_ARCH.value) +169 +170 if model_arch is not None: +171 model_arch.load_state_dict(torch.load(path)) +172 model_arch.eval() +173 self.model = model_arch +174 +175 else: +176 self.model = torch.load(path) +
Load pytorch model from path
+ +178 def save_onnx(self, path: Path) -> ModelReturn: +179 """Saves an onnx model +180 +181 Args: +182 path: +183 Path to save model to +184 +185 Returns: +186 ModelReturn +187 """ +188 import onnxruntime as rt +189 +190 from opsml.model.onnx import _get_onnx_metadata +191 +192 if self.onnx_model is None: +193 self.convert_to_onnx(**{"path": path}) +194 +195 else: +196 # save onnx model +197 self.onnx_model.sess_to_path(path) +198 +199 # no need to save onnx to bytes since its done during onnx conversion +200 assert self.onnx_model is not None, "No onnx model detected in interface" +201 return _get_onnx_metadata(self, cast(rt.InferenceSession, self.onnx_model.sess)) +
Saves an onnx model
+ +++ModelReturn
+
210 def convert_to_onnx(self, **kwargs: Path) -> None: +211 # import packages for onnx conversion +212 OpsmlImportExceptions.try_torchonnx_imports() +213 if self.onnx_model is not None: +214 return None +215 +216 from opsml.model.onnx.torch_converter import _PyTorchOnnxModel +217 +218 path: Optional[Path] = kwargs.get("path") +219 +220 if path is None: +221 return self._convert_to_onnx_inplace() +222 +223 self.onnx_model = _PyTorchOnnxModel(self).convert_to_onnx(path=path) +224 return None +
Converts model to onnx format
+226 def save_preprocessor(self, path: Path) -> None: +227 """Saves preprocessor to path if present. Base implementation use Joblib +228 +229 Args: +230 path: +231 Pathlib object +232 """ +233 assert self.preprocessor is not None, "No preprocessor detected in interface" +234 joblib.dump(self.preprocessor, path) +
Saves preprocessor to path if present. Base implementation use Joblib
+ +236 def load_preprocessor(self, path: Path) -> None: +237 """Load preprocessor from pathlib object +238 +239 Args: +240 path: +241 Pathlib object +242 """ +243 self.preprocessor = joblib.load(path) +
Load preprocessor from pathlib object
+ +245 @property +246 def preprocessor_suffix(self) -> str: +247 """Returns suffix for storage""" +248 return Suffix.JOBLIB.value +
Returns suffix for storage
+250 @property +251 def model_suffix(self) -> str: +252 """Returns suffix for storage""" +253 return Suffix.PT.value +
Returns suffix for storage
+1from pathlib import Path + 2from typing import Any, Dict, Optional + 3 + 4from pydantic import model_validator + 5 + 6from opsml.helpers.utils import OpsmlImportExceptions, get_class_name + 7from opsml.model.interfaces.base import ( + 8 SamplePrediction, + 9 get_model_args, + 10 get_processor_name, + 11) + 12from opsml.model.interfaces.pytorch import TorchModel + 13from opsml.types import CommonKwargs, Suffix, TorchOnnxArgs, TrainedModelType + 14 + 15try: + 16 from lightning import LightningModule, Trainer + 17 + 18 class LightningModel(TorchModel): + 19 """Model interface for Pytorch Lightning models. + 20 + 21 Args: + 22 model: + 23 Torch lightning model + 24 preprocessor: + 25 Optional preprocessor + 26 sample_data: + 27 Sample data to be used for type inference. + 28 This should match exactly what the model expects as input. + 29 task_type: + 30 Task type for model. Defaults to undefined. + 31 model_type: + 32 Optional model type. This is inferred automatically. + 33 preprocessor_name: + 34 Optional preprocessor. This is inferred automatically if a + 35 preprocessor is provided. + 36 + 37 Returns: + 38 LightningModel + 39 """ + 40 + 41 model: Optional[Trainer] = None # type: ignore[assignment] + 42 onnx_args: Optional[TorchOnnxArgs] = None + 43 + 44 @property + 45 def model_class(self) -> str: + 46 return TrainedModelType.PYTORCH_LIGHTNING.value + 47 + 48 @model_validator(mode="before") + 49 @classmethod + 50 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 51 model = model_args.get("model") + 52 + 53 if model_args.get("modelcard_uid", False): + 54 return model_args + 55 + 56 model, module, bases = get_model_args(model) + 57 + 58 if "lightning.pytorch" in module: + 59 model_args[CommonKwargs.MODEL_TYPE.value] = model.model.__class__.__name__ + 60 + 61 for base in bases: + 62 if "lightning.pytorch" in base: + 63 model_args[CommonKwargs.MODEL_TYPE.value] = "subclass" + 64 + 65 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) + 66 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data + 67 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) + 68 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( + 69 model_args.get(CommonKwargs.PREPROCESSOR.value), + 70 ) + 71 + 72 return model_args + 73 + 74 def get_sample_prediction(self) -> SamplePrediction: + 75 assert self.model is not None, "Trainer is not defined" + 76 assert self.sample_data is not None, "Sample data must be provided" + 77 + 78 trainer_model = self.model.model + 79 assert trainer_model is not None, "No model provided to trainer" + 80 + 81 # test dict input + 82 if isinstance(self.sample_data, dict): + 83 try: + 84 prediction = trainer_model(**self.sample_data) + 85 except Exception as _: # pylint: disable=broad-except + 86 prediction = trainer_model(self.sample_data) + 87 + 88 # test list and tuple inputs + 89 elif isinstance(self.sample_data, (list, tuple)): + 90 try: + 91 prediction = trainer_model(*self.sample_data) + 92 except Exception as _: # pylint: disable=broad-except + 93 prediction = trainer_model(self.sample_data) + 94 + 95 # all others + 96 else: + 97 prediction = trainer_model(self.sample_data) + 98 + 99 prediction_type = get_class_name(prediction) +100 +101 return SamplePrediction(prediction_type, prediction) +102 +103 def save_model(self, path: Path) -> None: +104 assert self.model is not None, "No model detected in interface" +105 self.model.save_checkpoint(path) +106 +107 def load_model(self, path: Path, **kwargs: Any) -> None: +108 """Load lightning model from path""" +109 +110 model_arch = kwargs.get(CommonKwargs.MODEL_ARCH.value) +111 +112 try: +113 if model_arch is not None: +114 # attempt to load checkpoint into model +115 assert issubclass( +116 model_arch, LightningModule +117 ), "Model architecture must be a subclass of LightningModule" +118 self.model = model_arch.load_from_checkpoint(checkpoint_path=path, **kwargs) +119 +120 else: +121 # load via torch +122 import torch +123 +124 self.model = torch.load(path) +125 +126 except Exception as exc: +127 raise ValueError(f"Unable to load pytorch lightning model: {exc}") from exc +128 +129 def convert_to_onnx(self, **kwargs: Path) -> None: +130 """Converts model to onnx""" +131 # import packages for onnx conversion +132 OpsmlImportExceptions.try_torchonnx_imports() +133 +134 if self.onnx_model is not None: +135 return None +136 +137 from opsml.model.onnx.torch_converter import _PyTorchLightningOnnxModel +138 +139 path: Optional[Path] = kwargs.get("path") +140 if path is None: +141 return self._convert_to_onnx_inplace() +142 +143 self.onnx_model = _PyTorchLightningOnnxModel(self).convert_to_onnx(**{"path": path}) +144 return None +145 +146 @property +147 def model_suffix(self) -> str: +148 """Returns suffix for storage""" +149 return Suffix.CKPT.value +150 +151 @staticmethod +152 def name() -> str: +153 return LightningModel.__name__ +154 +155except ModuleNotFoundError: +156 from opsml.model.interfaces.backups import LightningModelNoModule as LightningModel +
19 class LightningModel(TorchModel): + 20 """Model interface for Pytorch Lightning models. + 21 + 22 Args: + 23 model: + 24 Torch lightning model + 25 preprocessor: + 26 Optional preprocessor + 27 sample_data: + 28 Sample data to be used for type inference. + 29 This should match exactly what the model expects as input. + 30 task_type: + 31 Task type for model. Defaults to undefined. + 32 model_type: + 33 Optional model type. This is inferred automatically. + 34 preprocessor_name: + 35 Optional preprocessor. This is inferred automatically if a + 36 preprocessor is provided. + 37 + 38 Returns: + 39 LightningModel + 40 """ + 41 + 42 model: Optional[Trainer] = None # type: ignore[assignment] + 43 onnx_args: Optional[TorchOnnxArgs] = None + 44 + 45 @property + 46 def model_class(self) -> str: + 47 return TrainedModelType.PYTORCH_LIGHTNING.value + 48 + 49 @model_validator(mode="before") + 50 @classmethod + 51 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 52 model = model_args.get("model") + 53 + 54 if model_args.get("modelcard_uid", False): + 55 return model_args + 56 + 57 model, module, bases = get_model_args(model) + 58 + 59 if "lightning.pytorch" in module: + 60 model_args[CommonKwargs.MODEL_TYPE.value] = model.model.__class__.__name__ + 61 + 62 for base in bases: + 63 if "lightning.pytorch" in base: + 64 model_args[CommonKwargs.MODEL_TYPE.value] = "subclass" + 65 + 66 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) + 67 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data + 68 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) + 69 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( + 70 model_args.get(CommonKwargs.PREPROCESSOR.value), + 71 ) + 72 + 73 return model_args + 74 + 75 def get_sample_prediction(self) -> SamplePrediction: + 76 assert self.model is not None, "Trainer is not defined" + 77 assert self.sample_data is not None, "Sample data must be provided" + 78 + 79 trainer_model = self.model.model + 80 assert trainer_model is not None, "No model provided to trainer" + 81 + 82 # test dict input + 83 if isinstance(self.sample_data, dict): + 84 try: + 85 prediction = trainer_model(**self.sample_data) + 86 except Exception as _: # pylint: disable=broad-except + 87 prediction = trainer_model(self.sample_data) + 88 + 89 # test list and tuple inputs + 90 elif isinstance(self.sample_data, (list, tuple)): + 91 try: + 92 prediction = trainer_model(*self.sample_data) + 93 except Exception as _: # pylint: disable=broad-except + 94 prediction = trainer_model(self.sample_data) + 95 + 96 # all others + 97 else: + 98 prediction = trainer_model(self.sample_data) + 99 +100 prediction_type = get_class_name(prediction) +101 +102 return SamplePrediction(prediction_type, prediction) +103 +104 def save_model(self, path: Path) -> None: +105 assert self.model is not None, "No model detected in interface" +106 self.model.save_checkpoint(path) +107 +108 def load_model(self, path: Path, **kwargs: Any) -> None: +109 """Load lightning model from path""" +110 +111 model_arch = kwargs.get(CommonKwargs.MODEL_ARCH.value) +112 +113 try: +114 if model_arch is not None: +115 # attempt to load checkpoint into model +116 assert issubclass( +117 model_arch, LightningModule +118 ), "Model architecture must be a subclass of LightningModule" +119 self.model = model_arch.load_from_checkpoint(checkpoint_path=path, **kwargs) +120 +121 else: +122 # load via torch +123 import torch +124 +125 self.model = torch.load(path) +126 +127 except Exception as exc: +128 raise ValueError(f"Unable to load pytorch lightning model: {exc}") from exc +129 +130 def convert_to_onnx(self, **kwargs: Path) -> None: +131 """Converts model to onnx""" +132 # import packages for onnx conversion +133 OpsmlImportExceptions.try_torchonnx_imports() +134 +135 if self.onnx_model is not None: +136 return None +137 +138 from opsml.model.onnx.torch_converter import _PyTorchLightningOnnxModel +139 +140 path: Optional[Path] = kwargs.get("path") +141 if path is None: +142 return self._convert_to_onnx_inplace() +143 +144 self.onnx_model = _PyTorchLightningOnnxModel(self).convert_to_onnx(**{"path": path}) +145 return None +146 +147 @property +148 def model_suffix(self) -> str: +149 """Returns suffix for storage""" +150 return Suffix.CKPT.value +151 +152 @staticmethod +153 def name() -> str: +154 return LightningModel.__name__ +
Model interface for Pytorch Lightning models.
+ +Returns: +LightningModel
+49 @model_validator(mode="before") +50 @classmethod +51 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: +52 model = model_args.get("model") +53 +54 if model_args.get("modelcard_uid", False): +55 return model_args +56 +57 model, module, bases = get_model_args(model) +58 +59 if "lightning.pytorch" in module: +60 model_args[CommonKwargs.MODEL_TYPE.value] = model.model.__class__.__name__ +61 +62 for base in bases: +63 if "lightning.pytorch" in base: +64 model_args[CommonKwargs.MODEL_TYPE.value] = "subclass" +65 +66 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) +67 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +68 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +69 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( +70 model_args.get(CommonKwargs.PREPROCESSOR.value), +71 ) +72 +73 return model_args +
75 def get_sample_prediction(self) -> SamplePrediction: + 76 assert self.model is not None, "Trainer is not defined" + 77 assert self.sample_data is not None, "Sample data must be provided" + 78 + 79 trainer_model = self.model.model + 80 assert trainer_model is not None, "No model provided to trainer" + 81 + 82 # test dict input + 83 if isinstance(self.sample_data, dict): + 84 try: + 85 prediction = trainer_model(**self.sample_data) + 86 except Exception as _: # pylint: disable=broad-except + 87 prediction = trainer_model(self.sample_data) + 88 + 89 # test list and tuple inputs + 90 elif isinstance(self.sample_data, (list, tuple)): + 91 try: + 92 prediction = trainer_model(*self.sample_data) + 93 except Exception as _: # pylint: disable=broad-except + 94 prediction = trainer_model(self.sample_data) + 95 + 96 # all others + 97 else: + 98 prediction = trainer_model(self.sample_data) + 99 +100 prediction_type = get_class_name(prediction) +101 +102 return SamplePrediction(prediction_type, prediction) +
104 def save_model(self, path: Path) -> None: +105 assert self.model is not None, "No model detected in interface" +106 self.model.save_checkpoint(path) +
Save pytorch model to path
+ +108 def load_model(self, path: Path, **kwargs: Any) -> None: +109 """Load lightning model from path""" +110 +111 model_arch = kwargs.get(CommonKwargs.MODEL_ARCH.value) +112 +113 try: +114 if model_arch is not None: +115 # attempt to load checkpoint into model +116 assert issubclass( +117 model_arch, LightningModule +118 ), "Model architecture must be a subclass of LightningModule" +119 self.model = model_arch.load_from_checkpoint(checkpoint_path=path, **kwargs) +120 +121 else: +122 # load via torch +123 import torch +124 +125 self.model = torch.load(path) +126 +127 except Exception as exc: +128 raise ValueError(f"Unable to load pytorch lightning model: {exc}") from exc +
Load lightning model from path
+130 def convert_to_onnx(self, **kwargs: Path) -> None: +131 """Converts model to onnx""" +132 # import packages for onnx conversion +133 OpsmlImportExceptions.try_torchonnx_imports() +134 +135 if self.onnx_model is not None: +136 return None +137 +138 from opsml.model.onnx.torch_converter import _PyTorchLightningOnnxModel +139 +140 path: Optional[Path] = kwargs.get("path") +141 if path is None: +142 return self._convert_to_onnx_inplace() +143 +144 self.onnx_model = _PyTorchLightningOnnxModel(self).convert_to_onnx(**{"path": path}) +145 return None +
Converts model to onnx
+147 @property +148 def model_suffix(self) -> str: +149 """Returns suffix for storage""" +150 return Suffix.CKPT.value +
Returns suffix for storage
+1from pathlib import Path + 2from typing import Any, Dict, Optional, Union + 3 + 4import joblib + 5import pandas as pd + 6from numpy.typing import NDArray + 7from pydantic import model_validator + 8 + 9from opsml.helpers.utils import get_class_name + 10from opsml.model.interfaces.base import ( + 11 ModelInterface, + 12 get_model_args, + 13 get_processor_name, + 14) + 15from opsml.types import CommonKwargs, Suffix, TrainedModelType + 16 + 17try: + 18 from sklearn.base import BaseEstimator + 19 + 20 class SklearnModel(ModelInterface): + 21 """Model interface for Sklearn models. + 22 + 23 Args: + 24 model: + 25 Sklearn model + 26 preprocessor: + 27 Optional preprocessor + 28 sample_data: + 29 Sample data to be used for type inference. + 30 For sklearn models this should be a pandas DataFrame or numpy array. + 31 This should match exactly what the model expects as input. + 32 task_type: + 33 Task type for model. Defaults to undefined. + 34 model_type: + 35 Optional model type. This is inferred automatically. + 36 preprocessor_name: + 37 Optional preprocessor name. This is inferred automatically if a + 38 preprocessor is provided. + 39 + 40 Returns: + 41 SklearnModel + 42 """ + 43 + 44 model: Optional[BaseEstimator] = None + 45 sample_data: Optional[Union[pd.DataFrame, NDArray[Any]]] = None + 46 preprocessor: Optional[Any] = None + 47 preprocessor_name: str = CommonKwargs.UNDEFINED.value + 48 + 49 @property + 50 def model_class(self) -> str: + 51 return TrainedModelType.SKLEARN_ESTIMATOR.value + 52 + 53 @model_validator(mode="before") + 54 @classmethod + 55 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 56 model = model_args.get("model") + 57 + 58 if model_args.get("modelcard_uid", False): + 59 return model_args + 60 + 61 model, module, bases = get_model_args(model) + 62 + 63 if "sklearn" in module: + 64 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ + 65 + 66 else: + 67 for base in bases: + 68 if "sklearn" in base: + 69 model_args[CommonKwargs.MODEL_TYPE.value] = "subclass" + 70 + 71 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) + 72 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data + 73 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) + 74 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( + 75 model_args.get(CommonKwargs.PREPROCESSOR.value), + 76 ) + 77 + 78 return model_args + 79 + 80 def save_preprocessor(self, path: Path) -> None: + 81 """Saves preprocessor to path if present. Base implementation use Joblib + 82 + 83 Args: + 84 path: + 85 Pathlib object + 86 """ + 87 assert self.preprocessor is not None, "No preprocessor detected in interface" + 88 joblib.dump(self.preprocessor, path) + 89 + 90 def load_preprocessor(self, path: Path) -> None: + 91 """Load preprocessor from pathlib object + 92 + 93 Args: + 94 path: + 95 Pathlib object + 96 """ + 97 self.preprocessor = joblib.load(path) + 98 + 99 @property +100 def preprocessor_suffix(self) -> str: +101 """Returns suffix for storage""" +102 return Suffix.JOBLIB.value +103 +104 @staticmethod +105 def name() -> str: +106 return SklearnModel.__name__ +107 +108except ModuleNotFoundError: +109 from opsml.model.interfaces.backups import SklearnModelNoModule as SklearnModel +
21 class SklearnModel(ModelInterface): + 22 """Model interface for Sklearn models. + 23 + 24 Args: + 25 model: + 26 Sklearn model + 27 preprocessor: + 28 Optional preprocessor + 29 sample_data: + 30 Sample data to be used for type inference. + 31 For sklearn models this should be a pandas DataFrame or numpy array. + 32 This should match exactly what the model expects as input. + 33 task_type: + 34 Task type for model. Defaults to undefined. + 35 model_type: + 36 Optional model type. This is inferred automatically. + 37 preprocessor_name: + 38 Optional preprocessor name. This is inferred automatically if a + 39 preprocessor is provided. + 40 + 41 Returns: + 42 SklearnModel + 43 """ + 44 + 45 model: Optional[BaseEstimator] = None + 46 sample_data: Optional[Union[pd.DataFrame, NDArray[Any]]] = None + 47 preprocessor: Optional[Any] = None + 48 preprocessor_name: str = CommonKwargs.UNDEFINED.value + 49 + 50 @property + 51 def model_class(self) -> str: + 52 return TrainedModelType.SKLEARN_ESTIMATOR.value + 53 + 54 @model_validator(mode="before") + 55 @classmethod + 56 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 57 model = model_args.get("model") + 58 + 59 if model_args.get("modelcard_uid", False): + 60 return model_args + 61 + 62 model, module, bases = get_model_args(model) + 63 + 64 if "sklearn" in module: + 65 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ + 66 + 67 else: + 68 for base in bases: + 69 if "sklearn" in base: + 70 model_args[CommonKwargs.MODEL_TYPE.value] = "subclass" + 71 + 72 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) + 73 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data + 74 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) + 75 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( + 76 model_args.get(CommonKwargs.PREPROCESSOR.value), + 77 ) + 78 + 79 return model_args + 80 + 81 def save_preprocessor(self, path: Path) -> None: + 82 """Saves preprocessor to path if present. Base implementation use Joblib + 83 + 84 Args: + 85 path: + 86 Pathlib object + 87 """ + 88 assert self.preprocessor is not None, "No preprocessor detected in interface" + 89 joblib.dump(self.preprocessor, path) + 90 + 91 def load_preprocessor(self, path: Path) -> None: + 92 """Load preprocessor from pathlib object + 93 + 94 Args: + 95 path: + 96 Pathlib object + 97 """ + 98 self.preprocessor = joblib.load(path) + 99 +100 @property +101 def preprocessor_suffix(self) -> str: +102 """Returns suffix for storage""" +103 return Suffix.JOBLIB.value +104 +105 @staticmethod +106 def name() -> str: +107 return SklearnModel.__name__ +
Model interface for Sklearn models.
+ +Returns: +SklearnModel
+54 @model_validator(mode="before") +55 @classmethod +56 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: +57 model = model_args.get("model") +58 +59 if model_args.get("modelcard_uid", False): +60 return model_args +61 +62 model, module, bases = get_model_args(model) +63 +64 if "sklearn" in module: +65 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ +66 +67 else: +68 for base in bases: +69 if "sklearn" in base: +70 model_args[CommonKwargs.MODEL_TYPE.value] = "subclass" +71 +72 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) +73 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +74 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +75 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( +76 model_args.get(CommonKwargs.PREPROCESSOR.value), +77 ) +78 +79 return model_args +
81 def save_preprocessor(self, path: Path) -> None: +82 """Saves preprocessor to path if present. Base implementation use Joblib +83 +84 Args: +85 path: +86 Pathlib object +87 """ +88 assert self.preprocessor is not None, "No preprocessor detected in interface" +89 joblib.dump(self.preprocessor, path) +
Saves preprocessor to path if present. Base implementation use Joblib
+ +91 def load_preprocessor(self, path: Path) -> None: +92 """Load preprocessor from pathlib object +93 +94 Args: +95 path: +96 Pathlib object +97 """ +98 self.preprocessor = joblib.load(path) +
Load preprocessor from pathlib object
+ +100 @property +101 def preprocessor_suffix(self) -> str: +102 """Returns suffix for storage""" +103 return Suffix.JOBLIB.value +
Returns suffix for storage
+1from pathlib import Path + 2from typing import Any, Dict, List, Optional, Tuple, Union + 3 + 4import joblib + 5import numpy as np + 6from numpy.typing import NDArray + 7from pydantic import model_validator + 8 + 9from opsml.helpers.utils import get_class_name + 10from opsml.model.interfaces.base import ( + 11 ModelInterface, + 12 get_model_args, + 13 get_processor_name, + 14) + 15from opsml.types import CommonKwargs, Suffix, TrainedModelType + 16 + 17try: + 18 import tensorflow as tf + 19 + 20 ArrayType = Union[NDArray[Any], tf.Tensor] + 21 + 22 class TensorFlowModel(ModelInterface): + 23 """Model interface for Tensorflow models. + 24 + 25 Args: + 26 model: + 27 Tensorflow model + 28 preprocessor: + 29 Optional preprocessor + 30 sample_data: + 31 Sample data to be used for type inference and ONNX conversion/validation. + 32 This should match exactly what the model expects as input. ArrayType = Union[NDArray[Any], tf.Tensor] + 33 task_type: + 34 Task type for model. Defaults to undefined. + 35 model_type: + 36 Optional model type. This is inferred automatically. + 37 preprocessor_name: + 38 Optional preprocessor. This is inferred automatically if a + 39 preprocessor is provided. + 40 + 41 Returns: + 42 TensorFlowModel + 43 """ + 44 + 45 model: Optional[tf.keras.Model] = None + 46 sample_data: Optional[Union[ArrayType, Dict[str, ArrayType], List[ArrayType], Tuple[ArrayType]]] = None + 47 preprocessor: Optional[Any] = None + 48 preprocessor_name: str = CommonKwargs.UNDEFINED.value + 49 + 50 @property + 51 def model_class(self) -> str: + 52 return TrainedModelType.TF_KERAS.value + 53 + 54 @classmethod + 55 def _get_sample_data(cls, sample_data: Any) -> Any: + 56 """Check sample data and returns one record to be used + 57 during type inference and ONNX conversion/validation. + 58 + 59 Returns: + 60 Sample data with only one record + 61 """ + 62 + 63 if isinstance(sample_data, (np.ndarray, tf.Tensor)): + 64 return sample_data[0:1] + 65 + 66 if isinstance(sample_data, list): + 67 return [data[0:1] for data in sample_data] + 68 + 69 if isinstance(sample_data, tuple): + 70 return (data[0:1] for data in sample_data) + 71 + 72 if isinstance(sample_data, dict): + 73 sample_dict = {} + 74 for key, value in sample_data.items(): + 75 sample_dict[key] = value[0:1] + 76 return sample_dict + 77 + 78 raise ValueError("Provided sample data is not a valid type") + 79 + 80 @model_validator(mode="before") + 81 @classmethod + 82 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 83 model = model_args.get("model") + 84 + 85 if model_args.get("modelcard_uid", False): + 86 return model_args + 87 + 88 model, module, bases = get_model_args(model) + 89 + 90 assert isinstance(model, tf.keras.Model), "Model must be a tensorflow keras model" + 91 + 92 if "keras" in module: + 93 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ + 94 + 95 else: + 96 for base in bases: + 97 if "keras" in base: + 98 model_args[CommonKwargs.MODEL_TYPE.value] = "subclass" + 99 +100 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) +101 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +102 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +103 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( +104 model_args.get(CommonKwargs.PREPROCESSOR.value), +105 ) +106 +107 return model_args +108 +109 def save_model(self, path: Path) -> None: +110 """Save tensorflow model to path +111 +112 Args: +113 path: +114 pathlib object +115 """ +116 assert self.model is not None, "Model is not initialized" +117 self.model.save(path) +118 +119 def load_model(self, path: Path, **kwargs: Any) -> None: +120 """Load tensorflow model from path +121 +122 Args: +123 path: +124 pathlib object +125 kwargs: +126 Additional arguments to be passed to load_model +127 """ +128 self.model = tf.keras.models.load_model(path, **kwargs) +129 +130 def save_preprocessor(self, path: Path) -> None: +131 """Saves preprocessor to path if present. Base implementation use Joblib +132 +133 Args: +134 path: +135 Pathlib object +136 """ +137 assert self.preprocessor is not None, "No preprocessor detected in interface" +138 joblib.dump(self.preprocessor, path) +139 +140 def load_preprocessor(self, path: Path) -> None: +141 """Load preprocessor from pathlib object +142 +143 Args: +144 path: +145 Pathlib object +146 """ +147 self.preprocessor = joblib.load(path) +148 +149 @property +150 def preprocessor_suffix(self) -> str: +151 """Returns suffix for storage""" +152 return Suffix.JOBLIB.value +153 +154 @property +155 def model_suffix(self) -> str: +156 """Returns suffix for storage""" +157 return "" +158 +159 @staticmethod +160 def name() -> str: +161 return TensorFlowModel.__name__ +162 +163except ModuleNotFoundError: +164 from opsml.model.interfaces.backups import ( +165 TensorFlowModelNoModule as TensorFlowModel, +166 ) +
23 class TensorFlowModel(ModelInterface): + 24 """Model interface for Tensorflow models. + 25 + 26 Args: + 27 model: + 28 Tensorflow model + 29 preprocessor: + 30 Optional preprocessor + 31 sample_data: + 32 Sample data to be used for type inference and ONNX conversion/validation. + 33 This should match exactly what the model expects as input. ArrayType = Union[NDArray[Any], tf.Tensor] + 34 task_type: + 35 Task type for model. Defaults to undefined. + 36 model_type: + 37 Optional model type. This is inferred automatically. + 38 preprocessor_name: + 39 Optional preprocessor. This is inferred automatically if a + 40 preprocessor is provided. + 41 + 42 Returns: + 43 TensorFlowModel + 44 """ + 45 + 46 model: Optional[tf.keras.Model] = None + 47 sample_data: Optional[Union[ArrayType, Dict[str, ArrayType], List[ArrayType], Tuple[ArrayType]]] = None + 48 preprocessor: Optional[Any] = None + 49 preprocessor_name: str = CommonKwargs.UNDEFINED.value + 50 + 51 @property + 52 def model_class(self) -> str: + 53 return TrainedModelType.TF_KERAS.value + 54 + 55 @classmethod + 56 def _get_sample_data(cls, sample_data: Any) -> Any: + 57 """Check sample data and returns one record to be used + 58 during type inference and ONNX conversion/validation. + 59 + 60 Returns: + 61 Sample data with only one record + 62 """ + 63 + 64 if isinstance(sample_data, (np.ndarray, tf.Tensor)): + 65 return sample_data[0:1] + 66 + 67 if isinstance(sample_data, list): + 68 return [data[0:1] for data in sample_data] + 69 + 70 if isinstance(sample_data, tuple): + 71 return (data[0:1] for data in sample_data) + 72 + 73 if isinstance(sample_data, dict): + 74 sample_dict = {} + 75 for key, value in sample_data.items(): + 76 sample_dict[key] = value[0:1] + 77 return sample_dict + 78 + 79 raise ValueError("Provided sample data is not a valid type") + 80 + 81 @model_validator(mode="before") + 82 @classmethod + 83 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 84 model = model_args.get("model") + 85 + 86 if model_args.get("modelcard_uid", False): + 87 return model_args + 88 + 89 model, module, bases = get_model_args(model) + 90 + 91 assert isinstance(model, tf.keras.Model), "Model must be a tensorflow keras model" + 92 + 93 if "keras" in module: + 94 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ + 95 + 96 else: + 97 for base in bases: + 98 if "keras" in base: + 99 model_args[CommonKwargs.MODEL_TYPE.value] = "subclass" +100 +101 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) +102 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +103 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +104 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( +105 model_args.get(CommonKwargs.PREPROCESSOR.value), +106 ) +107 +108 return model_args +109 +110 def save_model(self, path: Path) -> None: +111 """Save tensorflow model to path +112 +113 Args: +114 path: +115 pathlib object +116 """ +117 assert self.model is not None, "Model is not initialized" +118 self.model.save(path) +119 +120 def load_model(self, path: Path, **kwargs: Any) -> None: +121 """Load tensorflow model from path +122 +123 Args: +124 path: +125 pathlib object +126 kwargs: +127 Additional arguments to be passed to load_model +128 """ +129 self.model = tf.keras.models.load_model(path, **kwargs) +130 +131 def save_preprocessor(self, path: Path) -> None: +132 """Saves preprocessor to path if present. Base implementation use Joblib +133 +134 Args: +135 path: +136 Pathlib object +137 """ +138 assert self.preprocessor is not None, "No preprocessor detected in interface" +139 joblib.dump(self.preprocessor, path) +140 +141 def load_preprocessor(self, path: Path) -> None: +142 """Load preprocessor from pathlib object +143 +144 Args: +145 path: +146 Pathlib object +147 """ +148 self.preprocessor = joblib.load(path) +149 +150 @property +151 def preprocessor_suffix(self) -> str: +152 """Returns suffix for storage""" +153 return Suffix.JOBLIB.value +154 +155 @property +156 def model_suffix(self) -> str: +157 """Returns suffix for storage""" +158 return "" +159 +160 @staticmethod +161 def name() -> str: +162 return TensorFlowModel.__name__ +
Model interface for Tensorflow models.
+ +++TensorFlowModel
+
81 @model_validator(mode="before") + 82 @classmethod + 83 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 84 model = model_args.get("model") + 85 + 86 if model_args.get("modelcard_uid", False): + 87 return model_args + 88 + 89 model, module, bases = get_model_args(model) + 90 + 91 assert isinstance(model, tf.keras.Model), "Model must be a tensorflow keras model" + 92 + 93 if "keras" in module: + 94 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ + 95 + 96 else: + 97 for base in bases: + 98 if "keras" in base: + 99 model_args[CommonKwargs.MODEL_TYPE.value] = "subclass" +100 +101 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) +102 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +103 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +104 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( +105 model_args.get(CommonKwargs.PREPROCESSOR.value), +106 ) +107 +108 return model_args +
110 def save_model(self, path: Path) -> None: +111 """Save tensorflow model to path +112 +113 Args: +114 path: +115 pathlib object +116 """ +117 assert self.model is not None, "Model is not initialized" +118 self.model.save(path) +
Save tensorflow model to path
+ +120 def load_model(self, path: Path, **kwargs: Any) -> None: +121 """Load tensorflow model from path +122 +123 Args: +124 path: +125 pathlib object +126 kwargs: +127 Additional arguments to be passed to load_model +128 """ +129 self.model = tf.keras.models.load_model(path, **kwargs) +
Load tensorflow model from path
+ +131 def save_preprocessor(self, path: Path) -> None: +132 """Saves preprocessor to path if present. Base implementation use Joblib +133 +134 Args: +135 path: +136 Pathlib object +137 """ +138 assert self.preprocessor is not None, "No preprocessor detected in interface" +139 joblib.dump(self.preprocessor, path) +
Saves preprocessor to path if present. Base implementation use Joblib
+ +141 def load_preprocessor(self, path: Path) -> None: +142 """Load preprocessor from pathlib object +143 +144 Args: +145 path: +146 Pathlib object +147 """ +148 self.preprocessor = joblib.load(path) +
Load preprocessor from pathlib object
+ +150 @property +151 def preprocessor_suffix(self) -> str: +152 """Returns suffix for storage""" +153 return Suffix.JOBLIB.value +
Returns suffix for storage
+155 @property +156 def model_suffix(self) -> str: +157 """Returns suffix for storage""" +158 return "" +
Returns suffix for storage
+1from pathlib import Path + 2from typing import Any, Dict, Optional + 3 + 4from pydantic import model_validator + 5 + 6from opsml.helpers.utils import get_class_name + 7from opsml.model.interfaces.base import ModelInterface + 8from opsml.types import CommonKwargs, ModelReturn, Suffix, TrainedModelType + 9 + 10try: + 11 import vowpalwabbit as vw + 12 + 13 class VowpalWabbitModel(ModelInterface): + 14 """Model interface for VowPal Wabbit model. + 15 + 16 Args: + 17 model: + 18 vowpal wabbit workspace + 19 sample_data: + 20 Sample data to be used for type inference. + 21 For vowpal wabbit models this should be a string. + 22 arguments: + 23 Vowpal Wabbit arguments. This will be inferred automatically from the workspace + 24 + 25 Returns: + 26 VowpalWabbitModel + 27 """ + 28 + 29 model: Optional[vw.Workspace] = None + 30 sample_data: Optional[str] = None + 31 arguments: str = "" + 32 + 33 @property + 34 def model_class(self) -> str: + 35 return TrainedModelType.VOWPAL.value + 36 + 37 @classmethod + 38 def _get_sample_data(cls, sample_data: str) -> str: + 39 """Check sample data and returns one record to be used + 40 during type inference and ONNX conversion/validation. + 41 + 42 Returns: + 43 Sample data with only one record + 44 """ + 45 + 46 assert isinstance(sample_data, str), "Sample data must be a string" + 47 return sample_data + 48 + 49 @model_validator(mode="before") + 50 @classmethod + 51 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 52 model = model_args.get("model") + 53 + 54 if model_args.get("modelcard_uid", False): + 55 return model_args + 56 + 57 assert model is not None, "Model must not be None" + 58 + 59 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) + 60 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data + 61 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) + 62 model_args[CommonKwargs.VOWPAL_ARGS.value] = model.get_arguments() + 63 + 64 return model_args + 65 + 66 def save_model(self, path: Path) -> None: + 67 """Saves vowpal model to ".model" file. + 68 + 69 Args: + 70 path: + 71 base path to save model to + 72 """ + 73 assert self.model is not None, "No model found" + 74 self.model.save(path.as_posix()) + 75 + 76 def load_model(self, path: Path, **kwargs: Any) -> None: + 77 """Loads a vowpal model from ".model" file with arguments. + 78 + 79 Args: + 80 path: + 81 base path to load from + 82 **kwargs: + 83 Additional arguments to be passed to the workspace. This is supplied via + 84 "arguments" key. + 85 + 86 Example: + 87 arguments="--cb 4" + 88 or + 89 arguments=["--cb", "4"] + 90 + 91 There is no need to specify "-i" argument. This is done automatically during loading. + 92 """ + 93 args = kwargs.get("arguments", self.arguments) + 94 + 95 if args is None: + 96 args = self.arguments + 97 + 98 elif isinstance(args, str): + 99 args = args + f" -i {path.as_posix()}" +100 +101 elif isinstance(args, list): +102 args.append(f"-i {path.as_posix()}") +103 args = " ".join(args) +104 +105 else: +106 raise ValueError("Arguments must be a string or a list") +107 +108 self.model = vw.Workspace(args) +109 +110 def save_onnx(self, path: Path) -> ModelReturn: # pylint: disable=redundant-returns-doc +111 """Onnx is not supported for vowpal wabbit models. +112 +113 Args: +114 path: +115 Path to save +116 +117 Returns: +118 ModelReturn +119 """ +120 raise ValueError("Onnx is not supported for vowpal wabbit models") +121 +122 @property +123 def model_suffix(self) -> str: +124 """Returns suffix for model storage""" +125 return Suffix.MODEL.value +126 +127 @staticmethod +128 def name() -> str: +129 return VowpalWabbitModel.__name__ +130 +131except ModuleNotFoundError: +132 from opsml.model.interfaces.backups import ( +133 VowpalWabbitModelNoModule as VowpalWabbitModel, +134 ) +
14 class VowpalWabbitModel(ModelInterface): + 15 """Model interface for VowPal Wabbit model. + 16 + 17 Args: + 18 model: + 19 vowpal wabbit workspace + 20 sample_data: + 21 Sample data to be used for type inference. + 22 For vowpal wabbit models this should be a string. + 23 arguments: + 24 Vowpal Wabbit arguments. This will be inferred automatically from the workspace + 25 + 26 Returns: + 27 VowpalWabbitModel + 28 """ + 29 + 30 model: Optional[vw.Workspace] = None + 31 sample_data: Optional[str] = None + 32 arguments: str = "" + 33 + 34 @property + 35 def model_class(self) -> str: + 36 return TrainedModelType.VOWPAL.value + 37 + 38 @classmethod + 39 def _get_sample_data(cls, sample_data: str) -> str: + 40 """Check sample data and returns one record to be used + 41 during type inference and ONNX conversion/validation. + 42 + 43 Returns: + 44 Sample data with only one record + 45 """ + 46 + 47 assert isinstance(sample_data, str), "Sample data must be a string" + 48 return sample_data + 49 + 50 @model_validator(mode="before") + 51 @classmethod + 52 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 53 model = model_args.get("model") + 54 + 55 if model_args.get("modelcard_uid", False): + 56 return model_args + 57 + 58 assert model is not None, "Model must not be None" + 59 + 60 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) + 61 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data + 62 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) + 63 model_args[CommonKwargs.VOWPAL_ARGS.value] = model.get_arguments() + 64 + 65 return model_args + 66 + 67 def save_model(self, path: Path) -> None: + 68 """Saves vowpal model to ".model" file. + 69 + 70 Args: + 71 path: + 72 base path to save model to + 73 """ + 74 assert self.model is not None, "No model found" + 75 self.model.save(path.as_posix()) + 76 + 77 def load_model(self, path: Path, **kwargs: Any) -> None: + 78 """Loads a vowpal model from ".model" file with arguments. + 79 + 80 Args: + 81 path: + 82 base path to load from + 83 **kwargs: + 84 Additional arguments to be passed to the workspace. This is supplied via + 85 "arguments" key. + 86 + 87 Example: + 88 arguments="--cb 4" + 89 or + 90 arguments=["--cb", "4"] + 91 + 92 There is no need to specify "-i" argument. This is done automatically during loading. + 93 """ + 94 args = kwargs.get("arguments", self.arguments) + 95 + 96 if args is None: + 97 args = self.arguments + 98 + 99 elif isinstance(args, str): +100 args = args + f" -i {path.as_posix()}" +101 +102 elif isinstance(args, list): +103 args.append(f"-i {path.as_posix()}") +104 args = " ".join(args) +105 +106 else: +107 raise ValueError("Arguments must be a string or a list") +108 +109 self.model = vw.Workspace(args) +110 +111 def save_onnx(self, path: Path) -> ModelReturn: # pylint: disable=redundant-returns-doc +112 """Onnx is not supported for vowpal wabbit models. +113 +114 Args: +115 path: +116 Path to save +117 +118 Returns: +119 ModelReturn +120 """ +121 raise ValueError("Onnx is not supported for vowpal wabbit models") +122 +123 @property +124 def model_suffix(self) -> str: +125 """Returns suffix for model storage""" +126 return Suffix.MODEL.value +127 +128 @staticmethod +129 def name() -> str: +130 return VowpalWabbitModel.__name__ +
Model interface for VowPal Wabbit model.
+ +++VowpalWabbitModel
+
50 @model_validator(mode="before") +51 @classmethod +52 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: +53 model = model_args.get("model") +54 +55 if model_args.get("modelcard_uid", False): +56 return model_args +57 +58 assert model is not None, "Model must not be None" +59 +60 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) +61 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +62 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +63 model_args[CommonKwargs.VOWPAL_ARGS.value] = model.get_arguments() +64 +65 return model_args +
67 def save_model(self, path: Path) -> None: +68 """Saves vowpal model to ".model" file. +69 +70 Args: +71 path: +72 base path to save model to +73 """ +74 assert self.model is not None, "No model found" +75 self.model.save(path.as_posix()) +
Saves vowpal model to ".model" file.
+ +77 def load_model(self, path: Path, **kwargs: Any) -> None: + 78 """Loads a vowpal model from ".model" file with arguments. + 79 + 80 Args: + 81 path: + 82 base path to load from + 83 **kwargs: + 84 Additional arguments to be passed to the workspace. This is supplied via + 85 "arguments" key. + 86 + 87 Example: + 88 arguments="--cb 4" + 89 or + 90 arguments=["--cb", "4"] + 91 + 92 There is no need to specify "-i" argument. This is done automatically during loading. + 93 """ + 94 args = kwargs.get("arguments", self.arguments) + 95 + 96 if args is None: + 97 args = self.arguments + 98 + 99 elif isinstance(args, str): +100 args = args + f" -i {path.as_posix()}" +101 +102 elif isinstance(args, list): +103 args.append(f"-i {path.as_posix()}") +104 args = " ".join(args) +105 +106 else: +107 raise ValueError("Arguments must be a string or a list") +108 +109 self.model = vw.Workspace(args) +
Loads a vowpal model from ".model" file with arguments.
+ +**kwargs: Additional arguments to be passed to the workspace. This is supplied via +"arguments" key.
+ +Example: + arguments="--cb 4" + or + arguments=["--cb", "4"]
+ +There is no need to specify "-i" argument. This is done automatically during loading.
111 def save_onnx(self, path: Path) -> ModelReturn: # pylint: disable=redundant-returns-doc +112 """Onnx is not supported for vowpal wabbit models. +113 +114 Args: +115 path: +116 Path to save +117 +118 Returns: +119 ModelReturn +120 """ +121 raise ValueError("Onnx is not supported for vowpal wabbit models") +
Onnx is not supported for vowpal wabbit models.
