From 255961beaf1b45cc649f373c19d87eca01cf9c6f Mon Sep 17 00:00:00 2001 From: Stuart Siegel Date: Wed, 31 Jul 2024 22:25:28 -0400 Subject: [PATCH 01/29] custom kserve runtime --- services/inference/Dockerfile | 44 + services/inference/Makefile | 30 + services/inference/README.md | 76 + services/inference/poetry.lock | 2911 +++++++++++++++++ services/inference/pyproject.toml | 36 + services/inference/tests/test_inference.py | 153 + services/inference/tsfmservices/__init__.py | 47 + .../inference/tsfmservices/common/__init__.py | 0 .../tsfmservices/common/constants.py | 4 + .../tsfmservices/common/logging.conf | 35 + .../inference/tsfmservices/common/util.py | 162 + .../tsfmservices/config/default_config.yml | 8 + .../tsfmservices/inference/__init__.py | 1 + .../tsfmservices/inference/inference.py | 178 + .../inference/tsfmservices/inference/main.py | 51 + .../tsfmservices/inference/payloads.py | 138 + 16 files changed, 3874 insertions(+) create mode 100644 services/inference/Dockerfile create mode 100644 services/inference/Makefile create mode 100644 services/inference/README.md create mode 100644 services/inference/poetry.lock create mode 100644 services/inference/pyproject.toml create mode 100644 services/inference/tests/test_inference.py create mode 100644 services/inference/tsfmservices/__init__.py create mode 100644 services/inference/tsfmservices/common/__init__.py create mode 100644 services/inference/tsfmservices/common/constants.py create mode 100644 services/inference/tsfmservices/common/logging.conf create mode 100644 services/inference/tsfmservices/common/util.py create mode 100644 services/inference/tsfmservices/config/default_config.yml create mode 100644 services/inference/tsfmservices/inference/__init__.py create mode 100644 services/inference/tsfmservices/inference/inference.py create mode 100644 services/inference/tsfmservices/inference/main.py create mode 100644 services/inference/tsfmservices/inference/payloads.py diff --git a/services/inference/Dockerfile b/services/inference/Dockerfile new file mode 100644 index 00000000..0df00f8d --- /dev/null +++ b/services/inference/Dockerfile @@ -0,0 +1,44 @@ +# based on https://github.com/opendatahub-io/caikit-tgis-serving/blob/main/Dockerfile + +FROM registry.access.redhat.com/ubi9/ubi-minimal:latest as builder + +RUN microdnf -y update && \ + microdnf -y install \ + git shadow-utils python3.11-pip python-wheel && \ + pip3.11 install --no-cache-dir --upgrade pip wheel && \ + microdnf clean all + +ENV POETRY_VIRTUALENVS_IN_PROJECT=1 + +COPY tsfmservices* /tsfmservices/tsfmservices +COPY pyproject.toml /tsfmservices/ +COPY poetry.lock /tsfmservices/ +WORKDIR /tsfmservices +# RUN python3.11 -m venv /tsfmservices/.venv +#RUN source /tsfmservices/.venv/bin/activate && \ +# pip3.11 install --no-cache-dir ".[services]" +RUN pip3.11 install poetry && poetry install + +FROM registry.access.redhat.com/ubi9/ubi-minimal:latest as deploy +RUN microdnf -y update && \ + microdnf -y install \ + shadow-utils python3.11 && \ + microdnf clean all + +WORKDIR /tsfmservices + +COPY --from=builder /tsfmservices/ /tsfmservices/ + +ENV VIRTUAL_ENV=/tsfmservices/.venv +ENV PATH="$VIRTUAL_ENV/bin:$PATH" +ENV HF_HOME=/tmp + +RUN groupadd --system tsfmservices --gid 1001 && \ + adduser --system --uid 1001 --gid 0 --groups tsfmservices \ + --create-home --home-dir /tsfmservices --shell /sbin/nologin \ + --comment "TSFMServices User" tsfmservices + +USER tsfmservices + +# CMD ["python", "-m", "tsfmservices.inference.main"] +CMD ["python", "-m", "uvicorn","tsfmservices.inference.main:app", "--host", "0.0.0.0", "--port", "8000" ] \ No newline at end of file diff --git a/services/inference/Makefile b/services/inference/Makefile new file mode 100644 index 00000000..9d515e20 --- /dev/null +++ b/services/inference/Makefile @@ -0,0 +1,30 @@ +CONTAINER_BUILDER ?= docker + +# starts the inference service (used mainly for test cases) +start_service_local: + python -m tsfmservices.inference.main & + sleep 10 +stop_service_local: + pkill -f 'python.*tsfmservices.*' + sleep 10 + +image: + $(CONTAINER_BUILDER) build -t tsfmservices -f Dockerfile . + +start_service_image: image + $(CONTAINER_BUILDER) run -p 8000:8000 -d --rm --name tsfmserver tsfmservices + sleep 10 +stop_service_image: + $(CONTAINER_BUILDER) stop tsfmserver + +test_local: start_service_local + pytest tests + $(MAKE) stop_service_local + +test_image: start_service_image + pytest tests + $(MAKE) stop_service_image + + + + diff --git a/services/inference/README.md b/services/inference/README.md new file mode 100644 index 00000000..90683065 --- /dev/null +++ b/services/inference/README.md @@ -0,0 +1,76 @@ +# TSFM Services + +This component provides basic RESTful services for the IBM tsfm-granite +class of timeseries foundation models. At present it can serve the following models: + +* https://huggingface.co/ibm-granite/granite-timeseries-ttm-v1 +* https://huggingface.co/ibm-granite/granite-timeseries-patchtst +* https://huggingface.co/ibm-granite/granite-timeseries-patchtsmixer + + + +## Prerequisites: + +* GNU make +* python 3.10 or 3.11 +* poetry (`pip install poetry`) +* zsh or bash (zsh is preferred) + +_Note that our primary target environment for services deployment is x86_64 Linux. You may encounter hiccups if you try to use this on a different environment. If so, please +file an issue. Some of our developers do use a Mac so you're likely to find a quick resolution. None of our developers use native Windows, however._ + +## Known issues: + +* Use of pkill statements in Makefile may not work properly on Mac OS. This will be apparent if you have left over processs after running test related make targets. Please help us put OS-specific checks into our Makefile to handle these cases by filing a PR. + +### Installation + +```sh +pip install poetry && poetry install --with dev +``` + +### Testing using a local server instance + +```sh +make test_local +``` + +### Creating an image + +_You must have either docker or podman installed on your system for this to +work. You must also have proper permissions on your system to build images._ + +```sh +CONTAINER_BUILDER= make image +# e.g, CONTAINER_BUILDER=docker make image +``` + +After a successful build you should have a local image named `tsfmservices:latest` + +```sh +(py311) ➜ tsfm-services git:(revised-build-system) ✗ docker images | grep tsfmservices | head -n 1 +tsfmservices latest df592dcb0533 46 seconds ago 1.49GB +# some of the numeric and hash values on your machine could be different +``` + +### Testing using the built image + +```sh +make test_image +``` + +### Viewing the OpenAPI 3.x specification and swagger page + +```sh +make start_local_server +``` + +Then open your browser to http://127.0.0.1:8000/docs, click on the `/openapi.json` link at the top of this page. + +To stop the server run: + +```sh +# may not work properly on a Mac +# if not, kill the uvicorn process manually. +make stop_local_server +``` diff --git a/services/inference/poetry.lock b/services/inference/poetry.lock new 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+[tool.poetry] +name = "tsfmservices" +version = "0.0.1" +description = "Service layer for TSFM granite models." +authors = ["IBM"] +license = "https://github.com/ibm-granite/granite-tsfm/blob/main/LICENSE" +packages = [{ include = "tsfmservices/**/*.py" }] + +[tool.poetry.dependencies] +# including 3.9 causes poetry lock to run forever +python = ">=3.10,<3.12" + +numpy = { version = "<2" } +tsfm_public = { git = "https://github.com/IBM/tsfm.git", tag = "v0.2.5" } + +# trying to pick up cpu version for inference +# to make image smaller +torch = { version = ">2,<3", source = "pytorch" } + +fastapi = { version = "*" } +pydantic = { version = "*" } +uvicorn = { version = "*" } + +[[tool.poetry.source]] +name = "pytorch" +url = "https://download.pytorch.org/whl/cpu" +priority = "explicit" + +[tool.poetry.group.dev] +optional = true +[tool.poetry.group.dev.dependencies] +pytest = "*" + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/services/inference/tests/test_inference.py b/services/inference/tests/test_inference.py new file mode 100644 index 00000000..5fb8ed0c --- /dev/null +++ b/services/inference/tests/test_inference.py @@ -0,0 +1,153 @@ +# Standard +from datetime import datetime +from io import BytesIO +from typing import Any, Dict +import json + +# Third Party +import numpy as np +import pandas as pd +import pytest +import requests + +# First Party +from tsfm_public.toolkit.util import select_by_index +from tsfm_public import TinyTimeMixerForPrediction + +from transformers import PatchTSTForPrediction, PatchTSMixerForPrediction + + +@pytest.fixture(scope="module") +def ts_data(): + dataset_path = ( + "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh1.csv" + ) + + forecast_length = 96 + context_length = 512 + timestamp_column = "date" + + data = pd.read_csv( + dataset_path, + parse_dates=[timestamp_column], + ) + + test_data = select_by_index( + data, + start_index=12 * 30 * 24 + 4 * 30 * 24 - context_length * 5, + end_index=12 * 30 * 24 + 4 * 30 * 24, + ).reset_index(drop=True) + + test_data["id"] = np.array(["a", "b", "c", "d", "e"]).repeat(context_length) + + return test_data, { + "timestamp_column": timestamp_column, + "id_columns": ["id"], + "target_columns": ["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL", "OT"], + "prediction_length": forecast_length, + } + + +def get_inference_response( + msg: Dict[str, Any], +) -> pd.DataFrame: + URL = "http://127.0.0.1:8000/v1/inference/forecasting" + headers = {} + req = requests.post(URL, json=msg, headers=headers) + resp = req.json() + + df = [pd.DataFrame.from_dict(r) for r in resp["results"]] + return df + + +def encode_data(df: pd.DataFrame, timestamp_column: str) -> Dict[str, Any]: + df[timestamp_column] = df[timestamp_column].apply(lambda x: x.isoformat()) + data_payload = df.to_dict(orient="list") + return data_payload + + +def test_zero_shot_forecast_inference(ts_data): + test_data, params = ts_data + id_columns = params["id_columns"] + + prediction_length = 96 + num_ids = test_data[id_columns[0]].nunique() + + # test single + test_data_ = test_data[test_data[id_columns[0]] == "a"].copy() + + msg = { + "model_id": "ibm-granite/granite-timeseries-ttm-v1", + "parameters": { + # "prediction_length": params["prediction_length"], + }, + "metadata": { + "timestamp_column": params["timestamp_column"], + "id_columns": params["id_columns"], + "target_columns": params["target_columns"], + }, + "data": encode_data(test_data_, params["timestamp_column"]), + "future_data": {}, + } + + df_out = get_inference_response(msg) + assert len(df_out) == 1 + assert df_out[0].shape[0] == prediction_length + + # test multi-time series + test_data_ = test_data.copy() + + msg = { + "model_id": "ibm-granite/granite-timeseries-ttm-v1", + "parameters": { + # "prediction_length": params["prediction_length"], + }, + "metadata": { + "timestamp_column": params["timestamp_column"], + "id_columns": params["id_columns"], + "target_columns": params["target_columns"], + }, + "data": encode_data(test_data_, params["timestamp_column"]), + "future_data": {}, + } + + df_out = get_inference_response(msg) + + assert len(df_out) == 1 + assert df_out[0].shape[0] == prediction_length * num_ids + + +@pytest.mark.parametrize( + "model_path", + [ + "ibm-granite/granite-timeseries-patchtst", + "ibm-granite/granite-timeseries-patchtsmixer", + ], +) +def test_trained_model_inference(ts_data, model_path): + test_data, params = ts_data + id_columns = params["id_columns"] + + prediction_length = 96 + + # test single + test_data_ = test_data[test_data[id_columns[0]] == "a"].copy() + encoded_data = encode_data(test_data_, params["timestamp_column"]) + + msg = { + "model_id": model_path, + "parameters": { + # "prediction_length": params["prediction_length"], + }, + "metadata": { + "timestamp_column": params["timestamp_column"], + "id_columns": params["id_columns"], + "target_columns": params["target_columns"], + }, + "data": encoded_data, + "future_data": {}, + } + + df_out = get_inference_response(msg) + assert len(df_out) == 1 + assert df_out[0].shape[0] == prediction_length diff --git a/services/inference/tsfmservices/__init__.py b/services/inference/tsfmservices/__init__.py new file mode 100644 index 00000000..34dd794f --- /dev/null +++ b/services/inference/tsfmservices/__init__.py @@ -0,0 +1,47 @@ +import logging +import os + +TSFM_PYTHON_LOGGING_LEVEL = os.getenv("TSFM_PYTHON_LOGGING_LEVEL", "INFO") + +LevelNamesMapping = dict( + INFO=logging.INFO, + WARN=logging.WARN, + WARNING=logging.WARNING, + ERROR=logging.ERROR, + CRITICAL=logging.CRITICAL, + DEBUG=logging.DEBUG, + FATAL=logging.FATAL, +) + +TSFM_PYTHON_LOGGING_LEVEL = ( + logging.getLevelNamesMapping()[TSFM_PYTHON_LOGGING_LEVEL] + if hasattr(logging, "getLevelNamesMapping") + else LevelNamesMapping[TSFM_PYTHON_LOGGING_LEVEL] +) +TSFM_PYTHON_LOGGING_FORMAT = os.getenv( + "TSFM_PYTHON_LOGGING_FORMAT", + "%(levelname)s:p-%(process)d:t-%(thread)d:%(filename)s:%(funcName)s:%(message)s", +) + +# Do we have redis? + +try: + # Third Party + import redis + import rq + + HAVE_REDIS_BACKEND = True +except ImportError: + HAVE_REDIS_BACKEND = False + +TSFM_USE_KFTO_ASYNC_BACKEND = int(os.getenv("TSFM_USE_KFTO_ASYNC_BACKEND", "0")) == 1 +USE_REDIS_ASYNC_BACKEND = HAVE_REDIS_BACKEND and not TSFM_USE_KFTO_ASYNC_BACKEND + +TSFM_ALLOW_LOAD_FROM_HF_HUB = int(os.getenv("TSFM_ALLOW_LOAD_FROM_HF_HUB", "1")) == 1 + +TSFM_CONFIG_FILE = os.getenv( + "TSFM_CONFIG_FILE", + os.path.realpath( + os.path.join(os.path.dirname(__file__), "config", "default_config.yml") + ), +) diff --git a/services/inference/tsfmservices/common/__init__.py b/services/inference/tsfmservices/common/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/services/inference/tsfmservices/common/constants.py b/services/inference/tsfmservices/common/constants.py new file mode 100644 index 00000000..15f42cd3 --- /dev/null +++ b/services/inference/tsfmservices/common/constants.py @@ -0,0 +1,4 @@ +"""Holds constant values used across modules.""" + +# What will appear in our paths for fast API version +API_VERSION = "v1" diff --git a/services/inference/tsfmservices/common/logging.conf b/services/inference/tsfmservices/common/logging.conf new file mode 100644 index 00000000..2e2b37e9 --- /dev/null +++ b/services/inference/tsfmservices/common/logging.conf @@ -0,0 +1,35 @@ +[loggers] +keys=root,file + +[handlers] +keys=console,file + +[formatters] +keys=console,file + +[logger_root] +level=DEBUG +handlers=console,file + +[logger_file] +level=DEBUG +handlers=file +qualname=file + +[handler_console] +class=StreamHandler +level=DEBUG +formatter=console +args=(sys.stdout,) + +[handler_file] +class=FileHandler +level=DEBUG +formatter=file +args=('server.log','w',) + +[formatter_console] +format=%(asctime)s - %(levelname)s - %(name)s - %(message)s + +[formatter_file] +format=%(asctime)s - %(levelname)s - %(name)s - %(funcName)s:%(lineno)d - %(message)s diff --git a/services/inference/tsfmservices/common/util.py b/services/inference/tsfmservices/common/util.py new file mode 100644 index 00000000..bcb551ec --- /dev/null +++ b/services/inference/tsfmservices/common/util.py @@ -0,0 +1,162 @@ +"""Utilities to support custom time series models""" + +# Standard +import importlib +import logging +import pathlib +from typing import Optional, Union + +import transformers + +# Third Party +from transformers import AutoConfig, AutoModel, PretrainedConfig, PreTrainedModel + +LOGGER = logging.getLogger(__file__) + + +def register_config(model_type: str, model_config_name: str, module_path: str) -> None: + """Register a configuration for a particular model architecture + + Args: + model_type (Optional[str], optional): The type of the model, from the model implementation. Defaults to None. + model_config_name (Optional[str], optional): The name of configuration class for the model. Defaults to None. + module_path (Optional[str], optional): Python module path that can be used to load the + config/model. Defaults to None. + + Raises: + RuntimeError: Raised when the module cannot be imported from the provided module path. + """ + # example + # model_type: "tinytimemixer" + # model_config_name: "TinyTimeMixerConfig" + # module_path: "tsfm" # place where config should be importable + + # AutoConfig.register("tinytimemixer", TinyTimeMixerConfig) + try: + mod = importlib.import_module(module_path) + conf_class = getattr(mod, model_config_name, None) + except ModuleNotFoundError as exc: # modulenot found, key error ? + raise RuntimeError( + f"Could not load {model_config_name} from {module_path}" + ) from exc + + if conf_class is not None: + AutoConfig.register(model_type, conf_class) + else: + # issue warning? + pass + + +def load_config( + model_path: Union[str, pathlib.Path], + model_type: Optional[str] = None, + model_config_name: Optional[str] = None, + module_path: Optional[str] = None, +) -> PretrainedConfig: + """Load configuration + + Attempts to load the configuration, if it is not loadable, then we register it with the AutoConfig mechanism. + + Args: + model_path (pathlib.Path): The path from which to load the config. + model_type (Optional[str], optional): The type of the model, from the model implementation. Defaults to None. + model_config_name (Optional[str], optional): The name of configuration class for the model. Defaults to None. + module_path (Optional[str], optional): Python module path that can be used to load the + config/model. Defaults to None. + + Returns: + PretrainedConfig: The configuration object corresponding to the pretrained model. + """ + # load config first try autoconfig, if not then we register and load + + try: + conf = AutoConfig.from_pretrained(model_path) + except (KeyError, ValueError) as exc: # determine error raised by autoconfig + if model_type is None or model_config_name is None or module_path is None: + raise ValueError( + "model_type, model_config_name, and module_path should be specified." + ) from exc + + register_config(model_type, model_config_name, module_path) + conf = AutoConfig.from_pretrained(model_path) + + return conf + + +def _get_model_class( + config: PretrainedConfig, module_path: Optional[str] = None +) -> type: + """Helper to find model class based on config object + + First the module_path will be checked if it can be loaded in the current environment. If not + then the transformers library will be used. + + Args: + config (PretrainedConfig): HF configuration for the model. + module_path (Optional[str], optional): Python module path that can be used to load the + config/model. Defaults to None. + + Raises: + AttributeError: Raised if the module at module_path cannot be loaded. + AttributeError: If the architecture provided by the config cannot be loaded from + the module. + + Returns: + type: The class for the model. + """ + if module_path is not None: + try: + mod = importlib.import_module(module_path) + except ModuleNotFoundError as exc: + raise AttributeError("Could not load module '{module_path}'.") from exc + else: + mod = transformers + + # get architecture from model config + architectures = getattr(config, "architectures", []) + for arch in architectures: + try: + model_class = getattr(mod, arch) + return model_class + except AttributeError as exc: + # catch specific error import error or attribute error + raise AttributeError( + "Could not load model class for architecture '{arch}'." + ) from exc + + +def load_model( + model_path: Union[str, pathlib.Path], + config: Optional[PretrainedConfig] = None, + module_path: Optional[str] = None, +) -> PreTrainedModel: + """Load a pretrained model. + If module_path is provided, load the model using the provided module path. + + Args: + model_path (Union[str, pathlib.Path]): Path to a location where the model can be loaded. + config (Optional[PretrainedConfig], optional): HF Configuration object. Defaults to None. + module_path (Optional[str], optional): Python module path that can be used to load the + config/model. Defaults to None. + + Raises: + ValueError: Raised if loading from a module_path and a configuration object is not provided. + + Returns: + PreTrainedModel: The loaded pretrained model. + """ + + if module_path is not None and config is None: + raise ValueError( + "Config must be provided when loading from a custom module_path" + ) + + if config is not None: + model_class = _get_model_class(config, module_path=module_path) + LOGGER.info(f"Found model class: {model_class.__name__}") + model = model_class.from_pretrained(model_path, config=config) + return model + + model = AutoModel.from_pretrained(model_path) + LOGGER.info(f"Found model class: {model.__class__.__name__}") + return model diff --git a/services/inference/tsfmservices/config/default_config.yml b/services/inference/tsfmservices/config/default_config.yml new file mode 100644 index 00000000..0acedeaf --- /dev/null +++ b/services/inference/tsfmservices/config/default_config.yml @@ -0,0 +1,8 @@ +# custom models that will be supported +custom_modules: + - module_path: tsfm_public + model_type: tinytimemixer + model_config_name: TinyTimeMixerConfig + +# path where local models will be loaded from +model_dir: /Users/wmgifford/Downloads/ \ No newline at end of file diff --git a/services/inference/tsfmservices/inference/__init__.py b/services/inference/tsfmservices/inference/__init__.py new file mode 100644 index 00000000..e016c88e --- /dev/null +++ b/services/inference/tsfmservices/inference/__init__.py @@ -0,0 +1 @@ +from .inference import InferenceRuntime diff --git a/services/inference/tsfmservices/inference/inference.py b/services/inference/tsfmservices/inference/inference.py new file mode 100644 index 00000000..ad91e87c --- /dev/null +++ b/services/inference/tsfmservices/inference/inference.py @@ -0,0 +1,178 @@ +"""Inference Runtime""" + +import datetime +import logging +from typing import Any, Dict, List +from pathlib import Path + +import pandas as pd +from fastapi import APIRouter, HTTPException +from tsfm_public import TimeSeriesForecastingPipeline, TimeSeriesPreprocessor + +from ..common.constants import API_VERSION +from ..common.util import load_config, load_model, register_config +from .payloads import ForecastingInferenceInput, PredictOutput + +from tsfmservices import TSFM_ALLOW_LOAD_FROM_HF_HUB + + +LOGGER = logging.getLogger(__file__) + + +class InferenceRuntime: + + def __init__(self, config: Dict[str, Any] = {}): + + self.config = config + model_map = {} + + if "custom_modules" in config: + for custom_module in config["custom_modules"]: + register_config( + custom_module["model_type"], + custom_module["model_config_name"], + custom_module["module_path"], + ) + LOGGER.info(f"registered {custom_module['model_type']}") + + model_map[custom_module["model_config_name"]] = custom_module[ + "module_path" + ] + + self.model_to_module_map = model_map + + def add_routes(self, app): + + self.router = APIRouter(prefix=f"/{API_VERSION}/inference", tags=["inference"]) + self.router.add_api_route( + "/forecasting", + self.forecast, + methods=["POST"], + response_model=PredictOutput, + ) + app.include_router(self.router) + + def load(self, model_path: str): + + try: + preprocessor = TimeSeriesPreprocessor.from_pretrained(model_path) + LOGGER.info("Successfully loaded preprocessor") + except OSError: + preprocessor = None + LOGGER.info("No preprocessor found") + + # load config and model + conf = load_config( + model_path, + ) + + model = load_model( + model_path, + config=conf, + module_path=self.model_to_module_map.get(conf.__class__.__name__, None), + ) + LOGGER.info("Successfully loaded model") + return model, preprocessor + + def forecast(self, input: ForecastingInferenceInput): + try: + LOGGER.info("calling forecast_common") + answer = self._forecast_common(input) + LOGGER.info("done, returning.") + return answer + except Exception as e: + LOGGER.exception(e) + raise HTTPException(status_code=500, detail=repr(e)) + + def _forecast_common( + self, input_payload: ForecastingInferenceInput + ) -> PredictOutput: + + # we need some sort of model registry + # payload = input_payload.model_dump() # do we need? + + data = decode_data(input_payload.data, input_payload.metadata) + future_data = decode_data(input_payload.future_data, input_payload.metadata) + + model_path = Path(self.config["model_dir"]) / input_payload.model_id + + if not model_path.is_dir(): + LOGGER.info(f"Could not find model at path: {model_path}") + if TSFM_ALLOW_LOAD_FROM_HF_HUB: + model_path = input_payload.model_id + LOGGER.info(f"Using HuggingFace Hub: {model_path}") + else: + raise RuntimeError( + f"Could not load model {input_payload.model_id} from {self.config['model_dir']}. If trying to load directly from the HuggingFace Hub please ensure that `TSFM_ALLOW_LOAD_FROM_HF_HUB=1`" + ) + + model, preprocessor = self.load(model_path) + + preprocessor_params = input_payload.metadata.model_dump() + + # preprocess + if preprocessor is None: + preprocessor = TimeSeriesPreprocessor( + **preprocessor_params, + scaling=False, + encode_categorical=False, + ) + # we don't set context length or prediction length above because it is not needed for inference + + # train to estimate freq if not available + preprocessor.train(data) + + # warn if future data is not provided, but is needed by the model + if preprocessor.exogenous_channel_indices and future_data is None: + raise ValueError( + "Future data should be provided for exogenous columns where the future is known (`control_columns` and `observable_columns`)" + ) + + forecast_pipeline = TimeSeriesForecastingPipeline( + model=model, + explode_forecasts=True, + feature_extractor=preprocessor, + add_known_ground_truth=False, + ) + + # warn if future data is not provided, but is needed by the model + if preprocessor.exogenous_channel_indices and future_data is None: + raise ValueError( + "Future data should be provided for exogenous columns where the future is known (`control_columns` and `observable_columns`)" + ) + + # truncate data length when exploding + # context_length = model.config.context_length + # if explode_forecasts and len(data) > context_length: + # data = select_by_index( + # data, id_columns=input.id_columns, start_index=-context_length + # ) + + test_data = preprocessor.preprocess(data) + + if