+ +++ModelReturn
+
123 @property +124 def model_suffix(self) -> str: +125 """Returns suffix for model storage""" +126 return Suffix.MODEL.value +
Returns suffix for model storage
+1from pathlib import Path + 2from typing import Any, Dict, Optional, Union + 3 + 4import joblib + 5import pandas as pd + 6from numpy.typing import NDArray + 7from pydantic import model_validator + 8 + 9from opsml.helpers.logging import ArtifactLogger + 10from opsml.helpers.utils import get_class_name + 11from opsml.model import ModelInterface + 12from opsml.model.interfaces.base import get_model_args, get_processor_name + 13from opsml.types import CommonKwargs, ModelReturn, Suffix, TrainedModelType + 14 + 15logger = ArtifactLogger.get_logger() + 16 + 17try: + 18 from xgboost import Booster, DMatrix, XGBModel + 19 + 20 class XGBoostModel(ModelInterface): + 21 """Model interface for XGBoost model class. Currently, only Sklearn flavor of XGBoost + 22 regressor and classifier are supported. + 23 + 24 Args: + 25 model: + 26 XGBoost model. Can be either a Booster or XGBModel. + 27 preprocessor: + 28 Optional preprocessor + 29 sample_data: + 30 Sample data to be used for type inference and ONNX conversion/validation. + 31 This should match exactly what the model expects as input. + 32 task_type: + 33 Task type for model. Defaults to undefined. + 34 model_type: + 35 Optional model type. This is inferred automatically. + 36 preprocessor_name: + 37 Optional preprocessor. This is inferred automatically if a + 38 preprocessor is provided. + 39 + 40 Returns: + 41 XGBoostModel + 42 """ + 43 + 44 model: Optional[Union[Booster, XGBModel]] = None + 45 sample_data: Optional[Union[pd.DataFrame, NDArray[Any], DMatrix]] = None + 46 preprocessor: Optional[Any] = None + 47 preprocessor_name: str = CommonKwargs.UNDEFINED.value + 48 + 49 @property + 50 def model_class(self) -> str: + 51 if "Booster" in self.model_type: + 52 return TrainedModelType.XGB_BOOSTER.value + 53 return TrainedModelType.SKLEARN_ESTIMATOR.value + 54 + 55 @classmethod + 56 def _get_sample_data(cls, sample_data: Any) -> Union[pd.DataFrame, NDArray[Any], DMatrix]: + 57 """Check sample data and returns one record to be used + 58 during type inference and ONNX conversion/validation. + 59 + 60 Returns: + 61 Sample data with only one record + 62 """ + 63 if isinstance(sample_data, DMatrix): + 64 return sample_data.slice([0]) + 65 return super()._get_sample_data(sample_data) + 66 + 67 @model_validator(mode="before") + 68 @classmethod + 69 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 70 model = model_args.get("model") + 71 + 72 if model_args.get("modelcard_uid", False): + 73 return model_args + 74 + 75 model, _, bases = get_model_args(model) + 76 + 77 if isinstance(model, XGBModel): + 78 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ + 79 + 80 elif isinstance(model, Booster): + 81 model_args[CommonKwargs.MODEL_TYPE.value] = "Booster" + 82 + 83 else: + 84 for base in bases: + 85 if "sklearn" in base: + 86 model_args[CommonKwargs.MODEL_TYPE.value] = "subclass" + 87 + 88 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) + 89 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data + 90 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) + 91 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( + 92 model_args.get(CommonKwargs.PREPROCESSOR.value), + 93 ) + 94 + 95 return model_args + 96 + 97 def save_model(self, path: Path) -> None: + 98 """Saves lgb model according to model format. Booster models are saved to text. + 99 Sklearn models are saved via joblib. +100 +101 Args: +102 path: +103 base path to save model to +104 """ +105 assert self.model is not None, "No model found" +106 if isinstance(self.model, Booster): +107 self.model.save_model(path) +108 +109 else: +110 super().save_model(path) +111 +112 def load_model(self, path: Path, **kwargs: Any) -> None: +113 """Loads lightgbm booster or sklearn model +114 +115 +116 Args: +117 path: +118 base path to load from +119 **kwargs: +120 Additional keyword arguments +121 """ +122 +123 if self.model_type == TrainedModelType.LGBM_BOOSTER.value: +124 self.model = Booster(model_file=path) +125 else: +126 super().load_model(path) +127 +128 def save_preprocessor(self, path: Path) -> None: +129 """Saves preprocessor to path if present. Base implementation use Joblib +130 +131 Args: +132 path: +133 Pathlib object +134 """ +135 assert self.preprocessor is not None, "No preprocessor detected in interface" +136 joblib.dump(self.preprocessor, path) +137 +138 def load_preprocessor(self, path: Path) -> None: +139 """Load preprocessor from pathlib object +140 +141 Args: +142 path: +143 Pathlib object +144 """ +145 self.preprocessor = joblib.load(path) +146 +147 def save_onnx(self, path: Path) -> ModelReturn: +148 """Saves the onnx model +149 +150 Args: +151 path: +152 Path to save +153 +154 Returns: +155 ModelReturn +156 """ +157 +158 if self.model_class == TrainedModelType.XGB_BOOSTER.value: +159 logger.warning("ONNX conversion for XGBoost Booster is not supported") +160 +161 return super().save_onnx(path) +162 +163 def save_sample_data(self, path: Path) -> None: +164 """Serialized and save sample data to path. +165 +166 Args: +167 path: +168 Pathlib object +169 """ +170 if isinstance(self.sample_data, DMatrix): +171 self.sample_data.save_binary(path) +172 +173 else: +174 joblib.dump(self.sample_data, path) +175 +176 def load_sample_data(self, path: Path) -> None: +177 """Serialized and save sample data to path. +178 +179 Args: +180 path: +181 Pathlib object +182 """ +183 if self.model_class == TrainedModelType.XGB_BOOSTER.value: +184 self.sample_data = DMatrix(path) +185 else: +186 self.sample_data = joblib.load(path) +187 +188 @property +189 def model_suffix(self) -> str: +190 if self.model_type == TrainedModelType.XGB_BOOSTER.value: +191 return Suffix.JSON.value +192 +193 return super().model_suffix +194 +195 @property +196 def preprocessor_suffix(self) -> str: +197 """Returns suffix for storage""" +198 return Suffix.JOBLIB.value +199 +200 @property +201 def data_suffix(self) -> str: +202 """Returns suffix for storage""" +203 if self.model_class == TrainedModelType.XGB_BOOSTER.value: +204 return Suffix.DMATRIX.value +205 return Suffix.JOBLIB.value +206 +207 @staticmethod +208 def name() -> str: +209 return XGBoostModel.__name__ +210 +211except ModuleNotFoundError: +212 from opsml.model.interfaces.backups import XGBoostModelNoModule as XGBoostModel +
21 class XGBoostModel(ModelInterface): + 22 """Model interface for XGBoost model class. Currently, only Sklearn flavor of XGBoost + 23 regressor and classifier are supported. + 24 + 25 Args: + 26 model: + 27 XGBoost model. Can be either a Booster or XGBModel. + 28 preprocessor: + 29 Optional preprocessor + 30 sample_data: + 31 Sample data to be used for type inference and ONNX conversion/validation. + 32 This should match exactly what the model expects as input. + 33 task_type: + 34 Task type for model. Defaults to undefined. + 35 model_type: + 36 Optional model type. This is inferred automatically. + 37 preprocessor_name: + 38 Optional preprocessor. This is inferred automatically if a + 39 preprocessor is provided. + 40 + 41 Returns: + 42 XGBoostModel + 43 """ + 44 + 45 model: Optional[Union[Booster, XGBModel]] = None + 46 sample_data: Optional[Union[pd.DataFrame, NDArray[Any], DMatrix]] = None + 47 preprocessor: Optional[Any] = None + 48 preprocessor_name: str = CommonKwargs.UNDEFINED.value + 49 + 50 @property + 51 def model_class(self) -> str: + 52 if "Booster" in self.model_type: + 53 return TrainedModelType.XGB_BOOSTER.value + 54 return TrainedModelType.SKLEARN_ESTIMATOR.value + 55 + 56 @classmethod + 57 def _get_sample_data(cls, sample_data: Any) -> Union[pd.DataFrame, NDArray[Any], DMatrix]: + 58 """Check sample data and returns one record to be used + 59 during type inference and ONNX conversion/validation. + 60 + 61 Returns: + 62 Sample data with only one record + 63 """ + 64 if isinstance(sample_data, DMatrix): + 65 return sample_data.slice([0]) + 66 return super()._get_sample_data(sample_data) + 67 + 68 @model_validator(mode="before") + 69 @classmethod + 70 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: + 71 model = model_args.get("model") + 72 + 73 if model_args.get("modelcard_uid", False): + 74 return model_args + 75 + 76 model, _, bases = get_model_args(model) + 77 + 78 if isinstance(model, XGBModel): + 79 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ + 80 + 81 elif isinstance(model, Booster): + 82 model_args[CommonKwargs.MODEL_TYPE.value] = "Booster" + 83 + 84 else: + 85 for base in bases: + 86 if "sklearn" in base: + 87 model_args[CommonKwargs.MODEL_TYPE.value] = "subclass" + 88 + 89 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) + 90 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data + 91 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) + 92 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( + 93 model_args.get(CommonKwargs.PREPROCESSOR.value), + 94 ) + 95 + 96 return model_args + 97 + 98 def save_model(self, path: Path) -> None: + 99 """Saves lgb model according to model format. Booster models are saved to text. +100 Sklearn models are saved via joblib. +101 +102 Args: +103 path: +104 base path to save model to +105 """ +106 assert self.model is not None, "No model found" +107 if isinstance(self.model, Booster): +108 self.model.save_model(path) +109 +110 else: +111 super().save_model(path) +112 +113 def load_model(self, path: Path, **kwargs: Any) -> None: +114 """Loads lightgbm booster or sklearn model +115 +116 +117 Args: +118 path: +119 base path to load from +120 **kwargs: +121 Additional keyword arguments +122 """ +123 +124 if self.model_type == TrainedModelType.LGBM_BOOSTER.value: +125 self.model = Booster(model_file=path) +126 else: +127 super().load_model(path) +128 +129 def save_preprocessor(self, path: Path) -> None: +130 """Saves preprocessor to path if present. Base implementation use Joblib +131 +132 Args: +133 path: +134 Pathlib object +135 """ +136 assert self.preprocessor is not None, "No preprocessor detected in interface" +137 joblib.dump(self.preprocessor, path) +138 +139 def load_preprocessor(self, path: Path) -> None: +140 """Load preprocessor from pathlib object +141 +142 Args: +143 path: +144 Pathlib object +145 """ +146 self.preprocessor = joblib.load(path) +147 +148 def save_onnx(self, path: Path) -> ModelReturn: +149 """Saves the onnx model +150 +151 Args: +152 path: +153 Path to save +154 +155 Returns: +156 ModelReturn +157 """ +158 +159 if self.model_class == TrainedModelType.XGB_BOOSTER.value: +160 logger.warning("ONNX conversion for XGBoost Booster is not supported") +161 +162 return super().save_onnx(path) +163 +164 def save_sample_data(self, path: Path) -> None: +165 """Serialized and save sample data to path. +166 +167 Args: +168 path: +169 Pathlib object +170 """ +171 if isinstance(self.sample_data, DMatrix): +172 self.sample_data.save_binary(path) +173 +174 else: +175 joblib.dump(self.sample_data, path) +176 +177 def load_sample_data(self, path: Path) -> None: +178 """Serialized and save sample data to path. +179 +180 Args: +181 path: +182 Pathlib object +183 """ +184 if self.model_class == TrainedModelType.XGB_BOOSTER.value: +185 self.sample_data = DMatrix(path) +186 else: +187 self.sample_data = joblib.load(path) +188 +189 @property +190 def model_suffix(self) -> str: +191 if self.model_type == TrainedModelType.XGB_BOOSTER.value: +192 return Suffix.JSON.value +193 +194 return super().model_suffix +195 +196 @property +197 def preprocessor_suffix(self) -> str: +198 """Returns suffix for storage""" +199 return Suffix.JOBLIB.value +200 +201 @property +202 def data_suffix(self) -> str: +203 """Returns suffix for storage""" +204 if self.model_class == TrainedModelType.XGB_BOOSTER.value: +205 return Suffix.DMATRIX.value +206 return Suffix.JOBLIB.value +207 +208 @staticmethod +209 def name() -> str: +210 return XGBoostModel.__name__ +
Model interface for XGBoost model class. Currently, only Sklearn flavor of XGBoost +regressor and classifier are supported.
+ +++XGBoostModel
+
68 @model_validator(mode="before") +69 @classmethod +70 def check_model(cls, model_args: Dict[str, Any]) -> Dict[str, Any]: +71 model = model_args.get("model") +72 +73 if model_args.get("modelcard_uid", False): +74 return model_args +75 +76 model, _, bases = get_model_args(model) +77 +78 if isinstance(model, XGBModel): +79 model_args[CommonKwargs.MODEL_TYPE.value] = model.__class__.__name__ +80 +81 elif isinstance(model, Booster): +82 model_args[CommonKwargs.MODEL_TYPE.value] = "Booster" +83 +84 else: +85 for base in bases: +86 if "sklearn" in base: +87 model_args[CommonKwargs.MODEL_TYPE.value] = "subclass" +88 +89 sample_data = cls._get_sample_data(sample_data=model_args[CommonKwargs.SAMPLE_DATA.value]) +90 model_args[CommonKwargs.SAMPLE_DATA.value] = sample_data +91 model_args[CommonKwargs.DATA_TYPE.value] = get_class_name(sample_data) +92 model_args[CommonKwargs.PREPROCESSOR_NAME.value] = get_processor_name( +93 model_args.get(CommonKwargs.PREPROCESSOR.value), +94 ) +95 +96 return model_args +
98 def save_model(self, path: Path) -> None: + 99 """Saves lgb model according to model format. Booster models are saved to text. +100 Sklearn models are saved via joblib. +101 +102 Args: +103 path: +104 base path to save model to +105 """ +106 assert self.model is not None, "No model found" +107 if isinstance(self.model, Booster): +108 self.model.save_model(path) +109 +110 else: +111 super().save_model(path) +
Saves lgb model according to model format. Booster models are saved to text. +Sklearn models are saved via joblib.
+ +113 def load_model(self, path: Path, **kwargs: Any) -> None: +114 """Loads lightgbm booster or sklearn model +115 +116 +117 Args: +118 path: +119 base path to load from +120 **kwargs: +121 Additional keyword arguments +122 """ +123 +124 if self.model_type == TrainedModelType.LGBM_BOOSTER.value: +125 self.model = Booster(model_file=path) +126 else: +127 super().load_model(path) +
Loads lightgbm booster or sklearn model
+ +129 def save_preprocessor(self, path: Path) -> None: +130 """Saves preprocessor to path if present. Base implementation use Joblib +131 +132 Args: +133 path: +134 Pathlib object +135 """ +136 assert self.preprocessor is not None, "No preprocessor detected in interface" +137 joblib.dump(self.preprocessor, path) +
Saves preprocessor to path if present. Base implementation use Joblib
+ +139 def load_preprocessor(self, path: Path) -> None: +140 """Load preprocessor from pathlib object +141 +142 Args: +143 path: +144 Pathlib object +145 """ +146 self.preprocessor = joblib.load(path) +
Load preprocessor from pathlib object
+ +148 def save_onnx(self, path: Path) -> ModelReturn: +149 """Saves the onnx model +150 +151 Args: +152 path: +153 Path to save +154 +155 Returns: +156 ModelReturn +157 """ +158 +159 if self.model_class == TrainedModelType.XGB_BOOSTER.value: +160 logger.warning("ONNX conversion for XGBoost Booster is not supported") +161 +162 return super().save_onnx(path) +
Saves the onnx model
+ +++ModelReturn
+
164 def save_sample_data(self, path: Path) -> None: +165 """Serialized and save sample data to path. +166 +167 Args: +168 path: +169 Pathlib object +170 """ +171 if isinstance(self.sample_data, DMatrix): +172 self.sample_data.save_binary(path) +173 +174 else: +175 joblib.dump(self.sample_data, path) +
Serialized and save sample data to path.
+ +177 def load_sample_data(self, path: Path) -> None: +178 """Serialized and save sample data to path. +179 +180 Args: +181 path: +182 Pathlib object +183 """ +184 if self.model_class == TrainedModelType.XGB_BOOSTER.value: +185 self.sample_data = DMatrix(path) +186 else: +187 self.sample_data = joblib.load(path) +
Serialized and save sample data to path.
+ +189 @property +190 def model_suffix(self) -> str: +191 if self.model_type == TrainedModelType.XGB_BOOSTER.value: +192 return Suffix.JSON.value +193 +194 return super().model_suffix +
Returns suffix for storage
+196 @property +197 def preprocessor_suffix(self) -> str: +198 """Returns suffix for storage""" +199 return Suffix.JOBLIB.value +
Returns suffix for storage
+201 @property +202 def data_suffix(self) -> str: +203 """Returns suffix for storage""" +204 if self.model_class == TrainedModelType.XGB_BOOSTER.value: +205 return Suffix.DMATRIX.value +206 return Suffix.JOBLIB.value +
Returns suffix for storage
+1# Copyright (c) Shipt, Inc. + 2# This source code is licensed under the MIT license found in the + 3# LICENSE file in the root directory of this source tree. + 4 + 5# mypy: disable-error-code="arg-type" + 6 + 7import json + 8from pathlib import Path + 9from typing import Any + 10 + 11from opsml.model import HuggingFaceModel, ModelInterface + 12from opsml.types import ModelMetadata, OnnxModel, SaveName, Suffix + 13 + 14 + 15class ModelLoader: + 16 """Helper class for loading models from disk and downloading via opsml-cli""" + 17 + 18 def __init__(self, path: Path): + 19 """Initialize ModelLoader + 20 + 21 Args: + 22 interface: + 23 ModelInterface for the model + 24 path: + 25 Directory path to the model artifacts + 26 """ + 27 + 28 self.path = path + 29 self.metadata = self._load_metadata() + 30 self.interface = self._load_interface() + 31 + 32 def _load_interface(self) -> ModelInterface: + 33 """Loads a ModelInterface from disk using metadata + 34 + 35 Args: + 36 interface: + 37 ModelInterface to load + 38 + 39 Returns: + 40 ModelInterface + 41 """ + 42 from opsml.storage.card_loader import _get_model_interface + 43 + 44 Interface = _get_model_interface(self.metadata.model_interface) # pylint: disable=invalid-name + 45 + 46 loaded_interface = Interface.model_construct( + 47 _fields_set={"name", "repository", "version"}, + 48 **{ + 49 "name": self.metadata.model_name, + 50 "repository": self.metadata.model_repository, + 51 "version": self.metadata.model_version, + 52 }, + 53 ) + 54 + 55 loaded_interface.model_type = self.metadata.model_type + 56 + 57 if hasattr(self.metadata, "prepocessor_name"): + 58 loaded_interface.preprocessor_name = self.metadata.preprocessor_name + 59 + 60 if hasattr(self.metadata, "tokenizer_name"): + 61 loaded_interface.tokenizer_name = self.metadata.tokenizer_name + 62 + 63 if hasattr(self.metadata, "feature_extractor_name"): + 64 loaded_interface.feature_extractor_name = self.metadata.feature_extractor_name + 65 + 66 return loaded_interface + 67 + 68 @property + 69 def model(self) -> Any: + 70 return self.interface.model + 71 + 72 @property + 73 def onnx_model(self) -> OnnxModel: + 74 assert self.interface.onnx_model is not None, "OnnxModel not loaded" + 75 return self.interface.onnx_model + 76 + 77 @property + 78 def preprocessor(self) -> Any: + 79 """Quick access to preprocessor from interface""" + 80 + 81 if hasattr(self.interface, "preprocessor"): + 82 return self.interface.preprocessor + 83 + 84 if hasattr(self.interface, "tokenizer"): + 85 if self.interface.tokenizer is not None: + 86 return self.interface.tokenizer + 87 + 88 if hasattr(self.interface, "feature_extractor"): + 89 if self.interface.feature_extractor is not None: + 90 return self.interface.feature_extractor + 91 + 92 return None + 93 + 94 def _load_metadata(self) -> ModelMetadata: + 95 """Load metadata from disk""" + 96 metadata_path = (self.path / SaveName.MODEL_METADATA.value).with_suffix(Suffix.JSON.value) + 97 + 98 with metadata_path.open("r") as file_: + 99 return ModelMetadata(**json.load(file_)) +100 +101 def _load_huggingface_preprocessors(self) -> None: +102 """Load huggingface preprocessors from disk""" +103 +104 assert isinstance(self.interface, HuggingFaceModel), "HuggingFaceModel interface required" +105 +106 if self.preprocessor is not None: +107 return +108 +109 if hasattr(self.metadata, "tokenizer_name"): +110 load_path = (self.path / SaveName.TOKENIZER.value).with_suffix("") +111 self.interface.load_tokenizer(load_path) +112 return +113 +114 if hasattr(self.metadata, "feature_extractor_name"): +115 load_path = (self.path / SaveName.FEATURE_EXTRACTOR.value).with_suffix("") +116 self.interface.load_feature_extractor(load_path) +117 return +118 +119 return +120 +121 def load_preprocessor(self) -> None: +122 """Load preprocessor from disk""" +123 +124 if isinstance(self.interface, HuggingFaceModel): +125 self._load_huggingface_preprocessors() +126 return +127 +128 if hasattr(self.metadata, "preprocessor_name"): +129 load_path = (self.path / SaveName.PREPROCESSOR.value).with_suffix(self.interface.preprocessor_suffix) +130 self.interface.load_preprocessor(load_path) +131 return +132 +133 return +134 +135 def load_model(self, **kwargs: Any) -> None: +136 load_path = (self.path / SaveName.TRAINED_MODEL.value).with_suffix(self.interface.model_suffix) +137 self.interface.load_model(load_path, **kwargs) +138 +139 if isinstance(self.interface, HuggingFaceModel): +140 if self.interface.is_pipeline: +141 self.interface.to_pipeline() +142 +143 def _load_huggingface_onnx_model(self, **kwargs: Any) -> None: +144 assert isinstance(self.interface, HuggingFaceModel), "Expected HuggingFaceModel" +145 load_quantized = kwargs.get("load_quantized", False) +146 save_name = SaveName.QUANTIZED_MODEL.value if load_quantized else SaveName.ONNX_MODEL.value +147 +148 if self.interface.is_pipeline: +149 self._load_huggingface_preprocessors() +150 +151 load_path = (self.path / save_name).with_suffix(self.interface.model_suffix) +152 self.interface.onnx_model = OnnxModel(onnx_version=self.metadata.onnx_version) +153 self.interface.load_onnx_model(load_path) +154 +155 def load_onnx_model(self, **kwargs: Any) -> None: +156 """Load onnx model from disk +157 +158 Kwargs: +159 +160 ------Note: These kwargs only apply to HuggingFace models------ +161 +162 kwargs: +163 load_quantized: +164 If True, load quantized model +165 +166 onnx_args: +167 Additional onnx args needed to load the model +168 +169 """ +170 if isinstance(self.interface, HuggingFaceModel): +171 self.interface.onnx_args = kwargs.get("onnx_args", None) +172 self._load_huggingface_onnx_model(**kwargs) +173 return +174 +175 load_path = (self.path / SaveName.ONNX_MODEL.value).with_suffix(Suffix.ONNX.value) +176 self.interface.onnx_model = OnnxModel(onnx_version=self.metadata.onnx_version) +177 self.interface.load_onnx_model(load_path) +178 return +
16class ModelLoader: + 17 """Helper class for loading models from disk and downloading via opsml-cli""" + 18 + 19 def __init__(self, path: Path): + 20 """Initialize ModelLoader + 21 + 22 Args: + 23 interface: + 24 ModelInterface for the model + 25 path: + 26 Directory path to the model artifacts + 27 """ + 28 + 29 self.path = path + 30 self.metadata = self._load_metadata() + 31 self.interface = self._load_interface() + 32 + 33 def _load_interface(self) -> ModelInterface: + 34 """Loads a ModelInterface from disk using metadata + 35 + 36 Args: + 37 interface: + 38 ModelInterface to load + 39 + 40 Returns: + 41 ModelInterface + 42 """ + 43 from opsml.storage.card_loader import _get_model_interface + 44 + 45 Interface = _get_model_interface(self.metadata.model_interface) # pylint: disable=invalid-name + 46 + 47 loaded_interface = Interface.model_construct( + 48 _fields_set={"name", "repository", "version"}, + 49 **{ + 50 "name": self.metadata.model_name, + 51 "repository": self.metadata.model_repository, + 52 "version": self.metadata.model_version, + 53 }, + 54 ) + 55 + 56 loaded_interface.model_type = self.metadata.model_type + 57 + 58 if hasattr(self.metadata, "prepocessor_name"): + 59 loaded_interface.preprocessor_name = self.metadata.preprocessor_name + 60 + 61 if hasattr(self.metadata, "tokenizer_name"): + 62 loaded_interface.tokenizer_name = self.metadata.tokenizer_name + 63 + 64 if hasattr(self.metadata, "feature_extractor_name"): + 65 loaded_interface.feature_extractor_name = self.metadata.feature_extractor_name + 66 + 67 return loaded_interface + 68 + 69 @property + 70 def model(self) -> Any: + 71 return self.interface.model + 72 + 73 @property + 74 def onnx_model(self) -> OnnxModel: + 75 assert self.interface.onnx_model is not None, "OnnxModel not loaded" + 76 return self.interface.onnx_model + 77 + 78 @property + 79 def preprocessor(self) -> Any: + 80 """Quick access to preprocessor from interface""" + 81 + 82 if hasattr(self.interface, "preprocessor"): + 83 return self.interface.preprocessor + 84 + 85 if hasattr(self.interface, "tokenizer"): + 86 if self.interface.tokenizer is not None: + 87 return self.interface.tokenizer + 88 + 89 if hasattr(self.interface, "feature_extractor"): + 90 if self.interface.feature_extractor is not None: + 91 return self.interface.feature_extractor + 92 + 93 return None + 94 + 95 def _load_metadata(self) -> ModelMetadata: + 96 """Load metadata from disk""" + 97 metadata_path = (self.path / SaveName.MODEL_METADATA.value).with_suffix(Suffix.JSON.value) + 98 + 99 with metadata_path.open("r") as file_: +100 return ModelMetadata(**json.load(file_)) +101 +102 def _load_huggingface_preprocessors(self) -> None: +103 """Load huggingface preprocessors from disk""" +104 +105 assert isinstance(self.interface, HuggingFaceModel), "HuggingFaceModel interface required" +106 +107 if self.preprocessor is not None: +108 return +109 +110 if hasattr(self.metadata, "tokenizer_name"): +111 load_path = (self.path / SaveName.TOKENIZER.value).with_suffix("") +112 self.interface.load_tokenizer(load_path) +113 return +114 +115 if hasattr(self.metadata, "feature_extractor_name"): +116 load_path = (self.path / SaveName.FEATURE_EXTRACTOR.value).with_suffix("") +117 self.interface.load_feature_extractor(load_path) +118 return +119 +120 return +121 +122 def load_preprocessor(self) -> None: +123 """Load preprocessor from disk""" +124 +125 if isinstance(self.interface, HuggingFaceModel): +126 self._load_huggingface_preprocessors() +127 return +128 +129 if hasattr(self.metadata, "preprocessor_name"): +130 load_path = (self.path / SaveName.PREPROCESSOR.value).with_suffix(self.interface.preprocessor_suffix) +131 self.interface.load_preprocessor(load_path) +132 return +133 +134 return +135 +136 def load_model(self, **kwargs: Any) -> None: +137 load_path = (self.path / SaveName.TRAINED_MODEL.value).with_suffix(self.interface.model_suffix) +138 self.interface.load_model(load_path, **kwargs) +139 +140 if isinstance(self.interface, HuggingFaceModel): +141 if self.interface.is_pipeline: +142 self.interface.to_pipeline() +143 +144 def _load_huggingface_onnx_model(self, **kwargs: Any) -> None: +145 assert isinstance(self.interface, HuggingFaceModel), "Expected HuggingFaceModel" +146 load_quantized = kwargs.get("load_quantized", False) +147 save_name = SaveName.QUANTIZED_MODEL.value if load_quantized else SaveName.ONNX_MODEL.value +148 +149 if self.interface.is_pipeline: +150 self._load_huggingface_preprocessors() +151 +152 load_path = (self.path / save_name).with_suffix(self.interface.model_suffix) +153 self.interface.onnx_model = OnnxModel(onnx_version=self.metadata.onnx_version) +154 self.interface.load_onnx_model(load_path) +155 +156 def load_onnx_model(self, **kwargs: Any) -> None: +157 """Load onnx model from disk +158 +159 Kwargs: +160 +161 ------Note: These kwargs only apply to HuggingFace models------ +162 +163 kwargs: +164 load_quantized: +165 If True, load quantized model +166 +167 onnx_args: +168 Additional onnx args needed to load the model +169 +170 """ +171 if isinstance(self.interface, HuggingFaceModel): +172 self.interface.onnx_args = kwargs.get("onnx_args", None) +173 self._load_huggingface_onnx_model(**kwargs) +174 return +175 +176 load_path = (self.path / SaveName.ONNX_MODEL.value).with_suffix(Suffix.ONNX.value) +177 self.interface.onnx_model = OnnxModel(onnx_version=self.metadata.onnx_version) +178 self.interface.load_onnx_model(load_path) +179 return +
Helper class for loading models from disk and downloading via opsml-cli
+19 def __init__(self, path: Path): +20 """Initialize ModelLoader +21 +22 Args: +23 interface: +24 ModelInterface for the model +25 path: +26 Directory path to the model artifacts +27 """ +28 +29 self.path = path +30 self.metadata = self._load_metadata() +31 self.interface = self._load_interface() +
Initialize ModelLoader
+ +78 @property +79 def preprocessor(self) -> Any: +80 """Quick access to preprocessor from interface""" +81 +82 if hasattr(self.interface, "preprocessor"): +83 return self.interface.preprocessor +84 +85 if hasattr(self.interface, "tokenizer"): +86 if self.interface.tokenizer is not None: +87 return self.interface.tokenizer +88 +89 if hasattr(self.interface, "feature_extractor"): +90 if self.interface.feature_extractor is not None: +91 return self.interface.feature_extractor +92 +93 return None +
Quick access to preprocessor from interface
+122 def load_preprocessor(self) -> None: +123 """Load preprocessor from disk""" +124 +125 if isinstance(self.interface, HuggingFaceModel): +126 self._load_huggingface_preprocessors() +127 return +128 +129 if hasattr(self.metadata, "preprocessor_name"): +130 load_path = (self.path / SaveName.PREPROCESSOR.value).with_suffix(self.interface.preprocessor_suffix) +131 self.interface.load_preprocessor(load_path) +132 return +133 +134 return +
Load preprocessor from disk
+136 def load_model(self, **kwargs: Any) -> None: +137 load_path = (self.path / SaveName.TRAINED_MODEL.value).with_suffix(self.interface.model_suffix) +138 self.interface.load_model(load_path, **kwargs) +139 +140 if isinstance(self.interface, HuggingFaceModel): +141 if self.interface.is_pipeline: +142 self.interface.to_pipeline() +
156 def load_onnx_model(self, **kwargs: Any) -> None: +157 """Load onnx model from disk +158 +159 Kwargs: +160 +161 ------Note: These kwargs only apply to HuggingFace models------ +162 +163 kwargs: +164 load_quantized: +165 If True, load quantized model +166 +167 onnx_args: +168 Additional onnx args needed to load the model +169 +170 """ +171 if isinstance(self.interface, HuggingFaceModel): +172 self.interface.onnx_args = kwargs.get("onnx_args", None) +173 self._load_huggingface_onnx_model(**kwargs) +174 return +175 +176 load_path = (self.path / SaveName.ONNX_MODEL.value).with_suffix(Suffix.ONNX.value) +177 self.interface.onnx_model = OnnxModel(onnx_version=self.metadata.onnx_version) +178 self.interface.load_onnx_model(load_path) +179 return +
Load onnx model from disk
+ +++------Note: These kwargs only apply to HuggingFace models------
+ +kwargs: + load_quantized: + If True, load quantized model
+ ++onnx_args: + Additional onnx args needed to load the model +
1# pylint: disable=redefined-outer-name,import-outside-toplevel + 2# Copyright (c) Shipt, Inc. + 3# This source code is licensed under the MIT license found in the + 4# LICENSE file in the root directory of this source tree. + 5 + 6import os + 7from typing import Any, List, Union + 8 + 9import pandas as pd + 10import polars as pl + 11 + 12DIR_PATH = os.path.dirname(__file__) + 13ProfileReport = Any + 14 + 15 + 16class DataProfiler: + 17 @staticmethod + 18 def create_profile_report( + 19 data: Union[pd.DataFrame, pl.DataFrame], + 20 name: str, + 21 sample_perc: float = 1, + 22 ) -> ProfileReport: + 23 """ + 24 Creates a `ydata-profiling` report + 25 + 26 Args: + 27 data: + 28 Pandas dataframe + 29 sample_perc: + 30 Percentage to use for sampling + 31 name: + 32 Name of the report + 33 + 34 Returns: + 35 `ProfileReport` + 36 """ + 37 from ydata_profiling import ProfileReport + 38 + 39 kwargs = {"title": f"Profile report for {name}"} + 40 + 41 if isinstance(data, pl.DataFrame): + 42 if sample_perc < 1: + 43 return ProfileReport( + 44 df=data.sample(fraction=sample_perc, with_replacement=False, shuffle=True).to_pandas(), + 45 config_file=os.path.join(DIR_PATH, "profile_config.yml"), + 46 lazy=False, + 47 **kwargs, + 48 ) + 49 + 50 return ProfileReport( + 51 df=data.to_pandas(), + 52 config_file=os.path.join(DIR_PATH, "profile_config.yml"), + 53 lazy=False, + 54 **kwargs, + 55 ) + 56 + 57 if sample_perc < 1: + 58 return ProfileReport( + 59 df=data.sample(frac=sample_perc, replace=False), + 60 config_file=os.path.join(DIR_PATH, "profile_config.yml"), + 61 lazy=False, + 62 **kwargs, + 63 ) + 64 + 65 return ProfileReport( + 66 df=data, + 67 config_file=os.path.join(DIR_PATH, "profile_config.yml"), + 68 lazy=False, + 69 **kwargs, + 70 ) + 71 + 72 @staticmethod + 73 def load_profile(data: bytes) -> ProfileReport: + 74 """Loads a `ProfileReport` from data bytes + 75 + 76 Args: + 77 data: + 78 `ProfileReport` in bytes + 79 + 80 Returns: + 81 `ProfileReport` + 82 """ + 83 from ydata_profiling import ProfileReport + 84 + 85 profile = ProfileReport() + 86 profile.loads(data) + 87 return profile + 88 + 89 @staticmethod + 90 def compare_reports(reports: List[ProfileReport]) -> ProfileReport: + 91 """Compares ProfileReports + 92 + 93 Args: + 94 reports: + 95 List of `ProfileReport` + 96 + 97 Returns: + 98 `ProfileReport` + 99 """ +100 from ydata_profiling import compare +101 +102 return compare(reports=reports) +
17class DataProfiler: + 18 @staticmethod + 19 def create_profile_report( + 20 data: Union[pd.DataFrame, pl.DataFrame], + 21 name: str, + 22 sample_perc: float = 1, + 23 ) -> ProfileReport: + 24 """ + 25 Creates a `ydata-profiling` report + 26 + 27 Args: + 28 data: + 29 Pandas dataframe + 30 sample_perc: + 31 Percentage to use for sampling + 32 name: + 33 Name of the report + 34 + 35 Returns: + 36 `ProfileReport` + 37 """ + 38 from ydata_profiling import ProfileReport + 39 + 40 kwargs = {"title": f"Profile report for {name}"} + 41 + 42 if isinstance(data, pl.DataFrame): + 43 if sample_perc < 1: + 44 return ProfileReport( + 45 df=data.sample(fraction=sample_perc, with_replacement=False, shuffle=True).to_pandas(), + 46 config_file=os.path.join(DIR_PATH, "profile_config.yml"), + 47 lazy=False, + 48 **kwargs, + 49 ) + 50 + 51 return ProfileReport( + 52 df=data.to_pandas(), + 53 config_file=os.path.join(DIR_PATH, "profile_config.yml"), + 54 lazy=False, + 55 **kwargs, + 56 ) + 57 + 58 if sample_perc < 1: + 59 return ProfileReport( + 60 df=data.sample(frac=sample_perc, replace=False), + 61 config_file=os.path.join(DIR_PATH, "profile_config.yml"), + 62 lazy=False, + 63 **kwargs, + 64 ) + 65 + 66 return ProfileReport( + 67 df=data, + 68 config_file=os.path.join(DIR_PATH, "profile_config.yml"), + 69 lazy=False, + 70 **kwargs, + 71 ) + 72 + 73 @staticmethod + 74 def load_profile(data: bytes) -> ProfileReport: + 75 """Loads a `ProfileReport` from data bytes + 76 + 77 Args: + 78 data: + 79 `ProfileReport` in bytes + 80 + 81 Returns: + 82 `ProfileReport` + 83 """ + 84 from ydata_profiling import ProfileReport + 85 + 86 profile = ProfileReport() + 87 profile.loads(data) + 88 return profile + 89 + 90 @staticmethod + 91 def compare_reports(reports: List[ProfileReport]) -> ProfileReport: + 92 """Compares ProfileReports + 93 + 94 Args: + 95 reports: + 96 List of `ProfileReport` + 97 + 98 Returns: + 99 `ProfileReport` +100 """ +101 from ydata_profiling import compare +102 +103 return compare(reports=reports) +
18 @staticmethod +19 def create_profile_report( +20 data: Union[pd.DataFrame, pl.DataFrame], +21 name: str, +22 sample_perc: float = 1, +23 ) -> ProfileReport: +24 """ +25 Creates a `ydata-profiling` report +26 +27 Args: +28 data: +29 Pandas dataframe +30 sample_perc: +31 Percentage to use for sampling +32 name: +33 Name of the report +34 +35 Returns: +36 `ProfileReport` +37 """ +38 from ydata_profiling import ProfileReport +39 +40 kwargs = {"title": f"Profile report for {name}"} +41 +42 if isinstance(data, pl.DataFrame): +43 if sample_perc < 1: +44 return ProfileReport( +45 df=data.sample(fraction=sample_perc, with_replacement=False, shuffle=True).to_pandas(), +46 config_file=os.path.join(DIR_PATH, "profile_config.yml"), +47 lazy=False, +48 **kwargs, +49 ) +50 +51 return ProfileReport( +52 df=data.to_pandas(), +53 config_file=os.path.join(DIR_PATH, "profile_config.yml"), +54 lazy=False, +55 **kwargs, +56 ) +57 +58 if sample_perc < 1: +59 return ProfileReport( +60 df=data.sample(frac=sample_perc, replace=False), +61 config_file=os.path.join(DIR_PATH, "profile_config.yml"), +62 lazy=False, +63 **kwargs, +64 ) +65 +66 return ProfileReport( +67 df=data, +68 config_file=os.path.join(DIR_PATH, "profile_config.yml"), +69 lazy=False, +70 **kwargs, +71 ) +
Creates a ydata-profiling report
+ ++
73 @staticmethod +74 def load_profile(data: bytes) -> ProfileReport: +75 """Loads a `ProfileReport` from data bytes +76 +77 Args: +78 data: +79 `ProfileReport` in bytes +80 +81 Returns: +82 `ProfileReport` +83 """ +84 from ydata_profiling import ProfileReport +85 +86 profile = ProfileReport() +87 profile.loads(data) +88 return profile +
Loads a ProfileReport from data bytes
ProfileReport in bytes+ ++
90 @staticmethod + 91 def compare_reports(reports: List[ProfileReport]) -> ProfileReport: + 92 """Compares ProfileReports + 93 + 94 Args: + 95 reports: + 96 List of `ProfileReport` + 97 + 98 Returns: + 99 `ProfileReport` +100 """ +101 from ydata_profiling import compare +102 +103 return compare(reports=reports) +
1# pylint: disable=invalid-envvar-value,invalid-name + 2 + 3# Copyright (c) Shipt, Inc. + 4# This source code is licensed under the MIT license found in the + 5# LICENSE file in the root directory of this source tree. + 6 + 7from pathlib import Path + 8from typing import Any, Dict, List, Optional, Union, cast + 9 + 10from numpy.typing import NDArray + 11 + 12from opsml.cards.base import ArtifactCard + 13from opsml.cards.data import DataCard + 14from opsml.cards.model import ModelCard + 15from opsml.cards.run import RunCard + 16from opsml.helpers.logging import ArtifactLogger + 17from opsml.registry.registry import CardRegistries, CardRegistry + 18from opsml.registry.semver import VersionType + 19from opsml.storage import client + 20from opsml.types import ( + 21 ArtifactUris, + 22 CardInfo, + 23 CardType, + 24 CommonKwargs, + 25 Metrics, + 26 Params, + 27 SaveName, + 28) + 29 + 30logger = ArtifactLogger.get_logger() + 31 + 32 + 33# dataclass inheritance doesnt handle default vals well for <= py3.9 + 34class RunInfo: + 35 def __init__( + 36 self, + 37 runcard: RunCard, + 38 run_id: str, + 39 run_name: Optional[str] = None, + 40 ): + 41 self.storage_client = client.storage_client + 42 self.registries = CardRegistries() + 43 self.runcard = runcard + 44 self.run_id = run_id + 45 self.run_name = run_name + 46 + 47 + 48class CardHandler: + 49 """DRY helper class for ActiveRun and OpsmlProject""" + 50 + 51 @staticmethod + 52 def register_card( + 53 registries: CardRegistries, + 54 card: ArtifactCard, + 55 version_type: Union[VersionType, str] = VersionType.MINOR, + 56 ) -> None: + 57 """Registers and ArtifactCard""" + 58 + 59 registry: CardRegistry = getattr(registries, card.card_type) + 60 registry.register_card(card=card, version_type=version_type) + 61 + 62 @staticmethod + 63 def load_card(registries: CardRegistries, registry_name: str, info: CardInfo) -> ArtifactCard: + 64 """Loads an ArtifactCard""" + 65 + 66 registry: CardRegistry = getattr(registries, registry_name) + 67 return registry.load_card(name=info.name, version=info.version, uid=info.uid) + 68 + 69 @staticmethod + 70 def update_card(registries: CardRegistries, card: ArtifactCard) -> None: + 71 """Updates an ArtifactCard""" + 72 registry: CardRegistry = getattr(registries, card.card_type) + 73 registry.update_card(card=card) + 74 + 75 + 76class ActiveRun: + 77 def __init__(self, run_info: RunInfo): + 78 """ + 79 Run object that handles logging artifacts, metrics, cards, and tags for a given run of a Project + 80 + 81 Args: + 82 run_info: + 83 Run info for a given active run + 84 """ + 85 self._info = run_info + 86 self._active = True # should be active upon instantiation + 87 self.runcard = run_info.runcard + 88 + 89 @property + 90 def run_id(self) -> str: + 91 """Id for current run""" + 92 return self._info.run_id + 93 + 94 @property + 95 def run_name(self) -> Optional[str]: + 96 """Name for current run""" + 97 return self._info.run_name + 98 + 99 @property +100 def active(self) -> bool: +101 return self._active +102 +103 def _verify_active(self) -> None: +104 if not self.active: +105 raise ValueError("""Run is not active""") +106 +107 def add_tag(self, key: str, value: str) -> None: +108 """ +109 Adds a tag to the current run +110 +111 Args: +112 key: +113 Name of the tag +114 value: +115 Value to associate with tag +116 """ +117 self.runcard.add_tag(key=key, value=value) +118 +119 def add_tags(self, tags: Dict[str, Union[str, float, int]]) -> None: +120 """ +121 Adds a tag to the current run +122 +123 Args: +124 tags: +125 Dictionary of key, value tags +126 +127 """ +128 for key, value in tags.items(): +129 self.add_tag(key=key, value=cast(str, value)) +130 +131 def register_card( +132 self, +133 card: ArtifactCard, +134 version_type: Union[VersionType, str] = VersionType.MINOR, +135 ) -> None: +136 """ +137 Register a given artifact card. +138 +139 Args: +140 card: +141 The card to register +142 version_type: +143 Version type for increment. Options are "major", "minor" and +144 "patch". Defaults to "minor". +145 """ +146 self._verify_active() +147 +148 # add runuid to card +149 if isinstance(card, (DataCard, ModelCard)): +150 card.metadata.runcard_uid = self.runcard.uid +151 +152 CardHandler.register_card( +153 registries=self._info.registries, +154 card=card, +155 version_type=version_type, +156 ) +157 +158 tag_key = f"{card.card_type}:{card.name}" +159 self.add_tag(key=tag_key, value=card.version) +160 +161 # add uid to RunCard +162 self.runcard.add_card_uid(card_type=card.card_type, uid=str(card.uid)) +163 +164 def load_card(self, registry_name: str, info: CardInfo) -> ArtifactCard: +165 """ +166 Loads an ArtifactCard. +167 +168 Args: +169 registry_name: +170 Type of card to load (data, model, run, pipeline) +171 info: +172 Card information to retrieve. `uid` takes precedence if it +173 exists. If the optional `version` is specified, that version +174 will be loaded. If it doesn't exist, the most recent version will +175 be loaded. +176 +177 Returns +178 `ArtifactCard` +179 """ +180 card_type = CardType(registry_name.lower()).value +181 +182 return CardHandler.load_card(registries=self._info.registries, registry_name=card_type, info=info) +183 +184 def log_artifact_from_file( +185 self, +186 name: str, +187 local_path: Union[str, Path], +188 artifact_path: Optional[Union[str, Path]] = None, +189 ) -> None: +190 """ +191 Log a local file or directory to the opsml server and associate with the current run. +192 +193 Args: +194 name: +195 Name to assign to artifact(s) +196 local_path: +197 Local path to file or directory. Can be string or pathlike object +198 artifact_path: +199 Optional path to store artifact in opsml server. If not provided, 'artifacts' will be used +200 """ +201 +202 self._verify_active() +203 +204 lpath = Path(local_path) +205 rpath = self.runcard.uri / (artifact_path or SaveName.ARTIFACTS.value) +206 +207 if lpath.is_file(): +208 rpath = rpath / lpath.name +209 +210 self._info.storage_client.put(lpath, rpath) +211 self.runcard._add_artifact_uri( # pylint: disable=protected-access +212 name=name, +213 local_path=lpath.as_posix(), +214 remote_path=rpath.as_posix(), +215 ) +216 +217 def log_metric( +218 self, +219 key: str, +220 value: float, +221 timestamp: Optional[int] = None, +222 step: Optional[int] = None, +223 ) -> None: +224 """ +225 Log a metric for a given run +226 +227 Args: +228 key: +229 Metric name +230 value: +231 Metric value +232 timestamp: +233 Optional time indicating metric creation time +234 step: +235 Optional step in training when metric was created +236 +237 """ +238 self._verify_active() +239 self.runcard.log_metric( +240 key=key, +241 value=value, +242 timestamp=timestamp, +243 step=step, +244 ) +245 +246 def log_metrics( +247 self, +248 metrics: Dict[str, Union[float, int]], +249 step: Optional[int] = None, +250 ) -> None: +251 """Logs a collection of metrics for a run +252 +253 Args: +254 metrics: +255 Dictionary of metrics +256 step: +257 step the metrics are associated with +258 +259 """ +260 self._verify_active() +261 self.runcard.log_metrics(metrics=metrics, step=step) +262 +263 def log_parameter(self, key: str, value: str) -> None: +264 """ +265 Logs a parameter to project run +266 +267 Args: +268 key: +269 Parameter name +270 value: +271 Parameter value +272 """ +273 +274 self._verify_active() +275 self.runcard.log_parameter(key=key, value=value) +276 +277 def log_parameters(self, parameters: Dict[str, Union[float, int, str]]) -> None: +278 """ +279 Logs a collection of parameters for a run +280 +281 Args: +282 parameters: +283 Dictionary of parameters +284 """ +285 +286 self._verify_active() +287 self.runcard.log_parameters(parameters=parameters) +288 +289 def log_graph( +290 self, +291 name: str, +292 x: Union[List[Union[float, int]], NDArray[Any]], +293 y: Union[List[Union[float, int]], NDArray[Any], Dict[str, Union[List[Union[float, int]], NDArray[Any]]]], +294 x_label: str = "x", +295 y_label: str = "y", +296 graph_style: str = "line", +297 ) -> None: +298 """Logs a graph to the RunCard, which will be rendered in the UI as a line graph +299 +300 Args: +301 name: +302 Name of graph +303 x: +304 List or numpy array of x values +305 +306 x_label: +307 Label for x axis +308 y: +309 Either of the following: +310 (1) a list or numpy array of y values +311 (2) a dictionary of y values where key is the group label and +312 value is a list or numpy array of y values +313 y_label: +314 Label for y axis +315 graph_style: +316 Style of graph. Options are "line" or "scatter" +317 +318 example: +319 +320 ### single line graph +321 x = np.arange(1, 400, 0.5) +322 y = x * x +323 run.log_graph(name="graph1", x=x, y=y, x_label="x", y_label="y", graph_style="line") +324 +325 ### multi line graph +326 x = np.arange(1, 1000, 0.5) +327 y1 = x * x +328 y2 = y1 * 1.1 +329 y3 = y2 * 3 +330 run.log_graph( +331 name="multiline", +332 x=x, +333 y={"y1": y1, "y2": y2, "y3": y3}, +334 x_label="x", +335 y_label="y", +336 graph_style="line", +337 ) +338 +339 """ +340 self.runcard.log_graph(name=name, x=x, x_label=x_label, y=y, y_label=y_label, graph_style=graph_style) +341 +342 def create_or_update_runcard(self) -> None: +343 """Creates or updates an active RunCard""" +344 +345 self._verify_active() +346 +347 if self.runcard.uid is not None and self.runcard.version != CommonKwargs.BASE_VERSION.value: +348 CardHandler.update_card(registries=self._info.registries, card=self.runcard) +349 else: +350 CardHandler.register_card(registries=self._info.registries, card=self.runcard) +351 +352 @property +353 def run_data(self) -> Any: +354 raise NotImplementedError +355 +356 @property +357 def metrics(self) -> Metrics: +358 return self.runcard.metrics +359 +360 @property +361 def parameters(self) -> Params: +362 return self.runcard.parameters +363 +364 @property +365 def tags(self) -> dict[str, Union[str, int]]: +366 return self.runcard.tags +367 +368 @property +369 def artifact_uris(self) -> ArtifactUris: +370 return self.runcard.artifact_uris +
35class RunInfo: +36 def __init__( +37 self, +38 runcard: RunCard, +39 run_id: str, +40 run_name: Optional[str] = None, +41 ): +42 self.storage_client = client.storage_client +43 self.registries = CardRegistries() +44 self.runcard = runcard +45 self.run_id = run_id +46 self.run_name = run_name +
49class CardHandler: +50 """DRY helper class for ActiveRun and OpsmlProject""" +51 +52 @staticmethod +53 def register_card( +54 registries: CardRegistries, +55 card: ArtifactCard, +56 version_type: Union[VersionType, str] = VersionType.MINOR, +57 ) -> None: +58 """Registers and ArtifactCard""" +59 +60 registry: CardRegistry = getattr(registries, card.card_type) +61 registry.register_card(card=card, version_type=version_type) +62 +63 @staticmethod +64 def load_card(registries: CardRegistries, registry_name: str, info: CardInfo) -> ArtifactCard: +65 """Loads an ArtifactCard""" +66 +67 registry: CardRegistry = getattr(registries, registry_name) +68 return registry.load_card(name=info.name, version=info.version, uid=info.uid) +69 +70 @staticmethod +71 def update_card(registries: CardRegistries, card: ArtifactCard) -> None: +72 """Updates an ArtifactCard""" +73 registry: CardRegistry = getattr(registries, card.card_type) +74 registry.update_card(card=card) +
DRY helper class for ActiveRun and OpsmlProject
+52 @staticmethod +53 def register_card( +54 registries: CardRegistries, +55 card: ArtifactCard, +56 version_type: Union[VersionType, str] = VersionType.MINOR, +57 ) -> None: +58 """Registers and ArtifactCard""" +59 +60 registry: CardRegistry = getattr(registries, card.card_type) +61 registry.register_card(card=card, version_type=version_type) +
Registers and ArtifactCard
+63 @staticmethod +64 def load_card(registries: CardRegistries, registry_name: str, info: CardInfo) -> ArtifactCard: +65 """Loads an ArtifactCard""" +66 +67 registry: CardRegistry = getattr(registries, registry_name) +68 return registry.load_card(name=info.name, version=info.version, uid=info.uid) +
Loads an ArtifactCard
+70 @staticmethod +71 def update_card(registries: CardRegistries, card: ArtifactCard) -> None: +72 """Updates an ArtifactCard""" +73 registry: CardRegistry = getattr(registries, card.card_type) +74 registry.update_card(card=card) +
Updates an ArtifactCard
+77class ActiveRun: + 78 def __init__(self, run_info: RunInfo): + 79 """ + 80 Run object that handles logging artifacts, metrics, cards, and tags for a given run of a Project + 81 + 82 Args: + 83 run_info: + 84 Run info for a given active run + 85 """ + 86 self._info = run_info + 87 self._active = True # should be active upon instantiation + 88 self.runcard = run_info.runcard + 89 + 90 @property + 91 def run_id(self) -> str: + 92 """Id for current run""" + 93 return self._info.run_id + 94 + 95 @property + 96 def run_name(self) -> Optional[str]: + 97 """Name for current run""" + 98 return self._info.run_name + 99 +100 @property +101 def active(self) -> bool: +102 return self._active +103 +104 def _verify_active(self) -> None: +105 if not self.active: +106 raise ValueError("""Run is not active""") +107 +108 def add_tag(self, key: str, value: str) -> None: +109 """ +110 Adds a tag to the current run +111 +112 Args: +113 key: +114 Name of the tag +115 value: +116 Value to associate with tag +117 """ +118 self.runcard.add_tag(key=key, value=value) +119 +120 def add_tags(self, tags: Dict[str, Union[str, float, int]]) -> None: +121 """ +122 Adds a tag to the current run +123 +124 Args: +125 tags: +126 Dictionary of key, value tags +127 +128 """ +129 for key, value in tags.items(): +130 self.add_tag(key=key, value=cast(str, value)) +131 +132 def register_card( +133 self, +134 card: ArtifactCard, +135 version_type: Union[VersionType, str] = VersionType.MINOR, +136 ) -> None: +137 """ +138 Register a given artifact card. +139 +140 Args: +141 card: +142 The card to register +143 version_type: +144 Version type for increment. Options are "major", "minor" and +145 "patch". Defaults to "minor". +146 """ +147 self._verify_active() +148 +149 # add runuid to card +150 if isinstance(card, (DataCard, ModelCard)): +151 card.metadata.runcard_uid = self.runcard.uid +152 +153 CardHandler.register_card( +154 registries=self._info.registries, +155 card=card, +156 version_type=version_type, +157 ) +158 +159 tag_key = f"{card.card_type}:{card.name}" +160 self.add_tag(key=tag_key, value=card.version) +161 +162 # add uid to RunCard +163 self.runcard.add_card_uid(card_type=card.card_type, uid=str(card.uid)) +164 +165 def load_card(self, registry_name: str, info: CardInfo) -> ArtifactCard: +166 """ +167 Loads an ArtifactCard. +168 +169 Args: +170 registry_name: +171 Type of card to load (data, model, run, pipeline) +172 info: +173 Card information to retrieve. `uid` takes precedence if it +174 exists. If the optional `version` is specified, that version +175 will be loaded. If it doesn't exist, the most recent version will +176 be loaded. +177 +178 Returns +179 `ArtifactCard` +180 """ +181 card_type = CardType(registry_name.lower()).value +182 +183 return CardHandler.load_card(registries=self._info.registries, registry_name=card_type, info=info) +184 +185 def log_artifact_from_file( +186 self, +187 name: str, +188 local_path: Union[str, Path], +189 artifact_path: Optional[Union[str, Path]] = None, +190 ) -> None: +191 """ +192 Log a local file or directory to the opsml server and associate with the current run. +193 +194 Args: +195 name: +196 Name to assign to artifact(s) +197 local_path: +198 Local path to file or directory. Can be string or pathlike object +199 artifact_path: +200 Optional path to store artifact in opsml server. If not provided, 'artifacts' will be used +201 """ +202 +203 self._verify_active() +204 +205 lpath = Path(local_path) +206 rpath = self.runcard.uri / (artifact_path or SaveName.ARTIFACTS.value) +207 +208 if lpath.is_file(): +209 rpath = rpath / lpath.name +210 +211 self._info.storage_client.put(lpath, rpath) +212 self.runcard._add_artifact_uri( # pylint: disable=protected-access +213 name=name, +214 local_path=lpath.as_posix(), +215 remote_path=rpath.as_posix(), +216 ) +217 +218 def log_metric( +219 self, +220 key: str, +221 value: float, +222 timestamp: Optional[int] = None, +223 step: Optional[int] = None, +224 ) -> None: +225 """ +226 Log a metric for a given run +227 +228 Args: +229 key: +230 Metric name +231 value: +232 Metric value +233 timestamp: +234 Optional time indicating metric creation time +235 step: +236 Optional step in training when metric was created +237 +238 """ +239 self._verify_active() +240 self.runcard.log_metric( +241 key=key, +242 value=value, +243 timestamp=timestamp, +244 step=step, +245 ) +246 +247 def log_metrics( +248 self, +249 metrics: Dict[str, Union[float, int]], +250 step: Optional[int] = None, +251 ) -> None: +252 """Logs a collection of metrics for a run +253 +254 Args: +255 metrics: +256 Dictionary of metrics +257 step: +258 step the metrics are associated with +259 +260 """ +261 self._verify_active() +262 self.runcard.log_metrics(metrics=metrics, step=step) +263 +264 def log_parameter(self, key: str, value: str) -> None: +265 """ +266 Logs a parameter to project run +267 +268 Args: +269 key: +270 Parameter name +271 value: +272 Parameter value +273 """ +274 +275 self._verify_active() +276 self.runcard.log_parameter(key=key, value=value) +277 +278 def log_parameters(self, parameters: Dict[str, Union[float, int, str]]) -> None: +279 """ +280 Logs a collection of parameters for a run +281 +282 Args: +283 parameters: +284 Dictionary of parameters +285 """ +286 +287 self._verify_active() +288 self.runcard.log_parameters(parameters=parameters) +289 +290 def log_graph( +291 self, +292 name: str, +293 x: Union[List[Union[float, int]], NDArray[Any]], +294 y: Union[List[Union[float, int]], NDArray[Any], Dict[str, Union[List[Union[float, int]], NDArray[Any]]]], +295 x_label: str = "x", +296 y_label: str = "y", +297 graph_style: str = "line", +298 ) -> None: +299 """Logs a graph to the RunCard, which will be rendered in the UI as a line graph +300 +301 Args: +302 name: +303 Name of graph +304 x: +305 List or numpy array of x values +306 +307 x_label: +308 Label for x axis +309 y: +310 Either of the following: +311 (1) a list or numpy array of y values +312 (2) a dictionary of y values where key is the group label and +313 value is a list or numpy array of y values +314 y_label: +315 Label for y axis +316 graph_style: +317 Style of graph. Options are "line" or "scatter" +318 +319 example: +320 +321 ### single line graph +322 x = np.arange(1, 400, 0.5) +323 y = x * x +324 run.log_graph(name="graph1", x=x, y=y, x_label="x", y_label="y", graph_style="line") +325 +326 ### multi line graph +327 x = np.arange(1, 1000, 0.5) +328 y1 = x * x +329 y2 = y1 * 1.1 +330 y3 = y2 * 3 +331 run.log_graph( +332 name="multiline", +333 x=x, +334 y={"y1": y1, "y2": y2, "y3": y3}, +335 x_label="x", +336 y_label="y", +337 graph_style="line", +338 ) +339 +340 """ +341 self.runcard.log_graph(name=name, x=x, x_label=x_label, y=y, y_label=y_label, graph_style=graph_style) +342 +343 def create_or_update_runcard(self) -> None: +344 """Creates or updates an active RunCard""" +345 +346 self._verify_active() +347 +348 if self.runcard.uid is not None and self.runcard.version != CommonKwargs.BASE_VERSION.value: +349 CardHandler.update_card(registries=self._info.registries, card=self.runcard) +350 else: +351 CardHandler.register_card(registries=self._info.registries, card=self.runcard) +352 +353 @property +354 def run_data(self) -> Any: +355 raise NotImplementedError +356 +357 @property +358 def metrics(self) -> Metrics: +359 return self.runcard.metrics +360 +361 @property +362 def parameters(self) -> Params: +363 return self.runcard.parameters +364 +365 @property +366 def tags(self) -> dict[str, Union[str, int]]: +367 return self.runcard.tags +368 +369 @property +370 def artifact_uris(self) -> ArtifactUris: +371 return self.runcard.artifact_uris +
78 def __init__(self, run_info: RunInfo): +79 """ +80 Run object that handles logging artifacts, metrics, cards, and tags for a given run of a Project +81 +82 Args: +83 run_info: +84 Run info for a given active run +85 """ +86 self._info = run_info +87 self._active = True # should be active upon instantiation +88 self.runcard = run_info.runcard +
Run object that handles logging artifacts, metrics, cards, and tags for a given run of a Project
+ +90 @property +91 def run_id(self) -> str: +92 """Id for current run""" +93 return self._info.run_id +
Id for current run
+95 @property +96 def run_name(self) -> Optional[str]: +97 """Name for current run""" +98 return self._info.run_name +
Name for current run
+108 def add_tag(self, key: str, value: str) -> None: +109 """ +110 Adds a tag to the current run +111 +112 Args: +113 key: +114 Name of the tag +115 value: +116 Value to associate with tag +117 """ +118 self.runcard.add_tag(key=key, value=value) +
Adds a tag to the current run
+ +132 def register_card( +133 self, +134 card: ArtifactCard, +135 version_type: Union[VersionType, str] = VersionType.MINOR, +136 ) -> None: +137 """ +138 Register a given artifact card. +139 +140 Args: +141 card: +142 The card to register +143 version_type: +144 Version type for increment. Options are "major", "minor" and +145 "patch". Defaults to "minor". +146 """ +147 self._verify_active() +148 +149 # add runuid to card +150 if isinstance(card, (DataCard, ModelCard)): +151 card.metadata.runcard_uid = self.runcard.uid +152 +153 CardHandler.register_card( +154 registries=self._info.registries, +155 card=card, +156 version_type=version_type, +157 ) +158 +159 tag_key = f"{card.card_type}:{card.name}" +160 self.add_tag(key=tag_key, value=card.version) +161 +162 # add uid to RunCard +163 self.runcard.add_card_uid(card_type=card.card_type, uid=str(card.uid)) +
Register a given artifact card.
+ +165 def load_card(self, registry_name: str, info: CardInfo) -> ArtifactCard: +166 """ +167 Loads an ArtifactCard. +168 +169 Args: +170 registry_name: +171 Type of card to load (data, model, run, pipeline) +172 info: +173 Card information to retrieve. `uid` takes precedence if it +174 exists. If the optional `version` is specified, that version +175 will be loaded. If it doesn't exist, the most recent version will +176 be loaded. +177 +178 Returns +179 `ArtifactCard` +180 """ +181 card_type = CardType(registry_name.lower()).value +182 +183 return CardHandler.load_card(registries=self._info.registries, registry_name=card_type, info=info) +
Loads an ArtifactCard.
+ +uid takes precedence if it
+exists. If the optional version is specified, that version
+will be loaded. If it doesn't exist, the most recent version will
+be loaded.Returns
+ ArtifactCard
185 def log_artifact_from_file( +186 self, +187 name: str, +188 local_path: Union[str, Path], +189 artifact_path: Optional[Union[str, Path]] = None, +190 ) -> None: +191 """ +192 Log a local file or directory to the opsml server and associate with the current run. +193 +194 Args: +195 name: +196 Name to assign to artifact(s) +197 local_path: +198 Local path to file or directory. Can be string or pathlike object +199 artifact_path: +200 Optional path to store artifact in opsml server. If not provided, 'artifacts' will be used +201 """ +202 +203 self._verify_active() +204 +205 lpath = Path(local_path) +206 rpath = self.runcard.uri / (artifact_path or SaveName.ARTIFACTS.value) +207 +208 if lpath.is_file(): +209 rpath = rpath / lpath.name +210 +211 self._info.storage_client.put(lpath, rpath) +212 self.runcard._add_artifact_uri( # pylint: disable=protected-access +213 name=name, +214 local_path=lpath.as_posix(), +215 remote_path=rpath.as_posix(), +216 ) +
Log a local file or directory to the opsml server and associate with the current run.
+ +218 def log_metric( +219 self, +220 key: str, +221 value: float, +222 timestamp: Optional[int] = None, +223 step: Optional[int] = None, +224 ) -> None: +225 """ +226 Log a metric for a given run +227 +228 Args: +229 key: +230 Metric name +231 value: +232 Metric value +233 timestamp: +234 Optional time indicating metric creation time +235 step: +236 Optional step in training when metric was created +237 +238 """ +239 self._verify_active() +240 self.runcard.log_metric( +241 key=key, +242 value=value, +243 timestamp=timestamp, +244 step=step, +245 ) +
Log a metric for a given run
+ +247 def log_metrics( +248 self, +249 metrics: Dict[str, Union[float, int]], +250 step: Optional[int] = None, +251 ) -> None: +252 """Logs a collection of metrics for a run +253 +254 Args: +255 metrics: +256 Dictionary of metrics +257 step: +258 step the metrics are associated with +259 +260 """ +261 self._verify_active() +262 self.runcard.log_metrics(metrics=metrics, step=step) +
Logs a collection of metrics for a run
+ +264 def log_parameter(self, key: str, value: str) -> None: +265 """ +266 Logs a parameter to project run +267 +268 Args: +269 key: +270 Parameter name +271 value: +272 Parameter value +273 """ +274 +275 self._verify_active() +276 self.runcard.log_parameter(key=key, value=value) +
Logs a parameter to project run
+ +278 def log_parameters(self, parameters: Dict[str, Union[float, int, str]]) -> None: +279 """ +280 Logs a collection of parameters for a run +281 +282 Args: +283 parameters: +284 Dictionary of parameters +285 """ +286 +287 self._verify_active() +288 self.runcard.log_parameters(parameters=parameters) +
Logs a collection of parameters for a run
+ +290 def log_graph( +291 self, +292 name: str, +293 x: Union[List[Union[float, int]], NDArray[Any]], +294 y: Union[List[Union[float, int]], NDArray[Any], Dict[str, Union[List[Union[float, int]], NDArray[Any]]]], +295 x_label: str = "x", +296 y_label: str = "y", +297 graph_style: str = "line", +298 ) -> None: +299 """Logs a graph to the RunCard, which will be rendered in the UI as a line graph +300 +301 Args: +302 name: +303 Name of graph +304 x: +305 List or numpy array of x values +306 +307 x_label: +308 Label for x axis +309 y: +310 Either of the following: +311 (1) a list or numpy array of y values +312 (2) a dictionary of y values where key is the group label and +313 value is a list or numpy array of y values +314 y_label: +315 Label for y axis +316 graph_style: +317 Style of graph. Options are "line" or "scatter" +318 +319 example: +320 +321 ### single line graph +322 x = np.arange(1, 400, 0.5) +323 y = x * x +324 run.log_graph(name="graph1", x=x, y=y, x_label="x", y_label="y", graph_style="line") +325 +326 ### multi line graph +327 x = np.arange(1, 1000, 0.5) +328 y1 = x * x +329 y2 = y1 * 1.1 +330 y3 = y2 * 3 +331 run.log_graph( +332 name="multiline", +333 x=x, +334 y={"y1": y1, "y2": y2, "y3": y3}, +335 x_label="x", +336 y_label="y", +337 graph_style="line", +338 ) +339 +340 """ +341 self.runcard.log_graph(name=name, x=x, x_label=x_label, y=y, y_label=y_label, graph_style=graph_style) +
Logs a graph to the RunCard, which will be rendered in the UI as a line graph
+ +example:
+ +### single line graph
+x = np.arange(1, 400, 0.5)
+y = x * x
+run.log_graph(name="graph1", x=x, y=y, x_label="x", y_label="y", graph_style="line")
+
+### multi line graph
+x = np.arange(1, 1000, 0.5)
+y1 = x * x
+y2 = y1 * 1.1
+y3 = y2 * 3
+run.log_graph(
+ name="multiline",
+ x=x,
+ y={"y1": y1, "y2": y2, "y3": y3},
+ x_label="x",
+ y_label="y",
+ graph_style="line",
+)
+343 def create_or_update_runcard(self) -> None: +344 """Creates or updates an active RunCard""" +345 +346 self._verify_active() +347 +348 if self.runcard.uid is not None and self.runcard.version != CommonKwargs.BASE_VERSION.value: +349 CardHandler.update_card(registries=self._info.registries, card=self.runcard) +350 else: +351 CardHandler.register_card(registries=self._info.registries, card=self.runcard) +
Creates or updates an active RunCard
+1# pylint: disable=invalid-envvar-value + 2# Copyright (c) Shipt, Inc. + 3# This source code is licensed under the MIT license found in the + 4# LICENSE file in the root directory of this source tree. + 5# pylint: disable=protected-access + 6 + 7from contextlib import contextmanager + 8from typing import Any, Dict, Iterator, List, Optional, Union, cast + 9 + 10from opsml.cards.base import ArtifactCard + 11from opsml.cards.project import ProjectCard + 12from opsml.cards.run import RunCard + 13from opsml.helpers.logging import ArtifactLogger + 14from opsml.projects._run_manager import ActiveRunException, _RunManager + 15from opsml.projects.active_run import ActiveRun, CardHandler + 16from opsml.projects.types import ProjectInfo + 17from opsml.registry import CardRegistries + 18from opsml.registry.sql.base.client import ClientProjectCardRegistry + 19from opsml.types import CardInfo, CardType, Metric, Metrics, Param, Params + 20 + 21logger = ArtifactLogger.get_logger() + 22 + 23 + 24class _ProjectRegistrar: + 25 def __init__(self, project_info: ProjectInfo): + 26 self._project_info = project_info + 27 self.registries = CardRegistries() + 28 + 29 @property + 30 def project_registry(self) -> ClientProjectCardRegistry: + 31 # both server and client registries are the same for methods + 32 return cast(ClientProjectCardRegistry, self.registries.project._registry) + 33 + 34 def register_project(self) -> int: + 35 """ + 36 Checks if the project name exists in the project registry. A ProjectCard is created if it + 37 doesn't exist. + 38 + 39 Returns: + 40 project_id: int + 41 """ + 42 + 43 card = ProjectCard( + 44 name=self._project_info.name, + 45 repository=self._project_info.repository, + 46 contact=self._project_info.contact, + 47 ) + 48 self.registries.project.register_card(card=card) + 49 + 50 return card.project_id + 51 + 52 + 53class OpsmlProject: + 54 def __init__(self, info: ProjectInfo): + 55 """ + 56 Instantiates a project which creates cards, metrics and parameters to + 57 the opsml registry via a "run" object. + 58 + 59 If info.run_id is set, that run_id will be loaded as read only. In read + 60 only mode, you can retrieve cards, metrics, and parameters, however you + 61 cannot write new data. If you wish to record data/create a new run, you will + 62 need to enter the run context. + 63 + 64 In order to create new cards, you need to create a run using the `run` + 65 context manager. + 66 + 67 Example: + 68 + 69 project: OpsmlProject = OpsmlProject( + 70 ProjectInfo( + 71 name="test-project", + 72 # If run_id is omitted, a new run is created. + 73 run_id="123ab123kaj8u8naskdfh813", + 74 ) + 75 ) + 76 # the project is in "read only" mode. all read operations will work + 77 for k, v in project.parameters: + 78 logger.info("{} = {}", k, v) + 79 + 80 # creating a project run + 81 with project.run() as run: + 82 # Now that the run context is entered, it's in read/write mode + 83 # You can write cards, parameters, and metrics to the project. + 84 run.log_parameter(key="my_param", value="12.34") + 85 + 86 Args: + 87 info: + 88 Run information. if a run_id is given, that run is set + 89 as the project's current run. + 90 """ + 91 # Set the run manager and project_id (creates ProjectCard if project doesn't exist) + 92 registrar = _ProjectRegistrar(project_info=info) + 93 + 94 # get project id or register new project + 95 info.project_id = registrar.register_project() + 96 + 97 # crete run manager + 98 self._run_mgr = _RunManager(project_info=info, registries=registrar.registries) + 99 +100 @property +101 def run_id(self) -> str: +102 """Current run id associated with project""" +103 if self._run_mgr.run_id is not None: +104 return self._run_mgr.run_id +105 raise ValueError("Run id not set for current project") +106 +107 @run_id.setter +108 def run_id(self, run_id: str) -> None: +109 """Set the run_id to use with the active project""" +110 self._run_mgr.run_id = run_id +111 +112 @property +113 def project_id(self) -> int: +114 return self._run_mgr.project_id +115 +116 @property +117 def project_name(self) -> str: +118 return self._run_mgr._project_info.name # pylint: disable=protected-access +119 +120 @contextmanager +121 def run(self, run_name: Optional[str] = None) -> Iterator[ActiveRun]: +122 """ +123 Starts a new run for the project +124 +125 Args: +126 run_name: +127 Optional run name +128 """ +129 +130 try: +131 yield self._run_mgr.start_run(run_name=run_name) # self._run_mgr.active_run +132 +133 except ActiveRunException as error: +134 logger.error("Run already active. Ending run.") +135 raise error +136 +137 except Exception as error: +138 logger.error("Error encountered. Ending run. {}", error) +139 self._run_mgr.end_run() +140 raise error +141 +142 self._run_mgr.end_run() +143 +144 def load_card(self, registry_name: str, info: CardInfo) -> ArtifactCard: +145 """ +146 Loads an ArtifactCard. +147 +148 Args: +149 registry_name: +150 Name of registry to load card from +151 info: +152 Card information to retrieve. `uid` takes precedence if it +153 exists. If the optional `version` is specified, that version +154 will be loaded. If it doesn't exist, the most recent ersion will +155 be loaded. +156 +157 Returns +158 `ArtifactCard` +159 """ +160 card_type = CardType(registry_name.lower()).value +161 return CardHandler.load_card( +162 registries=self._run_mgr.registries, +163 registry_name=card_type, +164 info=info, +165 ) +166 +167 def list_runs(self, limit: int = 100) -> List[Dict[str, Any]]: +168 """ +169 Lists all runs for the current project, sorted by timestamp +170 +171 Returns: +172 List of RunCard +173 """ +174 logger.info("Listing runs for project {}", self.project_name) +175 +176 project_runs = self._run_mgr.registries.run._registry.list_cards( # pylint: disable=protected-access +177 limit=limit, +178 query_terms={"project": self.project_name}, +179 ) +180 +181 return sorted(project_runs, key=lambda k: k["timestamp"], reverse=True) +182 +183 @property +184 def runcard(self) -> RunCard: +185 return cast(RunCard, self._run_mgr.registries.run.load_card(uid=self.run_id)) +186 +187 @property +188 def metrics(self) -> Metrics: +189 runcard = self.runcard +190 runcard.load_metrics() +191 return runcard.metrics +192 +193 def get_metric(self, name: str) -> Union[List[Metric], Metric]: +194 """ +195 Get metric by name +196 +197 Args: +198 name: str +199 +200 Returns: +201 List of Metric or Metric +202 +203 """ +204 return self.runcard.get_metric(name=name) +205 +206 @property +207 def parameters(self) -> Params: +208 return self.runcard.parameters +209 +210 def get_parameter(self, name: str) -> Union[List[Param], Param]: +211 """ +212 Get param by name +213 +214 Args: +215 name: str +216 +217 Returns: +218 List of Param or Param +219 +220 """ +221 return self.runcard.get_parameter(name=name) +222 +223 @property +224 def tags(self) -> Dict[str, Union[str, int]]: +225 return self.runcard.tags +226 +227 @property +228 def datacard_uids(self) -> List[str]: +229 """DataCards associated with the current run""" +230 return self.runcard.datacard_uids +231 +232 @property +233 def modelcard_uids(self) -> List[str]: +234 """ModelCards associated with the current run""" +235 return self.runcard.modelcard_uids +
54class OpsmlProject: + 55 def __init__(self, info: ProjectInfo): + 56 """ + 57 Instantiates a project which creates cards, metrics and parameters to + 58 the opsml registry via a "run" object. + 59 + 60 If info.run_id is set, that run_id will be loaded as read only. In read + 61 only mode, you can retrieve cards, metrics, and parameters, however you + 62 cannot write new data. If you wish to record data/create a new run, you will + 63 need to enter the run context. + 64 + 65 In order to create new cards, you need to create a run using the `run` + 66 context manager. + 67 + 68 Example: + 69 + 70 project: OpsmlProject = OpsmlProject( + 71 ProjectInfo( + 72 name="test-project", + 73 # If run_id is omitted, a new run is created. + 74 run_id="123ab123kaj8u8naskdfh813", + 75 ) + 76 ) + 77 # the project is in "read only" mode. all read operations will work + 78 for k, v in project.parameters: + 79 logger.info("{} = {}", k, v) + 80 + 81 # creating a project run + 82 with project.run() as run: + 83 # Now that the run context is entered, it's in read/write mode + 84 # You can write cards, parameters, and metrics to the project. + 85 run.log_parameter(key="my_param", value="12.34") + 86 + 87 Args: + 88 info: + 89 Run information. if a run_id is given, that run is set + 90 as the project's current run. + 91 """ + 92 # Set the run manager and project_id (creates ProjectCard if project doesn't exist) + 93 registrar = _ProjectRegistrar(project_info=info) + 94 + 95 # get project id or register new project + 96 info.project_id = registrar.register_project() + 97 + 98 # crete run manager + 99 self._run_mgr = _RunManager(project_info=info, registries=registrar.registries) +100 +101 @property +102 def run_id(self) -> str: +103 """Current run id associated with project""" +104 if self._run_mgr.run_id is not None: +105 return self._run_mgr.run_id +106 raise ValueError("Run id not set for current project") +107 +108 @run_id.setter +109 def run_id(self, run_id: str) -> None: +110 """Set the run_id to use with the active project""" +111 self._run_mgr.run_id = run_id +112 +113 @property +114 def project_id(self) -> int: +115 return self._run_mgr.project_id +116 +117 @property +118 def project_name(self) -> str: +119 return self._run_mgr._project_info.name # pylint: disable=protected-access +120 +121 @contextmanager +122 def run(self, run_name: Optional[str] = None) -> Iterator[ActiveRun]: +123 """ +124 Starts a new run for the project +125 +126 Args: +127 run_name: +128 Optional run name +129 """ +130 +131 try: +132 yield self._run_mgr.start_run(run_name=run_name) # self._run_mgr.active_run +133 +134 except ActiveRunException as error: +135 logger.error("Run already active. Ending run.") +136 raise error +137 +138 except Exception as error: +139 logger.error("Error encountered. Ending run. {}", error) +140 self._run_mgr.end_run() +141 raise error +142 +143 self._run_mgr.end_run() +144 +145 def load_card(self, registry_name: str, info: CardInfo) -> ArtifactCard: +146 """ +147 Loads an ArtifactCard. +148 +149 Args: +150 registry_name: +151 Name of registry to load card from +152 info: +153 Card information to retrieve. `uid` takes precedence if it +154 exists. If the optional `version` is specified, that version +155 will be loaded. If it doesn't exist, the most recent ersion will +156 be loaded. +157 +158 Returns +159 `ArtifactCard` +160 """ +161 card_type = CardType(registry_name.lower()).value +162 return CardHandler.load_card( +163 registries=self._run_mgr.registries, +164 registry_name=card_type, +165 info=info, +166 ) +167 +168 def list_runs(self, limit: int = 100) -> List[Dict[str, Any]]: +169 """ +170 Lists all runs for the current project, sorted by timestamp +171 +172 Returns: +173 List of RunCard +174 """ +175 logger.info("Listing runs for project {}", self.project_name) +176 +177 project_runs = self._run_mgr.registries.run._registry.list_cards( # pylint: disable=protected-access +178 limit=limit, +179 query_terms={"project": self.project_name}, +180 ) +181 +182 return sorted(project_runs, key=lambda k: k["timestamp"], reverse=True) +183 +184 @property +185 def runcard(self) -> RunCard: +186 return cast(RunCard, self._run_mgr.registries.run.load_card(uid=self.run_id)) +187 +188 @property +189 def metrics(self) -> Metrics: +190 runcard = self.runcard +191 runcard.load_metrics() +192 return runcard.metrics +193 +194 def get_metric(self, name: str) -> Union[List[Metric], Metric]: +195 """ +196 Get metric by name +197 +198 Args: +199 name: str +200 +201 Returns: +202 List of Metric or Metric +203 +204 """ +205 return self.runcard.get_metric(name=name) +206 +207 @property +208 def parameters(self) -> Params: +209 return self.runcard.parameters +210 +211 def get_parameter(self, name: str) -> Union[List[Param], Param]: +212 """ +213 Get param by name +214 +215 Args: +216 name: str +217 +218 Returns: +219 List of Param or Param +220 +221 """ +222 return self.runcard.get_parameter(name=name) +223 +224 @property +225 def tags(self) -> Dict[str, Union[str, int]]: +226 return self.runcard.tags +227 +228 @property +229 def datacard_uids(self) -> List[str]: +230 """DataCards associated with the current run""" +231 return self.runcard.datacard_uids +232 +233 @property +234 def modelcard_uids(self) -> List[str]: +235 """ModelCards associated with the current run""" +236 return self.runcard.modelcard_uids +
55 def __init__(self, info: ProjectInfo): +56 """ +57 Instantiates a project which creates cards, metrics and parameters to +58 the opsml registry via a "run" object. +59 +60 If info.run_id is set, that run_id will be loaded as read only. In read +61 only mode, you can retrieve cards, metrics, and parameters, however you +62 cannot write new data. If you wish to record data/create a new run, you will +63 need to enter the run context. +64 +65 In order to create new cards, you need to create a run using the `run` +66 context manager. +67 +68 Example: +69 +70 project: OpsmlProject = OpsmlProject( +71 ProjectInfo( +72 name="test-project", +73 # If run_id is omitted, a new run is created. +74 run_id="123ab123kaj8u8naskdfh813", +75 ) +76 ) +77 # the project is in "read only" mode. all read operations will work +78 for k, v in project.parameters: +79 logger.info("{} = {}", k, v) +80 +81 # creating a project run +82 with project.run() as run: +83 # Now that the run context is entered, it's in read/write mode +84 # You can write cards, parameters, and metrics to the project. +85 run.log_parameter(key="my_param", value="12.34") +86 +87 Args: +88 info: +89 Run information. if a run_id is given, that run is set +90 as the project's current run. +91 """ +92 # Set the run manager and project_id (creates ProjectCard if project doesn't exist) +93 registrar = _ProjectRegistrar(project_info=info) +94 +95 # get project id or register new project +96 info.project_id = registrar.register_project() +97 +98 # crete run manager +99 self._run_mgr = _RunManager(project_info=info, registries=registrar.registries) +
Instantiates a project which creates cards, metrics and parameters to +the opsml registry via a "run" object.
+ +If info.run_id is set, that run_id will be loaded as read only. In read +only mode, you can retrieve cards, metrics, and parameters, however you +cannot write new data. If you wish to record data/create a new run, you will +need to enter the run context.
+ +In order to create new cards, you need to create a run using the run
+context manager.
++ +project: OpsmlProject = OpsmlProject( + ProjectInfo( + name="test-project", + # If run_id is omitted, a new run is created. + run_id="123ab123kaj8u8naskdfh813", + ) + )
+ +the project is in "read only" mode. all read operations will work
+ +for k, v in project.parameters: + logger.info("{} = {}", k, v)
+ +creating a project run
+ +with project.run() as run: + # Now that the run context is entered, it's in read/write mode + # You can write cards, parameters, and metrics to the project. + run.log_parameter(key="my_param", value="12.34")
+
101 @property +102 def run_id(self) -> str: +103 """Current run id associated with project""" +104 if self._run_mgr.run_id is not None: +105 return self._run_mgr.run_id +106 raise ValueError("Run id not set for current project") +
Current run id associated with project
+121 @contextmanager +122 def run(self, run_name: Optional[str] = None) -> Iterator[ActiveRun]: +123 """ +124 Starts a new run for the project +125 +126 Args: +127 run_name: +128 Optional run name +129 """ +130 +131 try: +132 yield self._run_mgr.start_run(run_name=run_name) # self._run_mgr.active_run +133 +134 except ActiveRunException as error: +135 logger.error("Run already active. Ending run.") +136 raise error +137 +138 except Exception as error: +139 logger.error("Error encountered. Ending run. {}", error) +140 self._run_mgr.end_run() +141 raise error +142 +143 self._run_mgr.end_run() +
Starts a new run for the project
+ +145 def load_card(self, registry_name: str, info: CardInfo) -> ArtifactCard: +146 """ +147 Loads an ArtifactCard. +148 +149 Args: +150 registry_name: +151 Name of registry to load card from +152 info: +153 Card information to retrieve. `uid` takes precedence if it +154 exists. If the optional `version` is specified, that version +155 will be loaded. If it doesn't exist, the most recent ersion will +156 be loaded. +157 +158 Returns +159 `ArtifactCard` +160 """ +161 card_type = CardType(registry_name.lower()).value +162 return CardHandler.load_card( +163 registries=self._run_mgr.registries, +164 registry_name=card_type, +165 info=info, +166 ) +
Loads an ArtifactCard.