future_data is not None: + # future data needs some values for targets, but they are unused + # Eventually this will be part of the forecast pipeline. + future_data[input_payload.target_columns] = 0 + future_data = preprocessor.preprocess(future_data) + future_data.drop(columns=input_payload.target_columns) + + forecasts = forecast_pipeline( + test_data, future_time_series=future_data, inverse_scale_outputs=True + ) + + return PredictOutput( + model_id=input_payload.model_id, + created_at=datetime.datetime.now().isoformat(), + results=[forecasts.to_dict(orient="list")], + ) + + +def decode_data(data: Dict[str, List[Any]], metadata: Dict[str, Any]) -> pd.DataFrame: + if not data: + return None + + df = pd.DataFrame.from_dict(data) + if ts_col := metadata.timestamp_column: + df[ts_col] = pd.to_datetime(df[ts_col]) + return df diff --git a/services/inference/tsfmservices/inference/main.py b/services/inference/tsfmservices/inference/main.py new file mode 100644 index 00000000..3a31a1db --- /dev/null +++ b/services/inference/tsfmservices/inference/main.py @@ -0,0 +1,51 @@ +"""Primary entry point for inference services""" + +import logging +from fastapi import FastAPI +import yaml + +from tsfmservices import ( + TSFM_PYTHON_LOGGING_FORMAT, + TSFM_PYTHON_LOGGING_LEVEL, + TSFM_CONFIG_FILE, +) +from tsfmservices.common.constants import API_VERSION +from tsfmservices.inference import InferenceRuntime + +logging.basicConfig( + format=TSFM_PYTHON_LOGGING_FORMAT, + level=TSFM_PYTHON_LOGGING_LEVEL, +) + + +if TSFM_CONFIG_FILE: + with open(TSFM_CONFIG_FILE, "r") as file: + config = yaml.safe_load(file) +else: + config = {} + + +app = FastAPI( + title="FM for Time Series API", + version=API_VERSION, + description="This FastAPI application provides service endpoints for performing inference tasks on TSFM HF models.", +) +ir = InferenceRuntime(config=config) +ir.add_routes(app) + + +@app.get("/") +def root(): + # Endpoint at the root URL ("/") returns a welcome message with a clickable link + message = "Welcome. Go to /docs to access the API documentation." + return {"message": message} + + +if __name__ == "__main__": + + # Third Party + import uvicorn + + # Run the FastAPI application on the local host and port 7860 + # CMD["uvicorn", "app:app", "--host", "127.0.0.1", "--port", "7860", "--reload"] + uvicorn.run(app, host="127.0.0.1", port=8000) diff --git a/services/inference/tsfmservices/inference/payloads.py b/services/inference/tsfmservices/inference/payloads.py new file mode 100644 index 00000000..0ecb0661 --- /dev/null +++ b/services/inference/tsfmservices/inference/payloads.py @@ -0,0 +1,138 @@ +"""Payload definitions for inference""" + +# Standard +from typing import Annotated, Any, Dict, List, Optional + +# Third Party +from pydantic import BaseModel, ConfigDict, Field, model_validator + +# WARNING: DO NOT IMPORT util here or else you'll get a circular dependency + +EverythingPatternedString = Annotated[ + str, Field(min_length=0, max_length=100, pattern=".*") +] + + +class BaseMetadataInput(BaseModel): + model_config = ConfigDict(extra="forbid", protected_namespaces=()) + + timestamp_column: str = Field( + description="A valid column in the data that should be treated as the timestamp.", + pattern=".*", + min_length=1, + max_length=100, + example="date", + ) + id_columns: List[EverythingPatternedString] = Field( + description="Columns that define a unique key for time series.", + default_factory=list, + max_length=10, + example=["ID1", "ID2"], + min_length=0, + ) + freq: Optional[str] = Field( + description="""A freqency indicator for the given timestamp_column. + See https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#period-aliases for a description of the allowed values. + If not provided, we will attempt to infer it from the data.""", + default=None, + pattern="\d+[B|D|W|M|Q|Y|h|min|s|ms|us|ns]|^\s*$", + min_length=0, + max_length=100, + example="1h", + ) + + +class ForecastingMetadataInput(BaseMetadataInput): + target_columns: List[EverythingPatternedString] = Field( + default_factory=list, + max_length=500, + min_length=0, + example=["HUFL", "HULL"], + description="An array of column headings which constitute the target variables.", + ) + observable_columns: List[EverythingPatternedString] = Field( + default_factory=list, + max_length=500, + min_length=0, + example=["OBS1", "OBS2"], + description="An optional array of column headings which constitute the observable variables.", + ) + control_columns: List[EverythingPatternedString] = Field( + default_factory=list, + max_length=500, + min_length=0, + example=["CNTRL1", "CNTRL2"], + description="An optional array of column headings which constitute the control variables.", + ) + conditional_columns: List[EverythingPatternedString] = Field( + default_factory=list, + max_length=500, + min_length=0, + example=["CONDL1", "CONDL2"], + description="An optional array of column headings which constitute the conditional variables.", + ) + static_categorical_columns: List[EverythingPatternedString] = Field( + default_factory=list, + max_length=500, + min_length=0, + example=["SCV1", "SCV2"], + description="An optional array of column headings which constitute the static categorical variables.", + ) + + +class ForecastingParameters(BaseModel): + model_config = ConfigDict(extra="forbid", protected_namespaces=()) + + prediction_length: Optional[int] = Field( + description="The prediction length for the forecast.", + default=None, + ) + + +class BaseInferenceInput(BaseModel): + model_config = ConfigDict(extra="forbid", protected_namespaces=()) + + model_id: str = Field( + description="A model identifier.", + pattern="^\S+$", + min_length=1, + max_length=100, + example="ibm/tinytimemixer-monash-fl_96", + ) + + +class ForecastingInferenceInput(BaseInferenceInput): + metadata: ForecastingMetadataInput + + parameters: ForecastingParameters + + data: Dict[str, List[Any]] = Field( + description="Data", + ) + + future_data: Optional[Dict[str, List[Any]]] = Field( + description="Future data", default=None + ) + + @model_validator(mode="before") + @classmethod + def grpcswaparoos(cls, data: Any) -> Any: + if isinstance(data, dict): + if "future_data" in data and data["future_data"] is None: + data["future_data"] = "" + return data + + +class PredictOutput(BaseModel): + model_id: str = Field( + description="Model ID for the model that produced the prediction.", + default=None, + ) + created_at: str = Field( + description="Timestamp indicating when the prediction was created. ISO 8601 format.", + default=None, + ) + results: List[Dict[str, List[Any]]] = Field( + description="List of prediction results.", + default=None, + ) From 95cd3156c8f86bafc7377404d00f44c99f511978 Mon Sep 17 00:00:00 2001 From: Stuart Siegel Date: Wed, 31 Jul 2024 22:25:28 -0400 Subject: [PATCH 02/29] custom kserve runtime Co-Authored-By: Wesley Gifford <79663411+wgifford@users.noreply.github.com> --- services/inference/Dockerfile | 44 + services/inference/Makefile | 30 + services/inference/README.md | 76 + services/inference/poetry.lock | 2911 +++++++++++++++++ services/inference/pyproject.toml | 36 + services/inference/tests/test_inference.py | 153 + services/inference/tsfmservices/__init__.py | 47 + .../inference/tsfmservices/common/__init__.py | 0 .../tsfmservices/common/constants.py | 4 + .../tsfmservices/common/logging.conf | 35 + .../inference/tsfmservices/common/util.py | 162 + .../tsfmservices/config/default_config.yml | 8 + .../tsfmservices/inference/__init__.py | 1 + .../tsfmservices/inference/inference.py | 178 + .../inference/tsfmservices/inference/main.py | 51 + .../tsfmservices/inference/payloads.py | 138 + 16 files changed, 3874 insertions(+) create mode 100644 services/inference/Dockerfile create mode 100644 services/inference/Makefile create mode 100644 services/inference/README.md create mode 100644 services/inference/poetry.lock create mode 100644 services/inference/pyproject.toml create mode 100644 services/inference/tests/test_inference.py create mode 100644 services/inference/tsfmservices/__init__.py create mode 100644 services/inference/tsfmservices/common/__init__.py create mode 100644 services/inference/tsfmservices/common/constants.py create mode 100644 services/inference/tsfmservices/common/logging.conf create mode 100644 services/inference/tsfmservices/common/util.py create mode 100644 services/inference/tsfmservices/config/default_config.yml create mode 100644 services/inference/tsfmservices/inference/__init__.py create mode 100644 services/inference/tsfmservices/inference/inference.py create mode 100644 services/inference/tsfmservices/inference/main.py create mode 100644 services/inference/tsfmservices/inference/payloads.py diff --git a/services/inference/Dockerfile b/services/inference/Dockerfile new file mode 100644 index 00000000..0df00f8d --- /dev/null +++ b/services/inference/Dockerfile @@ -0,0 +1,44 @@ +# based on https://github.com/opendatahub-io/caikit-tgis-serving/blob/main/Dockerfile + +FROM registry.access.redhat.com/ubi9/ubi-minimal:latest as builder + +RUN microdnf -y update && \ + microdnf -y install \ + git shadow-utils python3.11-pip python-wheel && \ + pip3.11 install --no-cache-dir --upgrade pip wheel && \ + microdnf clean all + +ENV POETRY_VIRTUALENVS_IN_PROJECT=1 + +COPY tsfmservices* /tsfmservices/tsfmservices +COPY pyproject.toml /tsfmservices/ +COPY poetry.lock /tsfmservices/ +WORKDIR /tsfmservices +# RUN python3.11 -m venv /tsfmservices/.venv +#RUN source /tsfmservices/.venv/bin/activate && \ +# pip3.11 install --no-cache-dir ".