+ +uid takes precedence if it
+exists. If the optional version is specified, that version
+will be loaded. If it doesn't exist, the most recent ersion will
+be loaded.Returns
+ ArtifactCard
168 def list_runs(self, limit: int = 100) -> List[Dict[str, Any]]: +169 """ +170 Lists all runs for the current project, sorted by timestamp +171 +172 Returns: +173 List of RunCard +174 """ +175 logger.info("Listing runs for project {}", self.project_name) +176 +177 project_runs = self._run_mgr.registries.run._registry.list_cards( # pylint: disable=protected-access +178 limit=limit, +179 query_terms={"project": self.project_name}, +180 ) +181 +182 return sorted(project_runs, key=lambda k: k["timestamp"], reverse=True) +
Lists all runs for the current project, sorted by timestamp
+ +++List of RunCard
+
194 def get_metric(self, name: str) -> Union[List[Metric], Metric]: +195 """ +196 Get metric by name +197 +198 Args: +199 name: str +200 +201 Returns: +202 List of Metric or Metric +203 +204 """ +205 return self.runcard.get_metric(name=name) +
Get metric by name
+ +++List of Metric or Metric
+
211 def get_parameter(self, name: str) -> Union[List[Param], Param]: +212 """ +213 Get param by name +214 +215 Args: +216 name: str +217 +218 Returns: +219 List of Param or Param +220 +221 """ +222 return self.runcard.get_parameter(name=name) +
Get param by name
+ +++List of Param or Param
+
1# pylint: disable=protected-access + 2# Copyright (c) Shipt, Inc. + 3# This source code is licensed under the MIT license found in the + 4# LICENSE file in the root directory of this source tree. + 5import textwrap + 6from typing import Any, Dict, List, Optional, Type, Union + 7 + 8from opsml.cards import ArtifactCard, CardInfo + 9from opsml.data import DataInterface + 10from opsml.helpers.logging import ArtifactLogger + 11from opsml.helpers.utils import clean_string + 12from opsml.model import ModelInterface + 13from opsml.registry.backend import _set_registry + 14from opsml.registry.semver import VersionType + 15from opsml.storage.card_loader import CardLoader + 16from opsml.types import CommonKwargs, RegistryType + 17 + 18logger = ArtifactLogger.get_logger() + 19 + 20 + 21class CardRegistry: + 22 def __init__(self, registry_type: Union[RegistryType, str]): + 23 """ + 24 Interface for connecting to any of the ArtifactCard registries + 25 + 26 Args: + 27 registry_type: + 28 Type of card registry to create + 29 settings: + 30 Storage settings + 31 + 32 Returns: + 33 Instantiated connection to specific Card registry + 34 + 35 Example: + 36 data_registry = CardRegistry(RegistryType.DATA) + 37 data_registry.list_cards() + 38 + 39 or + 40 data_registry = CardRegistry("data") + 41 data_registry.list_cards() + 42 """ + 43 + 44 _registry_type = ( + 45 registry_type if isinstance(registry_type, RegistryType) else RegistryType.from_str(registry_type) + 46 ) + 47 + 48 self._registry = _set_registry(_registry_type) + 49 self.table_name = self._registry.table_name + 50 + 51 @property + 52 def registry_type(self) -> RegistryType: + 53 "Registry type for card registry" + 54 return self._registry.registry_type + 55 + 56 def list_cards( + 57 self, + 58 uid: Optional[str] = None, + 59 name: Optional[str] = None, + 60 repository: Optional[str] = None, + 61 version: Optional[str] = None, + 62 tags: Optional[Dict[str, str]] = None, + 63 info: Optional[CardInfo] = None, + 64 max_date: Optional[str] = None, + 65 limit: Optional[int] = None, + 66 ignore_release_candidates: bool = False, + 67 ) -> List[Dict[str, Any]]: + 68 """Retrieves records from registry + 69 + 70 Args: + 71 name: + 72 Card name + 73 repository: + 74 Repository associated with card + 75 version: + 76 Optional version number of existing data. If not specified, the + 77 most recent version will be used + 78 tags: + 79 Dictionary of key, value tags to search for + 80 uid: + 81 Unique identifier for Card. If present, the uid takes precedence + 82 max_date: + 83 Max date to search. (e.g. "2023-05-01" would search for cards up to and including "2023-05-01") + 84 limit: + 85 Places a limit on result list. Results are sorted by SemVer + 86 info: + 87 CardInfo object. If present, the info object takes precedence + 88 ignore_release_candidates: + 89 If True, ignores release candidates + 90 + 91 Returns: + 92 pandas dataframe of records or list of dictionaries + 93 """ + 94 + 95 if info is not None: + 96 name = name or info.name + 97 repository = repository or info.repository + 98 uid = uid or info.uid + 99 version = version or info.version +100 tags = tags or info.tags +101 +102 if name is not None: +103 name = name.lower() +104 +105 if repository is not None: +106 repository = repository.lower() +107 +108 if all(not bool(var) for var in [name, repository, version, uid, tags]): +109 limit = limit or 25 +110 +111 card_list = self._registry.list_cards( +112 uid=uid, +113 name=name, +114 repository=repository, +115 version=version, +116 max_date=max_date, +117 limit=limit, +118 tags=tags, +119 ignore_release_candidates=ignore_release_candidates, +120 ) +121 +122 return card_list +123 +124 def load_card( +125 self, +126 name: Optional[str] = None, +127 repository: Optional[str] = None, +128 uid: Optional[str] = None, +129 tags: Optional[Dict[str, str]] = None, +130 version: Optional[str] = None, +131 info: Optional[CardInfo] = None, +132 ignore_release_candidates: bool = False, +133 interface: Optional[Union[Type[ModelInterface], Type[DataInterface]]] = None, +134 ) -> ArtifactCard: +135 """Loads a specific card +136 +137 Args: +138 name: +139 Optional Card name +140 uid: +141 Unique identifier for card. If present, the uid takes +142 precedence. +143 tags: +144 Optional tags associated with model. +145 repository: +146 Optional repository associated with card +147 version: +148 Optional version number of existing data. If not specified, the +149 most recent version will be used +150 info: +151 Optional CardInfo object. If present, the info takes precedence +152 ignore_release_candidates: +153 If True, ignores release candidates +154 interface: +155 Optional interface to use for loading card. This is required for when using +156 subclassed interfaces. +157 +158 Returns +159 ArtifactCard +160 """ +161 +162 # find better way to do this later +163 if info is not None: +164 name = name or info.name +165 uid = uid or info.uid +166 version = version or info.version +167 tags = tags or info.tags +168 +169 name = clean_string(name) +170 +171 records = self.list_cards( +172 uid=uid, +173 name=name, +174 repository=repository, +175 version=version, +176 tags=tags, +177 ignore_release_candidates=ignore_release_candidates, +178 limit=1, +179 ) +180 +181 return CardLoader( +182 card_args=records[0], +183 registry_type=self.registry_type, +184 ).load_card(interface=interface) +185 +186 def register_card( +187 self, +188 card: ArtifactCard, +189 version_type: Union[VersionType, str] = VersionType.MINOR, +190 pre_tag: str = "rc", +191 build_tag: str = "build", +192 ) -> None: +193 """ +194 Adds a new `Card` record to registry. Registration will be skipped if the card already exists. +195 +196 Args: +197 card: +198 card to register +199 version_type: +200 Version type for increment. Options are "major", "minor" and +201 "patch". Defaults to "minor". +202 pre_tag: +203 pre-release tag to add to card version +204 build_tag: +205 build tag to add to card version +206 """ +207 +208 _version_type = version_type if isinstance(version_type, VersionType) else VersionType.from_str(version_type) +209 +210 if card.uid is not None and card.version != CommonKwargs.BASE_VERSION.value: +211 logger.info( +212 textwrap.dedent( +213 f""" +214 Card {card.uid} already exists. Skipping registration. If you'd like to register +215 a new card, please instantiate a new Card object. If you'd like to update the +216 existing card, please use the update_card method. +217 """ +218 ) +219 ) +220 +221 else: +222 self._registry.register_card( +223 card=card, +224 version_type=_version_type, +225 pre_tag=pre_tag, +226 build_tag=build_tag, +227 ) +228 +229 def update_card(self, card: ArtifactCard) -> None: +230 """ +231 Update an artifact card based on current registry +232 +233 Args: +234 card: +235 Card to register +236 """ +237 return self._registry.update_card(card=card) +238 +239 def query_value_from_card(self, uid: str, columns: List[str]) -> Dict[str, Any]: +240 """ +241 Query column values from a specific Card +242 +243 Args: +244 uid: +245 Uid of Card +246 columns: +247 List of columns to query +248 +249 Returns: +250 Dictionary of column, values pairs +251 """ +252 results = self._registry.list_cards(uid=uid)[0] +253 return {col: results[col] for col in columns} +254 +255 def delete_card(self, card: ArtifactCard) -> None: +256 """ +257 Delete a specific Card +258 +259 Args: +260 card: +261 Card to delete +262 """ +263 return self._registry.delete_card(card) +264 +265 +266class CardRegistries: +267 def __init__(self) -> None: +268 """Instantiates class that contains all registries""" +269 +270 self.data = CardRegistry(registry_type=RegistryType.DATA) +271 self.model = CardRegistry(registry_type=RegistryType.MODEL) +272 self.run = CardRegistry(registry_type=RegistryType.RUN) +273 self.pipeline = CardRegistry(registry_type=RegistryType.PIPELINE) +274 self.project = CardRegistry(registry_type=RegistryType.PROJECT) +275 self.audit = CardRegistry(registry_type=RegistryType.AUDIT) +
22class CardRegistry: + 23 def __init__(self, registry_type: Union[RegistryType, str]): + 24 """ + 25 Interface for connecting to any of the ArtifactCard registries + 26 + 27 Args: + 28 registry_type: + 29 Type of card registry to create + 30 settings: + 31 Storage settings + 32 + 33 Returns: + 34 Instantiated connection to specific Card registry + 35 + 36 Example: + 37 data_registry = CardRegistry(RegistryType.DATA) + 38 data_registry.list_cards() + 39 + 40 or + 41 data_registry = CardRegistry("data") + 42 data_registry.list_cards() + 43 """ + 44 + 45 _registry_type = ( + 46 registry_type if isinstance(registry_type, RegistryType) else RegistryType.from_str(registry_type) + 47 ) + 48 + 49 self._registry = _set_registry(_registry_type) + 50 self.table_name = self._registry.table_name + 51 + 52 @property + 53 def registry_type(self) -> RegistryType: + 54 "Registry type for card registry" + 55 return self._registry.registry_type + 56 + 57 def list_cards( + 58 self, + 59 uid: Optional[str] = None, + 60 name: Optional[str] = None, + 61 repository: Optional[str] = None, + 62 version: Optional[str] = None, + 63 tags: Optional[Dict[str, str]] = None, + 64 info: Optional[CardInfo] = None, + 65 max_date: Optional[str] = None, + 66 limit: Optional[int] = None, + 67 ignore_release_candidates: bool = False, + 68 ) -> List[Dict[str, Any]]: + 69 """Retrieves records from registry + 70 + 71 Args: + 72 name: + 73 Card name + 74 repository: + 75 Repository associated with card + 76 version: + 77 Optional version number of existing data. If not specified, the + 78 most recent version will be used + 79 tags: + 80 Dictionary of key, value tags to search for + 81 uid: + 82 Unique identifier for Card. If present, the uid takes precedence + 83 max_date: + 84 Max date to search. (e.g. "2023-05-01" would search for cards up to and including "2023-05-01") + 85 limit: + 86 Places a limit on result list. Results are sorted by SemVer + 87 info: + 88 CardInfo object. If present, the info object takes precedence + 89 ignore_release_candidates: + 90 If True, ignores release candidates + 91 + 92 Returns: + 93 pandas dataframe of records or list of dictionaries + 94 """ + 95 + 96 if info is not None: + 97 name = name or info.name + 98 repository = repository or info.repository + 99 uid = uid or info.uid +100 version = version or info.version +101 tags = tags or info.tags +102 +103 if name is not None: +104 name = name.lower() +105 +106 if repository is not None: +107 repository = repository.lower() +108 +109 if all(not bool(var) for var in [name, repository, version, uid, tags]): +110 limit = limit or 25 +111 +112 card_list = self._registry.list_cards( +113 uid=uid, +114 name=name, +115 repository=repository, +116 version=version, +117 max_date=max_date, +118 limit=limit, +119 tags=tags, +120 ignore_release_candidates=ignore_release_candidates, +121 ) +122 +123 return card_list +124 +125 def load_card( +126 self, +127 name: Optional[str] = None, +128 repository: Optional[str] = None, +129 uid: Optional[str] = None, +130 tags: Optional[Dict[str, str]] = None, +131 version: Optional[str] = None, +132 info: Optional[CardInfo] = None, +133 ignore_release_candidates: bool = False, +134 interface: Optional[Union[Type[ModelInterface], Type[DataInterface]]] = None, +135 ) -> ArtifactCard: +136 """Loads a specific card +137 +138 Args: +139 name: +140 Optional Card name +141 uid: +142 Unique identifier for card. If present, the uid takes +143 precedence. +144 tags: +145 Optional tags associated with model. +146 repository: +147 Optional repository associated with card +148 version: +149 Optional version number of existing data. If not specified, the +150 most recent version will be used +151 info: +152 Optional CardInfo object. If present, the info takes precedence +153 ignore_release_candidates: +154 If True, ignores release candidates +155 interface: +156 Optional interface to use for loading card. This is required for when using +157 subclassed interfaces. +158 +159 Returns +160 ArtifactCard +161 """ +162 +163 # find better way to do this later +164 if info is not None: +165 name = name or info.name +166 uid = uid or info.uid +167 version = version or info.version +168 tags = tags or info.tags +169 +170 name = clean_string(name) +171 +172 records = self.list_cards( +173 uid=uid, +174 name=name, +175 repository=repository, +176 version=version, +177 tags=tags, +178 ignore_release_candidates=ignore_release_candidates, +179 limit=1, +180 ) +181 +182 return CardLoader( +183 card_args=records[0], +184 registry_type=self.registry_type, +185 ).load_card(interface=interface) +186 +187 def register_card( +188 self, +189 card: ArtifactCard, +190 version_type: Union[VersionType, str] = VersionType.MINOR, +191 pre_tag: str = "rc", +192 build_tag: str = "build", +193 ) -> None: +194 """ +195 Adds a new `Card` record to registry. Registration will be skipped if the card already exists. +196 +197 Args: +198 card: +199 card to register +200 version_type: +201 Version type for increment. Options are "major", "minor" and +202 "patch". Defaults to "minor". +203 pre_tag: +204 pre-release tag to add to card version +205 build_tag: +206 build tag to add to card version +207 """ +208 +209 _version_type = version_type if isinstance(version_type, VersionType) else VersionType.from_str(version_type) +210 +211 if card.uid is not None and card.version != CommonKwargs.BASE_VERSION.value: +212 logger.info( +213 textwrap.dedent( +214 f""" +215 Card {card.uid} already exists. Skipping registration. If you'd like to register +216 a new card, please instantiate a new Card object. If you'd like to update the +217 existing card, please use the update_card method. +218 """ +219 ) +220 ) +221 +222 else: +223 self._registry.register_card( +224 card=card, +225 version_type=_version_type, +226 pre_tag=pre_tag, +227 build_tag=build_tag, +228 ) +229 +230 def update_card(self, card: ArtifactCard) -> None: +231 """ +232 Update an artifact card based on current registry +233 +234 Args: +235 card: +236 Card to register +237 """ +238 return self._registry.update_card(card=card) +239 +240 def query_value_from_card(self, uid: str, columns: List[str]) -> Dict[str, Any]: +241 """ +242 Query column values from a specific Card +243 +244 Args: +245 uid: +246 Uid of Card +247 columns: +248 List of columns to query +249 +250 Returns: +251 Dictionary of column, values pairs +252 """ +253 results = self._registry.list_cards(uid=uid)[0] +254 return {col: results[col] for col in columns} +255 +256 def delete_card(self, card: ArtifactCard) -> None: +257 """ +258 Delete a specific Card +259 +260 Args: +261 card: +262 Card to delete +263 """ +264 return self._registry.delete_card(card) +
23 def __init__(self, registry_type: Union[RegistryType, str]): +24 """ +25 Interface for connecting to any of the ArtifactCard registries +26 +27 Args: +28 registry_type: +29 Type of card registry to create +30 settings: +31 Storage settings +32 +33 Returns: +34 Instantiated connection to specific Card registry +35 +36 Example: +37 data_registry = CardRegistry(RegistryType.DATA) +38 data_registry.list_cards() +39 +40 or +41 data_registry = CardRegistry("data") +42 data_registry.list_cards() +43 """ +44 +45 _registry_type = ( +46 registry_type if isinstance(registry_type, RegistryType) else RegistryType.from_str(registry_type) +47 ) +48 +49 self._registry = _set_registry(_registry_type) +50 self.table_name = self._registry.table_name +
Interface for connecting to any of the ArtifactCard registries
+ +++ +Instantiated connection to specific Card registry
+
++data_registry = CardRegistry(RegistryType.DATA) + data_registry.list_cards()
+ +or + data_registry = CardRegistry("data") + data_registry.list_cards()
+
52 @property +53 def registry_type(self) -> RegistryType: +54 "Registry type for card registry" +55 return self._registry.registry_type +
Registry type for card registry
+57 def list_cards( + 58 self, + 59 uid: Optional[str] = None, + 60 name: Optional[str] = None, + 61 repository: Optional[str] = None, + 62 version: Optional[str] = None, + 63 tags: Optional[Dict[str, str]] = None, + 64 info: Optional[CardInfo] = None, + 65 max_date: Optional[str] = None, + 66 limit: Optional[int] = None, + 67 ignore_release_candidates: bool = False, + 68 ) -> List[Dict[str, Any]]: + 69 """Retrieves records from registry + 70 + 71 Args: + 72 name: + 73 Card name + 74 repository: + 75 Repository associated with card + 76 version: + 77 Optional version number of existing data. If not specified, the + 78 most recent version will be used + 79 tags: + 80 Dictionary of key, value tags to search for + 81 uid: + 82 Unique identifier for Card. If present, the uid takes precedence + 83 max_date: + 84 Max date to search. (e.g. "2023-05-01" would search for cards up to and including "2023-05-01") + 85 limit: + 86 Places a limit on result list. Results are sorted by SemVer + 87 info: + 88 CardInfo object. If present, the info object takes precedence + 89 ignore_release_candidates: + 90 If True, ignores release candidates + 91 + 92 Returns: + 93 pandas dataframe of records or list of dictionaries + 94 """ + 95 + 96 if info is not None: + 97 name = name or info.name + 98 repository = repository or info.repository + 99 uid = uid or info.uid +100 version = version or info.version +101 tags = tags or info.tags +102 +103 if name is not None: +104 name = name.lower() +105 +106 if repository is not None: +107 repository = repository.lower() +108 +109 if all(not bool(var) for var in [name, repository, version, uid, tags]): +110 limit = limit or 25 +111 +112 card_list = self._registry.list_cards( +113 uid=uid, +114 name=name, +115 repository=repository, +116 version=version, +117 max_date=max_date, +118 limit=limit, +119 tags=tags, +120 ignore_release_candidates=ignore_release_candidates, +121 ) +122 +123 return card_list +
Retrieves records from registry
+ +++pandas dataframe of records or list of dictionaries
+
125 def load_card( +126 self, +127 name: Optional[str] = None, +128 repository: Optional[str] = None, +129 uid: Optional[str] = None, +130 tags: Optional[Dict[str, str]] = None, +131 version: Optional[str] = None, +132 info: Optional[CardInfo] = None, +133 ignore_release_candidates: bool = False, +134 interface: Optional[Union[Type[ModelInterface], Type[DataInterface]]] = None, +135 ) -> ArtifactCard: +136 """Loads a specific card +137 +138 Args: +139 name: +140 Optional Card name +141 uid: +142 Unique identifier for card. If present, the uid takes +143 precedence. +144 tags: +145 Optional tags associated with model. +146 repository: +147 Optional repository associated with card +148 version: +149 Optional version number of existing data. If not specified, the +150 most recent version will be used +151 info: +152 Optional CardInfo object. If present, the info takes precedence +153 ignore_release_candidates: +154 If True, ignores release candidates +155 interface: +156 Optional interface to use for loading card. This is required for when using +157 subclassed interfaces. +158 +159 Returns +160 ArtifactCard +161 """ +162 +163 # find better way to do this later +164 if info is not None: +165 name = name or info.name +166 uid = uid or info.uid +167 version = version or info.version +168 tags = tags or info.tags +169 +170 name = clean_string(name) +171 +172 records = self.list_cards( +173 uid=uid, +174 name=name, +175 repository=repository, +176 version=version, +177 tags=tags, +178 ignore_release_candidates=ignore_release_candidates, +179 limit=1, +180 ) +181 +182 return CardLoader( +183 card_args=records[0], +184 registry_type=self.registry_type, +185 ).load_card(interface=interface) +
Loads a specific card
+ +Returns + ArtifactCard
+187 def register_card( +188 self, +189 card: ArtifactCard, +190 version_type: Union[VersionType, str] = VersionType.MINOR, +191 pre_tag: str = "rc", +192 build_tag: str = "build", +193 ) -> None: +194 """ +195 Adds a new `Card` record to registry. Registration will be skipped if the card already exists. +196 +197 Args: +198 card: +199 card to register +200 version_type: +201 Version type for increment. Options are "major", "minor" and +202 "patch". Defaults to "minor". +203 pre_tag: +204 pre-release tag to add to card version +205 build_tag: +206 build tag to add to card version +207 """ +208 +209 _version_type = version_type if isinstance(version_type, VersionType) else VersionType.from_str(version_type) +210 +211 if card.uid is not None and card.version != CommonKwargs.BASE_VERSION.value: +212 logger.info( +213 textwrap.dedent( +214 f""" +215 Card {card.uid} already exists. Skipping registration. If you'd like to register +216 a new card, please instantiate a new Card object. If you'd like to update the +217 existing card, please use the update_card method. +218 """ +219 ) +220 ) +221 +222 else: +223 self._registry.register_card( +224 card=card, +225 version_type=_version_type, +226 pre_tag=pre_tag, +227 build_tag=build_tag, +228 ) +
Adds a new Card record to registry. Registration will be skipped if the card already exists.
230 def update_card(self, card: ArtifactCard) -> None: +231 """ +232 Update an artifact card based on current registry +233 +234 Args: +235 card: +236 Card to register +237 """ +238 return self._registry.update_card(card=card) +
Update an artifact card based on current registry
+ +240 def query_value_from_card(self, uid: str, columns: List[str]) -> Dict[str, Any]: +241 """ +242 Query column values from a specific Card +243 +244 Args: +245 uid: +246 Uid of Card +247 columns: +248 List of columns to query +249 +250 Returns: +251 Dictionary of column, values pairs +252 """ +253 results = self._registry.list_cards(uid=uid)[0] +254 return {col: results[col] for col in columns} +
Query column values from a specific Card
+ +++Dictionary of column, values pairs
+
267class CardRegistries: +268 def __init__(self) -> None: +269 """Instantiates class that contains all registries""" +270 +271 self.data = CardRegistry(registry_type=RegistryType.DATA) +272 self.model = CardRegistry(registry_type=RegistryType.MODEL) +273 self.run = CardRegistry(registry_type=RegistryType.RUN) +274 self.pipeline = CardRegistry(registry_type=RegistryType.PIPELINE) +275 self.project = CardRegistry(registry_type=RegistryType.PROJECT) +276 self.audit = CardRegistry(registry_type=RegistryType.AUDIT) +
268 def __init__(self) -> None: +269 """Instantiates class that contains all registries""" +270 +271 self.data = CardRegistry(registry_type=RegistryType.DATA) +272 self.model = CardRegistry(registry_type=RegistryType.MODEL) +273 self.run = CardRegistry(registry_type=RegistryType.RUN) +274 self.pipeline = CardRegistry(registry_type=RegistryType.PIPELINE) +275 self.project = CardRegistry(registry_type=RegistryType.PROJECT) +276 self.audit = CardRegistry(registry_type=RegistryType.AUDIT) +
Instantiates class that contains all registries
+1# Copyright (c) Shipt, Inc. + 2# This source code is licensed under the MIT license found in the + 3# LICENSE file in the root directory of this source tree. + 4import re + 5from enum import Enum + 6from typing import Any, List, Optional + 7 + 8import semver + 9from pydantic import BaseModel, model_validator + 10 + 11from opsml.helpers.exceptions import VersionError + 12from opsml.helpers.logging import ArtifactLogger + 13 + 14logger = ArtifactLogger.get_logger() + 15 + 16 + 17class VersionType(str, Enum): + 18 MAJOR = "major" + 19 MINOR = "minor" + 20 PATCH = "patch" + 21 PRE = "pre" + 22 BUILD = "build" + 23 PRE_BUILD = "pre_build" + 24 + 25 @staticmethod + 26 def from_str(name: str) -> "VersionType": + 27 l_name = name.strip().lower() + 28 if l_name == "major": + 29 return VersionType.MAJOR + 30 if l_name == "minor": + 31 return VersionType.MINOR + 32 if l_name == "patch": + 33 return VersionType.PATCH + 34 if l_name == "pre": + 35 return VersionType.PRE + 36 if l_name == "build": + 37 return VersionType.BUILD + 38 if l_name == "pre_build": + 39 return VersionType.PRE_BUILD + 40 raise NotImplementedError() + 41 + 42 + 43class CardVersion(BaseModel): + 44 version: str + 45 version_splits: List[str] = [] + 46 is_full_semver: bool = False + 47 + 48 @model_validator(mode="before") + 49 @classmethod + 50 def validate_inputs(cls, values: Any) -> Any: + 51 """Validates a user-supplied version""" + 52 version = values.get("version") + 53 splits = version.split(".") + 54 values["version_splits"] = splits + 55 + 56 if cls.check_full_semver(splits): + 57 values["is_full_semver"] = True + 58 cls._validate_full_semver(version) + 59 else: + 60 values["is_full_semver"] = False + 61 cls._validate_partial_semver(splits) + 62 + 63 return values + 64 + 65 @classmethod + 66 def check_full_semver(cls, version_splits: List[str]) -> bool: + 67 """Checks if a version is a full semver""" + 68 return len(version_splits) >= 3 + 69 + 70 @classmethod + 71 def _validate_full_semver(cls, version: str) -> None: + 72 """Validates a full semver""" + 73 if not semver.VersionInfo.isvalid(version): + 74 raise ValueError("Version is not a valid Semver") + 75 + 76 @classmethod + 77 def _validate_partial_semver(cls, version_splits: List[str]) -> None: + 78 """Validates a partial semver""" + 79 try: + 80 assert all((i.isdigit() for i in version_splits)) + 81 except AssertionError as exc: + 82 version = ".".join(version_splits) + 83 raise AssertionError(f"Version {version} is not a valid semver or partial semver") from exc + 84 + 85 def _get_version_split(self, split: int) -> str: + 86 """Splits a version into its major, minor, and patch components""" + 87 + 88 try: + 89 return self.version_splits[split] + 90 except IndexError as exc: + 91 raise IndexError(f"Version split {split} not found: {self.version}") from exc + 92 + 93 @property + 94 def has_major_minor(self) -> bool: + 95 """Checks if a version has a major and minor component""" + 96 return len(self.version_splits) >= 2 + 97 + 98 @property + 99 def major(self) -> str: +100 return self._get_version_split(0) +101 +102 @property +103 def minor(self) -> str: +104 return self._get_version_split(1) +105 +106 @property +107 def patch(self) -> str: +108 return self._get_version_split(2) +109 +110 @property +111 def valid_version(self) -> str: +112 if self.is_full_semver: +113 return str(semver.VersionInfo.parse(self.version).finalize_version()) +114 return self.version +115 +116 @staticmethod +117 def finalize_partial_version(version: str) -> str: +118 """Finalizes a partial semver version +119 +120 Args: +121 version: +122 version to finalize +123 Returns: +124 str: finalized version +125 """ +126 version_splits = version.split(".") +127 +128 if len(version_splits) == 1: +129 return f"{version}.0.0" +130 if len(version_splits) == 2: +131 return f"{version}.0" +132 +133 return version +134 +135 def get_version_to_search(self, version_type: VersionType) -> Optional[str]: +136 """Gets a version to search for in the database +137 +138 Args: +139 version_type: +140 type of version to search for +141 Returns: +142 str: version to search for +143 """ +144 +145 if version_type == VersionType.PATCH: # want to search major and minor if exists +146 if self.has_major_minor: +147 return f"{self.major}.{self.minor}" +148 return str(self.major) +149 +150 if version_type == VersionType.MINOR: # want to search major +151 return str(self.major) +152 +153 if version_type in [VersionType.PRE, VersionType.BUILD, VersionType.PRE_BUILD]: +154 return self.valid_version +155 +156 return None +157 +158 +159class SemVerUtils: +160 """Class for general semver-related functions""" +161 +162 @staticmethod +163 def sort_semvers(versions: List[str]) -> List[str]: +164 """Implements bubble sort for semvers +165 +166 Args: +167 versions: +168 list of versions to sort +169 +170 Returns: +171 sorted list of versions with highest version first +172 """ +173 +174 n_ver = len(versions) +175 +176 for i in range(n_ver): +177 already_sorted = True +178 +179 for j in range(n_ver - i - 1): +180 j_version = semver.VersionInfo.parse(versions[j]) +181 j1_version = semver.VersionInfo.parse(versions[j + 1]) +182 +183 # use semver comparison logic +184 if j_version > j1_version: +185 # swap +186 versions[j], versions[j + 1] = versions[j + 1], versions[j] +187 +188 already_sorted = False +189 +190 if already_sorted: +191 break +192 +193 versions.reverse() +194 return versions +195 +196 @staticmethod +197 def is_release_candidate(version: str) -> bool: +198 """Ignores pre-release versions""" +199 ver = semver.VersionInfo.parse(version) +200 return bool(ver.prerelease) +201 +202 @staticmethod +203 def increment_version( +204 version: str, +205 version_type: VersionType, +206 pre_tag: str, +207 build_tag: str, +208 ) -> str: +209 """ +210 Increments a version based on version type +211 +212 Args: +213 version: +214 Current version +215 version_type: +216 Type of version increment. +217 pre_tag: +218 Pre-release tag +219 build_tag: +220 Build tag +221 +222 Raises: +223 ValueError: +224 unknown version_type +225 +226 Returns: +227 New version +228 """ +229 ver: semver.VersionInfo = semver.VersionInfo.parse(version) +230 +231 # Set major, minor, patch +232 if version_type == VersionType.MAJOR: +233 return str(ver.bump_major()) +234 if version_type == VersionType.MINOR: +235 return str(ver.bump_minor()) +236 if version_type == VersionType.PATCH: +237 return str(ver.bump_patch()) +238 +239 # Set pre-release +240 if version_type == VersionType.PRE: +241 return str(ver.bump_prerelease(token=pre_tag)) +242 +243 # Set build +244 if version_type == VersionType.BUILD: +245 return str(ver.bump_build(token=build_tag)) +246 +247 if version_type == VersionType.PRE_BUILD: +248 ver = ver.bump_prerelease(token=pre_tag) +249 ver = ver.bump_build(token=build_tag) +250 +251 return str(ver) +252 +253 raise ValueError(f"Unknown version_type: {version_type}") +254 +255 @staticmethod +256 def add_tags( +257 version: str, +258 pre_tag: Optional[str] = None, +259 build_tag: Optional[str] = None, +260 ) -> str: +261 if pre_tag is not None: +262 version = f"{version}-{pre_tag}" +263 if build_tag is not None: +264 version = f"{version}+{build_tag}" +265 +266 return version +267 +268 +269class SemVerRegistryValidator: +270 """Class for obtaining the correct registry version""" +271 +272 def __init__( +273 self, +274 name: str, +275 version_type: VersionType, +276 pre_tag: str, +277 build_tag: str, +278 version: Optional[CardVersion] = None, +279 ) -> None: +280 """Instantiate SemverValidator +281 +282 Args: +283 name: +284 name of the artifact +285 version_type: +286 type of version increment +287 version: +288 version to use +289 pre_tag: +290 pre-release tag +291 build_tag: +292 build tag +293 +294 Returns: +295 None +296 """ +297 self.version = version +298 self._version_to_search = None +299 self.final_version = None +300 self.version_type = version_type +301 self.name = name +302 self.pre_tag = pre_tag +303 self.build_tag = build_tag +304 +305 @property +306 def version_to_search(self) -> Optional[str]: +307 """Parses version and returns version to search for in the registry""" +308 if self.version is not None: +309 return self.version.get_version_to_search(version_type=self.version_type) +310 return self._version_to_search +311 +312 def _set_version_from_existing(self, versions: List[str]) -> str: +313 """Search existing versions to find the correct version to use +314 +315 Args: +316 versions: +317 list of existing versions +318 +319 Returns: +320 str: version to use +321 """ +322 version = versions[0] +323 recent_ver = semver.VersionInfo.parse(version) +324 # first need to check if increment is mmp +325 if self.version_type in [VersionType.MAJOR, VersionType.MINOR, VersionType.PATCH]: +326 # check if most recent version is a pre-release or build +327 if recent_ver.prerelease is not None: +328 version = str(recent_ver.finalize_version()) +329 try: +330 # if all versions are pre-release use finalized version +331 # if not, increment version +332 for ver in versions: +333 parsed_ver = semver.VersionInfo.parse(ver) +334 if parsed_ver.prerelease is None: +335 raise VersionError("Major, minor and patch version combination already exists") +336 return version +337 except VersionError: +338 logger.info("Major, minor and patch version combination already exists") +339 +340 while version in versions: +341 version = SemVerUtils.increment_version( +342 version=version, +343 version_type=self.version_type, +344 pre_tag=self.pre_tag, +345 build_tag=self.build_tag, +346 ) +347 +348 return version +349 +350 def set_version(self, versions: List[str]) -> str: +351 """Sets the correct version to use for incrementing and adding the the registry +352 +353 Args: +354 versions: +355 list of existing versions +356 +357 Returns: +358 str: version to use +359 """ +360 if bool(versions): +361 return self._set_version_from_existing(versions=versions) +362 +363 final_version = None +364 if self.version is not None: +365 final_version = CardVersion.finalize_partial_version(version=self.version.valid_version) +366 +367 version = final_version or "1.0.0" +368 +369 if self.version_type in [VersionType.PRE, VersionType.BUILD, VersionType.PRE_BUILD]: +370 return SemVerUtils.increment_version( +371 version=version, +372 version_type=self.version_type, +373 pre_tag=self.pre_tag, +374 build_tag=self.build_tag, +375 ) +376 +377 return version +378 +379 +380class SemVerSymbols(str, Enum): +381 STAR = "*" +382 CARET = "^" +383 TILDE = "~" +384 +385 +386class SemVerParser: +387 """Base class for semver parsing""" +388 +389 @staticmethod +390 def parse_version(version: str) -> str: +391 raise NotImplementedError +392 +393 @staticmethod +394 def validate(version: str) -> bool: +395 raise NotImplementedError +396 +397 +398class StarParser(SemVerParser): +399 """Parses versions that contain * symbol""" +400 +401 @staticmethod +402 def parse_version(version: str) -> str: +403 version_ = version.split(SemVerSymbols.STAR)[0] +404 return re.sub(".$", "", version_) +405 +406 @staticmethod +407 def validate(version: str) -> bool: +408 return SemVerSymbols.STAR in version +409 +410 +411class CaretParser(SemVerParser): +412 """Parses versions that contain ^ symbol""" +413 +414 @staticmethod +415 def parse_version(version: str) -> str: +416 return version.split(".")[0].replace(SemVerSymbols.CARET, "") +417 +418 @staticmethod +419 def validate(version: str) -> bool: +420 return SemVerSymbols.CARET in version +421 +422 +423class TildeParser(SemVerParser): +424 """Parses versions that contain ~ symbol""" +425 +426 @staticmethod +427 def parse_version(version: str) -> str: +428 return ".".join(version.split(".")[0:2]).replace(SemVerSymbols.TILDE, "") +429 +430 @staticmethod +431 def validate(version: str) -> bool: +432 return SemVerSymbols.TILDE in version +433 +434 +435class NoParser(SemVerParser): +436 """Does not parse version""" +437 +438 @staticmethod +439 def parse_version(version: str) -> str: +440 return version +441 +442 @staticmethod +443 def validate(version: str) -> bool: +444 return version not in list(SemVerSymbols) +445 +446 +447def get_version_to_search(version: str) -> str: +448 """Parses a current version based on SemVer characters. +449 +450 Args: +451 version (str): ArtifactCard version +452 +453 Returns: +454 Version (str) to search based on presence of SemVer characters +455 """ +456 +457 # gut check +458 if sum(symbol in version for symbol in SemVerSymbols) > 1: +459 raise ValueError("Only one SemVer character is allowed in the version string") +460 +461 parser = next( +462 (parser for parser in SemVerParser.__subclasses__() if parser.validate(version=version)), +463 NoParser, +464 ) +465 return parser.parse_version(version=version) +
18class VersionType(str, Enum): +19 MAJOR = "major" +20 MINOR = "minor" +21 PATCH = "patch" +22 PRE = "pre" +23 BUILD = "build" +24 PRE_BUILD = "pre_build" +25 +26 @staticmethod +27 def from_str(name: str) -> "VersionType": +28 l_name = name.strip().lower() +29 if l_name == "major": +30 return VersionType.MAJOR +31 if l_name == "minor": +32 return VersionType.MINOR +33 if l_name == "patch": +34 return VersionType.PATCH +35 if l_name == "pre": +36 return VersionType.PRE +37 if l_name == "build": +38 return VersionType.BUILD +39 if l_name == "pre_build": +40 return VersionType.PRE_BUILD +41 raise NotImplementedError() +
An enumeration.
+26 @staticmethod +27 def from_str(name: str) -> "VersionType": +28 l_name = name.strip().lower() +29 if l_name == "major": +30 return VersionType.MAJOR +31 if l_name == "minor": +32 return VersionType.MINOR +33 if l_name == "patch": +34 return VersionType.PATCH +35 if l_name == "pre": +36 return VersionType.PRE +37 if l_name == "build": +38 return VersionType.BUILD +39 if l_name == "pre_build": +40 return VersionType.PRE_BUILD +41 raise NotImplementedError() +
44class CardVersion(BaseModel): + 45 version: str + 46 version_splits: List[str] = [] + 47 is_full_semver: bool = False + 48 + 49 @model_validator(mode="before") + 50 @classmethod + 51 def validate_inputs(cls, values: Any) -> Any: + 52 """Validates a user-supplied version""" + 53 version = values.get("version") + 54 splits = version.split(".") + 55 values["version_splits"] = splits + 56 + 57 if cls.check_full_semver(splits): + 58 values["is_full_semver"] = True + 59 cls._validate_full_semver(version) + 60 else: + 61 values["is_full_semver"] = False + 62 cls._validate_partial_semver(splits) + 63 + 64 return values + 65 + 66 @classmethod + 67 def check_full_semver(cls, version_splits: List[str]) -> bool: + 68 """Checks if a version is a full semver""" + 69 return len(version_splits) >= 3 + 70 + 71 @classmethod + 72 def _validate_full_semver(cls, version: str) -> None: + 73 """Validates a full semver""" + 74 if not semver.VersionInfo.isvalid(version): + 75 raise ValueError("Version is not a valid Semver") + 76 + 77 @classmethod + 78 def _validate_partial_semver(cls, version_splits: List[str]) -> None: + 79 """Validates a partial semver""" + 80 try: + 81 assert all((i.isdigit() for i in version_splits)) + 82 except AssertionError as exc: + 83 version = ".".join(version_splits) + 84 raise AssertionError(f"Version {version} is not a valid semver or partial semver") from exc + 85 + 86 def _get_version_split(self, split: int) -> str: + 87 """Splits a version into its major, minor, and patch components""" + 88 + 89 try: + 90 return self.version_splits[split] + 91 except IndexError as exc: + 92 raise IndexError(f"Version split {split} not found: {self.version}") from exc + 93 + 94 @property + 95 def has_major_minor(self) -> bool: + 96 """Checks if a version has a major and minor component""" + 97 return len(self.version_splits) >= 2 + 98 + 99 @property +100 def major(self) -> str: +101 return self._get_version_split(0) +102 +103 @property +104 def minor(self) -> str: +105 return self._get_version_split(1) +106 +107 @property +108 def patch(self) -> str: +109 return self._get_version_split(2) +110 +111 @property +112 def valid_version(self) -> str: +113 if self.is_full_semver: +114 return str(semver.VersionInfo.parse(self.version).finalize_version()) +115 return self.version +116 +117 @staticmethod +118 def finalize_partial_version(version: str) -> str: +119 """Finalizes a partial semver version +120 +121 Args: +122 version: +123 version to finalize +124 Returns: +125 str: finalized version +126 """ +127 version_splits = version.split(".") +128 +129 if len(version_splits) == 1: +130 return f"{version}.0.0" +131 if len(version_splits) == 2: +132 return f"{version}.0" +133 +134 return version +135 +136 def get_version_to_search(self, version_type: VersionType) -> Optional[str]: +137 """Gets a version to search for in the database +138 +139 Args: +140 version_type: +141 type of version to search for +142 Returns: +143 str: version to search for +144 """ +145 +146 if version_type == VersionType.PATCH: # want to search major and minor if exists +147 if self.has_major_minor: +148 return f"{self.major}.{self.minor}" +149 return str(self.major) +150 +151 if version_type == VersionType.MINOR: # want to search major +152 return str(self.major) +153 +154 if version_type in [VersionType.PRE, VersionType.BUILD, VersionType.PRE_BUILD]: +155 return self.valid_version +156 +157 return None +
Usage docs: https://docs.pydantic.dev/2.6/concepts/models/
+ +A base class for creating Pydantic models.
+ +__init__ function.Model.__validators__ and Model.__root_validators__ from Pydantic V1.RootModel.model_config['extra'] == 'allow'.49 @model_validator(mode="before") +50 @classmethod +51 def validate_inputs(cls, values: Any) -> Any: +52 """Validates a user-supplied version""" +53 version = values.get("version") +54 splits = version.split(".") +55 values["version_splits"] = splits +56 +57 if cls.check_full_semver(splits): +58 values["is_full_semver"] = True +59 cls._validate_full_semver(version) +60 else: +61 values["is_full_semver"] = False +62 cls._validate_partial_semver(splits) +63 +64 return values +
Validates a user-supplied version
+66 @classmethod +67 def check_full_semver(cls, version_splits: List[str]) -> bool: +68 """Checks if a version is a full semver""" +69 return len(version_splits) >= 3 +
Checks if a version is a full semver
+94 @property +95 def has_major_minor(self) -> bool: +96 """Checks if a version has a major and minor component""" +97 return len(self.version_splits) >= 2 +
Checks if a version has a major and minor component
+117 @staticmethod +118 def finalize_partial_version(version: str) -> str: +119 """Finalizes a partial semver version +120 +121 Args: +122 version: +123 version to finalize +124 Returns: +125 str: finalized version +126 """ +127 version_splits = version.split(".") +128 +129 if len(version_splits) == 1: +130 return f"{version}.0.0" +131 if len(version_splits) == 2: +132 return f"{version}.0" +133 +134 return version +
Finalizes a partial semver version
+ +++str: finalized version
+
136 def get_version_to_search(self, version_type: VersionType) -> Optional[str]: +137 """Gets a version to search for in the database +138 +139 Args: +140 version_type: +141 type of version to search for +142 Returns: +143 str: version to search for +144 """ +145 +146 if version_type == VersionType.PATCH: # want to search major and minor if exists +147 if self.has_major_minor: +148 return f"{self.major}.{self.minor}" +149 return str(self.major) +150 +151 if version_type == VersionType.MINOR: # want to search major +152 return str(self.major) +153 +154 if version_type in [VersionType.PRE, VersionType.BUILD, VersionType.PRE_BUILD]: +155 return self.valid_version +156 +157 return None +
Gets a version to search for in the database
+ +++str: version to search for
+
160class SemVerUtils: +161 """Class for general semver-related functions""" +162 +163 @staticmethod +164 def sort_semvers(versions: List[str]) -> List[str]: +165 """Implements bubble sort for semvers +166 +167 Args: +168 versions: +169 list of versions to sort +170 +171 Returns: +172 sorted list of versions with highest version first +173 """ +174 +175 n_ver = len(versions) +176 +177 for i in range(n_ver): +178 already_sorted = True +179 +180 for j in range(n_ver - i - 1): +181 j_version = semver.VersionInfo.parse(versions[j]) +182 j1_version = semver.VersionInfo.parse(versions[j + 1]) +183 +184 # use semver comparison logic +185 if j_version > j1_version: +186 # swap +187 versions[j], versions[j + 1] = versions[j + 1], versions[j] +188 +189 already_sorted = False +190 +191 if already_sorted: +192 break +193 +194 versions.reverse() +195 return versions +196 +197 @staticmethod +198 def is_release_candidate(version: str) -> bool: +199 """Ignores pre-release versions""" +200 ver = semver.VersionInfo.parse(version) +201 return bool(ver.prerelease) +202 +203 @staticmethod +204 def increment_version( +205 version: str, +206 version_type: VersionType, +207 pre_tag: str, +208 build_tag: str, +209 ) -> str: +210 """ +211 Increments a version based on version type +212 +213 Args: +214 version: +215 Current version +216 version_type: +217 Type of version increment. +218 pre_tag: +219 Pre-release tag +220 build_tag: +221 Build tag +222 +223 Raises: +224 ValueError: +225 unknown version_type +226 +227 Returns: +228 New version +229 """ +230 ver: semver.VersionInfo = semver.VersionInfo.parse(version) +231 +232 # Set major, minor, patch +233 if version_type == VersionType.MAJOR: +234 return str(ver.bump_major()) +235 if version_type == VersionType.MINOR: +236 return str(ver.bump_minor()) +237 if version_type == VersionType.PATCH: +238 return str(ver.bump_patch()) +239 +240 # Set pre-release +241 if version_type == VersionType.PRE: +242 return str(ver.bump_prerelease(token=pre_tag)) +243 +244 # Set build +245 if version_type == VersionType.BUILD: +246 return str(ver.bump_build(token=build_tag)) +247 +248 if version_type == VersionType.PRE_BUILD: +249 ver = ver.bump_prerelease(token=pre_tag) +250 ver = ver.bump_build(token=build_tag) +251 +252 return str(ver) +253 +254 raise ValueError(f"Unknown version_type: {version_type}") +255 +256 @staticmethod +257 def add_tags( +258 version: str, +259 pre_tag: Optional[str] = None, +260 build_tag: Optional[str] = None, +261 ) -> str: +262 if pre_tag is not None: +263 version = f"{version}-{pre_tag}" +264 if build_tag is not None: +265 version = f"{version}+{build_tag}" +266 +267 return version +
Class for general semver-related functions
+163 @staticmethod +164 def sort_semvers(versions: List[str]) -> List[str]: +165 """Implements bubble sort for semvers +166 +167 Args: +168 versions: +169 list of versions to sort +170 +171 Returns: +172 sorted list of versions with highest version first +173 """ +174 +175 n_ver = len(versions) +176 +177 for i in range(n_ver): +178 already_sorted = True +179 +180 for j in range(n_ver - i - 1): +181 j_version = semver.VersionInfo.parse(versions[j]) +182 j1_version = semver.VersionInfo.parse(versions[j + 1]) +183 +184 # use semver comparison logic +185 if j_version > j1_version: +186 # swap +187 versions[j], versions[j + 1] = versions[j + 1], versions[j] +188 +189 already_sorted = False +190 +191 if already_sorted: +192 break +193 +194 versions.reverse() +195 return versions +
Implements bubble sort for semvers
+ +++sorted list of versions with highest version first
+
197 @staticmethod +198 def is_release_candidate(version: str) -> bool: +199 """Ignores pre-release versions""" +200 ver = semver.VersionInfo.parse(version) +201 return bool(ver.prerelease) +
Ignores pre-release versions
+203 @staticmethod +204 def increment_version( +205 version: str, +206 version_type: VersionType, +207 pre_tag: str, +208 build_tag: str, +209 ) -> str: +210 """ +211 Increments a version based on version type +212 +213 Args: +214 version: +215 Current version +216 version_type: +217 Type of version increment. +218 pre_tag: +219 Pre-release tag +220 build_tag: +221 Build tag +222 +223 Raises: +224 ValueError: +225 unknown version_type +226 +227 Returns: +228 New version +229 """ +230 ver: semver.VersionInfo = semver.VersionInfo.parse(version) +231 +232 # Set major, minor, patch +233 if version_type == VersionType.MAJOR: +234 return str(ver.bump_major()) +235 if version_type == VersionType.MINOR: +236 return str(ver.bump_minor()) +237 if version_type == VersionType.PATCH: +238 return str(ver.bump_patch()) +239 +240 # Set pre-release +241 if version_type == VersionType.PRE: +242 return str(ver.bump_prerelease(token=pre_tag)) +243 +244 # Set build +245 if version_type == VersionType.BUILD: +246 return str(ver.bump_build(token=build_tag)) +247 +248 if version_type == VersionType.PRE_BUILD: +249 ver = ver.bump_prerelease(token=pre_tag) +250 ver = ver.bump_build(token=build_tag) +251 +252 return str(ver) +253 +254 raise ValueError(f"Unknown version_type: {version_type}") +
Increments a version based on version type
+ +++New version
+
270class SemVerRegistryValidator: +271 """Class for obtaining the correct registry version""" +272 +273 def __init__( +274 self, +275 name: str, +276 version_type: VersionType, +277 pre_tag: str, +278 build_tag: str, +279 version: Optional[CardVersion] = None, +280 ) -> None: +281 """Instantiate SemverValidator +282 +283 Args: +284 name: +285 name of the artifact +286 version_type: +287 type of version increment +288 version: +289 version to use +290 pre_tag: +291 pre-release tag +292 build_tag: +293 build tag +294 +295 Returns: +296 None +297 """ +298 self.version = version +299 self._version_to_search = None +300 self.final_version = None +301 self.version_type = version_type +302 self.name = name +303 self.pre_tag = pre_tag +304 self.build_tag = build_tag +305 +306 @property +307 def version_to_search(self) -> Optional[str]: +308 """Parses version and returns version to search for in the registry""" +309 if self.version is not None: +310 return self.version.get_version_to_search(version_type=self.version_type) +311 return self._version_to_search +312 +313 def _set_version_from_existing(self, versions: List[str]) -> str: +314 """Search existing versions to find the correct version to use +315 +316 Args: +317 versions: +318 list of existing versions +319 +320 Returns: +321 str: version to use +322 """ +323 version = versions[0] +324 recent_ver = semver.VersionInfo.parse(version) +325 # first need to check if increment is mmp +326 if self.version_type in [VersionType.MAJOR, VersionType.MINOR, VersionType.PATCH]: +327 # check if most recent version is a pre-release or build +328 if recent_ver.prerelease is not None: +329 version = str(recent_ver.finalize_version()) +330 try: +331 # if all versions are pre-release use finalized version +332 # if not, increment version +333 for ver in versions: +334 parsed_ver = semver.VersionInfo.parse(ver) +335 if parsed_ver.prerelease is None: +336 raise VersionError("Major, minor and patch version combination already exists") +337 return version +338 except VersionError: +339 logger.info("Major, minor and patch version combination already exists") +340 +341 while version in versions: +342 version = SemVerUtils.increment_version( +343 version=version, +344 version_type=self.version_type, +345 pre_tag=self.pre_tag, +346 build_tag=self.build_tag, +347 ) +348 +349 return version +350 +351 def set_version(self, versions: List[str]) -> str: +352 """Sets the correct version to use for incrementing and adding the the registry +353 +354 Args: +355 versions: +356 list of existing versions +357 +358 Returns: +359 str: version to use +360 """ +361 if bool(versions): +362 return self._set_version_from_existing(versions=versions) +363 +364 final_version = None +365 if self.version is not None: +366 final_version = CardVersion.finalize_partial_version(version=self.version.valid_version) +367 +368 version = final_version or "1.0.0" +369 +370 if self.version_type in [VersionType.PRE, VersionType.BUILD, VersionType.PRE_BUILD]: +371 return SemVerUtils.increment_version( +372 version=version, +373 version_type=self.version_type, +374 pre_tag=self.pre_tag, +375 build_tag=self.build_tag, +376 ) +377 +378 return version +
Class for obtaining the correct registry version
+273 def __init__( +274 self, +275 name: str, +276 version_type: VersionType, +277 pre_tag: str, +278 build_tag: str, +279 version: Optional[CardVersion] = None, +280 ) -> None: +281 """Instantiate SemverValidator +282 +283 Args: +284 name: +285 name of the artifact +286 version_type: +287 type of version increment +288 version: +289 version to use +290 pre_tag: +291 pre-release tag +292 build_tag: +293 build tag +294 +295 Returns: +296 None +297 """ +298 self.version = version +299 self._version_to_search = None +300 self.final_version = None +301 self.version_type = version_type +302 self.name = name +303 self.pre_tag = pre_tag +304 self.build_tag = build_tag +
Instantiate SemverValidator
+ +++None
+
306 @property +307 def version_to_search(self) -> Optional[str]: +308 """Parses version and returns version to search for in the registry""" +309 if self.version is not None: +310 return self.version.get_version_to_search(version_type=self.version_type) +311 return self._version_to_search +
Parses version and returns version to search for in the registry
+351 def set_version(self, versions: List[str]) -> str: +352 """Sets the correct version to use for incrementing and adding the the registry +353 +354 Args: +355 versions: +356 list of existing versions +357 +358 Returns: +359 str: version to use +360 """ +361 if bool(versions): +362 return self._set_version_from_existing(versions=versions) +363 +364 final_version = None +365 if self.version is not None: +366 final_version = CardVersion.finalize_partial_version(version=self.version.valid_version) +367 +368 version = final_version or "1.0.0" +369 +370 if self.version_type in [VersionType.PRE, VersionType.BUILD, VersionType.PRE_BUILD]: +371 return SemVerUtils.increment_version( +372 version=version, +373 version_type=self.version_type, +374 pre_tag=self.pre_tag, +375 build_tag=self.build_tag, +376 ) +377 +378 return version +
Sets the correct version to use for incrementing and adding the the registry
+ +++str: version to use
+
An enumeration.
+387class SemVerParser: +388 """Base class for semver parsing""" +389 +390 @staticmethod +391 def parse_version(version: str) -> str: +392 raise NotImplementedError +393 +394 @staticmethod +395 def validate(version: str) -> bool: +396 raise NotImplementedError +
Base class for semver parsing
+399class StarParser(SemVerParser): +400 """Parses versions that contain * symbol""" +401 +402 @staticmethod +403 def parse_version(version: str) -> str: +404 version_ = version.split(SemVerSymbols.STAR)[0] +405 return re.sub(".$", "", version_) +406 +407 @staticmethod +408 def validate(version: str) -> bool: +409 return SemVerSymbols.STAR in version +
Parses versions that contain * symbol
+412class CaretParser(SemVerParser): +413 """Parses versions that contain ^ symbol""" +414 +415 @staticmethod +416 def parse_version(version: str) -> str: +417 return version.split(".")[0].replace(SemVerSymbols.CARET, "") +418 +419 @staticmethod +420 def validate(version: str) -> bool: +421 return SemVerSymbols.CARET in version +
Parses versions that contain ^ symbol
+424class TildeParser(SemVerParser): +425 """Parses versions that contain ~ symbol""" +426 +427 @staticmethod +428 def parse_version(version: str) -> str: +429 return ".".join(version.split(".")[0:2]).replace(SemVerSymbols.TILDE, "") +430 +431 @staticmethod +432 def validate(version: str) -> bool: +433 return SemVerSymbols.TILDE in version +
Parses versions that contain ~ symbol
+436class NoParser(SemVerParser): +437 """Does not parse version""" +438 +439 @staticmethod +440 def parse_version(version: str) -> str: +441 return version +442 +443 @staticmethod +444 def validate(version: str) -> bool: +445 return version not in list(SemVerSymbols) +
Does not parse version
+448def get_version_to_search(version: str) -> str: +449 """Parses a current version based on SemVer characters. +450 +451 Args: +452 version (str): ArtifactCard version +453 +454 Returns: +455 Version (str) to search based on presence of SemVer characters +456 """ +457 +458 # gut check +459 if sum(symbol in version for symbol in SemVerSymbols) > 1: +460 raise ValueError("Only one SemVer character is allowed in the version string") +461 +462 parser = next( +463 (parser for parser in SemVerParser.__subclasses__() if parser.validate(version=version)), +464 NoParser, +465 ) +466 return parser.parse_version(version=version) +
Parses a current version based on SemVer characters.
+ +++Version (str) to search based on presence of SemVer characters
+
1# Copyright (c) Shipt, Inc. + 2# This source code is licensed under the MIT license found in the + 3# LICENSE file in the root directory of this source tree. + 4 + 5import re + 6from pathlib import Path + 7from typing import Optional + 8 + 9from pydantic import field_validator +10from pydantic_settings import BaseSettings +11 +12from opsml.types import StorageSystem +13 +14 +15class OpsmlConfig(BaseSettings): +16 app_name: str = "opsml" +17 app_env: str = "development" +18 +19 opsml_storage_uri: str = "./mlruns" +20 opsml_tracking_uri: str = "sqlite:///tmp.db" +21 opsml_prod_token: str = "staging" +22 opsml_proxy_root: str = "opsml-root:/" +23 opsml_registry_path: str = "model_registry" +24 opsml_testing: bool = bool(0) +25 download_chunk_size: int = 31457280 # 30MB +26 upload_chunk_size: int = 31457280 # 30MB +27 +28 # API client username / password +29 opsml_username: Optional[str] = None +30 opsml_password: Optional[str] = None +31 +32 # The current RUN_ID to load when creating a new project +33 opsml_run_id: Optional[str] = None +34 +35 @field_validator("opsml_storage_uri", mode="before") +36 @classmethod +37 def set_opsml_storage_uri(cls, opsml_storage_uri: str) -> str: +38 """Opsml uses storage cients that follow fsspec guidelines. LocalFileSytem only deals +39 in absolutes, so we need to convert relative paths to absolute paths. +40 """ +41 if opsml_storage_uri.startswith("gs://") or opsml_storage_uri.startswith("s3://"): +42 return opsml_storage_uri +43 +44 return Path(opsml_storage_uri).absolute().as_posix() +45 +46 @property +47 def is_tracking_local(self) -> bool: +48 """Used to determine if an API client will be used. +49 +50 If tracking is local, the [server] extra is required. +51 """ +52 return not self.opsml_tracking_uri.lower().strip().startswith("http") +53 +54 @property +55 def storage_system(self) -> StorageSystem: +56 """Returns the storage system used for the current tracking URI""" +57 if self.is_tracking_local: +58 if self.opsml_storage_uri.startswith("gs://"): +59 return StorageSystem.GCS +60 if self.opsml_storage_uri.startswith("s3://"): +61 return StorageSystem.S3 +62 return StorageSystem.LOCAL +63 return StorageSystem.API +64 +65 @property +66 def storage_root(self) -> str: +67 """Returns the root of the storage URI""" +68 if self.is_tracking_local: +69 storage_uri_lower = self.opsml_storage_uri.lower() +70 if storage_uri_lower.startswith("gs://"): +71 return re.sub("^gs://", "", storage_uri_lower) +72 if storage_uri_lower.startswith("s3://"): +73 return re.sub("^s3://", "", storage_uri_lower) +74 return storage_uri_lower +75 return self.opsml_proxy_root +76 +77 +78config = OpsmlConfig() +
16class OpsmlConfig(BaseSettings): +17 app_name: str = "opsml" +18 app_env: str = "development" +19 +20 opsml_storage_uri: str = "./mlruns" +21 opsml_tracking_uri: str = "sqlite:///tmp.db" +22 opsml_prod_token: str = "staging" +23 opsml_proxy_root: str = "opsml-root:/" +24 opsml_registry_path: str = "model_registry" +25 opsml_testing: bool = bool(0) +26 download_chunk_size: int = 31457280 # 30MB +27 upload_chunk_size: int = 31457280 # 30MB +28 +29 # API client username / password +30 opsml_username: Optional[str] = None +31 opsml_password: Optional[str] = None +32 +33 # The current RUN_ID to load when creating a new project +34 opsml_run_id: Optional[str] = None +35 +36 @field_validator("opsml_storage_uri", mode="before") +37 @classmethod +38 def set_opsml_storage_uri(cls, opsml_storage_uri: str) -> str: +39 """Opsml uses storage cients that follow fsspec guidelines. LocalFileSytem only deals +40 in absolutes, so we need to convert relative paths to absolute paths. +41 """ +42 if opsml_storage_uri.startswith("gs://") or opsml_storage_uri.startswith("s3://"): +43 return opsml_storage_uri +44 +45 return Path(opsml_storage_uri).absolute().as_posix() +46 +47 @property +48 def is_tracking_local(self) -> bool: +49 """Used to determine if an API client will be used. +50 +51 If tracking is local, the [server] extra is required. +52 """ +53 return not self.opsml_tracking_uri.lower().strip().startswith("http") +54 +55 @property +56 def storage_system(self) -> StorageSystem: +57 """Returns the storage system used for the current tracking URI""" +58 if self.is_tracking_local: +59 if self.opsml_storage_uri.startswith("gs://"): +60 return StorageSystem.GCS +61 if self.opsml_storage_uri.startswith("s3://"): +62 return StorageSystem.S3 +63 return StorageSystem.LOCAL +64 return StorageSystem.API +65 +66 @property +67 def storage_root(self) -> str: +68 """Returns the root of the storage URI""" +69 if self.is_tracking_local: +70 storage_uri_lower = self.opsml_storage_uri.lower() +71 if storage_uri_lower.startswith("gs://"): +72 return re.sub("^gs://", "", storage_uri_lower) +73 if storage_uri_lower.startswith("s3://"): +74 return re.sub("^s3://", "", storage_uri_lower) +75 return storage_uri_lower +76 return self.opsml_proxy_root +
Base class for settings, allowing values to be overridden by environment variables.
+ +This is useful in production for secrets you do not wish to save in code, it plays nicely with docker(-compose), +Heroku and any 12 factor app design.
+ +All the below attributes can be set via model_config.
None.None.Path(''), which
+means that the value from model_config['env_file'] should be used. You can also pass
+None to indicate that environment variables should not be loaded from an env file.'latin-1'. Defaults to None.False.None.None type(None). Defaults to None type(None), which means no parsing should occur.None.36 @field_validator("opsml_storage_uri", mode="before") +37 @classmethod +38 def set_opsml_storage_uri(cls, opsml_storage_uri: str) -> str: +39 """Opsml uses storage cients that follow fsspec guidelines. LocalFileSytem only deals +40 in absolutes, so we need to convert relative paths to absolute paths. +41 """ +42 if opsml_storage_uri.startswith("gs://") or opsml_storage_uri.startswith("s3://"): +43 return opsml_storage_uri +44 +45 return Path(opsml_storage_uri).absolute().as_posix() +
Opsml uses storage cients that follow fsspec guidelines. LocalFileSytem only deals +in absolutes, so we need to convert relative paths to absolute paths.
+47 @property +48 def is_tracking_local(self) -> bool: +49 """Used to determine if an API client will be used. +50 +51 If tracking is local, the [server] extra is required. +52 """ +53 return not self.opsml_tracking_uri.lower().strip().startswith("http") +
Used to determine if an API client will be used.
+ +If tracking is local, the [server] extra is required.
+55 @property +56 def storage_system(self) -> StorageSystem: +57 """Returns the storage system used for the current tracking URI""" +58 if self.is_tracking_local: +59 if self.opsml_storage_uri.startswith("gs://"): +60 return StorageSystem.GCS +61 if self.opsml_storage_uri.startswith("s3://"): +62 return StorageSystem.S3 +63 return StorageSystem.LOCAL +64 return StorageSystem.API +
Returns the storage system used for the current tracking URI
+66 @property +67 def storage_root(self) -> str: +68 """Returns the root of the storage URI""" +69 if self.is_tracking_local: +70 storage_uri_lower = self.opsml_storage_uri.lower() +71 if storage_uri_lower.startswith("gs://"): +72 return re.sub("^gs://", "", storage_uri_lower) +73 if storage_uri_lower.startswith("s3://"): +74 return re.sub("^s3://", "", storage_uri_lower) +75 return storage_uri_lower +76 return self.opsml_proxy_root +
Returns the root of the storage URI
+1from enum import Enum + 2 + 3 + 4class HuggingFaceTask(str, Enum): + 5 AUDIO_CLASSIFICATION = "audio-classification" + 6 AUTOMATIC_SPEECH_RECOGNITION = "automatic-speech-recognition" + 7 CONVERSATIONAL = "conversational" + 8 DEPTH_ESTIMATION = "depth-estimation" + 9 DOCUMENT_QUESTION_ANSWERING = "document-question-answering" +10 FEATURE_EXTRACTION = "feature-extraction" +11 FILL_MASK = "fill-mask" +12 IMAGE_CLASSIFICATION = "image-classification" +13 IMAGE_SEGMENTATION = "image-segmentation" +14 IMAGE_TO_IMAGE = "image-to-image" +15 IMAGE_TO_TEXT = "image-to-text" +16 MASK_GENERATION = "mask-generation" +17 OBJECT_DETECTION = "object-detection" +18 QUESTION_ANSWERING = "question-answering" +19 SUMMARIZATION = "summarization" +20 TABLE_QUESTION_ANSWERING = "table-question-answering" +21 TEXT2TEXT_GENERATION = "text2text-generation" +22 TEXT_CLASSIFICATION = "text-classification" +23 TEXT_GENERATION = "text-generation" +24 TEXT_TO_AUDIO = "text-to-audio" +25 TOKEN_CLASSIFICATION = "token-classification" +26 TRANSLATION = "translation" +27 TRANSLATION_XX_TO_YY = "translation_xx_to_yy" +28 VIDEO_CLASSIFICATION = "video-classification" +29 VISUAL_QUESTION_ANSWERING = "visual-question-answering" +30 ZERO_SHOT_CLASSIFICATION = "zero-shot-classification" +31 ZERO_SHOT_IMAGE_CLASSIFICATION = "zero-shot-image-classification" +32 ZERO_SHOT_AUDIO_CLASSIFICATION = "zero-shot-audio-classification" +33 ZERO_SHOT_OBJECT_DETECTION = "zero-shot-object-detection" +34 +35 +36GENERATION_TYPES = [ +37 HuggingFaceTask.MASK_GENERATION.value, +38 HuggingFaceTask.TEXT_GENERATION.value, +39 HuggingFaceTask.TEXT2TEXT_GENERATION.value, +40] +41 +42 +43class HuggingFaceORTModel(str, Enum): +44 ORT_AUDIO_CLASSIFICATION = "ORTModelForAudioClassification" +45 ORT_AUDIO_FRAME_CLASSIFICATION = "ORTModelForAudioFrameClassification" +46 ORT_AUDIO_XVECTOR = "ORTModelForAudioXVector" +47 ORT_CUSTOM_TASKS = "ORTModelForCustomTasks" +48 ORT_CTC = "ORTModelForCTC" +49 ORT_FEATURE_EXTRACTION = "ORTModelForFeatureExtraction" +50 ORT_IMAGE_CLASSIFICATION = "ORTModelForImageClassification" +51 ORT_MASKED_LM = "ORTModelForMaskedLM" +52 ORT_MULTIPLE_CHOICE = "ORTModelForMultipleChoice" +53 ORT_QUESTION_ANSWERING = "ORTModelForQuestionAnswering" +54 ORT_SEMANTIC_SEGMENTATION = "ORTModelForSemanticSegmentation" +55 ORT_SEQUENCE_CLASSIFICATION = "ORTModelForSequenceClassification" +56 ORT_TOKEN_CLASSIFICATION = "ORTModelForTokenClassification" +57 ORT_SEQ2SEQ_LM = "ORTModelForSeq2SeqLM" +58 ORT_SPEECH_SEQ2SEQ = "ORTModelForSpeechSeq2Seq" +59 ORT_VISION2SEQ = "ORTModelForVision2Seq" +60 ORT_PIX2STRUCT = "ORTModelForPix2Struct" +61 ORT_CAUSAL_LM = "ORTModelForCausalLM" +62 ORT_OPTIMIZER = "ORTOptimizer" +63 ORT_QUANTIZER = "ORTQuantizer" +64 ORT_TRAINER = "ORTTrainer" +65 ORT_SEQ2SEQ_TRAINER = "ORTSeq2SeqTrainer" +66 ORT_TRAINING_ARGUMENTS = "ORTTrainingArguments" +67 ORT_SEQ2SEQ_TRAINING_ARGUMENTS = "ORTSeq2SeqTrainingArguments" +68 ORT_STABLE_DIFFUSION_PIPELINE = "ORTStableDiffusionPipeline" +69 ORT_STABLE_DIFFUSION_IMG2IMG_PIPELINE = "ORTStableDiffusionImg2ImgPipeline" +70 ORT_STABLE_DIFFUSION_INPAINT_PIPELINE = "ORTStableDiffusionInpaintPipeline" +71 ORT_STABLE_DIFFUSION_XL_PIPELINE = "ORTStableDiffusionXLPipeline" +72 ORT_STABLE_DIFFUSION_XL_IMG2IMG_PIPELINE = "ORTStableDiffusionXLImg2ImgPipeline" +
5class HuggingFaceTask(str, Enum): + 6 AUDIO_CLASSIFICATION = "audio-classification" + 7 AUTOMATIC_SPEECH_RECOGNITION = "automatic-speech-recognition" + 8 CONVERSATIONAL = "conversational" + 9 DEPTH_ESTIMATION = "depth-estimation" +10 DOCUMENT_QUESTION_ANSWERING = "document-question-answering" +11 FEATURE_EXTRACTION = "feature-extraction" +12 FILL_MASK = "fill-mask" +13 IMAGE_CLASSIFICATION = "image-classification" +14 IMAGE_SEGMENTATION = "image-segmentation" +15 IMAGE_TO_IMAGE = "image-to-image" +16 IMAGE_TO_TEXT = "image-to-text" +17 MASK_GENERATION = "mask-generation" +18 OBJECT_DETECTION = "object-detection" +19 QUESTION_ANSWERING = "question-answering" +20 SUMMARIZATION = "summarization" +21 TABLE_QUESTION_ANSWERING = "table-question-answering" +22 TEXT2TEXT_GENERATION = "text2text-generation" +23 TEXT_CLASSIFICATION = "text-classification" +24 TEXT_GENERATION = "text-generation" +25 TEXT_TO_AUDIO = "text-to-audio" +26 TOKEN_CLASSIFICATION = "token-classification" +27 TRANSLATION = "translation" +28 TRANSLATION_XX_TO_YY = "translation_xx_to_yy" +29 VIDEO_CLASSIFICATION = "video-classification" +30 VISUAL_QUESTION_ANSWERING = "visual-question-answering" +31 ZERO_SHOT_CLASSIFICATION = "zero-shot-classification" +32 ZERO_SHOT_IMAGE_CLASSIFICATION = "zero-shot-image-classification" +33 ZERO_SHOT_AUDIO_CLASSIFICATION = "zero-shot-audio-classification" +34 ZERO_SHOT_OBJECT_DETECTION = "zero-shot-object-detection" +
An enumeration.
+44class HuggingFaceORTModel(str, Enum): +45 ORT_AUDIO_CLASSIFICATION = "ORTModelForAudioClassification" +46 ORT_AUDIO_FRAME_CLASSIFICATION = "ORTModelForAudioFrameClassification" +47 ORT_AUDIO_XVECTOR = "ORTModelForAudioXVector" +48 ORT_CUSTOM_TASKS = "ORTModelForCustomTasks" +49 ORT_CTC = "ORTModelForCTC" +50 ORT_FEATURE_EXTRACTION = "ORTModelForFeatureExtraction" +51 ORT_IMAGE_CLASSIFICATION = "ORTModelForImageClassification" +52 ORT_MASKED_LM = "ORTModelForMaskedLM" +53 ORT_MULTIPLE_CHOICE = "ORTModelForMultipleChoice" +54 ORT_QUESTION_ANSWERING = "ORTModelForQuestionAnswering" +55 ORT_SEMANTIC_SEGMENTATION = "ORTModelForSemanticSegmentation" +56 ORT_SEQUENCE_CLASSIFICATION = "ORTModelForSequenceClassification" +57 ORT_TOKEN_CLASSIFICATION = "ORTModelForTokenClassification" +58 ORT_SEQ2SEQ_LM = "ORTModelForSeq2SeqLM" +59 ORT_SPEECH_SEQ2SEQ = "ORTModelForSpeechSeq2Seq" +60 ORT_VISION2SEQ = "ORTModelForVision2Seq" +61 ORT_PIX2STRUCT = "ORTModelForPix2Struct" +62 ORT_CAUSAL_LM = "ORTModelForCausalLM" +63 ORT_OPTIMIZER = "ORTOptimizer" +64 ORT_QUANTIZER = "ORTQuantizer" +65 ORT_TRAINER = "ORTTrainer" +66 ORT_SEQ2SEQ_TRAINER = "ORTSeq2SeqTrainer" +67 ORT_TRAINING_ARGUMENTS = "ORTTrainingArguments" +68 ORT_SEQ2SEQ_TRAINING_ARGUMENTS = "ORTSeq2SeqTrainingArguments" +69 ORT_STABLE_DIFFUSION_PIPELINE = "ORTStableDiffusionPipeline" +70 ORT_STABLE_DIFFUSION_IMG2IMG_PIPELINE = "ORTStableDiffusionImg2ImgPipeline" +71 ORT_STABLE_DIFFUSION_INPAINT_PIPELINE = "ORTStableDiffusionInpaintPipeline" +72 ORT_STABLE_DIFFUSION_XL_PIPELINE = "ORTStableDiffusionXLPipeline" +73 ORT_STABLE_DIFFUSION_XL_IMG2IMG_PIPELINE = "ORTStableDiffusionXLImg2ImgPipeline" +
An enumeration.
+Base pydantic class for artifact cards
\n", "bases": "pydantic.main.BaseModel"}, "opsml.cards.base.ArtifactCard.model_config": {"fullname": "opsml.cards.base.ArtifactCard.model_config", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.model_config", "kind": "variable", "doc": "\n", "default_value": "{'arbitrary_types_allowed': True, 'validate_assignment': False, 'validate_default': True}"}, "opsml.cards.base.ArtifactCard.name": {"fullname": "opsml.cards.base.ArtifactCard.name", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.name", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.cards.base.ArtifactCard.repository": {"fullname": "opsml.cards.base.ArtifactCard.repository", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.repository", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.cards.base.ArtifactCard.contact": {"fullname": "opsml.cards.base.ArtifactCard.contact", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.contact", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.cards.base.ArtifactCard.version": {"fullname": "opsml.cards.base.ArtifactCard.version", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.version", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.cards.base.ArtifactCard.uid": {"fullname": "opsml.cards.base.ArtifactCard.uid", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.uid", "kind": "variable", "doc": "\n", "annotation": ": Optional[str]"}, "opsml.cards.base.ArtifactCard.info": {"fullname": "opsml.cards.base.ArtifactCard.info", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.info", "kind": "variable", "doc": "\n", "annotation": ": Optional[opsml.types.card.CardInfo]"}, "opsml.cards.base.ArtifactCard.tags": {"fullname": "opsml.cards.base.ArtifactCard.tags", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.tags", "kind": "variable", "doc": "\n", "annotation": ": Dict[str, str]"}, "opsml.cards.base.ArtifactCard.validate_args": {"fullname": "opsml.cards.base.ArtifactCard.validate_args", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.validate_args", "kind": "function", "doc": "Validate base args and Lowercase name and repository
\n\n\n\n", "signature": "(cls, card_args: Dict[str, Any]) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.cards.base.ArtifactCard.create_registry_record": {"fullname": "opsml.cards.base.ArtifactCard.create_registry_record", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.create_registry_record", "kind": "function", "doc": "validated card_args
\n
Creates a registry record from self attributes
\n", "signature": "(self) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.cards.base.ArtifactCard.add_tag": {"fullname": "opsml.cards.base.ArtifactCard.add_tag", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.add_tag", "kind": "function", "doc": "\n", "signature": "(self, key: str, value: str) -> None:", "funcdef": "def"}, "opsml.cards.base.ArtifactCard.uri": {"fullname": "opsml.cards.base.ArtifactCard.uri", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.uri", "kind": "variable", "doc": "The base URI to use for the card and it's artifacts.
\n", "annotation": ": pathlib.Path"}, "opsml.cards.base.ArtifactCard.artifact_uri": {"fullname": "opsml.cards.base.ArtifactCard.artifact_uri", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.artifact_uri", "kind": "variable", "doc": "Returns the root URI to which artifacts associated with this card should be saved.
\n", "annotation": ": pathlib.Path"}, "opsml.cards.base.ArtifactCard.card_type": {"fullname": "opsml.cards.base.ArtifactCard.card_type", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.card_type", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.cards.base.ArtifactCard.model_fields": {"fullname": "opsml.cards.base.ArtifactCard.model_fields", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'name': FieldInfo(annotation=str, required=False, default='undefined'), 'repository': FieldInfo(annotation=str, required=False, default='undefined'), 'contact': FieldInfo(annotation=str, required=False, default='undefined'), 'version': FieldInfo(annotation=str, required=False, default='0.0.0'), 'uid': FieldInfo(annotation=Union[str, NoneType], required=False), 'info': FieldInfo(annotation=Union[CardInfo, NoneType], required=False), 'tags': FieldInfo(annotation=Dict[str, str], required=False, default={})}"}, "opsml.cards.base.ArtifactCard.model_computed_fields": {"fullname": "opsml.cards.base.ArtifactCard.model_computed_fields", "modulename": "opsml.cards.base", "qualname": "ArtifactCard.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.cards.audit": {"fullname": "opsml.cards.audit", "modulename": "opsml.cards.audit", "kind": "module", "doc": "\n"}, "opsml.cards.audit.logger": {"fullname": "opsml.cards.audit.logger", "modulename": "opsml.cards.audit", "qualname": "logger", "kind": "variable", "doc": "\n", "default_value": "<builtins.Logger object>"}, "opsml.cards.audit.DIR_PATH": {"fullname": "opsml.cards.audit.DIR_PATH", "modulename": "opsml.cards.audit", "qualname": "DIR_PATH", "kind": "variable", "doc": "\n", "default_value": "'/home/steven_forrester/github/opsml/opsml/cards'"}, "opsml.cards.audit.AUDIT_TEMPLATE_PATH": {"fullname": "opsml.cards.audit.AUDIT_TEMPLATE_PATH", "modulename": "opsml.cards.audit", "qualname": "AUDIT_TEMPLATE_PATH", "kind": "variable", "doc": "\n", "default_value": "'/home/steven_forrester/github/opsml/opsml/cards/templates/audit_card.yaml'"}, "opsml.cards.audit.Question": {"fullname": "opsml.cards.audit.Question", "modulename": "opsml.cards.audit", "qualname": "Question", "kind": "class", "doc": "Usage docs: https://docs.pydantic.dev/2.6/concepts/models/
\n\nA base class for creating Pydantic models.
\n\n__init__ function.Model.__validators__ and Model.__root_validators__ from Pydantic V1.RootModel.model_config['extra'] == 'allow'.Usage docs: https://docs.pydantic.dev/2.6/concepts/models/
\n\nA base class for creating Pydantic models.
\n\n__init__ function.Model.__validators__ and Model.__root_validators__ from Pydantic V1.RootModel.model_config['extra'] == 'allow'.Loads audit sections from template if no values are provided
\n", "signature": "(cls, values: Dict[str, Any]) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.cards.audit.AuditSections.load_yaml_template": {"fullname": "opsml.cards.audit.AuditSections.load_yaml_template", "modulename": "opsml.cards.audit", "qualname": "AuditSections.load_yaml_template", "kind": "function", "doc": "\n", "signature": "() -> Dict[str, Dict[int, Dict[str, str]]]:", "funcdef": "def"}, "opsml.cards.audit.AuditSections.model_config": {"fullname": "opsml.cards.audit.AuditSections.model_config", "modulename": "opsml.cards.audit", "qualname": "AuditSections.model_config", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.cards.audit.AuditSections.model_fields": {"fullname": "opsml.cards.audit.AuditSections.model_fields", "modulename": "opsml.cards.audit", "qualname": "AuditSections.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'business_understanding': FieldInfo(annotation=Dict[int, Annotated[Question, SerializeAsAny]], required=True), 'data_understanding': FieldInfo(annotation=Dict[int, Annotated[Question, SerializeAsAny]], required=True), 'data_preparation': FieldInfo(annotation=Dict[int, Annotated[Question, SerializeAsAny]], required=True), 'modeling': FieldInfo(annotation=Dict[int, Annotated[Question, SerializeAsAny]], required=True), 'evaluation': FieldInfo(annotation=Dict[int, Annotated[Question, SerializeAsAny]], required=True), 'deployment_ops': FieldInfo(annotation=Dict[int, Annotated[Question, SerializeAsAny]], required=True), 'misc': FieldInfo(annotation=Dict[int, Annotated[Question, SerializeAsAny]], required=True)}"}, "opsml.cards.audit.AuditSections.model_computed_fields": {"fullname": "opsml.cards.audit.AuditSections.model_computed_fields", "modulename": "opsml.cards.audit", "qualname": "AuditSections.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.cards.audit.AuditQuestionTable": {"fullname": "opsml.cards.audit.AuditQuestionTable", "modulename": "opsml.cards.audit", "qualname": "AuditQuestionTable", "kind": "class", "doc": "Helper class for creating a rich table to be used with an AuditCard
\n"}, "opsml.cards.audit.AuditQuestionTable.table": {"fullname": "opsml.cards.audit.AuditQuestionTable.table", "modulename": "opsml.cards.audit", "qualname": "AuditQuestionTable.table", "kind": "variable", "doc": "\n"}, "opsml.cards.audit.AuditQuestionTable.create_table": {"fullname": "opsml.cards.audit.AuditQuestionTable.create_table", "modulename": "opsml.cards.audit", "qualname": "AuditQuestionTable.create_table", "kind": "function", "doc": "Create Rich table of Audit
\n", "signature": "(self) -> rich.table.Table:", "funcdef": "def"}, "opsml.cards.audit.AuditQuestionTable.add_row": {"fullname": "opsml.cards.audit.AuditQuestionTable.add_row", "modulename": "opsml.cards.audit", "qualname": "AuditQuestionTable.add_row", "kind": "function", "doc": "Add row to table
\n", "signature": "(\tself,\tsection_name: str,\tnbr: int,\tquestion: opsml.cards.audit.Question) -> None:", "funcdef": "def"}, "opsml.cards.audit.AuditQuestionTable.add_section": {"fullname": "opsml.cards.audit.AuditQuestionTable.add_section", "modulename": "opsml.cards.audit", "qualname": "AuditQuestionTable.add_section", "kind": "function", "doc": "Add section
\n", "signature": "(self) -> None:", "funcdef": "def"}, "opsml.cards.audit.AuditQuestionTable.print_table": {"fullname": "opsml.cards.audit.AuditQuestionTable.print_table", "modulename": "opsml.cards.audit", "qualname": "AuditQuestionTable.print_table", "kind": "function", "doc": "Print table
\n", "signature": "(self) -> None:", "funcdef": "def"}, "opsml.cards.audit.AuditCard": {"fullname": "opsml.cards.audit.AuditCard", "modulename": "opsml.cards.audit", "qualname": "AuditCard", "kind": "class", "doc": "Creates an AuditCard for storing audit-related information about a\nmachine learning project.
\n\ninfo: CardInfo object containing additional metadata. If provided, it will override any\nvalues provided for name, repository, contact, and version.
Name, repository, and contact are required arguments for all cards. They can be provided\ndirectly or through a CardInfo object.
Adds comment to AuditCard
\n\nCreates a registry record for a audit
\n", "signature": "(self) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.cards.audit.AuditCard.add_card": {"fullname": "opsml.cards.audit.AuditCard.add_card", "modulename": "opsml.cards.audit", "qualname": "AuditCard.add_card", "kind": "function", "doc": "Adds a card uid to the appropriate card uid list for tracking
\n\nLists all Audit Card questions in a rich table
\n\nAnswers a question in a section
\n\nCreate a DataCard from your data.
\n\nDataInterface that contains datainfo: CardInfo object containing additional metadata. If provided, it will override any\nvalues provided for name, repository, contact, and version.
Name, repository, and contact are required arguments for all cards. They can be provided\ndirectly or through a CardInfo object.
\n\n", "bases": "opsml.cards.base.ArtifactCard"}, "opsml.cards.data.DataCard.interface": {"fullname": "opsml.cards.data.DataCard.interface", "modulename": "opsml.cards.data", "qualname": "DataCard.interface", "kind": "variable", "doc": "\n", "annotation": ": Annotated[Union[opsml.data.interfaces._base.DataInterface, opsml.data.interfaces.custom_data.base.Dataset], SerializeAsAny()]"}, "opsml.cards.data.DataCard.metadata": {"fullname": "opsml.cards.data.DataCard.metadata", "modulename": "opsml.cards.data", "qualname": "DataCard.metadata", "kind": "variable", "doc": "\n", "annotation": ": opsml.types.data.DataCardMetadata"}, "opsml.cards.data.DataCard.load_data": {"fullname": "opsml.cards.data.DataCard.load_data", "modulename": "opsml.cards.data", "qualname": "DataCard.load_data", "kind": "function", "doc": "DataCard
\n
Load data to interface
\n\nDataset.Dataset.\nDefaults to 1000.chunk_size: How many files per batch to use when writing arrow back to local file.\nDefaults to 1000.
\n\nExample:
\n\nLoad data to interface
\n", "signature": "(self) -> None:", "funcdef": "def"}, "opsml.cards.data.DataCard.create_registry_record": {"fullname": "opsml.cards.data.DataCard.create_registry_record", "modulename": "opsml.cards.data", "qualname": "DataCard.create_registry_record", "kind": "function", "doc": "Creates required metadata for registering the current data card.\nImplemented with a DataRegistry object.\n Returns:\n Registry metadata
\n", "signature": "(self) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.cards.data.DataCard.add_info": {"fullname": "opsml.cards.data.DataCard.add_info", "modulename": "opsml.cards.data", "qualname": "DataCard.add_info", "kind": "function", "doc": "Adds metadata to the existing DataCard metadata dictionary
\n\nCreates a data profile report
\n\nSplits data interface according to data split logic
\n", "signature": "(self) -> Dict[str, opsml.data.splitter.Data]:", "funcdef": "def"}, "opsml.cards.data.DataCard.data_splits": {"fullname": "opsml.cards.data.DataCard.data_splits", "modulename": "opsml.cards.data", "qualname": "DataCard.data_splits", "kind": "variable", "doc": "Returns data splits
\n", "annotation": ": List[opsml.data.splitter.DataSplit]"}, "opsml.cards.data.DataCard.data": {"fullname": "opsml.cards.data.DataCard.data", "modulename": "opsml.cards.data", "qualname": "DataCard.data", "kind": "variable", "doc": "Returns data
\n", "annotation": ": Any"}, "opsml.cards.data.DataCard.data_profile": {"fullname": "opsml.cards.data.DataCard.data_profile", "modulename": "opsml.cards.data", "qualname": "DataCard.data_profile", "kind": "variable", "doc": "Returns data profile
\n", "annotation": ": Any"}, "opsml.cards.data.DataCard.card_type": {"fullname": "opsml.cards.data.DataCard.card_type", "modulename": "opsml.cards.data", "qualname": "DataCard.card_type", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.cards.data.DataCard.model_config": {"fullname": "opsml.cards.data.DataCard.model_config", "modulename": "opsml.cards.data", "qualname": "DataCard.model_config", "kind": "variable", "doc": "\n", "default_value": "{'arbitrary_types_allowed': True, 'validate_assignment': False, 'validate_default': True}"}, "opsml.cards.data.DataCard.model_fields": {"fullname": "opsml.cards.data.DataCard.model_fields", "modulename": "opsml.cards.data", "qualname": "DataCard.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'name': FieldInfo(annotation=str, required=False, default='undefined'), 'repository': FieldInfo(annotation=str, required=False, default='undefined'), 'contact': FieldInfo(annotation=str, required=False, default='undefined'), 'version': FieldInfo(annotation=str, required=False, default='0.0.0'), 'uid': FieldInfo(annotation=Union[str, NoneType], required=False), 'info': FieldInfo(annotation=Union[CardInfo, NoneType], required=False), 'tags': FieldInfo(annotation=Dict[str, str], required=False, default={}), 'interface': FieldInfo(annotation=Union[DataInterface, Dataset], required=True, metadata=[SerializeAsAny()]), 'metadata': FieldInfo(annotation=DataCardMetadata, required=False, default=DataCardMetadata(interface_type='', data_type='', description=Description(summary=None, sample_code=None, Notes=None), feature_map={}, additional_info={}, runcard_uid=None, pipelinecard_uid=None, auditcard_uid=None))}"}, "opsml.cards.data.DataCard.model_computed_fields": {"fullname": "opsml.cards.data.DataCard.model_computed_fields", "modulename": "opsml.cards.data", "qualname": "DataCard.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.cards.model": {"fullname": "opsml.cards.model", "modulename": "opsml.cards.model", "kind": "module", "doc": "\n"}, "opsml.cards.model.logger": {"fullname": "opsml.cards.model.logger", "modulename": "opsml.cards.model", "qualname": "logger", "kind": "variable", "doc": "\n", "default_value": "<builtins.Logger object>"}, "opsml.cards.model.ModelCard": {"fullname": "opsml.cards.model.ModelCard", "modulename": "opsml.cards.model", "qualname": "ModelCard", "kind": "class", "doc": "Create a ModelCard from your trained machine learning model.\nThis Card is used in conjunction with the ModelCardCreator class.
\n\ninfo: CardInfo object containing additional metadata. If provided, it will override any\nvalues provided for name, repository, contact, and version.
Name, repository, and contact are required arguments for all cards. They can be provided\ndirectly or through a CardInfo object.
ModelCardMetadata associated with the modelLoads model, preprocessor and sample data to interface
\n", "signature": "(self, **kwargs: Any) -> None:", "funcdef": "def"}, "opsml.cards.model.ModelCard.download_model": {"fullname": "opsml.cards.model.ModelCard.download_model", "modulename": "opsml.cards.model", "qualname": "ModelCard.download_model", "kind": "function", "doc": "Downloads model, preprocessor and metadata to path
\n\nLoads onnx model to interface
\n", "signature": "(self, **kwargs: Any) -> None:", "funcdef": "def"}, "opsml.cards.model.ModelCard.load_preprocessor": {"fullname": "opsml.cards.model.ModelCard.load_preprocessor", "modulename": "opsml.cards.model", "qualname": "ModelCard.load_preprocessor", "kind": "function", "doc": "Loads onnx model to interface
\n", "signature": "(self, **kwargs: Any) -> None:", "funcdef": "def"}, "opsml.cards.model.ModelCard.create_registry_record": {"fullname": "opsml.cards.model.ModelCard.create_registry_record", "modulename": "opsml.cards.model", "qualname": "ModelCard.create_registry_record", "kind": "function", "doc": "Creates a registry record from the current ModelCard
\n", "signature": "(self) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.cards.model.ModelCard.model": {"fullname": "opsml.cards.model.ModelCard.model", "modulename": "opsml.cards.model", "qualname": "ModelCard.model", "kind": "variable", "doc": "Quick access to model from interface
\n", "annotation": ": Any"}, "opsml.cards.model.ModelCard.sample_data": {"fullname": "opsml.cards.model.ModelCard.sample_data", "modulename": "opsml.cards.model", "qualname": "ModelCard.sample_data", "kind": "variable", "doc": "Quick access to sample data from interface
\n", "annotation": ": Any"}, "opsml.cards.model.ModelCard.preprocessor": {"fullname": "opsml.cards.model.ModelCard.preprocessor", "modulename": "opsml.cards.model", "qualname": "ModelCard.preprocessor", "kind": "variable", "doc": "Quick access to preprocessor from interface
\n", "annotation": ": Any"}, "opsml.cards.model.ModelCard.onnx_model": {"fullname": "opsml.cards.model.ModelCard.onnx_model", "modulename": "opsml.cards.model", "qualname": "ModelCard.onnx_model", "kind": "variable", "doc": "Quick access to onnx model from interface
\n", "annotation": ": Optional[opsml.types.model.OnnxModel]"}, "opsml.cards.model.ModelCard.model_metadata": {"fullname": "opsml.cards.model.ModelCard.model_metadata", "modulename": "opsml.cards.model", "qualname": "ModelCard.model_metadata", "kind": "variable", "doc": "Loads ModelMetadata class
Card containing project information
\n", "bases": "opsml.cards.base.ArtifactCard"}, "opsml.cards.project.ProjectCard.project_id": {"fullname": "opsml.cards.project.ProjectCard.project_id", "modulename": "opsml.cards.project", "qualname": "ProjectCard.project_id", "kind": "variable", "doc": "\n", "annotation": ": int"}, "opsml.cards.project.ProjectCard.create_registry_record": {"fullname": "opsml.cards.project.ProjectCard.create_registry_record", "modulename": "opsml.cards.project", "qualname": "ProjectCard.create_registry_record", "kind": "function", "doc": "Creates a registry record for a project
\n", "signature": "(self) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.cards.project.ProjectCard.card_type": {"fullname": "opsml.cards.project.ProjectCard.card_type", "modulename": "opsml.cards.project", "qualname": "ProjectCard.card_type", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.cards.project.ProjectCard.model_config": {"fullname": "opsml.cards.project.ProjectCard.model_config", "modulename": "opsml.cards.project", "qualname": "ProjectCard.model_config", "kind": "variable", "doc": "\n", "default_value": "{'arbitrary_types_allowed': True, 'validate_assignment': False, 'validate_default': True}"}, "opsml.cards.project.ProjectCard.model_fields": {"fullname": "opsml.cards.project.ProjectCard.model_fields", "modulename": "opsml.cards.project", "qualname": "ProjectCard.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'name': FieldInfo(annotation=str, required=False, default='undefined'), 'repository': FieldInfo(annotation=str, required=False, default='undefined'), 'contact': FieldInfo(annotation=str, required=False, default='undefined'), 'version': FieldInfo(annotation=str, required=False, default='0.0.0'), 'uid': FieldInfo(annotation=Union[str, NoneType], required=False), 'info': FieldInfo(annotation=Union[CardInfo, NoneType], required=False), 'tags': FieldInfo(annotation=Dict[str, str], required=False, default={}), 'project_id': FieldInfo(annotation=int, required=False, default=0)}"}, "opsml.cards.project.ProjectCard.model_computed_fields": {"fullname": "opsml.cards.project.ProjectCard.model_computed_fields", "modulename": "opsml.cards.project", "qualname": "ProjectCard.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.cards.run": {"fullname": "opsml.cards.run", "modulename": "opsml.cards.run", "kind": "module", "doc": "\n"}, "opsml.cards.run.logger": {"fullname": "opsml.cards.run.logger", "modulename": "opsml.cards.run", "qualname": "logger", "kind": "variable", "doc": "\n", "default_value": "<builtins.Logger object>"}, "opsml.cards.run.RunCard": {"fullname": "opsml.cards.run.RunCard", "modulename": "opsml.cards.run", "qualname": "RunCard", "kind": "class", "doc": "Create a RunCard from specified arguments.
\n\nApart from required args, a RunCard must be associated with one of\ndatacard_uid, modelcard_uids or pipelinecard_uid
\n\ninfo: CardInfo object containing additional metadata. If provided, it will override any\nvalues provided for name, repository, contact, and version.
Name, repository, and contact are required arguments for all cards. They can be provided\ndirectly or through a CardInfo object.
Logs tags to current RunCard
\n\nLogs tags to current RunCard
\n\nLogs a graph to the RunCard, which will be rendered in the UI as a line graph
\n\nexample:
\n\n### single line graph\nx = np.arange(1, 400, 0.5)\ny = x * x\nrun.log_graph(name=\"graph1\", x=x, y=y, x_label=\"x\", y_label=\"y\", graph_style=\"line\")\n\n### multi line graph\nx = np.arange(1, 1000, 0.5)\ny1 = x * x\ny2 = y1 * 1.1\ny3 = y2 * 3\nrun.log_graph(\n name=\"multiline\",\n x=x,\n y={\"y1\": y1, \"y2\": y2, \"y3\": y3},\n x_label=\"x\",\n y_label=\"y\",\n graph_style=\"line\",\n)\nLogs parameters to current RunCard
\n\nLogs parameter to current RunCard
\n\nLogs metric to the existing RunCard metric dictionary
\n\nLog metrics to the existing RunCard metric dictionary
\n\nLog a local file or directory to the opsml server and associate with the current run.
\n\nCreates a registry record from the current RunCard
\n", "signature": "(self) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.cards.run.RunCard.add_card_uid": {"fullname": "opsml.cards.run.RunCard.add_card_uid", "modulename": "opsml.cards.run", "qualname": "RunCard.add_card_uid", "kind": "function", "doc": "Adds a card uid to the appropriate card uid list for tracking
\n\nGets a metric by name
\n\n\n\n", "signature": "(\tself,\tname: str) -> Union[List[opsml.types.card.Metric], opsml.types.card.Metric]:", "funcdef": "def"}, "opsml.cards.run.RunCard.load_metrics": {"fullname": "opsml.cards.run.RunCard.load_metrics", "modulename": "opsml.cards.run", "qualname": "RunCard.load_metrics", "kind": "function", "doc": "List of dictionaries or dictionary containing value
\n
Reloads metrics from registry
\n", "signature": "(self) -> None:", "funcdef": "def"}, "opsml.cards.run.RunCard.get_parameter": {"fullname": "opsml.cards.run.RunCard.get_parameter", "modulename": "opsml.cards.run", "qualname": "RunCard.get_parameter", "kind": "function", "doc": "Gets a parameter by name
\n\n\n\n", "signature": "(\tself,\tname: str) -> Union[List[opsml.types.card.Param], opsml.types.card.Param]:", "funcdef": "def"}, "opsml.cards.run.RunCard.load_artifacts": {"fullname": "opsml.cards.run.RunCard.load_artifacts", "modulename": "opsml.cards.run", "qualname": "RunCard.load_artifacts", "kind": "function", "doc": "List of dictionaries or dictionary containing value
\n
Loads artifacts from artifact_uris
\n", "signature": "(self, name: Optional[str] = None) -> None:", "funcdef": "def"}, "opsml.cards.run.RunCard.uri": {"fullname": "opsml.cards.run.RunCard.uri", "modulename": "opsml.cards.run", "qualname": "RunCard.uri", "kind": "variable", "doc": "The base URI to use for the card and it's artifacts.
\n", "annotation": ": pathlib.Path"}, "opsml.cards.run.RunCard.card_type": {"fullname": "opsml.cards.run.RunCard.card_type", "modulename": "opsml.cards.run", "qualname": "RunCard.card_type", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.cards.run.RunCard.model_config": {"fullname": "opsml.cards.run.RunCard.model_config", "modulename": "opsml.cards.run", "qualname": "RunCard.model_config", "kind": "variable", "doc": "\n", "default_value": "{'arbitrary_types_allowed': True, 'validate_assignment': False, 'validate_default': True}"}, "opsml.cards.run.RunCard.model_fields": {"fullname": "opsml.cards.run.RunCard.model_fields", "modulename": "opsml.cards.run", "qualname": "RunCard.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'name': FieldInfo(annotation=str, required=False, default='undefined'), 'repository': FieldInfo(annotation=str, required=False, default='undefined'), 'contact': FieldInfo(annotation=str, required=False, default='undefined'), 'version': FieldInfo(annotation=str, required=False, default='0.0.0'), 'uid': FieldInfo(annotation=Union[str, NoneType], required=False), 'info': FieldInfo(annotation=Union[CardInfo, NoneType], required=False), 'tags': FieldInfo(annotation=Dict[str, Union[int, str]], required=False, default={}), 'datacard_uids': FieldInfo(annotation=List[str], required=False, default=[]), 'modelcard_uids': FieldInfo(annotation=List[str], required=False, default=[]), 'pipelinecard_uid': FieldInfo(annotation=Union[str, NoneType], required=False), 'metrics': FieldInfo(annotation=Dict[str, List[Metric]], required=False, default={}), 'parameters': FieldInfo(annotation=Dict[str, List[Param]], required=False, default={}), 'artifact_uris': FieldInfo(annotation=Dict[str, Artifact], required=False, default={}), 'project': FieldInfo(annotation=Union[str, NoneType], required=False)}"}, "opsml.cards.run.RunCard.model_computed_fields": {"fullname": "opsml.cards.run.RunCard.model_computed_fields", "modulename": "opsml.cards.run", "qualname": "RunCard.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.data.splitter": {"fullname": "opsml.data.splitter", "modulename": "opsml.data.splitter", "kind": "module", "doc": "\n"}, "opsml.data.splitter.Data": {"fullname": "opsml.data.splitter.Data", "modulename": "opsml.data.splitter", "qualname": "Data", "kind": "class", "doc": "\n"}, "opsml.data.splitter.Data.__init__": {"fullname": "opsml.data.splitter.Data.__init__", "modulename": "opsml.data.splitter", "qualname": "Data.__init__", "kind": "function", "doc": "\n", "signature": "(X: Any, y: Optional[Any] = None)"}, "opsml.data.splitter.Data.X": {"fullname": "opsml.data.splitter.Data.X", "modulename": "opsml.data.splitter", "qualname": "Data.X", "kind": "variable", "doc": "\n", "annotation": ": Any"}, "opsml.data.splitter.Data.y": {"fullname": "opsml.data.splitter.Data.y", "modulename": "opsml.data.splitter", "qualname": "Data.y", "kind": "variable", "doc": "\n", "annotation": ": Optional[Any]", "default_value": "None"}, "opsml.data.splitter.DataSplit": {"fullname": "opsml.data.splitter.DataSplit", "modulename": "opsml.data.splitter", "qualname": "DataSplit", "kind": "class", "doc": "Usage docs: https://docs.pydantic.dev/2.6/concepts/models/
\n\nA base class for creating Pydantic models.