[services]" +RUN pip3.11 install poetry && poetry install + +FROM registry.access.redhat.com/ubi9/ubi-minimal:latest as deploy +RUN microdnf -y update && \ + microdnf -y install \ + shadow-utils python3.11 && \ + microdnf clean all + +WORKDIR /tsfmservices + +COPY --from=builder /tsfmservices/ /tsfmservices/ + +ENV VIRTUAL_ENV=/tsfmservices/.venv +ENV PATH="$VIRTUAL_ENV/bin:$PATH" +ENV HF_HOME=/tmp + +RUN groupadd --system tsfmservices --gid 1001 && \ + adduser --system --uid 1001 --gid 0 --groups tsfmservices \ + --create-home --home-dir /tsfmservices --shell /sbin/nologin \ + --comment "TSFMServices User" tsfmservices + +USER tsfmservices + +# CMD ["python", "-m", "tsfmservices.inference.main"] +CMD ["python", "-m", "uvicorn","tsfmservices.inference.main:app", "--host", "0.0.0.0", "--port", "8000" ] \ No newline at end of file diff --git a/services/inference/Makefile b/services/inference/Makefile new file mode 100644 index 00000000..9d515e20 --- /dev/null +++ b/services/inference/Makefile @@ -0,0 +1,30 @@ +CONTAINER_BUILDER ?= docker + +# starts the inference service (used mainly for test cases) +start_service_local: + python -m tsfmservices.inference.main & + sleep 10 +stop_service_local: + pkill -f 'python.*tsfmservices.*' + sleep 10 + +image: + $(CONTAINER_BUILDER) build -t tsfmservices -f Dockerfile . + +start_service_image: image + $(CONTAINER_BUILDER) run -p 8000:8000 -d --rm --name tsfmserver tsfmservices + sleep 10 +stop_service_image: + $(CONTAINER_BUILDER) stop tsfmserver + +test_local: start_service_local + pytest tests + $(MAKE) stop_service_local + +test_image: start_service_image + pytest tests + $(MAKE) stop_service_image + + + + diff --git a/services/inference/README.md b/services/inference/README.md new file mode 100644 index 00000000..90683065 --- /dev/null +++ b/services/inference/README.md @@ -0,0 +1,76 @@ +# TSFM Services + +This component provides basic RESTful services for the IBM tsfm-granite +class of timeseries foundation models. At present it can serve the following models: + +* https://huggingface.co/ibm-granite/granite-timeseries-ttm-v1 +* https://huggingface.co/ibm-granite/granite-timeseries-patchtst +* https://huggingface.co/ibm-granite/granite-timeseries-patchtsmixer + + + +## Prerequisites: + +* GNU make +* python 3.10 or 3.11 +* poetry (`pip install poetry`) +* zsh or bash (zsh is preferred) + +_Note that our primary target environment for services deployment is x86_64 Linux. You may encounter hiccups if you try to use this on a different environment. If so, please +file an issue. Some of our developers do use a Mac so you're likely to find a quick resolution. None of our developers use native Windows, however._ + +## Known issues: + +* Use of pkill statements in Makefile may not work properly on Mac OS. This will be apparent if you have left over processs after running test related make targets. Please help us put OS-specific checks into our Makefile to handle these cases by filing a PR. + +### Installation + +```sh +pip install poetry && poetry install --with dev +``` + +### Testing using a local server instance + +```sh +make test_local +``` + +### Creating an image + +_You must have either docker or podman installed on your system for this to +work. You must also have proper permissions on your system to build images._ + +```sh +CONTAINER_BUILDER= make image +# e.g, CONTAINER_BUILDER=docker make image +``` + +After a successful build you should have a local image named `tsfmservices:latest` + +```sh +(py311) ➜ tsfm-services git:(revised-build-system) ✗ docker images | grep tsfmservices | head -n 1 +tsfmservices latest df592dcb0533 46 seconds ago 1.49GB +# some of the numeric and hash values on your machine could be different +``` + +### Testing using the built image + +```sh +make test_image +``` + +### Viewing the OpenAPI 3.x specification and swagger page + +```sh +make start_local_server +``` + +Then open your browser to http://127.0.0.1:8000/docs, click on the `/openapi.json` link at the top of this page. + +To stop the server run: + +```sh +# may not work properly on a Mac +# if not, kill the uvicorn process manually. +make stop_local_server +``` diff --git a/services/inference/poetry.lock b/services/inference/poetry.lock new 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+[tool.poetry] +name = "tsfmservices" +version = "0.0.1" +description = "Service layer for TSFM granite models." +authors = ["IBM"] +license = "https://github.com/ibm-granite/granite-tsfm/blob/main/LICENSE" +packages = [{ include = "tsfmservices/**/*.py" }] + +[tool.poetry.dependencies] +# including 3.9 causes poetry lock to run forever +python = ">=3.10,<3.12" + +numpy = { version = "<2" } +tsfm_public = { git = "https://github.com/IBM/tsfm.git", tag = "v0.2.5" } + +# trying to pick up cpu version for inference +# to make image smaller +torch = { version = ">2,<3", source = "pytorch" } + +fastapi = { version = "*" } +pydantic = { version = "*" } +uvicorn = { version = "*" } + +[[tool.poetry.source]] +name = "pytorch" +url = "https://download.pytorch.org/whl/cpu" +priority = "explicit" + +[tool.poetry.group.dev] +optional = true +[tool.poetry.group.dev.dependencies] +pytest = "*" + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/services/inference/tests/test_inference.py b/services/inference/tests/test_inference.py new file mode 100644 index 00000000..5fb8ed0c --- /dev/null +++ b/services/inference/tests/test_inference.py @@ -0,0 +1,153 @@ +# Standard +from datetime import datetime +from io import BytesIO +from typing import Any, Dict +import json + +# Third Party +import numpy as np +import pandas as pd +import pytest +import requests + +# First Party +from tsfm_public.toolkit.util import select_by_index +from tsfm_public import TinyTimeMixerForPrediction + +from transformers import PatchTSTForPrediction, PatchTSMixerForPrediction + + +@pytest.fixture(scope="module") +def ts_data(): + dataset_path = ( + "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh1.csv" + ) + + forecast_length = 96 + context_length = 512 + timestamp_column = "date" + + data = pd.read_csv( + dataset_path, + parse_dates=[timestamp_column], + ) + + test_data = select_by_index( + data, + start_index=12 * 30 * 24 + 4 * 30 * 24 - context_length * 5, + end_index=12 * 30 * 24 + 4 * 30 * 24, + ).reset_index(drop=True) + + test_data["id"] = np.array(["a", "b", "c", "d", "e"]).repeat(context_length) + + return test_data, { + "timestamp_column": timestamp_column, + "id_columns": ["id"], + "target_columns": ["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL", "OT"], + "prediction_length": forecast_length, + } + + +def get_inference_response( + msg: Dict[str, Any], +) -> pd.DataFrame: + URL = "http://127.0.0.1:8000/v1/inference/forecasting" + headers = {} + req = requests.post(URL, json=msg, headers=headers) + resp = req.json() + + df = [pd.DataFrame.from_dict(r) for r in resp["results"]] + return df + + +def encode_data(df: pd.DataFrame, timestamp_column: str) -> Dict[str, Any]: + df[timestamp_column] = df[timestamp_column].apply(lambda x: x.isoformat()) + data_payload = df.to_dict(orient="list") + return data_payload + + +def test_zero_shot_forecast_inference(ts_data): + test_data, params = ts_data + id_columns = params["id_columns"] + + prediction_length = 96 + num_ids = test_data[id_columns[0]].nunique() + + # test single + test_data_ = test_data[test_data[id_columns[0]] == "a"].copy() + + msg = { + "model_id": "ibm-granite/granite-timeseries-ttm-v1", + "parameters": { + # "prediction_length": params["prediction_length"], + }, + "metadata": { + "timestamp_column": params["timestamp_column"], + "id_columns": params["id_columns"], + "target_columns": params["target_columns"], + }, + "data": encode_data(test_data_, params["timestamp_column"]), + "future_data": {}, + } + + df_out = get_inference_response(msg) + assert len(df_out) == 1 + assert df_out[0].shape[0] == prediction_length + + # test multi-time series + test_data_ = test_data.copy() + + msg = { + "model_id": "ibm-granite/granite-timeseries-ttm-v1", + "parameters": { + # "prediction_length": params["prediction_length"], + }, + "metadata": { + "timestamp_column": params["timestamp_column"], + "id_columns": params["id_columns"], + "target_columns": params["target_columns"], + }, + "data": encode_data(test_data_, params["timestamp_column"]), + "future_data": {}, + } + + df_out = get_inference_response(msg) + + assert len(df_out) == 1 + assert df_out[0].shape[0] == prediction_length * num_ids + + +@pytest.mark.parametrize( + "model_path", + [ + "ibm-granite/granite-timeseries-patchtst", + "ibm-granite/granite-timeseries-patchtsmixer", + ], +) +def test_trained_model_inference(ts_data, model_path): + test_data, params = ts_data + id_columns = params["id_columns"] + + prediction_length = 96 + + # test single + test_data_ = test_data[test_data[id_columns[0]] == "a"].copy() + encoded_data = encode_data(test_data_, params["timestamp_column"]) + + msg = { + "model_id": model_path, + "parameters": { + # "prediction_length": params["prediction_length"], + }, + "metadata": { + "timestamp_column": params["timestamp_column"], + "id_columns": params["id_columns"], + "target_columns": params["target_columns"], + }, + "data": encoded_data, + "future_data": {}, + } + + df_out = get_inference_response(msg) + assert len(df_out) == 1 + assert df_out[0].shape[0] == prediction_length diff --git a/services/inference/tsfmservices/__init__.py b/services/inference/tsfmservices/__init__.py new file mode 100644 index 00000000..34dd794f --- /dev/null +++ b/services/inference/tsfmservices/__init__.py @@ -0,0 +1,47 @@ +import logging +import os + +TSFM_PYTHON_LOGGING_LEVEL = os.getenv("TSFM_PYTHON_LOGGING_LEVEL", "INFO") + +LevelNamesMapping = dict( + INFO=logging.INFO, + WARN=logging.WARN, + WARNING=logging.WARNING, + ERROR=logging.ERROR, + CRITICAL=logging.CRITICAL, + DEBUG=logging.DEBUG, + FATAL=logging.FATAL, +) + +TSFM_PYTHON_LOGGING_LEVEL = ( + logging.getLevelNamesMapping()[TSFM_PYTHON_LOGGING_LEVEL] + if hasattr(logging, "getLevelNamesMapping") + else LevelNamesMapping[TSFM_PYTHON_LOGGING_LEVEL] +) +TSFM_PYTHON_LOGGING_FORMAT = os.getenv( + "TSFM_PYTHON_LOGGING_FORMAT", + "%(levelname)s:p-%(process)d:t-%(thread)d:%(filename)s:%(funcName)s:%(message)s", +) + +# Do we have redis? + +try: + # Third Party + import redis + import rq + + HAVE_REDIS_BACKEND = True +except ImportError: + HAVE_REDIS_BACKEND = False + +TSFM_USE_KFTO_ASYNC_BACKEND = int(os.getenv("TSFM_USE_KFTO_ASYNC_BACKEND", "0")) == 1 +USE_REDIS_ASYNC_BACKEND = HAVE_REDIS_BACKEND and not TSFM_USE_KFTO_ASYNC_BACKEND + +TSFM_ALLOW_LOAD_FROM_HF_HUB = int(os.getenv("TSFM_ALLOW_LOAD_FROM_HF_HUB", "1")) == 1 + +TSFM_CONFIG_FILE = os.getenv( + "TSFM_CONFIG_FILE", + os.path.realpath( + os.path.join(os.path.dirname(__file__), "config", "default_config.yml") + ), +) diff --git a/services/inference/tsfmservices/common/__init__.py b/services/inference/tsfmservices/common/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/services/inference/tsfmservices/common/constants.py b/services/inference/tsfmservices/common/constants.py new file mode 100644 index 00000000..15f42cd3 --- /dev/null +++ b/services/inference/tsfmservices/common/constants.py @@ -0,0 +1,4 @@ +"""Holds constant values used across modules.""" + +# What will appear in our paths for fast API version +API_VERSION = "v1" diff --git a/services/inference/tsfmservices/common/logging.conf b/services/inference/tsfmservices/common/logging.conf new file mode 100644 index 00000000..2e2b37e9 --- /dev/null +++ b/services/inference/tsfmservices/common/logging.conf @@ -0,0 +1,35 @@ +[loggers] +keys=root,file + +[handlers] +keys=console,file + +[formatters] +keys=console,file + +[logger_root] +level=DEBUG +handlers=console,file + +[logger_file] +level=DEBUG +handlers=file +qualname=file + +[handler_console] +class=StreamHandler +level=DEBUG +formatter=console +args=(sys.stdout,) + +[handler_file] +class=FileHandler +level=DEBUG +formatter=file +args=('server.log','w',) + +[formatter_console] +format=%(asctime)s - %(levelname)s - %(name)s - %(message)s + +[formatter_file] +format=%(asctime)s - %(levelname)s - %(name)s - %(funcName)s:%(lineno)d - %(message)s diff --git a/services/inference/tsfmservices/common/util.py b/services/inference/tsfmservices/common/util.py new file mode 100644 index 00000000..bcb551ec --- /dev/null +++ b/services/inference/tsfmservices/common/util.py @@ -0,0 +1,162 @@ +"""Utilities to support custom time series models""" + +# Standard +import importlib +import logging +import pathlib +from typing import Optional, Union + +import transformers + +# Third Party +from transformers import AutoConfig, AutoModel, PretrainedConfig, PreTrainedModel + +LOGGER = logging.getLogger(__file__) + + +def register_config(model_type: str, model_config_name: str, module_path: str) -> None: + """Register a configuration for a particular model architecture + + Args: + model_type (Optional[str], optional): The type of the model, from the model implementation. Defaults to None. + model_config_name (Optional[str], optional): The name of configuration class for the model. Defaults to None. + module_path (Optional[str], optional): Python module path that can be used to load the + config/model. Defaults to None. + + Raises: + RuntimeError: Raised when the module cannot be imported from the provided module path. + """ + # example + # model_type: "tinytimemixer" + # model_config_name: "TinyTimeMixerConfig" + # module_path: "tsfm" # place where config should be importable + + # AutoConfig.register("tinytimemixer", TinyTimeMixerConfig) + try: + mod = importlib.import_module(module_path) + conf_class = getattr(mod, model_config_name, None) + except ModuleNotFoundError as exc: # modulenot found, key error ? + raise RuntimeError( + f"Could not load {model_config_name} from {module_path}" + ) from exc + + if conf_class is not None: + AutoConfig.register(model_type, conf_class) + else: + # issue warning? + pass + + +def load_config( + model_path: Union[str, pathlib.Path], + model_type: Optional[str] = None, + model_config_name: Optional[str] = None, + module_path: Optional[str] = None, +) -> PretrainedConfig: + """Load configuration + + Attempts to load the configuration, if it is not loadable, then we register it with the AutoConfig mechanism. + + Args: + model_path (pathlib.Path): The path from which to load the config. + model_type (Optional[str], optional): The type of the model, from the model implementation. Defaults to None. + model_config_name (Optional[str], optional): The name of configuration class for the model. Defaults to None. + module_path (Optional[str], optional): Python module path that can be used to load the + config/model. Defaults to None. + + Returns: + PretrainedConfig: The configuration object corresponding to the pretrained model. + """ + # load config first try autoconfig, if not then we register and load + + try: + conf = AutoConfig.from_pretrained(model_path) + except (KeyError, ValueError) as exc: # determine error raised by autoconfig + if model_type is None or model_config_name is None or module_path is None: + raise ValueError( + "model_type, model_config_name, and module_path should be specified." + ) from exc + + register_config(model_type, model_config_name, module_path) + conf = AutoConfig.from_pretrained(model_path) + + return conf + + +def _get_model_class( + config: PretrainedConfig, module_path: Optional[str] = None +) -> type: + """Helper to find model class based on config object + + First the module_path will be checked if it can be loaded in the current environment. If not + then the transformers library will be used. + + Args: + config (PretrainedConfig): HF configuration for the model. + module_path (Optional[str], optional): Python module path that can be used to load the + config/model. Defaults to None. + + Raises: + AttributeError: Raised if the module at module_path cannot be loaded. + AttributeError: If the architecture provided by the config cannot be loaded from + the module. + + Returns: + type: The class for the model. + """ + if module_path is not None: + try: + mod = importlib.import_module(module_path) + except ModuleNotFoundError as exc: + raise AttributeError("Could not load module '{module_path}'.") from exc + else: + mod = transformers + + # get architecture from model config + architectures = getattr(config, "architectures", []) + for arch in architectures: + try: + model_class = getattr(mod, arch) + return model_class + except AttributeError as exc: + # catch specific error import error or attribute error + raise AttributeError( + "Could not load model class for architecture '{arch}'." + ) from exc + + +def load_model( + model_path: Union[str, pathlib.Path], + config: Optional[PretrainedConfig] = None, + module_path: Optional[str] = None, +) -> PreTrainedModel: + """Load a pretrained model. + If module_path is provided, load the model using the provided module path. + + Args: + model_path (Union[str, pathlib.Path]): Path to a location where the model can be loaded. + config (Optional[PretrainedConfig], optional): HF Configuration object. Defaults to None. + module_path (Optional[str], optional): Python module path that can be used to load the + config/model. Defaults to None. + + Raises: + ValueError: Raised if loading from a module_path and a configuration object is not provided. + + Returns: + PreTrainedModel: The loaded pretrained model. + """ + + if module_path is not None and config is None: + raise ValueError( + "Config must be provided when loading from a custom module_path" + ) + + if config is not None: + model_class = _get_model_class(config, module_path=module_path) + LOGGER.info(f"Found model class: {model_class.