\n\n__init__ function.Model.__validators__ and Model.__root_validators__ from Pydantic V1.RootModel.model_config['extra'] == 'allow'.Pre to convert indices to list if not None
\n", "signature": "(cls, value: Optional[List[int]]) -> Optional[List[int]]:", "funcdef": "def"}, "opsml.data.splitter.DataSplit.trim_whitespace": {"fullname": "opsml.data.splitter.DataSplit.trim_whitespace", "modulename": "opsml.data.splitter", "qualname": "DataSplit.trim_whitespace", "kind": "function", "doc": "Trims whitespace from inequality signs
\n", "signature": "(cls, value: str) -> str:", "funcdef": "def"}, "opsml.data.splitter.DataSplit.model_fields": {"fullname": "opsml.data.splitter.DataSplit.model_fields", "modulename": "opsml.data.splitter", "qualname": "DataSplit.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'label': FieldInfo(annotation=str, required=True), 'column_name': FieldInfo(annotation=Union[str, NoneType], required=False), 'column_value': FieldInfo(annotation=Union[str, float, int, Timestamp, NoneType], required=False), 'inequality': FieldInfo(annotation=Union[str, NoneType], required=False), 'start': FieldInfo(annotation=Union[int, NoneType], required=False), 'stop': FieldInfo(annotation=Union[int, NoneType], required=False), 'indices': FieldInfo(annotation=Union[List[int], NoneType], required=False)}"}, "opsml.data.splitter.DataSplit.model_computed_fields": {"fullname": "opsml.data.splitter.DataSplit.model_computed_fields", "modulename": "opsml.data.splitter", "qualname": "DataSplit.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.data.splitter.DataSplitterBase": {"fullname": "opsml.data.splitter.DataSplitterBase", "modulename": "opsml.data.splitter", "qualname": "DataSplitterBase", "kind": "class", "doc": "\n"}, "opsml.data.splitter.DataSplitterBase.__init__": {"fullname": "opsml.data.splitter.DataSplitterBase.__init__", "modulename": "opsml.data.splitter", "qualname": "DataSplitterBase.__init__", "kind": "function", "doc": "\n", "signature": "(\tsplit: opsml.data.splitter.DataSplit,\tdependent_vars: List[Union[int, str]])"}, "opsml.data.splitter.DataSplitterBase.split": {"fullname": "opsml.data.splitter.DataSplitterBase.split", "modulename": "opsml.data.splitter", "qualname": "DataSplitterBase.split", "kind": "variable", "doc": "\n"}, "opsml.data.splitter.DataSplitterBase.dependent_vars": {"fullname": "opsml.data.splitter.DataSplitterBase.dependent_vars", "modulename": "opsml.data.splitter", "qualname": "DataSplitterBase.dependent_vars", "kind": "variable", "doc": "\n"}, "opsml.data.splitter.DataSplitterBase.column_name": {"fullname": "opsml.data.splitter.DataSplitterBase.column_name", "modulename": "opsml.data.splitter", "qualname": "DataSplitterBase.column_name", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.data.splitter.DataSplitterBase.column_value": {"fullname": "opsml.data.splitter.DataSplitterBase.column_value", "modulename": "opsml.data.splitter", "qualname": "DataSplitterBase.column_value", "kind": "variable", "doc": "\n", "annotation": ": Any"}, "opsml.data.splitter.DataSplitterBase.indices": {"fullname": "opsml.data.splitter.DataSplitterBase.indices", "modulename": "opsml.data.splitter", "qualname": "DataSplitterBase.indices", "kind": "variable", "doc": "\n", "annotation": ": List[int]"}, "opsml.data.splitter.DataSplitterBase.start": {"fullname": "opsml.data.splitter.DataSplitterBase.start", "modulename": "opsml.data.splitter", "qualname": "DataSplitterBase.start", "kind": "variable", "doc": "\n", "annotation": ": int"}, "opsml.data.splitter.DataSplitterBase.stop": {"fullname": "opsml.data.splitter.DataSplitterBase.stop", "modulename": "opsml.data.splitter", "qualname": "DataSplitterBase.stop", "kind": "variable", "doc": "\n", "annotation": ": int"}, "opsml.data.splitter.DataSplitterBase.get_x_cols": {"fullname": "opsml.data.splitter.DataSplitterBase.get_x_cols", "modulename": "opsml.data.splitter", "qualname": "DataSplitterBase.get_x_cols", "kind": "function", "doc": "\n", "signature": "(\tself,\tcolumns: List[str],\tdependent_vars: List[Union[int, str]]) -> List[str]:", "funcdef": "def"}, "opsml.data.splitter.DataSplitterBase.create_split": {"fullname": "opsml.data.splitter.DataSplitterBase.create_split", "modulename": "opsml.data.splitter", "qualname": "DataSplitterBase.create_split", "kind": "function", "doc": "\n", "signature": "(self, data: Any) -> Tuple[str, opsml.data.splitter.Data]:", "funcdef": "def"}, "opsml.data.splitter.DataSplitterBase.validate": {"fullname": "opsml.data.splitter.DataSplitterBase.validate", "modulename": "opsml.data.splitter", "qualname": "DataSplitterBase.validate", "kind": "function", "doc": "\n", "signature": "(data_type: str, split: opsml.data.splitter.DataSplit) -> bool:", "funcdef": "def"}, "opsml.data.splitter.PolarsColumnSplitter": {"fullname": "opsml.data.splitter.PolarsColumnSplitter", "modulename": "opsml.data.splitter", "qualname": "PolarsColumnSplitter", "kind": "class", "doc": "Column splitter for Polars dataframe
\n", "bases": "DataSplitterBase"}, "opsml.data.splitter.PolarsColumnSplitter.create_split": {"fullname": "opsml.data.splitter.PolarsColumnSplitter.create_split", "modulename": "opsml.data.splitter", "qualname": "PolarsColumnSplitter.create_split", "kind": "function", "doc": "\n", "signature": "(\tself,\tdata: polars.dataframe.frame.DataFrame) -> Tuple[str, opsml.data.splitter.Data]:", "funcdef": "def"}, "opsml.data.splitter.PolarsColumnSplitter.validate": {"fullname": "opsml.data.splitter.PolarsColumnSplitter.validate", "modulename": "opsml.data.splitter", "qualname": "PolarsColumnSplitter.validate", "kind": "function", "doc": "\n", "signature": "(data_type: str, split: opsml.data.splitter.DataSplit) -> bool:", "funcdef": "def"}, "opsml.data.splitter.PolarsIndexSplitter": {"fullname": "opsml.data.splitter.PolarsIndexSplitter", "modulename": "opsml.data.splitter", "qualname": "PolarsIndexSplitter", "kind": "class", "doc": "Split Polars DataFrame by rows index
\n", "bases": "DataSplitterBase"}, "opsml.data.splitter.PolarsIndexSplitter.create_split": {"fullname": "opsml.data.splitter.PolarsIndexSplitter.create_split", "modulename": "opsml.data.splitter", "qualname": "PolarsIndexSplitter.create_split", "kind": "function", "doc": "\n", "signature": "(\tself,\tdata: polars.dataframe.frame.DataFrame) -> Tuple[str, opsml.data.splitter.Data]:", "funcdef": "def"}, "opsml.data.splitter.PolarsIndexSplitter.validate": {"fullname": "opsml.data.splitter.PolarsIndexSplitter.validate", "modulename": "opsml.data.splitter", "qualname": "PolarsIndexSplitter.validate", "kind": "function", "doc": "\n", "signature": "(data_type: str, split: opsml.data.splitter.DataSplit) -> bool:", "funcdef": "def"}, "opsml.data.splitter.PolarsRowsSplitter": {"fullname": "opsml.data.splitter.PolarsRowsSplitter", "modulename": "opsml.data.splitter", "qualname": "PolarsRowsSplitter", "kind": "class", "doc": "Split Polars DataFrame by rows slice
\n", "bases": "DataSplitterBase"}, "opsml.data.splitter.PolarsRowsSplitter.create_split": {"fullname": "opsml.data.splitter.PolarsRowsSplitter.create_split", "modulename": "opsml.data.splitter", "qualname": "PolarsRowsSplitter.create_split", "kind": "function", "doc": "\n", "signature": "(\tself,\tdata: polars.dataframe.frame.DataFrame) -> Tuple[str, opsml.data.splitter.Data]:", "funcdef": "def"}, "opsml.data.splitter.PolarsRowsSplitter.validate": {"fullname": "opsml.data.splitter.PolarsRowsSplitter.validate", "modulename": "opsml.data.splitter", "qualname": "PolarsRowsSplitter.validate", "kind": "function", "doc": "\n", "signature": "(data_type: str, split: opsml.data.splitter.DataSplit) -> bool:", "funcdef": "def"}, "opsml.data.splitter.PandasIndexSplitter": {"fullname": "opsml.data.splitter.PandasIndexSplitter", "modulename": "opsml.data.splitter", "qualname": "PandasIndexSplitter", "kind": "class", "doc": "\n", "bases": "DataSplitterBase"}, "opsml.data.splitter.PandasIndexSplitter.create_split": {"fullname": "opsml.data.splitter.PandasIndexSplitter.create_split", "modulename": "opsml.data.splitter", "qualname": "PandasIndexSplitter.create_split", "kind": "function", "doc": "\n", "signature": "(\tself,\tdata: pandas.core.frame.DataFrame) -> Tuple[str, opsml.data.splitter.Data]:", "funcdef": "def"}, "opsml.data.splitter.PandasIndexSplitter.validate": {"fullname": "opsml.data.splitter.PandasIndexSplitter.validate", "modulename": "opsml.data.splitter", "qualname": "PandasIndexSplitter.validate", "kind": "function", "doc": "\n", "signature": "(data_type: str, split: opsml.data.splitter.DataSplit) -> bool:", "funcdef": "def"}, "opsml.data.splitter.PandasRowSplitter": {"fullname": "opsml.data.splitter.PandasRowSplitter", "modulename": "opsml.data.splitter", "qualname": "PandasRowSplitter", "kind": "class", "doc": "\n", "bases": "DataSplitterBase"}, "opsml.data.splitter.PandasRowSplitter.create_split": {"fullname": "opsml.data.splitter.PandasRowSplitter.create_split", "modulename": "opsml.data.splitter", "qualname": "PandasRowSplitter.create_split", "kind": "function", "doc": "\n", "signature": "(\tself,\tdata: pandas.core.frame.DataFrame) -> Tuple[str, opsml.data.splitter.Data]:", "funcdef": "def"}, "opsml.data.splitter.PandasRowSplitter.validate": {"fullname": "opsml.data.splitter.PandasRowSplitter.validate", "modulename": "opsml.data.splitter", "qualname": "PandasRowSplitter.validate", "kind": "function", "doc": "\n", "signature": "(data_type: str, split: opsml.data.splitter.DataSplit) -> bool:", "funcdef": "def"}, "opsml.data.splitter.PandasColumnSplitter": {"fullname": "opsml.data.splitter.PandasColumnSplitter", "modulename": "opsml.data.splitter", "qualname": "PandasColumnSplitter", "kind": "class", "doc": "\n", "bases": "DataSplitterBase"}, "opsml.data.splitter.PandasColumnSplitter.create_split": {"fullname": "opsml.data.splitter.PandasColumnSplitter.create_split", "modulename": "opsml.data.splitter", "qualname": "PandasColumnSplitter.create_split", "kind": "function", "doc": "\n", "signature": "(\tself,\tdata: pandas.core.frame.DataFrame) -> Tuple[str, opsml.data.splitter.Data]:", "funcdef": "def"}, "opsml.data.splitter.PandasColumnSplitter.validate": {"fullname": "opsml.data.splitter.PandasColumnSplitter.validate", "modulename": "opsml.data.splitter", "qualname": "PandasColumnSplitter.validate", "kind": "function", "doc": "\n", "signature": "(data_type: str, split: opsml.data.splitter.DataSplit) -> bool:", "funcdef": "def"}, "opsml.data.splitter.PyArrowIndexSplitter": {"fullname": "opsml.data.splitter.PyArrowIndexSplitter", "modulename": "opsml.data.splitter", "qualname": "PyArrowIndexSplitter", "kind": "class", "doc": "\n", "bases": "DataSplitterBase"}, "opsml.data.splitter.PyArrowIndexSplitter.create_split": {"fullname": "opsml.data.splitter.PyArrowIndexSplitter.create_split", "modulename": "opsml.data.splitter", "qualname": "PyArrowIndexSplitter.create_split", "kind": "function", "doc": "\n", "signature": "(self, data: pyarrow.lib.Table) -> Tuple[str, opsml.data.splitter.Data]:", "funcdef": "def"}, "opsml.data.splitter.PyArrowIndexSplitter.validate": {"fullname": "opsml.data.splitter.PyArrowIndexSplitter.validate", "modulename": "opsml.data.splitter", "qualname": "PyArrowIndexSplitter.validate", "kind": "function", "doc": "\n", "signature": "(data_type: str, split: opsml.data.splitter.DataSplit) -> bool:", "funcdef": "def"}, "opsml.data.splitter.NumpyIndexSplitter": {"fullname": "opsml.data.splitter.NumpyIndexSplitter", "modulename": "opsml.data.splitter", "qualname": "NumpyIndexSplitter", "kind": "class", "doc": "\n", "bases": "DataSplitterBase"}, "opsml.data.splitter.NumpyIndexSplitter.create_split": {"fullname": "opsml.data.splitter.NumpyIndexSplitter.create_split", "modulename": "opsml.data.splitter", "qualname": "NumpyIndexSplitter.create_split", "kind": "function", "doc": "\n", "signature": "(\tself,\tdata: numpy.ndarray[typing.Any, numpy.dtype[typing.Any]]) -> Tuple[str, opsml.data.splitter.Data]:", "funcdef": "def"}, "opsml.data.splitter.NumpyIndexSplitter.validate": {"fullname": "opsml.data.splitter.NumpyIndexSplitter.validate", "modulename": "opsml.data.splitter", "qualname": "NumpyIndexSplitter.validate", "kind": "function", "doc": "\n", "signature": "(data_type: str, split: opsml.data.splitter.DataSplit) -> bool:", "funcdef": "def"}, "opsml.data.splitter.NumpyRowSplitter": {"fullname": "opsml.data.splitter.NumpyRowSplitter", "modulename": "opsml.data.splitter", "qualname": "NumpyRowSplitter", "kind": "class", "doc": "\n", "bases": "DataSplitterBase"}, "opsml.data.splitter.NumpyRowSplitter.create_split": {"fullname": "opsml.data.splitter.NumpyRowSplitter.create_split", "modulename": "opsml.data.splitter", "qualname": "NumpyRowSplitter.create_split", "kind": "function", "doc": "\n", "signature": "(\tself,\tdata: numpy.ndarray[typing.Any, numpy.dtype[typing.Any]]) -> Tuple[str, opsml.data.splitter.Data]:", "funcdef": "def"}, "opsml.data.splitter.NumpyRowSplitter.validate": {"fullname": "opsml.data.splitter.NumpyRowSplitter.validate", "modulename": "opsml.data.splitter", "qualname": "NumpyRowSplitter.validate", "kind": "function", "doc": "\n", "signature": "(data_type: str, split: opsml.data.splitter.DataSplit) -> bool:", "funcdef": "def"}, "opsml.data.splitter.DataSplitter": {"fullname": "opsml.data.splitter.DataSplitter", "modulename": "opsml.data.splitter", "qualname": "DataSplitter", "kind": "class", "doc": "\n"}, "opsml.data.splitter.DataSplitter.split": {"fullname": "opsml.data.splitter.DataSplitter.split", "modulename": "opsml.data.splitter", "qualname": "DataSplitter.split", "kind": "function", "doc": "\n", "signature": "(\tsplit: opsml.data.splitter.DataSplit,\tdata: Union[pandas.core.frame.DataFrame, numpy.ndarray[Any, numpy.dtype[Any]], polars.dataframe.frame.DataFrame],\tdata_type: str,\tdependent_vars: List[Union[int, str]]) -> Tuple[str, opsml.data.splitter.Data]:", "funcdef": "def"}, "opsml.model.interfaces.base": {"fullname": "opsml.model.interfaces.base", "modulename": "opsml.model.interfaces.base", "kind": "module", "doc": "\n"}, "opsml.model.interfaces.base.get_processor_name": {"fullname": "opsml.model.interfaces.base.get_processor_name", "modulename": "opsml.model.interfaces.base", "qualname": "get_processor_name", "kind": "function", "doc": "\n", "signature": "(_class: Optional[Any] = None) -> str:", "funcdef": "def"}, "opsml.model.interfaces.base.get_model_args": {"fullname": "opsml.model.interfaces.base.get_model_args", "modulename": "opsml.model.interfaces.base", "qualname": "get_model_args", "kind": "function", "doc": "\n", "signature": "(model: Any) -> Tuple[Any, str, List[str]]:", "funcdef": "def"}, "opsml.model.interfaces.base.SamplePrediction": {"fullname": "opsml.model.interfaces.base.SamplePrediction", "modulename": "opsml.model.interfaces.base", "qualname": "SamplePrediction", "kind": "class", "doc": "Dataclass that holds sample prediction information
\n\nUsage docs: https://docs.pydantic.dev/2.6/concepts/models/
\n\nA base class for creating Pydantic models.
\n\n__init__ function.Model.__validators__ and Model.__root_validators__ from Pydantic V1.RootModel.model_config['extra'] == 'allow'.Saves model to path. Base implementation use Joblib
\n\nLoad model from pathlib object
\n\nSaves the onnx model
\n\n\n\n", "signature": "(self, path: pathlib.Path) -> opsml.types.model.ModelReturn:", "funcdef": "def"}, "opsml.model.interfaces.base.ModelInterface.convert_to_onnx": {"fullname": "opsml.model.interfaces.base.ModelInterface.convert_to_onnx", "modulename": "opsml.model.interfaces.base", "qualname": "ModelInterface.convert_to_onnx", "kind": "function", "doc": "ModelReturn
\n
Converts model to onnx format
\n", "signature": "(self, **kwargs: pathlib.Path) -> None:", "funcdef": "def"}, "opsml.model.interfaces.base.ModelInterface.load_onnx_model": {"fullname": "opsml.model.interfaces.base.ModelInterface.load_onnx_model", "modulename": "opsml.model.interfaces.base", "qualname": "ModelInterface.load_onnx_model", "kind": "function", "doc": "Load onnx model from pathlib object
\n\nSerialized and save sample data to path.
\n\nSerialized and save sample data to path.
\n\nReturns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.base.ModelInterface.data_suffix": {"fullname": "opsml.model.interfaces.base.ModelInterface.data_suffix", "modulename": "opsml.model.interfaces.base", "qualname": "ModelInterface.data_suffix", "kind": "variable", "doc": "Returns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.base.ModelInterface.name": {"fullname": "opsml.model.interfaces.base.ModelInterface.name", "modulename": "opsml.model.interfaces.base", "qualname": "ModelInterface.name", "kind": "function", "doc": "\n", "signature": "() -> str:", "funcdef": "def"}, "opsml.model.interfaces.base.ModelInterface.model_fields": {"fullname": "opsml.model.interfaces.base.ModelInterface.model_fields", "modulename": "opsml.model.interfaces.base", "qualname": "ModelInterface.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'model': FieldInfo(annotation=Union[Any, NoneType], required=False), 'sample_data': FieldInfo(annotation=Union[Any, NoneType], required=False), 'onnx_model': FieldInfo(annotation=Union[OnnxModel, NoneType], required=False), 'task_type': FieldInfo(annotation=str, required=False, default='undefined'), 'model_type': FieldInfo(annotation=str, required=False, default='undefined'), 'data_type': FieldInfo(annotation=str, required=False, default='undefined'), 'modelcard_uid': FieldInfo(annotation=str, required=False, default='')}"}, "opsml.model.interfaces.base.ModelInterface.model_computed_fields": {"fullname": "opsml.model.interfaces.base.ModelInterface.model_computed_fields", "modulename": "opsml.model.interfaces.base", "qualname": "ModelInterface.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.model.interfaces.catboost_": {"fullname": "opsml.model.interfaces.catboost_", "modulename": "opsml.model.interfaces.catboost_", "kind": "module", "doc": "\n"}, "opsml.model.interfaces.catboost_.logger": {"fullname": "opsml.model.interfaces.catboost_.logger", "modulename": "opsml.model.interfaces.catboost_", "qualname": "logger", "kind": "variable", "doc": "\n", "default_value": "<builtins.Logger object>"}, "opsml.model.interfaces.catboost_.CatBoostModel": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel", "kind": "class", "doc": "Model interface for CatBoost models.
\n\n\n\n", "bases": "opsml.model.interfaces.base.ModelInterface"}, "opsml.model.interfaces.catboost_.CatBoostModel.model": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel.model", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel.model", "kind": "variable", "doc": "\n", "annotation": ": Optional[catboost.core.CatBoost]"}, "opsml.model.interfaces.catboost_.CatBoostModel.sample_data": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel.sample_data", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel.sample_data", "kind": "variable", "doc": "\n", "annotation": ": Union[List[Any], numpy.ndarray[Any, numpy.dtype[Any]], NoneType]"}, "opsml.model.interfaces.catboost_.CatBoostModel.preprocessor": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel.preprocessor", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel.preprocessor", "kind": "variable", "doc": "\n", "annotation": ": Optional[Any]"}, "opsml.model.interfaces.catboost_.CatBoostModel.preprocessor_name": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel.preprocessor_name", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel.preprocessor_name", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.catboost_.CatBoostModel.get_sample_prediction": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel.get_sample_prediction", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel.get_sample_prediction", "kind": "function", "doc": "\n", "signature": "(self) -> opsml.model.interfaces.base.SamplePrediction:", "funcdef": "def"}, "opsml.model.interfaces.catboost_.CatBoostModel.model_class": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel.model_class", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel.model_class", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.catboost_.CatBoostModel.check_model": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel.check_model", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel.check_model", "kind": "function", "doc": "\n", "signature": "(cls, model_args: Dict[str, Any]) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.model.interfaces.catboost_.CatBoostModel.save_model": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel.save_model", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel.save_model", "kind": "function", "doc": "CatBoostModel
\n
Saves model to path. Base implementation use Joblib
\n\nLoad model from pathlib object
\n\nConverts model to onnx format
\n", "signature": "(self, **kwargs: pathlib.Path) -> None:", "funcdef": "def"}, "opsml.model.interfaces.catboost_.CatBoostModel.save_onnx": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel.save_onnx", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel.save_onnx", "kind": "function", "doc": "Saves the onnx model
\n\n\n\n", "signature": "(self, path: pathlib.Path) -> opsml.types.model.ModelReturn:", "funcdef": "def"}, "opsml.model.interfaces.catboost_.CatBoostModel.save_preprocessor": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel.save_preprocessor", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel.save_preprocessor", "kind": "function", "doc": "ModelReturn
\n
Saves preprocessor to path if present. Base implementation use Joblib
\n\nLoad preprocessor from pathlib object
\n\nReturns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.catboost_.CatBoostModel.model_suffix": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel.model_suffix", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel.model_suffix", "kind": "variable", "doc": "Returns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.catboost_.CatBoostModel.name": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel.name", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel.name", "kind": "function", "doc": "\n", "signature": "() -> str:", "funcdef": "def"}, "opsml.model.interfaces.catboost_.CatBoostModel.model_config": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel.model_config", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel.model_config", "kind": "variable", "doc": "\n", "default_value": "{'protected_namespaces': ('protect_',), 'arbitrary_types_allowed': True, 'validate_assignment': False, 'validate_default': True, 'extra': 'allow'}"}, "opsml.model.interfaces.catboost_.CatBoostModel.model_fields": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel.model_fields", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'model': FieldInfo(annotation=Union[CatBoost, NoneType], required=False), 'sample_data': FieldInfo(annotation=Union[List[Any], ndarray[Any, dtype[Any]], NoneType], required=False), 'onnx_model': FieldInfo(annotation=Union[OnnxModel, NoneType], required=False), 'task_type': FieldInfo(annotation=str, required=False, default='undefined'), 'model_type': FieldInfo(annotation=str, required=False, default='undefined'), 'data_type': FieldInfo(annotation=str, required=False, default='undefined'), 'modelcard_uid': FieldInfo(annotation=str, required=False, default=''), 'preprocessor': FieldInfo(annotation=Union[Any, NoneType], required=False), 'preprocessor_name': FieldInfo(annotation=str, required=False, default='undefined')}"}, "opsml.model.interfaces.catboost_.CatBoostModel.model_computed_fields": {"fullname": "opsml.model.interfaces.catboost_.CatBoostModel.model_computed_fields", "modulename": "opsml.model.interfaces.catboost_", "qualname": "CatBoostModel.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.model.interfaces.huggingface": {"fullname": "opsml.model.interfaces.huggingface", "modulename": "opsml.model.interfaces.huggingface", "kind": "module", "doc": "\n"}, "opsml.model.interfaces.huggingface.logger": {"fullname": "opsml.model.interfaces.huggingface.logger", "modulename": "opsml.model.interfaces.huggingface", "qualname": "logger", "kind": "variable", "doc": "\n", "default_value": "<builtins.Logger object>"}, "opsml.model.interfaces.huggingface.HuggingFaceModel": {"fullname": "opsml.model.interfaces.huggingface.HuggingFaceModel", "modulename": "opsml.model.interfaces.huggingface", "qualname": "HuggingFaceModel", "kind": "class", "doc": "Model interface for HuggingFace models
\n\nHuggingFaceTask for supported tasks.HuggingFaceOnnxArgs for supported arguments.\n\n\nHuggingFaceModel
\n
Example::
\n\nfrom transformers import BartModel, BartTokenizer\n\ntokenizer = BartTokenizer.from_pretrained(\"facebook/bart-base\")\nmodel = BartModel.from_pretrained(\"facebook/bart-base\")\ninputs = tokenizer([\"Hello. How are you\"], return_tensors=\"pt\")\n\n# this is fed to the ModelCard\nmodel = HuggingFaceModel(\n model=model,\n tokenizer=tokenizer,\n sample_data=inputs,\n task_type=HuggingFaceTask.TEXT_CLASSIFICATION.value,\n)\nCheck if task is a huggingface approved task
\n", "signature": "(cls, task_type: str) -> str:", "funcdef": "def"}, "opsml.model.interfaces.huggingface.HuggingFaceModel.get_sample_prediction": {"fullname": "opsml.model.interfaces.huggingface.HuggingFaceModel.get_sample_prediction", "modulename": "opsml.model.interfaces.huggingface", "qualname": "HuggingFaceModel.get_sample_prediction", "kind": "function", "doc": "Generates prediction from model and provided sample data
\n", "signature": "(self) -> opsml.model.interfaces.base.SamplePrediction:", "funcdef": "def"}, "opsml.model.interfaces.huggingface.HuggingFaceModel.convert_to_onnx": {"fullname": "opsml.model.interfaces.huggingface.HuggingFaceModel.convert_to_onnx", "modulename": "opsml.model.interfaces.huggingface", "qualname": "HuggingFaceModel.convert_to_onnx", "kind": "function", "doc": "Converts a huggingface model or pipeline to onnx via optimum library.\nConverted model or pipeline is accessible via the onnx_model attribute.
Saves model to path. Base implementation use Joblib
\n\nSaves the onnx model
\n\n\n\n", "signature": "(self, path: pathlib.Path) -> opsml.types.model.ModelReturn:", "funcdef": "def"}, "opsml.model.interfaces.huggingface.HuggingFaceModel.load_tokenizer": {"fullname": "opsml.model.interfaces.huggingface.HuggingFaceModel.load_tokenizer", "modulename": "opsml.model.interfaces.huggingface", "qualname": "HuggingFaceModel.load_tokenizer", "kind": "function", "doc": "\n", "signature": "(self, path: pathlib.Path) -> None:", "funcdef": "def"}, "opsml.model.interfaces.huggingface.HuggingFaceModel.load_feature_extractor": {"fullname": "opsml.model.interfaces.huggingface.HuggingFaceModel.load_feature_extractor", "modulename": "opsml.model.interfaces.huggingface", "qualname": "HuggingFaceModel.load_feature_extractor", "kind": "function", "doc": "\n", "signature": "(self, path: pathlib.Path) -> None:", "funcdef": "def"}, "opsml.model.interfaces.huggingface.HuggingFaceModel.load_model": {"fullname": "opsml.model.interfaces.huggingface.HuggingFaceModel.load_model", "modulename": "opsml.model.interfaces.huggingface", "qualname": "HuggingFaceModel.load_model", "kind": "function", "doc": "ModelReturn
\n
Load huggingface model from path
\n\nConverts model to pipeline
\n", "signature": "(self) -> None:", "funcdef": "def"}, "opsml.model.interfaces.huggingface.HuggingFaceModel.load_onnx_model": {"fullname": "opsml.model.interfaces.huggingface.HuggingFaceModel.load_onnx_model", "modulename": "opsml.model.interfaces.huggingface", "qualname": "HuggingFaceModel.load_onnx_model", "kind": "function", "doc": "Load onnx model from path
\n", "signature": "(self, path: pathlib.Path) -> None:", "funcdef": "def"}, "opsml.model.interfaces.huggingface.HuggingFaceModel.model_class": {"fullname": "opsml.model.interfaces.huggingface.HuggingFaceModel.model_class", "modulename": "opsml.model.interfaces.huggingface", "qualname": "HuggingFaceModel.model_class", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.huggingface.HuggingFaceModel.model_suffix": {"fullname": "opsml.model.interfaces.huggingface.HuggingFaceModel.model_suffix", "modulename": "opsml.model.interfaces.huggingface", "qualname": "HuggingFaceModel.model_suffix", "kind": "variable", "doc": "Returns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.huggingface.HuggingFaceModel.name": {"fullname": "opsml.model.interfaces.huggingface.HuggingFaceModel.name", "modulename": "opsml.model.interfaces.huggingface", "qualname": "HuggingFaceModel.name", "kind": "function", "doc": "\n", "signature": "() -> str:", "funcdef": "def"}, "opsml.model.interfaces.huggingface.HuggingFaceModel.model_config": {"fullname": "opsml.model.interfaces.huggingface.HuggingFaceModel.model_config", "modulename": "opsml.model.interfaces.huggingface", "qualname": "HuggingFaceModel.model_config", "kind": "variable", "doc": "\n", "default_value": "{'protected_namespaces': ('protect_',), 'arbitrary_types_allowed': True, 'validate_assignment': False, 'validate_default': True, 'extra': 'allow'}"}, "opsml.model.interfaces.huggingface.HuggingFaceModel.model_fields": {"fullname": "opsml.model.interfaces.huggingface.HuggingFaceModel.model_fields", "modulename": "opsml.model.interfaces.huggingface", "qualname": "HuggingFaceModel.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'model': FieldInfo(annotation=Union[Pipeline, PreTrainedModel, TFPreTrainedModel, NoneType], required=False), 'sample_data': FieldInfo(annotation=Union[Any, NoneType], required=False), 'onnx_model': FieldInfo(annotation=Union[OnnxModel, NoneType], required=False), 'task_type': FieldInfo(annotation=str, required=False, default='undefined'), 'model_type': FieldInfo(annotation=str, required=False, default='undefined'), 'data_type': FieldInfo(annotation=str, required=False, default='undefined'), 'modelcard_uid': FieldInfo(annotation=str, required=False, default=''), 'tokenizer': FieldInfo(annotation=Union[PreTrainedTokenizer, PreTrainedTokenizerFast, NoneType], required=False), 'feature_extractor': FieldInfo(annotation=Union[FeatureExtractionMixin, ImageProcessingMixin, NoneType], required=False), 'is_pipeline': FieldInfo(annotation=bool, required=False, default=False), 'backend': FieldInfo(annotation=str, required=False, default='pytorch'), 'onnx_args': FieldInfo(annotation=Union[HuggingFaceOnnxArgs, NoneType], required=False), 'tokenizer_name': FieldInfo(annotation=str, required=False, default='undefined'), 'feature_extractor_name': FieldInfo(annotation=str, required=False, default='undefined')}"}, "opsml.model.interfaces.huggingface.HuggingFaceModel.model_computed_fields": {"fullname": "opsml.model.interfaces.huggingface.HuggingFaceModel.model_computed_fields", "modulename": "opsml.model.interfaces.huggingface", "qualname": "HuggingFaceModel.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.model.interfaces.lgbm": {"fullname": "opsml.model.interfaces.lgbm", "modulename": "opsml.model.interfaces.lgbm", "kind": "module", "doc": "\n"}, "opsml.model.interfaces.lgbm.LightGBMModel": {"fullname": "opsml.model.interfaces.lgbm.LightGBMModel", "modulename": "opsml.model.interfaces.lgbm", "qualname": "LightGBMModel", "kind": "class", "doc": "Model interface for LightGBM Booster model class. If using the sklearn API, use SklearnModel instead.
\n\nTorchOnnxArgs for supported arguments.\n\n", "bases": "opsml.model.interfaces.base.ModelInterface"}, "opsml.model.interfaces.lgbm.LightGBMModel.model": {"fullname": "opsml.model.interfaces.lgbm.LightGBMModel.model", "modulename": "opsml.model.interfaces.lgbm", "qualname": "LightGBMModel.model", "kind": "variable", "doc": "\n", "annotation": ": Union[lightgbm.basic.Booster, lightgbm.sklearn.LGBMModel, NoneType]"}, "opsml.model.interfaces.lgbm.LightGBMModel.sample_data": {"fullname": "opsml.model.interfaces.lgbm.LightGBMModel.sample_data", "modulename": "opsml.model.interfaces.lgbm", "qualname": "LightGBMModel.sample_data", "kind": "variable", "doc": "\n", "annotation": ": Union[pandas.core.frame.DataFrame, numpy.ndarray[Any, numpy.dtype[Any]], NoneType]"}, "opsml.model.interfaces.lgbm.LightGBMModel.preprocessor": {"fullname": "opsml.model.interfaces.lgbm.LightGBMModel.preprocessor", "modulename": "opsml.model.interfaces.lgbm", "qualname": "LightGBMModel.preprocessor", "kind": "variable", "doc": "\n", "annotation": ": Optional[Any]"}, "opsml.model.interfaces.lgbm.LightGBMModel.preprocessor_name": {"fullname": "opsml.model.interfaces.lgbm.LightGBMModel.preprocessor_name", "modulename": "opsml.model.interfaces.lgbm", "qualname": "LightGBMModel.preprocessor_name", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.lgbm.LightGBMModel.model_class": {"fullname": "opsml.model.interfaces.lgbm.LightGBMModel.model_class", "modulename": "opsml.model.interfaces.lgbm", "qualname": "LightGBMModel.model_class", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.lgbm.LightGBMModel.check_model": {"fullname": "opsml.model.interfaces.lgbm.LightGBMModel.check_model", "modulename": "opsml.model.interfaces.lgbm", "qualname": "LightGBMModel.check_model", "kind": "function", "doc": "\n", "signature": "(cls, model_args: Dict[str, Any]) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.model.interfaces.lgbm.LightGBMModel.save_model": {"fullname": "opsml.model.interfaces.lgbm.LightGBMModel.save_model", "modulename": "opsml.model.interfaces.lgbm", "qualname": "LightGBMModel.save_model", "kind": "function", "doc": "LightGBMModel
\n
Saves lgb model according to model format. Booster models are saved to text.\nSklearn models are saved via joblib.
\n\nLoads lightgbm booster or sklearn model
\n\nSaves preprocessor to path if present. Base implementation use Joblib
\n\nLoad preprocessor from pathlib object
\n\nReturns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.lgbm.LightGBMModel.preprocessor_suffix": {"fullname": "opsml.model.interfaces.lgbm.LightGBMModel.preprocessor_suffix", "modulename": "opsml.model.interfaces.lgbm", "qualname": "LightGBMModel.preprocessor_suffix", "kind": "variable", "doc": "Returns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.lgbm.LightGBMModel.name": {"fullname": "opsml.model.interfaces.lgbm.LightGBMModel.name", "modulename": "opsml.model.interfaces.lgbm", "qualname": "LightGBMModel.name", "kind": "function", "doc": "\n", "signature": "() -> str:", "funcdef": "def"}, "opsml.model.interfaces.lgbm.LightGBMModel.model_config": {"fullname": "opsml.model.interfaces.lgbm.LightGBMModel.model_config", "modulename": "opsml.model.interfaces.lgbm", "qualname": "LightGBMModel.model_config", "kind": "variable", "doc": "\n", "default_value": "{'protected_namespaces': ('protect_',), 'arbitrary_types_allowed': True, 'validate_assignment': False, 'validate_default': True, 'extra': 'allow'}"}, "opsml.model.interfaces.lgbm.LightGBMModel.model_fields": {"fullname": "opsml.model.interfaces.lgbm.LightGBMModel.model_fields", "modulename": "opsml.model.interfaces.lgbm", "qualname": "LightGBMModel.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'model': FieldInfo(annotation=Union[Booster, LGBMModel, NoneType], required=False), 'sample_data': FieldInfo(annotation=Union[DataFrame, ndarray[Any, dtype[Any]], NoneType], required=False), 'onnx_model': FieldInfo(annotation=Union[OnnxModel, NoneType], required=False), 'task_type': FieldInfo(annotation=str, required=False, default='undefined'), 'model_type': FieldInfo(annotation=str, required=False, default='undefined'), 'data_type': FieldInfo(annotation=str, required=False, default='undefined'), 'modelcard_uid': FieldInfo(annotation=str, required=False, default=''), 'preprocessor': FieldInfo(annotation=Union[Any, NoneType], required=False), 'preprocessor_name': FieldInfo(annotation=str, required=False, default='undefined')}"}, "opsml.model.interfaces.lgbm.LightGBMModel.model_computed_fields": {"fullname": "opsml.model.interfaces.lgbm.LightGBMModel.model_computed_fields", "modulename": "opsml.model.interfaces.lgbm", "qualname": "LightGBMModel.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.model.interfaces.pytorch_lightning": {"fullname": "opsml.model.interfaces.pytorch_lightning", "modulename": "opsml.model.interfaces.pytorch_lightning", "kind": "module", "doc": "\n"}, "opsml.model.interfaces.pytorch_lightning.LightningModel": {"fullname": "opsml.model.interfaces.pytorch_lightning.LightningModel", "modulename": "opsml.model.interfaces.pytorch_lightning", "qualname": "LightningModel", "kind": "class", "doc": "Model interface for Pytorch Lightning models.
\n\nReturns:\nLightningModel
\n", "bases": "opsml.model.interfaces.pytorch.TorchModel"}, "opsml.model.interfaces.pytorch_lightning.LightningModel.model": {"fullname": "opsml.model.interfaces.pytorch_lightning.LightningModel.model", "modulename": "opsml.model.interfaces.pytorch_lightning", "qualname": "LightningModel.model", "kind": "variable", "doc": "\n", "annotation": ": Optional[lightning.pytorch.trainer.trainer.Trainer]"}, "opsml.model.interfaces.pytorch_lightning.LightningModel.onnx_args": {"fullname": "opsml.model.interfaces.pytorch_lightning.LightningModel.onnx_args", "modulename": "opsml.model.interfaces.pytorch_lightning", "qualname": "LightningModel.onnx_args", "kind": "variable", "doc": "\n", "annotation": ": Optional[opsml.types.model.TorchOnnxArgs]"}, "opsml.model.interfaces.pytorch_lightning.LightningModel.model_class": {"fullname": "opsml.model.interfaces.pytorch_lightning.LightningModel.model_class", "modulename": "opsml.model.interfaces.pytorch_lightning", "qualname": "LightningModel.model_class", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.pytorch_lightning.LightningModel.check_model": {"fullname": "opsml.model.interfaces.pytorch_lightning.LightningModel.check_model", "modulename": "opsml.model.interfaces.pytorch_lightning", "qualname": "LightningModel.check_model", "kind": "function", "doc": "\n", "signature": "(cls, model_args: Dict[str, Any]) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.model.interfaces.pytorch_lightning.LightningModel.get_sample_prediction": {"fullname": "opsml.model.interfaces.pytorch_lightning.LightningModel.get_sample_prediction", "modulename": "opsml.model.interfaces.pytorch_lightning", "qualname": "LightningModel.get_sample_prediction", "kind": "function", "doc": "\n", "signature": "(self) -> opsml.model.interfaces.base.SamplePrediction:", "funcdef": "def"}, "opsml.model.interfaces.pytorch_lightning.LightningModel.save_model": {"fullname": "opsml.model.interfaces.pytorch_lightning.LightningModel.save_model", "modulename": "opsml.model.interfaces.pytorch_lightning", "qualname": "LightningModel.save_model", "kind": "function", "doc": "Save pytorch model to path
\n\nLoad lightning model from path
\n", "signature": "(self, path: pathlib.Path, **kwargs: Any) -> None:", "funcdef": "def"}, "opsml.model.interfaces.pytorch_lightning.LightningModel.convert_to_onnx": {"fullname": "opsml.model.interfaces.pytorch_lightning.LightningModel.convert_to_onnx", "modulename": "opsml.model.interfaces.pytorch_lightning", "qualname": "LightningModel.convert_to_onnx", "kind": "function", "doc": "Converts model to onnx
\n", "signature": "(self, **kwargs: pathlib.Path) -> None:", "funcdef": "def"}, "opsml.model.interfaces.pytorch_lightning.LightningModel.model_suffix": {"fullname": "opsml.model.interfaces.pytorch_lightning.LightningModel.model_suffix", "modulename": "opsml.model.interfaces.pytorch_lightning", "qualname": "LightningModel.model_suffix", "kind": "variable", "doc": "Returns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.pytorch_lightning.LightningModel.name": {"fullname": "opsml.model.interfaces.pytorch_lightning.LightningModel.name", "modulename": "opsml.model.interfaces.pytorch_lightning", "qualname": "LightningModel.name", "kind": "function", "doc": "\n", "signature": "() -> str:", "funcdef": "def"}, "opsml.model.interfaces.pytorch_lightning.LightningModel.model_config": {"fullname": "opsml.model.interfaces.pytorch_lightning.LightningModel.model_config", "modulename": "opsml.model.interfaces.pytorch_lightning", "qualname": "LightningModel.model_config", "kind": "variable", "doc": "\n", "default_value": "{'protected_namespaces': ('protect_',), 'arbitrary_types_allowed': True, 'validate_assignment': False, 'validate_default': True, 'extra': 'allow'}"}, "opsml.model.interfaces.pytorch_lightning.LightningModel.model_fields": {"fullname": "opsml.model.interfaces.pytorch_lightning.LightningModel.model_fields", "modulename": "opsml.model.interfaces.pytorch_lightning", "qualname": "LightningModel.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'model': FieldInfo(annotation=Union[Trainer, NoneType], required=False), 'sample_data': FieldInfo(annotation=Union[Tensor, Dict[str, Tensor], List[Tensor], Tuple[Tensor], NoneType], required=False), 'onnx_model': FieldInfo(annotation=Union[OnnxModel, NoneType], required=False), 'task_type': FieldInfo(annotation=str, required=False, default='undefined'), 'model_type': FieldInfo(annotation=str, required=False, default='undefined'), 'data_type': FieldInfo(annotation=str, required=False, default='undefined'), 'modelcard_uid': FieldInfo(annotation=str, required=False, default=''), 'onnx_args': FieldInfo(annotation=Union[TorchOnnxArgs, NoneType], required=False), 'save_args': FieldInfo(annotation=TorchSaveArgs, required=False, default=TorchSaveArgs(as_state_dict=False)), 'preprocessor': FieldInfo(annotation=Union[Any, NoneType], required=False), 'preprocessor_name': FieldInfo(annotation=str, required=False, default='undefined')}"}, "opsml.model.interfaces.pytorch_lightning.LightningModel.model_computed_fields": {"fullname": "opsml.model.interfaces.pytorch_lightning.LightningModel.model_computed_fields", "modulename": "opsml.model.interfaces.pytorch_lightning", "qualname": "LightningModel.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.model.interfaces.pytorch": {"fullname": "opsml.model.interfaces.pytorch", "modulename": "opsml.model.interfaces.pytorch", "kind": "module", "doc": "\n"}, "opsml.model.interfaces.pytorch.TorchModel": {"fullname": "opsml.model.interfaces.pytorch.TorchModel", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel", "kind": "class", "doc": "Model interface for Pytorch models.
\n\nTorchSaveArgs for supported arguments.TorchOnnxArgs for supported arguments.Returns:\nTorchModel
\n", "bases": "opsml.model.interfaces.base.ModelInterface"}, "opsml.model.interfaces.pytorch.TorchModel.model": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.model", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.model", "kind": "variable", "doc": "\n", "annotation": ": Optional[torch.nn.modules.module.Module]"}, "opsml.model.interfaces.pytorch.TorchModel.sample_data": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.sample_data", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.sample_data", "kind": "variable", "doc": "\n", "annotation": ": Union[torch.Tensor, Dict[str, torch.Tensor], List[torch.Tensor], Tuple[torch.Tensor], NoneType]"}, "opsml.model.interfaces.pytorch.TorchModel.onnx_args": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.onnx_args", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.onnx_args", "kind": "variable", "doc": "\n", "annotation": ": Optional[opsml.types.model.TorchOnnxArgs]"}, "opsml.model.interfaces.pytorch.TorchModel.save_args": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.save_args", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.save_args", "kind": "variable", "doc": "\n", "annotation": ": opsml.types.model.TorchSaveArgs"}, "opsml.model.interfaces.pytorch.TorchModel.preprocessor": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.preprocessor", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.preprocessor", "kind": "variable", "doc": "\n", "annotation": ": Optional[Any]"}, "opsml.model.interfaces.pytorch.TorchModel.preprocessor_name": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.preprocessor_name", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.preprocessor_name", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.pytorch.TorchModel.model_class": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.model_class", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.model_class", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.pytorch.TorchModel.check_model": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.check_model", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.check_model", "kind": "function", "doc": "\n", "signature": "(cls, model_args: Dict[str, Any]) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.model.interfaces.pytorch.TorchModel.get_sample_prediction": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.get_sample_prediction", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.get_sample_prediction", "kind": "function", "doc": "\n", "signature": "(self) -> opsml.model.interfaces.base.SamplePrediction:", "funcdef": "def"}, "opsml.model.interfaces.pytorch.TorchModel.save_model": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.save_model", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.save_model", "kind": "function", "doc": "Save pytorch model to path
\n\nLoad pytorch model from path
\n\nSaves an onnx model
\n\n\n\n", "signature": "(self, path: pathlib.Path) -> opsml.types.model.ModelReturn:", "funcdef": "def"}, "opsml.model.interfaces.pytorch.TorchModel.convert_to_onnx": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.convert_to_onnx", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.convert_to_onnx", "kind": "function", "doc": "ModelReturn
\n
Converts model to onnx format
\n", "signature": "(self, **kwargs: pathlib.Path) -> None:", "funcdef": "def"}, "opsml.model.interfaces.pytorch.TorchModel.save_preprocessor": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.save_preprocessor", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.save_preprocessor", "kind": "function", "doc": "Saves preprocessor to path if present. Base implementation use Joblib
\n\nLoad preprocessor from pathlib object
\n\nReturns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.pytorch.TorchModel.model_suffix": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.model_suffix", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.model_suffix", "kind": "variable", "doc": "Returns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.pytorch.TorchModel.name": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.name", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.name", "kind": "function", "doc": "\n", "signature": "() -> str:", "funcdef": "def"}, "opsml.model.interfaces.pytorch.TorchModel.model_config": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.model_config", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.model_config", "kind": "variable", "doc": "\n", "default_value": "{'protected_namespaces': ('protect_',), 'arbitrary_types_allowed': True, 'validate_assignment': False, 'validate_default': True, 'extra': 'allow'}"}, "opsml.model.interfaces.pytorch.TorchModel.model_fields": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.model_fields", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'model': FieldInfo(annotation=Union[Module, NoneType], required=False), 'sample_data': FieldInfo(annotation=Union[Tensor, Dict[str, Tensor], List[Tensor], Tuple[Tensor], NoneType], required=False), 'onnx_model': FieldInfo(annotation=Union[OnnxModel, NoneType], required=False), 'task_type': FieldInfo(annotation=str, required=False, default='undefined'), 'model_type': FieldInfo(annotation=str, required=False, default='undefined'), 'data_type': FieldInfo(annotation=str, required=False, default='undefined'), 'modelcard_uid': FieldInfo(annotation=str, required=False, default=''), 'onnx_args': FieldInfo(annotation=Union[TorchOnnxArgs, NoneType], required=False), 'save_args': FieldInfo(annotation=TorchSaveArgs, required=False, default=TorchSaveArgs(as_state_dict=False)), 'preprocessor': FieldInfo(annotation=Union[Any, NoneType], required=False), 'preprocessor_name': FieldInfo(annotation=str, required=False, default='undefined')}"}, "opsml.model.interfaces.pytorch.TorchModel.model_computed_fields": {"fullname": "opsml.model.interfaces.pytorch.TorchModel.model_computed_fields", "modulename": "opsml.model.interfaces.pytorch", "qualname": "TorchModel.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.model.interfaces.sklearn": {"fullname": "opsml.model.interfaces.sklearn", "modulename": "opsml.model.interfaces.sklearn", "kind": "module", "doc": "\n"}, "opsml.model.interfaces.sklearn.SklearnModel": {"fullname": "opsml.model.interfaces.sklearn.SklearnModel", "modulename": "opsml.model.interfaces.sklearn", "qualname": "SklearnModel", "kind": "class", "doc": "Model interface for Sklearn models.
\n\nReturns:\nSklearnModel
\n", "bases": "opsml.model.interfaces.base.ModelInterface"}, "opsml.model.interfaces.sklearn.SklearnModel.model": {"fullname": "opsml.model.interfaces.sklearn.SklearnModel.model", "modulename": "opsml.model.interfaces.sklearn", "qualname": "SklearnModel.model", "kind": "variable", "doc": "\n", "annotation": ": Optional[sklearn.base.BaseEstimator]"}, "opsml.model.interfaces.sklearn.SklearnModel.sample_data": {"fullname": "opsml.model.interfaces.sklearn.SklearnModel.sample_data", "modulename": "opsml.model.interfaces.sklearn", "qualname": "SklearnModel.sample_data", "kind": "variable", "doc": "\n", "annotation": ": Union[pandas.core.frame.DataFrame, numpy.ndarray[Any, numpy.dtype[Any]], NoneType]"}, "opsml.model.interfaces.sklearn.SklearnModel.preprocessor": {"fullname": "opsml.model.interfaces.sklearn.SklearnModel.preprocessor", "modulename": "opsml.model.interfaces.sklearn", "qualname": "SklearnModel.preprocessor", "kind": "variable", "doc": "\n", "annotation": ": Optional[Any]"}, "opsml.model.interfaces.sklearn.SklearnModel.preprocessor_name": {"fullname": "opsml.model.interfaces.sklearn.SklearnModel.preprocessor_name", "modulename": "opsml.model.interfaces.sklearn", "qualname": "SklearnModel.preprocessor_name", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.sklearn.SklearnModel.model_class": {"fullname": "opsml.model.interfaces.sklearn.SklearnModel.model_class", "modulename": "opsml.model.interfaces.sklearn", "qualname": "SklearnModel.model_class", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.sklearn.SklearnModel.check_model": {"fullname": "opsml.model.interfaces.sklearn.SklearnModel.check_model", "modulename": "opsml.model.interfaces.sklearn", "qualname": "SklearnModel.check_model", "kind": "function", "doc": "\n", "signature": "(cls, model_args: Dict[str, Any]) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.model.interfaces.sklearn.SklearnModel.save_preprocessor": {"fullname": "opsml.model.interfaces.sklearn.SklearnModel.save_preprocessor", "modulename": "opsml.model.interfaces.sklearn", "qualname": "SklearnModel.save_preprocessor", "kind": "function", "doc": "Saves preprocessor to path if present. Base implementation use Joblib
\n\nLoad preprocessor from pathlib object
\n\nReturns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.sklearn.SklearnModel.name": {"fullname": "opsml.model.interfaces.sklearn.SklearnModel.name", "modulename": "opsml.model.interfaces.sklearn", "qualname": "SklearnModel.name", "kind": "function", "doc": "\n", "signature": "() -> str:", "funcdef": "def"}, "opsml.model.interfaces.sklearn.SklearnModel.model_config": {"fullname": "opsml.model.interfaces.sklearn.SklearnModel.model_config", "modulename": "opsml.model.interfaces.sklearn", "qualname": "SklearnModel.model_config", "kind": "variable", "doc": "\n", "default_value": "{'protected_namespaces': ('protect_',), 'arbitrary_types_allowed': True, 'validate_assignment': False, 'validate_default': True, 'extra': 'allow'}"}, "opsml.model.interfaces.sklearn.SklearnModel.model_fields": {"fullname": "opsml.model.interfaces.sklearn.SklearnModel.model_fields", "modulename": "opsml.model.interfaces.sklearn", "qualname": "SklearnModel.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'model': FieldInfo(annotation=Union[BaseEstimator, NoneType], required=False), 'sample_data': FieldInfo(annotation=Union[DataFrame, ndarray[Any, dtype[Any]], NoneType], required=False), 'onnx_model': FieldInfo(annotation=Union[OnnxModel, NoneType], required=False), 'task_type': FieldInfo(annotation=str, required=False, default='undefined'), 'model_type': FieldInfo(annotation=str, required=False, default='undefined'), 'data_type': FieldInfo(annotation=str, required=False, default='undefined'), 'modelcard_uid': FieldInfo(annotation=str, required=False, default=''), 'preprocessor': FieldInfo(annotation=Union[Any, NoneType], required=False), 'preprocessor_name': FieldInfo(annotation=str, required=False, default='undefined')}"}, "opsml.model.interfaces.sklearn.SklearnModel.model_computed_fields": {"fullname": "opsml.model.interfaces.sklearn.SklearnModel.model_computed_fields", "modulename": "opsml.model.interfaces.sklearn", "qualname": "SklearnModel.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.model.interfaces.tf": {"fullname": "opsml.model.interfaces.tf", "modulename": "opsml.model.interfaces.tf", "kind": "module", "doc": "\n"}, "opsml.model.interfaces.tf.TensorFlowModel": {"fullname": "opsml.model.interfaces.tf.TensorFlowModel", "modulename": "opsml.model.interfaces.tf", "qualname": "TensorFlowModel", "kind": "class", "doc": "Model interface for Tensorflow models.
\n\n\n\n", "bases": "opsml.model.interfaces.base.ModelInterface"}, "opsml.model.interfaces.tf.TensorFlowModel.model": {"fullname": "opsml.model.interfaces.tf.TensorFlowModel.model", "modulename": "opsml.model.interfaces.tf", "qualname": "TensorFlowModel.model", "kind": "variable", "doc": "\n", "annotation": ": Optional[keras.engine.training.Model]"}, "opsml.model.interfaces.tf.TensorFlowModel.sample_data": {"fullname": "opsml.model.interfaces.tf.TensorFlowModel.sample_data", "modulename": "opsml.model.interfaces.tf", "qualname": "TensorFlowModel.sample_data", "kind": "variable", "doc": "\n", "annotation": ": Union[numpy.ndarray[Any, numpy.dtype[Any]], tensorflow.python.framework.ops.Tensor, Dict[str, Union[numpy.ndarray[Any, numpy.dtype[Any]], tensorflow.python.framework.ops.Tensor]], List[Union[numpy.ndarray[Any, numpy.dtype[Any]], tensorflow.python.framework.ops.Tensor]], Tuple[Union[numpy.ndarray[Any, numpy.dtype[Any]], tensorflow.python.framework.ops.Tensor]], NoneType]"}, "opsml.model.interfaces.tf.TensorFlowModel.preprocessor": {"fullname": "opsml.model.interfaces.tf.TensorFlowModel.preprocessor", "modulename": "opsml.model.interfaces.tf", "qualname": "TensorFlowModel.preprocessor", "kind": "variable", "doc": "\n", "annotation": ": Optional[Any]"}, "opsml.model.interfaces.tf.TensorFlowModel.preprocessor_name": {"fullname": "opsml.model.interfaces.tf.TensorFlowModel.preprocessor_name", "modulename": "opsml.model.interfaces.tf", "qualname": "TensorFlowModel.preprocessor_name", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.tf.TensorFlowModel.model_class": {"fullname": "opsml.model.interfaces.tf.TensorFlowModel.model_class", "modulename": "opsml.model.interfaces.tf", "qualname": "TensorFlowModel.model_class", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.tf.TensorFlowModel.check_model": {"fullname": "opsml.model.interfaces.tf.TensorFlowModel.check_model", "modulename": "opsml.model.interfaces.tf", "qualname": "TensorFlowModel.check_model", "kind": "function", "doc": "\n", "signature": "(cls, model_args: Dict[str, Any]) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.model.interfaces.tf.TensorFlowModel.save_model": {"fullname": "opsml.model.interfaces.tf.TensorFlowModel.save_model", "modulename": "opsml.model.interfaces.tf", "qualname": "TensorFlowModel.save_model", "kind": "function", "doc": "TensorFlowModel
\n
Save tensorflow model to path
\n\nLoad tensorflow model from path
\n\nSaves preprocessor to path if present. Base implementation use Joblib
\n\nLoad preprocessor from pathlib object
\n\nReturns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.tf.TensorFlowModel.model_suffix": {"fullname": "opsml.model.interfaces.tf.TensorFlowModel.model_suffix", "modulename": "opsml.model.interfaces.tf", "qualname": "TensorFlowModel.model_suffix", "kind": "variable", "doc": "Returns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.tf.TensorFlowModel.name": {"fullname": "opsml.model.interfaces.tf.TensorFlowModel.name", "modulename": "opsml.model.interfaces.tf", "qualname": "TensorFlowModel.name", "kind": "function", "doc": "\n", "signature": "() -> str:", "funcdef": "def"}, "opsml.model.interfaces.tf.TensorFlowModel.model_config": {"fullname": "opsml.model.interfaces.tf.TensorFlowModel.model_config", "modulename": "opsml.model.interfaces.tf", "qualname": "TensorFlowModel.model_config", "kind": "variable", "doc": "\n", "default_value": "{'protected_namespaces': ('protect_',), 'arbitrary_types_allowed': True, 'validate_assignment': False, 'validate_default': True, 'extra': 'allow'}"}, "opsml.model.interfaces.tf.TensorFlowModel.model_fields": {"fullname": "opsml.model.interfaces.tf.TensorFlowModel.model_fields", "modulename": "opsml.model.interfaces.tf", "qualname": "TensorFlowModel.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'model': FieldInfo(annotation=Union[Model, NoneType], required=False), 'sample_data': FieldInfo(annotation=Union[ndarray[Any, dtype[Any]], Tensor, Dict[str, Union[ndarray[Any, dtype[Any]], Tensor]], List[Union[ndarray[Any, dtype[Any]], Tensor]], Tuple[Union[ndarray[Any, dtype[Any]], Tensor]], NoneType], required=False), 'onnx_model': FieldInfo(annotation=Union[OnnxModel, NoneType], required=False), 'task_type': FieldInfo(annotation=str, required=False, default='undefined'), 'model_type': FieldInfo(annotation=str, required=False, default='undefined'), 'data_type': FieldInfo(annotation=str, required=False, default='undefined'), 'modelcard_uid': FieldInfo(annotation=str, required=False, default=''), 'preprocessor': FieldInfo(annotation=Union[Any, NoneType], required=False), 'preprocessor_name': FieldInfo(annotation=str, required=False, default='undefined')}"}, "opsml.model.interfaces.tf.TensorFlowModel.model_computed_fields": {"fullname": "opsml.model.interfaces.tf.TensorFlowModel.model_computed_fields", "modulename": "opsml.model.interfaces.tf", "qualname": "TensorFlowModel.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.model.interfaces.vowpal": {"fullname": "opsml.model.interfaces.vowpal", "modulename": "opsml.model.interfaces.vowpal", "kind": "module", "doc": "\n"}, "opsml.model.interfaces.vowpal.VowpalWabbitModel": {"fullname": "opsml.model.interfaces.vowpal.VowpalWabbitModel", "modulename": "opsml.model.interfaces.vowpal", "qualname": "VowpalWabbitModel", "kind": "class", "doc": "Model interface for VowPal Wabbit model.
\n\n\n\n", "bases": "opsml.model.interfaces.base.ModelInterface"}, "opsml.model.interfaces.vowpal.VowpalWabbitModel.model": {"fullname": "opsml.model.interfaces.vowpal.VowpalWabbitModel.model", "modulename": "opsml.model.interfaces.vowpal", "qualname": "VowpalWabbitModel.model", "kind": "variable", "doc": "\n", "annotation": ": Optional[vowpalwabbit.pyvw.Workspace]"}, "opsml.model.interfaces.vowpal.VowpalWabbitModel.sample_data": {"fullname": "opsml.model.interfaces.vowpal.VowpalWabbitModel.sample_data", "modulename": "opsml.model.interfaces.vowpal", "qualname": "VowpalWabbitModel.sample_data", "kind": "variable", "doc": "\n", "annotation": ": Optional[str]"}, "opsml.model.interfaces.vowpal.VowpalWabbitModel.arguments": {"fullname": "opsml.model.interfaces.vowpal.VowpalWabbitModel.arguments", "modulename": "opsml.model.interfaces.vowpal", "qualname": "VowpalWabbitModel.arguments", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.vowpal.VowpalWabbitModel.model_class": {"fullname": "opsml.model.interfaces.vowpal.VowpalWabbitModel.model_class", "modulename": "opsml.model.interfaces.vowpal", "qualname": "VowpalWabbitModel.model_class", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.vowpal.VowpalWabbitModel.check_model": {"fullname": "opsml.model.interfaces.vowpal.VowpalWabbitModel.check_model", "modulename": "opsml.model.interfaces.vowpal", "qualname": "VowpalWabbitModel.check_model", "kind": "function", "doc": "\n", "signature": "(cls, model_args: Dict[str, Any]) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.model.interfaces.vowpal.VowpalWabbitModel.save_model": {"fullname": "opsml.model.interfaces.vowpal.VowpalWabbitModel.save_model", "modulename": "opsml.model.interfaces.vowpal", "qualname": "VowpalWabbitModel.save_model", "kind": "function", "doc": "VowpalWabbitModel
\n
Saves vowpal model to \".model\" file.
\n\nLoads a vowpal model from \".model\" file with arguments.
\n\n**kwargs: Additional arguments to be passed to the workspace. This is supplied via\n\"arguments\" key.
\n\nExample:\n arguments=\"--cb 4\"\n or\n arguments=[\"--cb\", \"4\"]
\n\nThere is no need to specify \"-i\" argument. This is done automatically during loading.
Onnx is not supported for vowpal wabbit models.
\n\n\n\n", "signature": "(self, path: pathlib.Path) -> opsml.types.model.ModelReturn:", "funcdef": "def"}, "opsml.model.interfaces.vowpal.VowpalWabbitModel.model_suffix": {"fullname": "opsml.model.interfaces.vowpal.VowpalWabbitModel.model_suffix", "modulename": "opsml.model.interfaces.vowpal", "qualname": "VowpalWabbitModel.model_suffix", "kind": "variable", "doc": "ModelReturn
\n
Returns suffix for model storage
\n", "annotation": ": str"}, "opsml.model.interfaces.vowpal.VowpalWabbitModel.name": {"fullname": "opsml.model.interfaces.vowpal.VowpalWabbitModel.name", "modulename": "opsml.model.interfaces.vowpal", "qualname": "VowpalWabbitModel.name", "kind": "function", "doc": "\n", "signature": "() -> str:", "funcdef": "def"}, "opsml.model.interfaces.vowpal.VowpalWabbitModel.model_config": {"fullname": "opsml.model.interfaces.vowpal.VowpalWabbitModel.model_config", "modulename": "opsml.model.interfaces.vowpal", "qualname": "VowpalWabbitModel.model_config", "kind": "variable", "doc": "\n", "default_value": "{'protected_namespaces': ('protect_',), 'arbitrary_types_allowed': True, 'validate_assignment': False, 'validate_default': True, 'extra': 'allow'}"}, "opsml.model.interfaces.vowpal.VowpalWabbitModel.model_fields": {"fullname": "opsml.model.interfaces.vowpal.VowpalWabbitModel.model_fields", "modulename": "opsml.model.interfaces.vowpal", "qualname": "VowpalWabbitModel.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'model': FieldInfo(annotation=Union[Workspace, NoneType], required=False), 'sample_data': FieldInfo(annotation=Union[str, NoneType], required=False), 'onnx_model': FieldInfo(annotation=Union[OnnxModel, NoneType], required=False), 'task_type': FieldInfo(annotation=str, required=False, default='undefined'), 'model_type': FieldInfo(annotation=str, required=False, default='undefined'), 'data_type': FieldInfo(annotation=str, required=False, default='undefined'), 'modelcard_uid': FieldInfo(annotation=str, required=False, default=''), 'arguments': FieldInfo(annotation=str, required=False, default='')}"}, "opsml.model.interfaces.vowpal.VowpalWabbitModel.model_computed_fields": {"fullname": "opsml.model.interfaces.vowpal.VowpalWabbitModel.model_computed_fields", "modulename": "opsml.model.interfaces.vowpal", "qualname": "VowpalWabbitModel.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.model.interfaces.xgb": {"fullname": "opsml.model.interfaces.xgb", "modulename": "opsml.model.interfaces.xgb", "kind": "module", "doc": "\n"}, "opsml.model.interfaces.xgb.logger": {"fullname": "opsml.model.interfaces.xgb.logger", "modulename": "opsml.model.interfaces.xgb", "qualname": "logger", "kind": "variable", "doc": "\n", "default_value": "<builtins.Logger object>"}, "opsml.model.interfaces.xgb.XGBoostModel": {"fullname": "opsml.model.interfaces.xgb.XGBoostModel", "modulename": "opsml.model.interfaces.xgb", "qualname": "XGBoostModel", "kind": "class", "doc": "Model interface for XGBoost model class. Currently, only Sklearn flavor of XGBoost\nregressor and classifier are supported.
\n\n\n\n", "bases": "opsml.model.interfaces.base.ModelInterface"}, "opsml.model.interfaces.xgb.XGBoostModel.model": {"fullname": "opsml.model.interfaces.xgb.XGBoostModel.model", "modulename": "opsml.model.interfaces.xgb", "qualname": "XGBoostModel.model", "kind": "variable", "doc": "\n", "annotation": ": Union[xgboost.core.Booster, xgboost.sklearn.XGBModel, NoneType]"}, "opsml.model.interfaces.xgb.XGBoostModel.sample_data": {"fullname": "opsml.model.interfaces.xgb.XGBoostModel.sample_data", "modulename": "opsml.model.interfaces.xgb", "qualname": "XGBoostModel.sample_data", "kind": "variable", "doc": "\n", "annotation": ": Union[pandas.core.frame.DataFrame, numpy.ndarray[Any, numpy.dtype[Any]], xgboost.core.DMatrix, NoneType]"}, "opsml.model.interfaces.xgb.XGBoostModel.preprocessor": {"fullname": "opsml.model.interfaces.xgb.XGBoostModel.preprocessor", "modulename": "opsml.model.interfaces.xgb", "qualname": "XGBoostModel.preprocessor", "kind": "variable", "doc": "\n", "annotation": ": Optional[Any]"}, "opsml.model.interfaces.xgb.XGBoostModel.preprocessor_name": {"fullname": "opsml.model.interfaces.xgb.XGBoostModel.preprocessor_name", "modulename": "opsml.model.interfaces.xgb", "qualname": "XGBoostModel.preprocessor_name", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.xgb.XGBoostModel.model_class": {"fullname": "opsml.model.interfaces.xgb.XGBoostModel.model_class", "modulename": "opsml.model.interfaces.xgb", "qualname": "XGBoostModel.model_class", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.model.interfaces.xgb.XGBoostModel.check_model": {"fullname": "opsml.model.interfaces.xgb.XGBoostModel.check_model", "modulename": "opsml.model.interfaces.xgb", "qualname": "XGBoostModel.check_model", "kind": "function", "doc": "\n", "signature": "(cls, model_args: Dict[str, Any]) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.model.interfaces.xgb.XGBoostModel.save_model": {"fullname": "opsml.model.interfaces.xgb.XGBoostModel.save_model", "modulename": "opsml.model.interfaces.xgb", "qualname": "XGBoostModel.save_model", "kind": "function", "doc": "XGBoostModel
\n
Saves lgb model according to model format. Booster models are saved to text.\nSklearn models are saved via joblib.
\n\nLoads lightgbm booster or sklearn model
\n\nSaves preprocessor to path if present. Base implementation use Joblib
\n\nLoad preprocessor from pathlib object
\n\nSaves the onnx model
\n\n\n\n", "signature": "(self, path: pathlib.Path) -> opsml.types.model.ModelReturn:", "funcdef": "def"}, "opsml.model.interfaces.xgb.XGBoostModel.save_sample_data": {"fullname": "opsml.model.interfaces.xgb.XGBoostModel.save_sample_data", "modulename": "opsml.model.interfaces.xgb", "qualname": "XGBoostModel.save_sample_data", "kind": "function", "doc": "ModelReturn
\n
Serialized and save sample data to path.
\n\nSerialized and save sample data to path.
\n\nReturns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.xgb.XGBoostModel.preprocessor_suffix": {"fullname": "opsml.model.interfaces.xgb.XGBoostModel.preprocessor_suffix", "modulename": "opsml.model.interfaces.xgb", "qualname": "XGBoostModel.preprocessor_suffix", "kind": "variable", "doc": "Returns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.xgb.XGBoostModel.data_suffix": {"fullname": "opsml.model.interfaces.xgb.XGBoostModel.data_suffix", "modulename": "opsml.model.interfaces.xgb", "qualname": "XGBoostModel.data_suffix", "kind": "variable", "doc": "Returns suffix for storage
\n", "annotation": ": str"}, "opsml.model.interfaces.xgb.XGBoostModel.name": {"fullname": "opsml.model.interfaces.xgb.XGBoostModel.name", "modulename": "opsml.model.interfaces.xgb", "qualname": "XGBoostModel.name", "kind": "function", "doc": "\n", "signature": "() -> str:", "funcdef": "def"}, "opsml.model.interfaces.xgb.XGBoostModel.model_config": {"fullname": "opsml.model.interfaces.xgb.XGBoostModel.model_config", "modulename": "opsml.model.interfaces.xgb", "qualname": "XGBoostModel.model_config", "kind": "variable", "doc": "\n", "default_value": "{'protected_namespaces': ('protect_',), 'arbitrary_types_allowed': True, 'validate_assignment': False, 'validate_default': True, 'extra': 'allow'}"}, "opsml.model.interfaces.xgb.XGBoostModel.model_fields": {"fullname": "opsml.model.interfaces.xgb.XGBoostModel.model_fields", "modulename": "opsml.model.interfaces.xgb", "qualname": "XGBoostModel.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'model': FieldInfo(annotation=Union[Booster, XGBModel, NoneType], required=False), 'sample_data': FieldInfo(annotation=Union[DataFrame, ndarray[Any, dtype[Any]], DMatrix, NoneType], required=False), 'onnx_model': FieldInfo(annotation=Union[OnnxModel, NoneType], required=False), 'task_type': FieldInfo(annotation=str, required=False, default='undefined'), 'model_type': FieldInfo(annotation=str, required=False, default='undefined'), 'data_type': FieldInfo(annotation=str, required=False, default='undefined'), 'modelcard_uid': FieldInfo(annotation=str, required=False, default=''), 'preprocessor': FieldInfo(annotation=Union[Any, NoneType], required=False), 'preprocessor_name': FieldInfo(annotation=str, required=False, default='undefined')}"}, "opsml.model.interfaces.xgb.XGBoostModel.model_computed_fields": {"fullname": "opsml.model.interfaces.xgb.XGBoostModel.model_computed_fields", "modulename": "opsml.model.interfaces.xgb", "qualname": "XGBoostModel.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.model.challenger": {"fullname": "opsml.model.challenger", "modulename": "opsml.model.challenger", "kind": "module", "doc": "\n"}, "opsml.model.challenger.logger": {"fullname": "opsml.model.challenger.logger", "modulename": "opsml.model.challenger", "qualname": "logger", "kind": "variable", "doc": "\n", "default_value": "<builtins.Logger object>"}, "opsml.model.challenger.BattleReport": {"fullname": "opsml.model.challenger.BattleReport", "modulename": "opsml.model.challenger", "qualname": "BattleReport", "kind": "class", "doc": "Usage docs: https://docs.pydantic.dev/2.6/concepts/models/
\n\nA base class for creating Pydantic models.
\n\n__init__ function.Model.__validators__ and Model.__root_validators__ from Pydantic V1.RootModel.model_config['extra'] == 'allow'.Usage docs: https://docs.pydantic.dev/2.6/concepts/models/
\n\nA base class for creating Pydantic models.
\n\n__init__ function.Model.__validators__ and Model.__root_validators__ from Pydantic V1.RootModel.model_config['extra'] == 'allow'.Instantiates ModelChallenger class
\n\nChallenges n champion models against the challenger model. If no champion is provided,\nthe latest model version is used as a champion.
\n\nReturns\n BattleReport
Helper class for loading models from disk and downloading via opsml-cli
\n"}, "opsml.model.loader.ModelLoader.__init__": {"fullname": "opsml.model.loader.ModelLoader.__init__", "modulename": "opsml.model.loader", "qualname": "ModelLoader.__init__", "kind": "function", "doc": "Initialize ModelLoader
\n\nQuick access to preprocessor from interface
\n", "annotation": ": Any"}, "opsml.model.loader.ModelLoader.load_preprocessor": {"fullname": "opsml.model.loader.ModelLoader.load_preprocessor", "modulename": "opsml.model.loader", "qualname": "ModelLoader.load_preprocessor", "kind": "function", "doc": "Load preprocessor from disk
\n", "signature": "(self) -> None:", "funcdef": "def"}, "opsml.model.loader.ModelLoader.load_model": {"fullname": "opsml.model.loader.ModelLoader.load_model", "modulename": "opsml.model.loader", "qualname": "ModelLoader.load_model", "kind": "function", "doc": "\n", "signature": "(self, **kwargs: Any) -> None:", "funcdef": "def"}, "opsml.model.loader.ModelLoader.load_onnx_model": {"fullname": "opsml.model.loader.ModelLoader.load_onnx_model", "modulename": "opsml.model.loader", "qualname": "ModelLoader.load_onnx_model", "kind": "function", "doc": "Load onnx model from disk
\n\n\n\n", "signature": "(self, **kwargs: Any) -> None:", "funcdef": "def"}, "opsml.profile.profile_data": {"fullname": "opsml.profile.profile_data", "modulename": "opsml.profile.profile_data", "kind": "module", "doc": "\n"}, "opsml.profile.profile_data.DIR_PATH": {"fullname": "opsml.profile.profile_data.DIR_PATH", "modulename": "opsml.profile.profile_data", "qualname": "DIR_PATH", "kind": "variable", "doc": "\n", "default_value": "'/home/steven_forrester/github/opsml/opsml/profile'"}, "opsml.profile.profile_data.ProfileReport": {"fullname": "opsml.profile.profile_data.ProfileReport", "modulename": "opsml.profile.profile_data", "qualname": "ProfileReport", "kind": "variable", "doc": "\n", "default_value": "typing.Any"}, "opsml.profile.profile_data.DataProfiler": {"fullname": "opsml.profile.profile_data.DataProfiler", "modulename": "opsml.profile.profile_data", "qualname": "DataProfiler", "kind": "class", "doc": "\n"}, "opsml.profile.profile_data.DataProfiler.create_profile_report": {"fullname": "opsml.profile.profile_data.DataProfiler.create_profile_report", "modulename": "opsml.profile.profile_data", "qualname": "DataProfiler.create_profile_report", "kind": "function", "doc": "------Note: These kwargs only apply to HuggingFace models------
\n \nkwargs:\n load_quantized:\n If True, load quantized model
\n\n\nonnx_args:\n Additional onnx args needed to load the model\n
Creates a ydata-profiling report
\n\n", "signature": "(\tdata: Union[pandas.core.frame.DataFrame, polars.dataframe.frame.DataFrame],\tname: str,\tsample_perc: float = 1) -> Any:", "funcdef": "def"}, "opsml.profile.profile_data.DataProfiler.load_profile": {"fullname": "opsml.profile.profile_data.DataProfiler.load_profile", "modulename": "opsml.profile.profile_data", "qualname": "DataProfiler.load_profile", "kind": "function", "doc": "\n
ProfileReport
Loads a ProfileReport from data bytes
ProfileReport in bytes\n\n", "signature": "(data: bytes) -> Any:", "funcdef": "def"}, "opsml.profile.profile_data.DataProfiler.compare_reports": {"fullname": "opsml.profile.profile_data.DataProfiler.compare_reports", "modulename": "opsml.profile.profile_data", "qualname": "DataProfiler.compare_reports", "kind": "function", "doc": "\n
ProfileReport
Compares ProfileReports
\n\nProfileReport\n\n", "signature": "(reports: List[Any]) -> Any:", "funcdef": "def"}, "opsml.projects.active_run": {"fullname": "opsml.projects.active_run", "modulename": "opsml.projects.active_run", "kind": "module", "doc": "\n"}, "opsml.projects.active_run.logger": {"fullname": "opsml.projects.active_run.logger", "modulename": "opsml.projects.active_run", "qualname": "logger", "kind": "variable", "doc": "\n", "default_value": "<builtins.Logger object>"}, "opsml.projects.active_run.RunInfo": {"fullname": "opsml.projects.active_run.RunInfo", "modulename": "opsml.projects.active_run", "qualname": "RunInfo", "kind": "class", "doc": "\n"}, "opsml.projects.active_run.RunInfo.__init__": {"fullname": "opsml.projects.active_run.RunInfo.__init__", "modulename": "opsml.projects.active_run", "qualname": "RunInfo.__init__", "kind": "function", "doc": "\n", "signature": "(\truncard: opsml.cards.run.RunCard,\trun_id: str,\trun_name: Optional[str] = None)"}, "opsml.projects.active_run.RunInfo.storage_client": {"fullname": "opsml.projects.active_run.RunInfo.storage_client", "modulename": "opsml.projects.active_run", "qualname": "RunInfo.storage_client", "kind": "variable", "doc": "\n"}, "opsml.projects.active_run.RunInfo.registries": {"fullname": "opsml.projects.active_run.RunInfo.registries", "modulename": "opsml.projects.active_run", "qualname": "RunInfo.registries", "kind": "variable", "doc": "\n"}, "opsml.projects.active_run.RunInfo.runcard": {"fullname": "opsml.projects.active_run.RunInfo.runcard", "modulename": "opsml.projects.active_run", "qualname": "RunInfo.runcard", "kind": "variable", "doc": "\n"}, "opsml.projects.active_run.RunInfo.run_id": {"fullname": "opsml.projects.active_run.RunInfo.run_id", "modulename": "opsml.projects.active_run", "qualname": "RunInfo.run_id", "kind": "variable", "doc": "\n"}, "opsml.projects.active_run.RunInfo.run_name": {"fullname": "opsml.projects.active_run.RunInfo.run_name", "modulename": "opsml.projects.active_run", "qualname": "RunInfo.run_name", "kind": "variable", "doc": "\n"}, "opsml.projects.active_run.CardHandler": {"fullname": "opsml.projects.active_run.CardHandler", "modulename": "opsml.projects.active_run", "qualname": "CardHandler", "kind": "class", "doc": "\n
ProfileReport
DRY helper class for ActiveRun and OpsmlProject
\n"}, "opsml.projects.active_run.CardHandler.register_card": {"fullname": "opsml.projects.active_run.CardHandler.register_card", "modulename": "opsml.projects.active_run", "qualname": "CardHandler.register_card", "kind": "function", "doc": "Registers and ArtifactCard
\n", "signature": "(\tregistries: opsml.registry.registry.CardRegistries,\tcard: opsml.cards.base.ArtifactCard,\tversion_type: Union[opsml.registry.semver.VersionType, str] = <VersionType.MINOR: 'minor'>) -> None:", "funcdef": "def"}, "opsml.projects.active_run.CardHandler.load_card": {"fullname": "opsml.projects.active_run.CardHandler.load_card", "modulename": "opsml.projects.active_run", "qualname": "CardHandler.load_card", "kind": "function", "doc": "Loads an ArtifactCard
\n", "signature": "(\tregistries: opsml.registry.registry.CardRegistries,\tregistry_name: str,\tinfo: opsml.types.card.CardInfo) -> opsml.cards.base.ArtifactCard:", "funcdef": "def"}, "opsml.projects.active_run.CardHandler.update_card": {"fullname": "opsml.projects.active_run.CardHandler.update_card", "modulename": "opsml.projects.active_run", "qualname": "CardHandler.update_card", "kind": "function", "doc": "Updates an ArtifactCard
\n", "signature": "(\tregistries: opsml.registry.registry.CardRegistries,\tcard: opsml.cards.base.ArtifactCard) -> None:", "funcdef": "def"}, "opsml.projects.active_run.ActiveRun": {"fullname": "opsml.projects.active_run.ActiveRun", "modulename": "opsml.projects.active_run", "qualname": "ActiveRun", "kind": "class", "doc": "\n"}, "opsml.projects.active_run.ActiveRun.__init__": {"fullname": "opsml.projects.active_run.ActiveRun.__init__", "modulename": "opsml.projects.active_run", "qualname": "ActiveRun.__init__", "kind": "function", "doc": "Run object that handles logging artifacts, metrics, cards, and tags for a given run of a Project
\n\nId for current run
\n", "annotation": ": str"}, "opsml.projects.active_run.ActiveRun.run_name": {"fullname": "opsml.projects.active_run.ActiveRun.run_name", "modulename": "opsml.projects.active_run", "qualname": "ActiveRun.run_name", "kind": "variable", "doc": "Name for current run
\n", "annotation": ": Optional[str]"}, "opsml.projects.active_run.ActiveRun.active": {"fullname": "opsml.projects.active_run.ActiveRun.active", "modulename": "opsml.projects.active_run", "qualname": "ActiveRun.active", "kind": "variable", "doc": "\n", "annotation": ": bool"}, "opsml.projects.active_run.ActiveRun.add_tag": {"fullname": "opsml.projects.active_run.ActiveRun.add_tag", "modulename": "opsml.projects.active_run", "qualname": "ActiveRun.add_tag", "kind": "function", "doc": "Adds a tag to the current run
\n\nAdds a tag to the current run
\n\nRegister a given artifact card.
\n\nLoads an ArtifactCard.
\n\nuid takes precedence if it\nexists. If the optional version is specified, that version\nwill be loaded. If it doesn't exist, the most recent version will\nbe loaded.Returns\n ArtifactCard
Log a local file or directory to the opsml server and associate with the current run.
\n\nLog a metric for a given run
\n\nLogs a collection of metrics for a run
\n\nLogs a parameter to project run
\n\nLogs a collection of parameters for a run
\n\nLogs a graph to the RunCard, which will be rendered in the UI as a line graph
\n\nexample:
\n\n### single line graph\nx = np.arange(1, 400, 0.5)\ny = x * x\nrun.log_graph(name=\"graph1\", x=x, y=y, x_label=\"x\", y_label=\"y\", graph_style=\"line\")\n\n### multi line graph\nx = np.arange(1, 1000, 0.5)\ny1 = x * x\ny2 = y1 * 1.1\ny3 = y2 * 3\nrun.log_graph(\n name=\"multiline\",\n x=x,\n y={\"y1\": y1, \"y2\": y2, \"y3\": y3},\n x_label=\"x\",\n y_label=\"y\",\n graph_style=\"line\",\n)\nCreates or updates an active RunCard
\n", "signature": "(self) -> None:", "funcdef": "def"}, "opsml.projects.active_run.ActiveRun.run_data": {"fullname": "opsml.projects.active_run.ActiveRun.run_data", "modulename": "opsml.projects.active_run", "qualname": "ActiveRun.run_data", "kind": "variable", "doc": "\n", "annotation": ": Any"}, "opsml.projects.active_run.ActiveRun.metrics": {"fullname": "opsml.projects.active_run.ActiveRun.metrics", "modulename": "opsml.projects.active_run", "qualname": "ActiveRun.metrics", "kind": "variable", "doc": "\n", "annotation": ": Dict[str, List[opsml.types.card.Metric]]"}, "opsml.projects.active_run.ActiveRun.parameters": {"fullname": "opsml.projects.active_run.ActiveRun.parameters", "modulename": "opsml.projects.active_run", "qualname": "ActiveRun.parameters", "kind": "variable", "doc": "\n", "annotation": ": Dict[str, List[opsml.types.card.Param]]"}, "opsml.projects.active_run.ActiveRun.tags": {"fullname": "opsml.projects.active_run.ActiveRun.tags", "modulename": "opsml.projects.active_run", "qualname": "ActiveRun.tags", "kind": "variable", "doc": "\n", "annotation": ": dict[str, typing.Union[str, int]]"}, "opsml.projects.active_run.ActiveRun.artifact_uris": {"fullname": "opsml.projects.active_run.ActiveRun.artifact_uris", "modulename": "opsml.projects.active_run", "qualname": "ActiveRun.artifact_uris", "kind": "variable", "doc": "\n", "annotation": ": Dict[str, opsml.types.card.Artifact]"}, "opsml.projects.project": {"fullname": "opsml.projects.project", "modulename": "opsml.projects.project", "kind": "module", "doc": "\n"}, "opsml.projects.project.logger": {"fullname": "opsml.projects.project.logger", "modulename": "opsml.projects.project", "qualname": "logger", "kind": "variable", "doc": "\n", "default_value": "<builtins.Logger object>"}, "opsml.projects.project.OpsmlProject": {"fullname": "opsml.projects.project.OpsmlProject", "modulename": "opsml.projects.project", "qualname": "OpsmlProject", "kind": "class", "doc": "\n"}, "opsml.projects.project.OpsmlProject.__init__": {"fullname": "opsml.projects.project.OpsmlProject.__init__", "modulename": "opsml.projects.project", "qualname": "OpsmlProject.__init__", "kind": "function", "doc": "Instantiates a project which creates cards, metrics and parameters to\nthe opsml registry via a \"run\" object.
\n\nIf info.run_id is set, that run_id will be loaded as read only. In read\nonly mode, you can retrieve cards, metrics, and parameters, however you\ncannot write new data. If you wish to record data/create a new run, you will\nneed to enter the run context.
\n\nIn order to create new cards, you need to create a run using the run\ncontext manager.
\n\n\nproject: OpsmlProject = OpsmlProject(\n ProjectInfo(\n name=\"test-project\",\n # If run_id is omitted, a new run is created.\n run_id=\"123ab123kaj8u8naskdfh813\",\n )\n )
\n \nthe project is in \"read only\" mode. all read operations will work
\n \nfor k, v in project.parameters:\n logger.info(\"{} = {}\", k, v)
\n \ncreating a project run
\n \nwith project.run() as run:\n # Now that the run context is entered, it's in read/write mode\n # You can write cards, parameters, and metrics to the project.\n run.log_parameter(key=\"my_param\", value=\"12.34\")
\n
Current run id associated with project
\n", "annotation": ": str"}, "opsml.projects.project.OpsmlProject.project_id": {"fullname": "opsml.projects.project.OpsmlProject.project_id", "modulename": "opsml.projects.project", "qualname": "OpsmlProject.project_id", "kind": "variable", "doc": "\n", "annotation": ": int"}, "opsml.projects.project.OpsmlProject.project_name": {"fullname": "opsml.projects.project.OpsmlProject.project_name", "modulename": "opsml.projects.project", "qualname": "OpsmlProject.project_name", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.projects.project.OpsmlProject.run": {"fullname": "opsml.projects.project.OpsmlProject.run", "modulename": "opsml.projects.project", "qualname": "OpsmlProject.run", "kind": "function", "doc": "Starts a new run for the project
\n\nLoads an ArtifactCard.