__name__}") + model = model_class.from_pretrained(model_path, config=config) + return model + + model = AutoModel.from_pretrained(model_path) + LOGGER.info(f"Found model class: {model.__class__.__name__}") + return model diff --git a/services/inference/tsfmservices/config/default_config.yml b/services/inference/tsfmservices/config/default_config.yml new file mode 100644 index 00000000..0acedeaf --- /dev/null +++ b/services/inference/tsfmservices/config/default_config.yml @@ -0,0 +1,8 @@ +# custom models that will be supported +custom_modules: + - module_path: tsfm_public + model_type: tinytimemixer + model_config_name: TinyTimeMixerConfig + +# path where local models will be loaded from +model_dir: /Users/wmgifford/Downloads/ \ No newline at end of file diff --git a/services/inference/tsfmservices/inference/__init__.py b/services/inference/tsfmservices/inference/__init__.py new file mode 100644 index 00000000..e016c88e --- /dev/null +++ b/services/inference/tsfmservices/inference/__init__.py @@ -0,0 +1 @@ +from .inference import InferenceRuntime diff --git a/services/inference/tsfmservices/inference/inference.py b/services/inference/tsfmservices/inference/inference.py new file mode 100644 index 00000000..ad91e87c --- /dev/null +++ b/services/inference/tsfmservices/inference/inference.py @@ -0,0 +1,178 @@ +"""Inference Runtime""" + +import datetime +import logging +from typing import Any, Dict, List +from pathlib import Path + +import pandas as pd +from fastapi import APIRouter, HTTPException +from tsfm_public import TimeSeriesForecastingPipeline, TimeSeriesPreprocessor + +from ..common.constants import API_VERSION +from ..common.util import load_config, load_model, register_config +from .payloads import ForecastingInferenceInput, PredictOutput + +from tsfmservices import TSFM_ALLOW_LOAD_FROM_HF_HUB + + +LOGGER = logging.getLogger(__file__) + + +class InferenceRuntime: + + def __init__(self, config: Dict[str, Any] = {}): + + self.config = config + model_map = {} + + if "custom_modules" in config: + for custom_module in config["custom_modules"]: + register_config( + custom_module["model_type"], + custom_module["model_config_name"], + custom_module["module_path"], + ) + LOGGER.info(f"registered {custom_module['model_type']}") + + model_map[custom_module["model_config_name"]] = custom_module[ + "module_path" + ] + + self.model_to_module_map = model_map + + def add_routes(self, app): + + self.router = APIRouter(prefix=f"/{API_VERSION}/inference", tags=["inference"]) + self.router.add_api_route( + "/forecasting", + self.forecast, + methods=["POST"], + response_model=PredictOutput, + ) + app.include_router(self.router) + + def load(self, model_path: str): + + try: + preprocessor = TimeSeriesPreprocessor.from_pretrained(model_path) + LOGGER.info("Successfully loaded preprocessor") + except OSError: + preprocessor = None + LOGGER.info("No preprocessor found") + + # load config and model + conf = load_config( + model_path, + ) + + model = load_model( + model_path, + config=conf, + module_path=self.model_to_module_map.get(conf.__class__.__name__, None), + ) + LOGGER.info("Successfully loaded model") + return model, preprocessor + + def forecast(self, input: ForecastingInferenceInput): + try: + LOGGER.info("calling forecast_common") + answer = self._forecast_common(input) + LOGGER.info("done, returning.") + return answer + except Exception as e: + LOGGER.exception(e) + raise HTTPException(status_code=500, detail=repr(e)) + + def _forecast_common( + self, input_payload: ForecastingInferenceInput + ) -> PredictOutput: + + # we need some sort of model registry + # payload = input_payload.model_dump() # do we need? + + data = decode_data(input_payload.data, input_payload.metadata) + future_data = decode_data(input_payload.future_data, input_payload.metadata) + + model_path = Path(self.config["model_dir"]) / input_payload.model_id + + if not model_path.is_dir(): + LOGGER.info(f"Could not find model at path: {model_path}") + if TSFM_ALLOW_LOAD_FROM_HF_HUB: + model_path = input_payload.model_id + LOGGER.info(f"Using HuggingFace Hub: {model_path}") + else: + raise RuntimeError( + f"Could not load model {input_payload.model_id} from {self.config['model_dir']}. If trying to load directly from the HuggingFace Hub please ensure that `TSFM_ALLOW_LOAD_FROM_HF_HUB=1`" + ) + + model, preprocessor = self.load(model_path) + + preprocessor_params = input_payload.metadata.model_dump() + + # preprocess + if preprocessor is None: + preprocessor = TimeSeriesPreprocessor( + **preprocessor_params, + scaling=False, + encode_categorical=False, + ) + # we don't set context length or prediction length above because it is not needed for inference + + # train to estimate freq if not available + preprocessor.train(data) + + # warn if future data is not provided, but is needed by the model + if preprocessor.exogenous_channel_indices and future_data is None: + raise ValueError( + "Future data should be provided for exogenous columns where the future is known (`control_columns` and `observable_columns`)" + ) + + forecast_pipeline = TimeSeriesForecastingPipeline( + model=model, + explode_forecasts=True, + feature_extractor=preprocessor, + add_known_ground_truth=False, + ) + + # warn if future data is not provided, but is needed by the model + if preprocessor.exogenous_channel_indices and future_data is None: + raise ValueError( + "Future data should be provided for exogenous columns where the future is known (`control_columns` and `observable_columns`)" + ) + + # truncate data length when exploding + # context_length = model.config.context_length + # if explode_forecasts and len(data) > context_length: + # data = select_by_index( + # data, id_columns=input.id_columns, start_index=-context_length + # ) + + test_data = preprocessor.preprocess(data) + + if future_data is not None: + # future data needs some values for targets, but they are unused + # Eventually this will be part of the forecast pipeline. + future_data[input_payload.target_columns] = 0 + future_data = preprocessor.preprocess(future_data) + future_data.drop(columns=input_payload.target_columns) + + forecasts = forecast_pipeline( + test_data, future_time_series=future_data, inverse_scale_outputs=True + ) + + return PredictOutput( + model_id=input_payload.model_id, + created_at=datetime.datetime.now().isoformat(), + results=[forecasts.to_dict(orient="list")], + ) + + +def decode_data(data: Dict[str, List[Any]], metadata: Dict[str, Any]) -> pd.DataFrame: + if not data: + return None + + df = pd.DataFrame.from_dict(data) + if ts_col := metadata.timestamp_column: + df[ts_col] = pd.to_datetime(df[ts_col]) + return df diff --git a/services/inference/tsfmservices/inference/main.py b/services/inference/tsfmservices/inference/main.py new file mode 100644 index 00000000..3a31a1db --- /dev/null +++ b/services/inference/tsfmservices/inference/main.py @@ -0,0 +1,51 @@ +"""Primary entry point for inference services""" + +import logging +from fastapi import FastAPI +import yaml + +from tsfmservices import ( + TSFM_PYTHON_LOGGING_FORMAT, + TSFM_PYTHON_LOGGING_LEVEL, + TSFM_CONFIG_FILE, +) +from tsfmservices.common.constants import API_VERSION +from tsfmservices.inference import InferenceRuntime + +logging.basicConfig( + format=TSFM_PYTHON_LOGGING_FORMAT, + level=TSFM_PYTHON_LOGGING_LEVEL, +) + + +if TSFM_CONFIG_FILE: + with open(TSFM_CONFIG_FILE, "r") as file: + config = yaml.safe_load(file) +else: + config = {} + + +app = FastAPI( + title="FM for Time Series API", + version=API_VERSION, + description="This FastAPI application provides service endpoints for performing inference tasks on TSFM HF models.", +) +ir = InferenceRuntime(config=config) +ir.add_routes(app) + + +@app.get("/") +def root(): + # Endpoint at the root URL ("/") returns a welcome message with a clickable link + message = "Welcome. Go to /docs to access the API documentation." + return {"message": message} + + +if __name__ == "__main__": + + # Third Party + import uvicorn + + # Run the FastAPI application on the local host and port 7860 + # CMD["uvicorn", "app:app", "--host", "127.0.0.1", "--port", "7860", "--reload"] + uvicorn.run(app, host="127.0.0.1", port=8000) diff --git a/services/inference/tsfmservices/inference/payloads.py b/services/inference/tsfmservices/inference/payloads.py new file mode 100644 index 00000000..0ecb0661 --- /dev/null +++ b/services/inference/tsfmservices/inference/payloads.py @@ -0,0 +1,138 @@ +"""Payload definitions for inference""" + +# Standard +from typing import Annotated, Any, Dict, List, Optional + +# Third Party +from pydantic import BaseModel, ConfigDict, Field, model_validator + +# WARNING: DO NOT IMPORT util here or else you'll get a circular dependency + +EverythingPatternedString = Annotated[ + str, Field(min_length=0, max_length=100, pattern=".