\n\nuid takes precedence if it\nexists. If the optional version is specified, that version\nwill be loaded. If it doesn't exist, the most recent ersion will\nbe loaded.Returns\n ArtifactCard
Lists all runs for the current project, sorted by timestamp
\n\n\n\n", "signature": "(self, limit: int = 100) -> List[Dict[str, Any]]:", "funcdef": "def"}, "opsml.projects.project.OpsmlProject.runcard": {"fullname": "opsml.projects.project.OpsmlProject.runcard", "modulename": "opsml.projects.project", "qualname": "OpsmlProject.runcard", "kind": "variable", "doc": "\n", "annotation": ": opsml.cards.run.RunCard"}, "opsml.projects.project.OpsmlProject.metrics": {"fullname": "opsml.projects.project.OpsmlProject.metrics", "modulename": "opsml.projects.project", "qualname": "OpsmlProject.metrics", "kind": "variable", "doc": "\n", "annotation": ": Dict[str, List[opsml.types.card.Metric]]"}, "opsml.projects.project.OpsmlProject.get_metric": {"fullname": "opsml.projects.project.OpsmlProject.get_metric", "modulename": "opsml.projects.project", "qualname": "OpsmlProject.get_metric", "kind": "function", "doc": "List of RunCard
\n
Get metric by name
\n\n\n\n", "signature": "(\tself,\tname: str) -> Union[List[opsml.types.card.Metric], opsml.types.card.Metric]:", "funcdef": "def"}, "opsml.projects.project.OpsmlProject.parameters": {"fullname": "opsml.projects.project.OpsmlProject.parameters", "modulename": "opsml.projects.project", "qualname": "OpsmlProject.parameters", "kind": "variable", "doc": "\n", "annotation": ": Dict[str, List[opsml.types.card.Param]]"}, "opsml.projects.project.OpsmlProject.get_parameter": {"fullname": "opsml.projects.project.OpsmlProject.get_parameter", "modulename": "opsml.projects.project", "qualname": "OpsmlProject.get_parameter", "kind": "function", "doc": "List of Metric or Metric
\n
Get param by name
\n\n\n\n", "signature": "(\tself,\tname: str) -> Union[List[opsml.types.card.Param], opsml.types.card.Param]:", "funcdef": "def"}, "opsml.projects.project.OpsmlProject.tags": {"fullname": "opsml.projects.project.OpsmlProject.tags", "modulename": "opsml.projects.project", "qualname": "OpsmlProject.tags", "kind": "variable", "doc": "\n", "annotation": ": Dict[str, Union[int, str]]"}, "opsml.projects.project.OpsmlProject.datacard_uids": {"fullname": "opsml.projects.project.OpsmlProject.datacard_uids", "modulename": "opsml.projects.project", "qualname": "OpsmlProject.datacard_uids", "kind": "variable", "doc": "List of Param or Param
\n
DataCards associated with the current run
\n", "annotation": ": List[str]"}, "opsml.projects.project.OpsmlProject.modelcard_uids": {"fullname": "opsml.projects.project.OpsmlProject.modelcard_uids", "modulename": "opsml.projects.project", "qualname": "OpsmlProject.modelcard_uids", "kind": "variable", "doc": "ModelCards associated with the current run
\n", "annotation": ": List[str]"}, "opsml.registry.registry": {"fullname": "opsml.registry.registry", "modulename": "opsml.registry.registry", "kind": "module", "doc": "\n"}, "opsml.registry.registry.logger": {"fullname": "opsml.registry.registry.logger", "modulename": "opsml.registry.registry", "qualname": "logger", "kind": "variable", "doc": "\n", "default_value": "<builtins.Logger object>"}, "opsml.registry.registry.CardRegistry": {"fullname": "opsml.registry.registry.CardRegistry", "modulename": "opsml.registry.registry", "qualname": "CardRegistry", "kind": "class", "doc": "\n"}, "opsml.registry.registry.CardRegistry.__init__": {"fullname": "opsml.registry.registry.CardRegistry.__init__", "modulename": "opsml.registry.registry", "qualname": "CardRegistry.__init__", "kind": "function", "doc": "Interface for connecting to any of the ArtifactCard registries
\n\n\n\n\nInstantiated connection to specific Card registry
\n
\n\n", "signature": "(registry_type: Union[opsml.types.card.RegistryType, str])"}, "opsml.registry.registry.CardRegistry.table_name": {"fullname": "opsml.registry.registry.CardRegistry.table_name", "modulename": "opsml.registry.registry", "qualname": "CardRegistry.table_name", "kind": "variable", "doc": "\n"}, "opsml.registry.registry.CardRegistry.registry_type": {"fullname": "opsml.registry.registry.CardRegistry.registry_type", "modulename": "opsml.registry.registry", "qualname": "CardRegistry.registry_type", "kind": "variable", "doc": "data_registry = CardRegistry(RegistryType.DATA)\n data_registry.list_cards()
\n \nor\n data_registry = CardRegistry(\"data\")\n data_registry.list_cards()
\n
Registry type for card registry
\n", "annotation": ": opsml.types.card.RegistryType"}, "opsml.registry.registry.CardRegistry.list_cards": {"fullname": "opsml.registry.registry.CardRegistry.list_cards", "modulename": "opsml.registry.registry", "qualname": "CardRegistry.list_cards", "kind": "function", "doc": "Retrieves records from registry
\n\n\n\n", "signature": "(\tself,\tuid: Optional[str] = None,\tname: Optional[str] = None,\trepository: Optional[str] = None,\tversion: Optional[str] = None,\ttags: Optional[Dict[str, str]] = None,\tinfo: Optional[opsml.types.card.CardInfo] = None,\tmax_date: Optional[str] = None,\tlimit: Optional[int] = None,\tignore_release_candidates: bool = False) -> List[Dict[str, Any]]:", "funcdef": "def"}, "opsml.registry.registry.CardRegistry.load_card": {"fullname": "opsml.registry.registry.CardRegistry.load_card", "modulename": "opsml.registry.registry", "qualname": "CardRegistry.load_card", "kind": "function", "doc": "pandas dataframe of records or list of dictionaries
\n
Loads a specific card
\n\nReturns\n ArtifactCard
\n", "signature": "(\tself,\tname: Optional[str] = None,\trepository: Optional[str] = None,\tuid: Optional[str] = None,\ttags: Optional[Dict[str, str]] = None,\tversion: Optional[str] = None,\tinfo: Optional[opsml.types.card.CardInfo] = None,\tignore_release_candidates: bool = False,\tinterface: Union[Type[opsml.data.interfaces._base.DataInterface], Type[opsml.model.interfaces.base.ModelInterface], NoneType] = None) -> opsml.cards.base.ArtifactCard:", "funcdef": "def"}, "opsml.registry.registry.CardRegistry.register_card": {"fullname": "opsml.registry.registry.CardRegistry.register_card", "modulename": "opsml.registry.registry", "qualname": "CardRegistry.register_card", "kind": "function", "doc": "Adds a new Card record to registry. Registration will be skipped if the card already exists.
Update an artifact card based on current registry
\n\nQuery column values from a specific Card
\n\n\n\n", "signature": "(self, uid: str, columns: List[str]) -> Dict[str, Any]:", "funcdef": "def"}, "opsml.registry.registry.CardRegistry.delete_card": {"fullname": "opsml.registry.registry.CardRegistry.delete_card", "modulename": "opsml.registry.registry", "qualname": "CardRegistry.delete_card", "kind": "function", "doc": "Dictionary of column, values pairs
\n
Delete a specific Card
\n\nInstantiates class that contains all registries
\n", "signature": "()"}, "opsml.registry.registry.CardRegistries.data": {"fullname": "opsml.registry.registry.CardRegistries.data", "modulename": "opsml.registry.registry", "qualname": "CardRegistries.data", "kind": "variable", "doc": "\n"}, "opsml.registry.registry.CardRegistries.model": {"fullname": "opsml.registry.registry.CardRegistries.model", "modulename": "opsml.registry.registry", "qualname": "CardRegistries.model", "kind": "variable", "doc": "\n"}, "opsml.registry.registry.CardRegistries.run": {"fullname": "opsml.registry.registry.CardRegistries.run", "modulename": "opsml.registry.registry", "qualname": "CardRegistries.run", "kind": "variable", "doc": "\n"}, "opsml.registry.registry.CardRegistries.pipeline": {"fullname": "opsml.registry.registry.CardRegistries.pipeline", "modulename": "opsml.registry.registry", "qualname": "CardRegistries.pipeline", "kind": "variable", "doc": "\n"}, "opsml.registry.registry.CardRegistries.project": {"fullname": "opsml.registry.registry.CardRegistries.project", "modulename": "opsml.registry.registry", "qualname": "CardRegistries.project", "kind": "variable", "doc": "\n"}, "opsml.registry.registry.CardRegistries.audit": {"fullname": "opsml.registry.registry.CardRegistries.audit", "modulename": "opsml.registry.registry", "qualname": "CardRegistries.audit", "kind": "variable", "doc": "\n"}, "opsml.registry.semver": {"fullname": "opsml.registry.semver", "modulename": "opsml.registry.semver", "kind": "module", "doc": "\n"}, "opsml.registry.semver.logger": {"fullname": "opsml.registry.semver.logger", "modulename": "opsml.registry.semver", "qualname": "logger", "kind": "variable", "doc": "\n", "default_value": "<builtins.Logger object>"}, "opsml.registry.semver.VersionType": {"fullname": "opsml.registry.semver.VersionType", "modulename": "opsml.registry.semver", "qualname": "VersionType", "kind": "class", "doc": "An enumeration.
\n", "bases": "builtins.str, enum.Enum"}, "opsml.registry.semver.VersionType.MAJOR": {"fullname": "opsml.registry.semver.VersionType.MAJOR", "modulename": "opsml.registry.semver", "qualname": "VersionType.MAJOR", "kind": "variable", "doc": "\n", "default_value": "<VersionType.MAJOR: 'major'>"}, "opsml.registry.semver.VersionType.MINOR": {"fullname": "opsml.registry.semver.VersionType.MINOR", "modulename": "opsml.registry.semver", "qualname": "VersionType.MINOR", "kind": "variable", "doc": "\n", "default_value": "<VersionType.MINOR: 'minor'>"}, "opsml.registry.semver.VersionType.PATCH": {"fullname": "opsml.registry.semver.VersionType.PATCH", "modulename": "opsml.registry.semver", "qualname": "VersionType.PATCH", "kind": "variable", "doc": "\n", "default_value": "<VersionType.PATCH: 'patch'>"}, "opsml.registry.semver.VersionType.PRE": {"fullname": "opsml.registry.semver.VersionType.PRE", "modulename": "opsml.registry.semver", "qualname": "VersionType.PRE", "kind": "variable", "doc": "\n", "default_value": "<VersionType.PRE: 'pre'>"}, "opsml.registry.semver.VersionType.BUILD": {"fullname": "opsml.registry.semver.VersionType.BUILD", "modulename": "opsml.registry.semver", "qualname": "VersionType.BUILD", "kind": "variable", "doc": "\n", "default_value": "<VersionType.BUILD: 'build'>"}, "opsml.registry.semver.VersionType.PRE_BUILD": {"fullname": "opsml.registry.semver.VersionType.PRE_BUILD", "modulename": "opsml.registry.semver", "qualname": "VersionType.PRE_BUILD", "kind": "variable", "doc": "\n", "default_value": "<VersionType.PRE_BUILD: 'pre_build'>"}, "opsml.registry.semver.VersionType.from_str": {"fullname": "opsml.registry.semver.VersionType.from_str", "modulename": "opsml.registry.semver", "qualname": "VersionType.from_str", "kind": "function", "doc": "\n", "signature": "(name: str) -> opsml.registry.semver.VersionType:", "funcdef": "def"}, "opsml.registry.semver.CardVersion": {"fullname": "opsml.registry.semver.CardVersion", "modulename": "opsml.registry.semver", "qualname": "CardVersion", "kind": "class", "doc": "Usage docs: https://docs.pydantic.dev/2.6/concepts/models/
\n\nA base class for creating Pydantic models.
\n\n__init__ function.Model.__validators__ and Model.__root_validators__ from Pydantic V1.RootModel.model_config['extra'] == 'allow'.Validates a user-supplied version
\n", "signature": "(cls, values: Any) -> Any:", "funcdef": "def"}, "opsml.registry.semver.CardVersion.check_full_semver": {"fullname": "opsml.registry.semver.CardVersion.check_full_semver", "modulename": "opsml.registry.semver", "qualname": "CardVersion.check_full_semver", "kind": "function", "doc": "Checks if a version is a full semver
\n", "signature": "(cls, version_splits: List[str]) -> bool:", "funcdef": "def"}, "opsml.registry.semver.CardVersion.has_major_minor": {"fullname": "opsml.registry.semver.CardVersion.has_major_minor", "modulename": "opsml.registry.semver", "qualname": "CardVersion.has_major_minor", "kind": "variable", "doc": "Checks if a version has a major and minor component
\n", "annotation": ": bool"}, "opsml.registry.semver.CardVersion.major": {"fullname": "opsml.registry.semver.CardVersion.major", "modulename": "opsml.registry.semver", "qualname": "CardVersion.major", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.registry.semver.CardVersion.minor": {"fullname": "opsml.registry.semver.CardVersion.minor", "modulename": "opsml.registry.semver", "qualname": "CardVersion.minor", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.registry.semver.CardVersion.patch": {"fullname": "opsml.registry.semver.CardVersion.patch", "modulename": "opsml.registry.semver", "qualname": "CardVersion.patch", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.registry.semver.CardVersion.valid_version": {"fullname": "opsml.registry.semver.CardVersion.valid_version", "modulename": "opsml.registry.semver", "qualname": "CardVersion.valid_version", "kind": "variable", "doc": "\n", "annotation": ": str"}, "opsml.registry.semver.CardVersion.finalize_partial_version": {"fullname": "opsml.registry.semver.CardVersion.finalize_partial_version", "modulename": "opsml.registry.semver", "qualname": "CardVersion.finalize_partial_version", "kind": "function", "doc": "Finalizes a partial semver version
\n\n\n\n", "signature": "(version: str) -> str:", "funcdef": "def"}, "opsml.registry.semver.CardVersion.get_version_to_search": {"fullname": "opsml.registry.semver.CardVersion.get_version_to_search", "modulename": "opsml.registry.semver", "qualname": "CardVersion.get_version_to_search", "kind": "function", "doc": "str: finalized version
\n
Gets a version to search for in the database
\n\n\n\n", "signature": "(self, version_type: opsml.registry.semver.VersionType) -> Optional[str]:", "funcdef": "def"}, "opsml.registry.semver.CardVersion.model_config": {"fullname": "opsml.registry.semver.CardVersion.model_config", "modulename": "opsml.registry.semver", "qualname": "CardVersion.model_config", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.registry.semver.CardVersion.model_fields": {"fullname": "opsml.registry.semver.CardVersion.model_fields", "modulename": "opsml.registry.semver", "qualname": "CardVersion.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'version': FieldInfo(annotation=str, required=True), 'version_splits': FieldInfo(annotation=List[str], required=False, default=[]), 'is_full_semver': FieldInfo(annotation=bool, required=False, default=False)}"}, "opsml.registry.semver.CardVersion.model_computed_fields": {"fullname": "opsml.registry.semver.CardVersion.model_computed_fields", "modulename": "opsml.registry.semver", "qualname": "CardVersion.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.registry.semver.SemVerUtils": {"fullname": "opsml.registry.semver.SemVerUtils", "modulename": "opsml.registry.semver", "qualname": "SemVerUtils", "kind": "class", "doc": "str: version to search for
\n
Class for general semver-related functions
\n"}, "opsml.registry.semver.SemVerUtils.sort_semvers": {"fullname": "opsml.registry.semver.SemVerUtils.sort_semvers", "modulename": "opsml.registry.semver", "qualname": "SemVerUtils.sort_semvers", "kind": "function", "doc": "Implements bubble sort for semvers
\n\n\n\n", "signature": "(versions: List[str]) -> List[str]:", "funcdef": "def"}, "opsml.registry.semver.SemVerUtils.is_release_candidate": {"fullname": "opsml.registry.semver.SemVerUtils.is_release_candidate", "modulename": "opsml.registry.semver", "qualname": "SemVerUtils.is_release_candidate", "kind": "function", "doc": "sorted list of versions with highest version first
\n
Ignores pre-release versions
\n", "signature": "(version: str) -> bool:", "funcdef": "def"}, "opsml.registry.semver.SemVerUtils.increment_version": {"fullname": "opsml.registry.semver.SemVerUtils.increment_version", "modulename": "opsml.registry.semver", "qualname": "SemVerUtils.increment_version", "kind": "function", "doc": "Increments a version based on version type
\n\n\n\n", "signature": "(\tversion: str,\tversion_type: opsml.registry.semver.VersionType,\tpre_tag: str,\tbuild_tag: str) -> str:", "funcdef": "def"}, "opsml.registry.semver.SemVerUtils.add_tags": {"fullname": "opsml.registry.semver.SemVerUtils.add_tags", "modulename": "opsml.registry.semver", "qualname": "SemVerUtils.add_tags", "kind": "function", "doc": "\n", "signature": "(\tversion: str,\tpre_tag: Optional[str] = None,\tbuild_tag: Optional[str] = None) -> str:", "funcdef": "def"}, "opsml.registry.semver.SemVerRegistryValidator": {"fullname": "opsml.registry.semver.SemVerRegistryValidator", "modulename": "opsml.registry.semver", "qualname": "SemVerRegistryValidator", "kind": "class", "doc": "New version
\n
Class for obtaining the correct registry version
\n"}, "opsml.registry.semver.SemVerRegistryValidator.__init__": {"fullname": "opsml.registry.semver.SemVerRegistryValidator.__init__", "modulename": "opsml.registry.semver", "qualname": "SemVerRegistryValidator.__init__", "kind": "function", "doc": "Instantiate SemverValidator
\n\n\n\n", "signature": "(\tname: str,\tversion_type: opsml.registry.semver.VersionType,\tpre_tag: str,\tbuild_tag: str,\tversion: Optional[opsml.registry.semver.CardVersion] = None)"}, "opsml.registry.semver.SemVerRegistryValidator.version": {"fullname": "opsml.registry.semver.SemVerRegistryValidator.version", "modulename": "opsml.registry.semver", "qualname": "SemVerRegistryValidator.version", "kind": "variable", "doc": "\n"}, "opsml.registry.semver.SemVerRegistryValidator.final_version": {"fullname": "opsml.registry.semver.SemVerRegistryValidator.final_version", "modulename": "opsml.registry.semver", "qualname": "SemVerRegistryValidator.final_version", "kind": "variable", "doc": "\n"}, "opsml.registry.semver.SemVerRegistryValidator.version_type": {"fullname": "opsml.registry.semver.SemVerRegistryValidator.version_type", "modulename": "opsml.registry.semver", "qualname": "SemVerRegistryValidator.version_type", "kind": "variable", "doc": "\n"}, "opsml.registry.semver.SemVerRegistryValidator.name": {"fullname": "opsml.registry.semver.SemVerRegistryValidator.name", "modulename": "opsml.registry.semver", "qualname": "SemVerRegistryValidator.name", "kind": "variable", "doc": "\n"}, "opsml.registry.semver.SemVerRegistryValidator.pre_tag": {"fullname": "opsml.registry.semver.SemVerRegistryValidator.pre_tag", "modulename": "opsml.registry.semver", "qualname": "SemVerRegistryValidator.pre_tag", "kind": "variable", "doc": "\n"}, "opsml.registry.semver.SemVerRegistryValidator.build_tag": {"fullname": "opsml.registry.semver.SemVerRegistryValidator.build_tag", "modulename": "opsml.registry.semver", "qualname": "SemVerRegistryValidator.build_tag", "kind": "variable", "doc": "\n"}, "opsml.registry.semver.SemVerRegistryValidator.version_to_search": {"fullname": "opsml.registry.semver.SemVerRegistryValidator.version_to_search", "modulename": "opsml.registry.semver", "qualname": "SemVerRegistryValidator.version_to_search", "kind": "variable", "doc": "None
\n
Parses version and returns version to search for in the registry
\n", "annotation": ": Optional[str]"}, "opsml.registry.semver.SemVerRegistryValidator.set_version": {"fullname": "opsml.registry.semver.SemVerRegistryValidator.set_version", "modulename": "opsml.registry.semver", "qualname": "SemVerRegistryValidator.set_version", "kind": "function", "doc": "Sets the correct version to use for incrementing and adding the the registry
\n\n\n\n", "signature": "(self, versions: List[str]) -> str:", "funcdef": "def"}, "opsml.registry.semver.SemVerSymbols": {"fullname": "opsml.registry.semver.SemVerSymbols", "modulename": "opsml.registry.semver", "qualname": "SemVerSymbols", "kind": "class", "doc": "str: version to use
\n
An enumeration.
\n", "bases": "builtins.str, enum.Enum"}, "opsml.registry.semver.SemVerSymbols.STAR": {"fullname": "opsml.registry.semver.SemVerSymbols.STAR", "modulename": "opsml.registry.semver", "qualname": "SemVerSymbols.STAR", "kind": "variable", "doc": "\n", "default_value": "<SemVerSymbols.STAR: '*'>"}, "opsml.registry.semver.SemVerSymbols.CARET": {"fullname": "opsml.registry.semver.SemVerSymbols.CARET", "modulename": "opsml.registry.semver", "qualname": "SemVerSymbols.CARET", "kind": "variable", "doc": "\n", "default_value": "<SemVerSymbols.CARET: '^'>"}, "opsml.registry.semver.SemVerSymbols.TILDE": {"fullname": "opsml.registry.semver.SemVerSymbols.TILDE", "modulename": "opsml.registry.semver", "qualname": "SemVerSymbols.TILDE", "kind": "variable", "doc": "\n", "default_value": "<SemVerSymbols.TILDE: '~'>"}, "opsml.registry.semver.SemVerParser": {"fullname": "opsml.registry.semver.SemVerParser", "modulename": "opsml.registry.semver", "qualname": "SemVerParser", "kind": "class", "doc": "Base class for semver parsing
\n"}, "opsml.registry.semver.SemVerParser.parse_version": {"fullname": "opsml.registry.semver.SemVerParser.parse_version", "modulename": "opsml.registry.semver", "qualname": "SemVerParser.parse_version", "kind": "function", "doc": "\n", "signature": "(version: str) -> str:", "funcdef": "def"}, "opsml.registry.semver.SemVerParser.validate": {"fullname": "opsml.registry.semver.SemVerParser.validate", "modulename": "opsml.registry.semver", "qualname": "SemVerParser.validate", "kind": "function", "doc": "\n", "signature": "(version: str) -> bool:", "funcdef": "def"}, "opsml.registry.semver.StarParser": {"fullname": "opsml.registry.semver.StarParser", "modulename": "opsml.registry.semver", "qualname": "StarParser", "kind": "class", "doc": "Parses versions that contain * symbol
\n", "bases": "SemVerParser"}, "opsml.registry.semver.StarParser.parse_version": {"fullname": "opsml.registry.semver.StarParser.parse_version", "modulename": "opsml.registry.semver", "qualname": "StarParser.parse_version", "kind": "function", "doc": "\n", "signature": "(version: str) -> str:", "funcdef": "def"}, "opsml.registry.semver.StarParser.validate": {"fullname": "opsml.registry.semver.StarParser.validate", "modulename": "opsml.registry.semver", "qualname": "StarParser.validate", "kind": "function", "doc": "\n", "signature": "(version: str) -> bool:", "funcdef": "def"}, "opsml.registry.semver.CaretParser": {"fullname": "opsml.registry.semver.CaretParser", "modulename": "opsml.registry.semver", "qualname": "CaretParser", "kind": "class", "doc": "Parses versions that contain ^ symbol
\n", "bases": "SemVerParser"}, "opsml.registry.semver.CaretParser.parse_version": {"fullname": "opsml.registry.semver.CaretParser.parse_version", "modulename": "opsml.registry.semver", "qualname": "CaretParser.parse_version", "kind": "function", "doc": "\n", "signature": "(version: str) -> str:", "funcdef": "def"}, "opsml.registry.semver.CaretParser.validate": {"fullname": "opsml.registry.semver.CaretParser.validate", "modulename": "opsml.registry.semver", "qualname": "CaretParser.validate", "kind": "function", "doc": "\n", "signature": "(version: str) -> bool:", "funcdef": "def"}, "opsml.registry.semver.TildeParser": {"fullname": "opsml.registry.semver.TildeParser", "modulename": "opsml.registry.semver", "qualname": "TildeParser", "kind": "class", "doc": "Parses versions that contain ~ symbol
\n", "bases": "SemVerParser"}, "opsml.registry.semver.TildeParser.parse_version": {"fullname": "opsml.registry.semver.TildeParser.parse_version", "modulename": "opsml.registry.semver", "qualname": "TildeParser.parse_version", "kind": "function", "doc": "\n", "signature": "(version: str) -> str:", "funcdef": "def"}, "opsml.registry.semver.TildeParser.validate": {"fullname": "opsml.registry.semver.TildeParser.validate", "modulename": "opsml.registry.semver", "qualname": "TildeParser.validate", "kind": "function", "doc": "\n", "signature": "(version: str) -> bool:", "funcdef": "def"}, "opsml.registry.semver.NoParser": {"fullname": "opsml.registry.semver.NoParser", "modulename": "opsml.registry.semver", "qualname": "NoParser", "kind": "class", "doc": "Does not parse version
\n", "bases": "SemVerParser"}, "opsml.registry.semver.NoParser.parse_version": {"fullname": "opsml.registry.semver.NoParser.parse_version", "modulename": "opsml.registry.semver", "qualname": "NoParser.parse_version", "kind": "function", "doc": "\n", "signature": "(version: str) -> str:", "funcdef": "def"}, "opsml.registry.semver.NoParser.validate": {"fullname": "opsml.registry.semver.NoParser.validate", "modulename": "opsml.registry.semver", "qualname": "NoParser.validate", "kind": "function", "doc": "\n", "signature": "(version: str) -> bool:", "funcdef": "def"}, "opsml.registry.semver.get_version_to_search": {"fullname": "opsml.registry.semver.get_version_to_search", "modulename": "opsml.registry.semver", "qualname": "get_version_to_search", "kind": "function", "doc": "Parses a current version based on SemVer characters.
\n\n\n\n", "signature": "(version: str) -> str:", "funcdef": "def"}, "opsml.settings.config": {"fullname": "opsml.settings.config", "modulename": "opsml.settings.config", "kind": "module", "doc": "\n"}, "opsml.settings.config.OpsmlConfig": {"fullname": "opsml.settings.config.OpsmlConfig", "modulename": "opsml.settings.config", "qualname": "OpsmlConfig", "kind": "class", "doc": "Version (str) to search based on presence of SemVer characters
\n
Base class for settings, allowing values to be overridden by environment variables.
\n\nThis is useful in production for secrets you do not wish to save in code, it plays nicely with docker(-compose),\nHeroku and any 12 factor app design.
\n\nAll the below attributes can be set via model_config.
None.None.Path(''), which\nmeans that the value from model_config['env_file'] should be used. You can also pass\nNone to indicate that environment variables should not be loaded from an env file.'latin-1'. Defaults to None.False.None.None type(None). Defaults to None type(None), which means no parsing should occur.None.Opsml uses storage cients that follow fsspec guidelines. LocalFileSytem only deals\nin absolutes, so we need to convert relative paths to absolute paths.
\n", "signature": "(cls, opsml_storage_uri: str) -> str:", "funcdef": "def"}, "opsml.settings.config.OpsmlConfig.is_tracking_local": {"fullname": "opsml.settings.config.OpsmlConfig.is_tracking_local", "modulename": "opsml.settings.config", "qualname": "OpsmlConfig.is_tracking_local", "kind": "variable", "doc": "Used to determine if an API client will be used.
\n\nIf tracking is local, the [server] extra is required.
\n", "annotation": ": bool"}, "opsml.settings.config.OpsmlConfig.storage_system": {"fullname": "opsml.settings.config.OpsmlConfig.storage_system", "modulename": "opsml.settings.config", "qualname": "OpsmlConfig.storage_system", "kind": "variable", "doc": "Returns the storage system used for the current tracking URI
\n", "annotation": ": opsml.types.storage.StorageSystem"}, "opsml.settings.config.OpsmlConfig.storage_root": {"fullname": "opsml.settings.config.OpsmlConfig.storage_root", "modulename": "opsml.settings.config", "qualname": "OpsmlConfig.storage_root", "kind": "variable", "doc": "Returns the root of the storage URI
\n", "annotation": ": str"}, "opsml.settings.config.OpsmlConfig.model_config": {"fullname": "opsml.settings.config.OpsmlConfig.model_config", "modulename": "opsml.settings.config", "qualname": "OpsmlConfig.model_config", "kind": "variable", "doc": "\n", "annotation": ": ClassVar[pydantic_settings.main.SettingsConfigDict]", "default_value": "{'extra': 'forbid', 'arbitrary_types_allowed': True, 'validate_default': True, 'case_sensitive': False, 'env_prefix': '', 'env_file': None, 'env_file_encoding': None, 'env_ignore_empty': False, 'env_nested_delimiter': None, 'env_parse_none_str': None, 'json_file': None, 'json_file_encoding': None, 'yaml_file': None, 'yaml_file_encoding': None, 'toml_file': None, 'secrets_dir': None, 'protected_namespaces': ('model_', 'settings_')}"}, "opsml.settings.config.OpsmlConfig.model_fields": {"fullname": "opsml.settings.config.OpsmlConfig.model_fields", "modulename": "opsml.settings.config", "qualname": "OpsmlConfig.model_fields", "kind": "variable", "doc": "\n", "default_value": "{'app_name': FieldInfo(annotation=str, required=False, default='opsml'), 'app_env': FieldInfo(annotation=str, required=False, default='development'), 'opsml_storage_uri': FieldInfo(annotation=str, required=False, default='./mlruns'), 'opsml_tracking_uri': FieldInfo(annotation=str, required=False, default='sqlite:///tmp.db'), 'opsml_prod_token': FieldInfo(annotation=str, required=False, default='staging'), 'opsml_proxy_root': FieldInfo(annotation=str, required=False, default='opsml-root:/'), 'opsml_registry_path': FieldInfo(annotation=str, required=False, default='model_registry'), 'opsml_testing': FieldInfo(annotation=bool, required=False, default=False), 'download_chunk_size': FieldInfo(annotation=int, required=False, default=31457280), 'upload_chunk_size': FieldInfo(annotation=int, required=False, default=31457280), 'opsml_username': FieldInfo(annotation=Union[str, NoneType], required=False), 'opsml_password': FieldInfo(annotation=Union[str, NoneType], required=False), 'opsml_run_id': FieldInfo(annotation=Union[str, NoneType], required=False)}"}, "opsml.settings.config.OpsmlConfig.model_computed_fields": {"fullname": "opsml.settings.config.OpsmlConfig.model_computed_fields", "modulename": "opsml.settings.config", "qualname": "OpsmlConfig.model_computed_fields", "kind": "variable", "doc": "\n", "default_value": "{}"}, "opsml.settings.config.config": {"fullname": "opsml.settings.config.config", "modulename": "opsml.settings.config", "qualname": "config", "kind": "variable", "doc": "\n", "default_value": "OpsmlConfig(app_name='opsml', app_env='development', opsml_storage_uri='gs://opsml-integration', opsml_tracking_uri='sqlite:///tmp.db', opsml_prod_token='staging', opsml_proxy_root='opsml-root:/', opsml_registry_path='model_registry', opsml_testing=False, download_chunk_size=31457280, upload_chunk_size=31457280, opsml_username=None, opsml_password=None, opsml_run_id=None)"}, "opsml.types.huggingface": {"fullname": "opsml.types.huggingface", "modulename": "opsml.types.huggingface", "kind": "module", "doc": "\n"}, "opsml.types.huggingface.HuggingFaceTask": {"fullname": "opsml.types.huggingface.HuggingFaceTask", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask", "kind": "class", "doc": "An enumeration.
\n", "bases": "builtins.str, enum.Enum"}, "opsml.types.huggingface.HuggingFaceTask.AUDIO_CLASSIFICATION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.AUDIO_CLASSIFICATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.AUDIO_CLASSIFICATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.AUDIO_CLASSIFICATION: 'audio-classification'>"}, "opsml.types.huggingface.HuggingFaceTask.AUTOMATIC_SPEECH_RECOGNITION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.AUTOMATIC_SPEECH_RECOGNITION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.AUTOMATIC_SPEECH_RECOGNITION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.AUTOMATIC_SPEECH_RECOGNITION: 'automatic-speech-recognition'>"}, "opsml.types.huggingface.HuggingFaceTask.CONVERSATIONAL": {"fullname": "opsml.types.huggingface.HuggingFaceTask.CONVERSATIONAL", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.CONVERSATIONAL", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.CONVERSATIONAL: 'conversational'>"}, "opsml.types.huggingface.HuggingFaceTask.DEPTH_ESTIMATION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.DEPTH_ESTIMATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.DEPTH_ESTIMATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.DEPTH_ESTIMATION: 'depth-estimation'>"}, "opsml.types.huggingface.HuggingFaceTask.DOCUMENT_QUESTION_ANSWERING": {"fullname": "opsml.types.huggingface.HuggingFaceTask.DOCUMENT_QUESTION_ANSWERING", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.DOCUMENT_QUESTION_ANSWERING", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.DOCUMENT_QUESTION_ANSWERING: 'document-question-answering'>"}, "opsml.types.huggingface.HuggingFaceTask.FEATURE_EXTRACTION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.FEATURE_EXTRACTION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.FEATURE_EXTRACTION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.FEATURE_EXTRACTION: 'feature-extraction'>"}, "opsml.types.huggingface.HuggingFaceTask.FILL_MASK": {"fullname": "opsml.types.huggingface.HuggingFaceTask.FILL_MASK", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.FILL_MASK", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.FILL_MASK: 'fill-mask'>"}, "opsml.types.huggingface.HuggingFaceTask.IMAGE_CLASSIFICATION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.IMAGE_CLASSIFICATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.IMAGE_CLASSIFICATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.IMAGE_CLASSIFICATION: 'image-classification'>"}, "opsml.types.huggingface.HuggingFaceTask.IMAGE_SEGMENTATION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.IMAGE_SEGMENTATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.IMAGE_SEGMENTATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.IMAGE_SEGMENTATION: 'image-segmentation'>"}, "opsml.types.huggingface.HuggingFaceTask.IMAGE_TO_IMAGE": {"fullname": "opsml.types.huggingface.HuggingFaceTask.IMAGE_TO_IMAGE", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.IMAGE_TO_IMAGE", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.IMAGE_TO_IMAGE: 'image-to-image'>"}, "opsml.types.huggingface.HuggingFaceTask.IMAGE_TO_TEXT": {"fullname": "opsml.types.huggingface.HuggingFaceTask.IMAGE_TO_TEXT", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.IMAGE_TO_TEXT", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.IMAGE_TO_TEXT: 'image-to-text'>"}, "opsml.types.huggingface.HuggingFaceTask.MASK_GENERATION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.MASK_GENERATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.MASK_GENERATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.MASK_GENERATION: 'mask-generation'>"}, "opsml.types.huggingface.HuggingFaceTask.OBJECT_DETECTION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.OBJECT_DETECTION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.OBJECT_DETECTION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.OBJECT_DETECTION: 'object-detection'>"}, "opsml.types.huggingface.HuggingFaceTask.QUESTION_ANSWERING": {"fullname": "opsml.types.huggingface.HuggingFaceTask.QUESTION_ANSWERING", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.QUESTION_ANSWERING", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.QUESTION_ANSWERING: 'question-answering'>"}, "opsml.types.huggingface.HuggingFaceTask.SUMMARIZATION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.SUMMARIZATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.SUMMARIZATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.SUMMARIZATION: 'summarization'>"}, "opsml.types.huggingface.HuggingFaceTask.TABLE_QUESTION_ANSWERING": {"fullname": "opsml.types.huggingface.HuggingFaceTask.TABLE_QUESTION_ANSWERING", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.TABLE_QUESTION_ANSWERING", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.TABLE_QUESTION_ANSWERING: 'table-question-answering'>"}, "opsml.types.huggingface.HuggingFaceTask.TEXT2TEXT_GENERATION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.TEXT2TEXT_GENERATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.TEXT2TEXT_GENERATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.TEXT2TEXT_GENERATION: 'text2text-generation'>"}, "opsml.types.huggingface.HuggingFaceTask.TEXT_CLASSIFICATION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.TEXT_CLASSIFICATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.TEXT_CLASSIFICATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.TEXT_CLASSIFICATION: 'text-classification'>"}, "opsml.types.huggingface.HuggingFaceTask.TEXT_GENERATION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.TEXT_GENERATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.TEXT_GENERATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.TEXT_GENERATION: 'text-generation'>"}, "opsml.types.huggingface.HuggingFaceTask.TEXT_TO_AUDIO": {"fullname": "opsml.types.huggingface.HuggingFaceTask.TEXT_TO_AUDIO", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.TEXT_TO_AUDIO", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.TEXT_TO_AUDIO: 'text-to-audio'>"}, "opsml.types.huggingface.HuggingFaceTask.TOKEN_CLASSIFICATION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.TOKEN_CLASSIFICATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.TOKEN_CLASSIFICATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.TOKEN_CLASSIFICATION: 'token-classification'>"}, "opsml.types.huggingface.HuggingFaceTask.TRANSLATION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.TRANSLATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.TRANSLATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.TRANSLATION: 'translation'>"}, "opsml.types.huggingface.HuggingFaceTask.TRANSLATION_XX_TO_YY": {"fullname": "opsml.types.huggingface.HuggingFaceTask.TRANSLATION_XX_TO_YY", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.TRANSLATION_XX_TO_YY", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.TRANSLATION_XX_TO_YY: 'translation_xx_to_yy'>"}, "opsml.types.huggingface.HuggingFaceTask.VIDEO_CLASSIFICATION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.VIDEO_CLASSIFICATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.VIDEO_CLASSIFICATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.VIDEO_CLASSIFICATION: 'video-classification'>"}, "opsml.types.huggingface.HuggingFaceTask.VISUAL_QUESTION_ANSWERING": {"fullname": "opsml.types.huggingface.HuggingFaceTask.VISUAL_QUESTION_ANSWERING", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.VISUAL_QUESTION_ANSWERING", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.VISUAL_QUESTION_ANSWERING: 'visual-question-answering'>"}, "opsml.types.huggingface.HuggingFaceTask.ZERO_SHOT_CLASSIFICATION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.ZERO_SHOT_CLASSIFICATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.ZERO_SHOT_CLASSIFICATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.ZERO_SHOT_CLASSIFICATION: 'zero-shot-classification'>"}, "opsml.types.huggingface.HuggingFaceTask.ZERO_SHOT_IMAGE_CLASSIFICATION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.ZERO_SHOT_IMAGE_CLASSIFICATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.ZERO_SHOT_IMAGE_CLASSIFICATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.ZERO_SHOT_IMAGE_CLASSIFICATION: 'zero-shot-image-classification'>"}, "opsml.types.huggingface.HuggingFaceTask.ZERO_SHOT_AUDIO_CLASSIFICATION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.ZERO_SHOT_AUDIO_CLASSIFICATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.ZERO_SHOT_AUDIO_CLASSIFICATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.ZERO_SHOT_AUDIO_CLASSIFICATION: 'zero-shot-audio-classification'>"}, "opsml.types.huggingface.HuggingFaceTask.ZERO_SHOT_OBJECT_DETECTION": {"fullname": "opsml.types.huggingface.HuggingFaceTask.ZERO_SHOT_OBJECT_DETECTION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceTask.ZERO_SHOT_OBJECT_DETECTION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceTask.ZERO_SHOT_OBJECT_DETECTION: 'zero-shot-object-detection'>"}, "opsml.types.huggingface.GENERATION_TYPES": {"fullname": "opsml.types.huggingface.GENERATION_TYPES", "modulename": "opsml.types.huggingface", "qualname": "GENERATION_TYPES", "kind": "variable", "doc": "\n", "default_value": "['mask-generation', 'text-generation', 'text2text-generation']"}, "opsml.types.huggingface.HuggingFaceORTModel": {"fullname": "opsml.types.huggingface.HuggingFaceORTModel", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceORTModel", "kind": "class", "doc": "An enumeration.
\n", "bases": "builtins.str, enum.Enum"}, "opsml.types.huggingface.HuggingFaceORTModel.ORT_AUDIO_CLASSIFICATION": {"fullname": "opsml.types.huggingface.HuggingFaceORTModel.ORT_AUDIO_CLASSIFICATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceORTModel.ORT_AUDIO_CLASSIFICATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceORTModel.ORT_AUDIO_CLASSIFICATION: 'ORTModelForAudioClassification'>"}, "opsml.types.huggingface.HuggingFaceORTModel.ORT_AUDIO_FRAME_CLASSIFICATION": {"fullname": "opsml.types.huggingface.HuggingFaceORTModel.ORT_AUDIO_FRAME_CLASSIFICATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceORTModel.ORT_AUDIO_FRAME_CLASSIFICATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceORTModel.ORT_AUDIO_FRAME_CLASSIFICATION: 'ORTModelForAudioFrameClassification'>"}, "opsml.types.huggingface.HuggingFaceORTModel.ORT_AUDIO_XVECTOR": {"fullname": "opsml.types.huggingface.HuggingFaceORTModel.ORT_AUDIO_XVECTOR", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceORTModel.ORT_AUDIO_XVECTOR", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceORTModel.ORT_AUDIO_XVECTOR: 'ORTModelForAudioXVector'>"}, "opsml.types.huggingface.HuggingFaceORTModel.ORT_CUSTOM_TASKS": {"fullname": "opsml.types.huggingface.HuggingFaceORTModel.ORT_CUSTOM_TASKS", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceORTModel.ORT_CUSTOM_TASKS", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceORTModel.ORT_CUSTOM_TASKS: 'ORTModelForCustomTasks'>"}, "opsml.types.huggingface.HuggingFaceORTModel.ORT_CTC": {"fullname": "opsml.types.huggingface.HuggingFaceORTModel.ORT_CTC", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceORTModel.ORT_CTC", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceORTModel.ORT_CTC: 'ORTModelForCTC'>"}, "opsml.types.huggingface.HuggingFaceORTModel.ORT_FEATURE_EXTRACTION": {"fullname": "opsml.types.huggingface.HuggingFaceORTModel.ORT_FEATURE_EXTRACTION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceORTModel.ORT_FEATURE_EXTRACTION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceORTModel.ORT_FEATURE_EXTRACTION: 'ORTModelForFeatureExtraction'>"}, "opsml.types.huggingface.HuggingFaceORTModel.ORT_IMAGE_CLASSIFICATION": {"fullname": "opsml.types.huggingface.HuggingFaceORTModel.ORT_IMAGE_CLASSIFICATION", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceORTModel.ORT_IMAGE_CLASSIFICATION", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceORTModel.ORT_IMAGE_CLASSIFICATION: 'ORTModelForImageClassification'>"}, "opsml.types.huggingface.HuggingFaceORTModel.ORT_MASKED_LM": {"fullname": "opsml.types.huggingface.HuggingFaceORTModel.ORT_MASKED_LM", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceORTModel.ORT_MASKED_LM", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceORTModel.ORT_MASKED_LM: 'ORTModelForMaskedLM'>"}, "opsml.types.huggingface.HuggingFaceORTModel.ORT_MULTIPLE_CHOICE": {"fullname": "opsml.types.huggingface.HuggingFaceORTModel.ORT_MULTIPLE_CHOICE", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceORTModel.ORT_MULTIPLE_CHOICE", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceORTModel.ORT_MULTIPLE_CHOICE: 'ORTModelForMultipleChoice'>"}, "opsml.types.huggingface.HuggingFaceORTModel.ORT_QUESTION_ANSWERING": {"fullname": "opsml.types.huggingface.HuggingFaceORTModel.ORT_QUESTION_ANSWERING", "modulename": "opsml.types.huggingface", "qualname": "HuggingFaceORTModel.ORT_QUESTION_ANSWERING", "kind": "variable", "doc": "\n", "default_value": "<HuggingFaceORTModel.ORT_QUESTION_ANSWERING: 'ORTModelForQuestionAnswering'>"}, "opsml.types.huggingface.HuggingFaceORTModel.ORT_SEMANTIC_SEGMENTATION": {"fullname": "opsml.types.huggingface.HuggingFaceORTModel.ORT_SEMANTIC_SEGMENTATION", "modulename": 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good enough. + elasticlunr.tokenizer.setSeperator(/[\s\-.;&_'"=,()]+|<[^>]*>/); + + let searchIndex; + if (docs._isPrebuiltIndex) { + console.info("using precompiled search index"); + searchIndex = elasticlunr.Index.load(docs); + } else { + console.time("building search index"); + // mirrored in build-search-index.js (part 2) + searchIndex = elasticlunr(function () { + this.pipeline.remove(elasticlunr.stemmer); + this.pipeline.remove(elasticlunr.stopWordFilter); + this.addField("qualname"); + this.addField("fullname"); + this.addField("annotation"); + this.addField("default_value"); + this.addField("signature"); + this.addField("bases"); + this.addField("doc"); + this.setRef("fullname"); + }); + for (let doc of docs) { + searchIndex.addDoc(doc); + } + console.timeEnd("building search index"); + } + + return (term) => searchIndex.search(term, { + fields: { + qualname: {boost: 4}, + fullname: {boost: 2}, + annotation: {boost: 2}, + default_value: {boost: 2}, + signature: {boost: 2}, + bases: {boost: 2}, + doc: {boost: 1}, + }, + expand: true + }); +})(); \ No newline at end of file diff --git a/assets/_mkdocstrings.css b/assets/_mkdocstrings.css new file mode 100644 index 000000000..4b7d98b83 --- /dev/null +++ b/assets/_mkdocstrings.css @@ -0,0 +1,109 @@ + +/* Avoid breaking parameter names, etc. in table cells. */ +.doc-contents td code { + word-break: normal !important; +} + +/* No line break before first paragraph of descriptions. */ +.doc-md-description, +.doc-md-description>p:first-child { + display: inline; +} + +/* Max width for docstring sections tables. */ +.doc .md-typeset__table, +.doc .md-typeset__table table { + display: table !important; + width: 100%; +} + +.doc .md-typeset__table tr { + display: table-row; +} + +/* Defaults in Spacy table style. */ +.doc-param-default { + float: right; +} + +/* Symbols in Navigation and ToC. */ +:root, +[data-md-color-scheme="default"] { + --doc-symbol-attribute-fg-color: #953800; + --doc-symbol-function-fg-color: #8250df; + --doc-symbol-method-fg-color: #8250df; + --doc-symbol-class-fg-color: #0550ae; + --doc-symbol-module-fg-color: #5cad0f; + + --doc-symbol-attribute-bg-color: #9538001a; + --doc-symbol-function-bg-color: #8250df1a; + --doc-symbol-method-bg-color: #8250df1a; + --doc-symbol-class-bg-color: #0550ae1a; + --doc-symbol-module-bg-color: #5cad0f1a; +} + +[data-md-color-scheme="slate"] { + --doc-symbol-attribute-fg-color: #ffa657; + --doc-symbol-function-fg-color: #d2a8ff; + --doc-symbol-method-fg-color: #d2a8ff; + --doc-symbol-class-fg-color: #79c0ff; + --doc-symbol-module-fg-color: #baff79; + + --doc-symbol-attribute-bg-color: #ffa6571a; + --doc-symbol-function-bg-color: #d2a8ff1a; + --doc-symbol-method-bg-color: #d2a8ff1a; + --doc-symbol-class-bg-color: #79c0ff1a; + --doc-symbol-module-bg-color: #baff791a; +} + +code.doc-symbol { + border-radius: .1rem; + font-size: .85em; + padding: 0 .3em; + font-weight: bold; +} + +code.doc-symbol-attribute { + color: var(--doc-symbol-attribute-fg-color); + background-color: var(--doc-symbol-attribute-bg-color); +} + +code.doc-symbol-attribute::after { + content: "attr"; +} + +code.doc-symbol-function { + color: var(--doc-symbol-function-fg-color); + background-color: var(--doc-symbol-function-bg-color); +} + +code.doc-symbol-function::after { + content: "func"; +} + +code.doc-symbol-method { + color: var(--doc-symbol-method-fg-color); + background-color: var(--doc-symbol-method-bg-color); +} + +code.doc-symbol-method::after { + content: "meth"; +} + +code.doc-symbol-class { + color: var(--doc-symbol-class-fg-color); + background-color: var(--doc-symbol-class-bg-color); +} + +code.doc-symbol-class::after { + content: "class"; +} + +code.doc-symbol-module { + color: var(--doc-symbol-module-fg-color); + background-color: var(--doc-symbol-module-bg-color); +} + +code.doc-symbol-module::after { + content: "mod"; +} \ No newline at end of file diff --git a/assets/images/favicon.png 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