*") +] + + +class BaseMetadataInput(BaseModel): + model_config = ConfigDict(extra="forbid", protected_namespaces=()) + + timestamp_column: str = Field( + description="A valid column in the data that should be treated as the timestamp.", + pattern=".*", + min_length=1, + max_length=100, + example="date", + ) + id_columns: List[EverythingPatternedString] = Field( + description="Columns that define a unique key for time series.", + default_factory=list, + max_length=10, + example=["ID1", "ID2"], + min_length=0, + ) + freq: Optional[str] = Field( + description="""A freqency indicator for the given timestamp_column. + See https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#period-aliases for a description of the allowed values. + If not provided, we will attempt to infer it from the data.""", + default=None, + pattern="\d+[B|D|W|M|Q|Y|h|min|s|ms|us|ns]|^\s*$", + min_length=0, + max_length=100, + example="1h", + ) + + +class ForecastingMetadataInput(BaseMetadataInput): + target_columns: List[EverythingPatternedString] = Field( + default_factory=list, + max_length=500, + min_length=0, + example=["HUFL", "HULL"], + description="An array of column headings which constitute the target variables.", + ) + observable_columns: List[EverythingPatternedString] = Field( + default_factory=list, + max_length=500, + min_length=0, + example=["OBS1", "OBS2"], + description="An optional array of column headings which constitute the observable variables.", + ) + control_columns: List[EverythingPatternedString] = Field( + default_factory=list, + max_length=500, + min_length=0, + example=["CNTRL1", "CNTRL2"], + description="An optional array of column headings which constitute the control variables.", + ) + conditional_columns: List[EverythingPatternedString] = Field( + default_factory=list, + max_length=500, + min_length=0, + example=["CONDL1", "CONDL2"], + description="An optional array of column headings which constitute the conditional variables.", + ) + static_categorical_columns: List[EverythingPatternedString] = Field( + default_factory=list, + max_length=500, + min_length=0, + example=["SCV1", "SCV2"], + description="An optional array of column headings which constitute the static categorical variables.", + ) + + +class ForecastingParameters(BaseModel): + model_config = ConfigDict(extra="forbid", protected_namespaces=()) + + prediction_length: Optional[int] = Field( + description="The prediction length for the forecast.", + default=None, + ) + + +class BaseInferenceInput(BaseModel): + model_config = ConfigDict(extra="forbid", protected_namespaces=()) + + model_id: str = Field( + description="A model identifier.", + pattern="^\S+$", + min_length=1, + max_length=100, + example="ibm/tinytimemixer-monash-fl_96", + ) + + +class ForecastingInferenceInput(BaseInferenceInput): + metadata: ForecastingMetadataInput + + parameters: ForecastingParameters + + data: Dict[str, List[Any]] = Field( + description="Data", + ) + + future_data: Optional[Dict[str, List[Any]]] = Field( + description="Future data", default=None + ) + + @model_validator(mode="before") + @classmethod + def grpcswaparoos(cls, data: Any) -> Any: + if isinstance(data, dict): + if "future_data" in data and data["future_data"] is None: + data["future_data"] = "" + return data + + +class PredictOutput(BaseModel): + model_id: str = Field( + description="Model ID for the model that produced the prediction.", + default=None, + ) + created_at: str = Field( + description="Timestamp indicating when the prediction was created. ISO 8601 format.", + default=None, + ) + results: List[Dict[str, List[Any]]] = Field( + description="List of prediction results.", + default=None, + ) From b7cd6816feda0ac83d8ef14fa3269024a6712fe2 Mon Sep 17 00:00:00 2001 From: "Stuart A. Siegel" Date: Thu, 1 Aug 2024 16:53:18 -0400 Subject: [PATCH 03/29] avoid a big context --- services/inference/.dockerignore | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 services/inference/.dockerignore diff --git a/services/inference/.dockerignore b/services/inference/.dockerignore new file mode 100644 index 00000000..ed4bba40 --- /dev/null +++ b/services/inference/.dockerignore @@ -0,0 +1,5 @@ +** +!tsfmservices +!poetry.lock +!pyproject.toml + From 56936a76901df0d0400fb2c8c6921c502b668b3f Mon Sep 17 00:00:00 2001 From: "Stuart A. Siegel" Date: Thu, 1 Aug 2024 16:53:32 -0400 Subject: [PATCH 04/29] casing..again --- services/inference/Dockerfile | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/services/inference/Dockerfile b/services/inference/Dockerfile index 0df00f8d..7a7f51d6 100644 --- a/services/inference/Dockerfile +++ b/services/inference/Dockerfile @@ -1,6 +1,6 @@ # based on https://github.com/opendatahub-io/caikit-tgis-serving/blob/main/Dockerfile -FROM registry.access.redhat.com/ubi9/ubi-minimal:latest as builder +FROM registry.access.redhat.com/ubi9/ubi-minimal:latest AS builder RUN microdnf -y update && \ microdnf -y install \ @@ -14,12 +14,9 @@ COPY tsfmservices* /tsfmservices/tsfmservices COPY pyproject.toml /tsfmservices/ COPY poetry.lock /tsfmservices/ WORKDIR /tsfmservices -# RUN python3.11 -m venv /tsfmservices/.venv -#RUN source /tsfmservices/.venv/bin/activate && \ -# pip3.11 install --no-cache-dir ".[services]" RUN pip3.11 install poetry && poetry install -FROM registry.access.redhat.com/ubi9/ubi-minimal:latest as deploy +FROM registry.access.redhat.com/ubi9/ubi-minimal:latest AS deploy RUN microdnf -y update && \ microdnf -y install \ shadow-utils python3.11 && \ From 385f26456341b81ceab1f505b96f3df22b162198 Mon Sep 17 00:00:00 2001 From: "Stuart A. Siegel" Date: Thu, 1 Aug 2024 16:59:10 -0400 Subject: [PATCH 05/29] limit checks to top level tests for now --- .github/workflows/python-package.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 6dfaed6d..71e0952d 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -36,4 +36,4 @@ jobs: make quality - name: Test with pytest run: | - pytest + pytest tests From 984fbfa4864e28f6df1c36ac060d6bc098687ec5 Mon Sep 17 00:00:00 2001 From: "Stuart A. Siegel" Date: Thu, 1 Aug 2024 17:51:05 -0400 Subject: [PATCH 06/29] first cut at runnign services test --- .github/workflows/services-test.yml | 43 +++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) create mode 100644 .github/workflows/services-test.yml diff --git a/.github/workflows/services-test.yml b/.github/workflows/services-test.yml new file mode 100644 index 00000000..a708e215 --- /dev/null +++ b/.github/workflows/services-test.yml @@ -0,0 +1,43 @@ +# This workflow will install Python dependencies, run tests and lint with a variety of Python versions +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: Python package + +on: + push: + branches: [ "main" ] + pull_request: + branches: [ "main" ] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.10", "3.11"] + + steps: + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v3 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + + python -m pip install --upgrade pip + pip install poetry + # if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + cd services/inference && poetry install -n --with dev + - name: Ruff quality checks + run: | + # use hf-style check + make quality + - name: Test with pytest + run: | + make start_service_local + sleep 20 + pytest tests + make stop_service_local || true From a5e896bbb93dcb2f5cf431c2b8538074db56b172 Mon Sep 17 00:00:00 2001 From: "Stuart A. Siegel" Date: Thu, 1 Aug 2024 18:00:44 -0400 Subject: [PATCH 07/29] skip ruff checks --- .github/workflows/services-test.yml | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/.github/workflows/services-test.yml b/.github/workflows/services-test.yml index a708e215..00c60591 100644 --- a/.github/workflows/services-test.yml +++ b/.github/workflows/services-test.yml @@ -25,16 +25,11 @@ jobs: with: python-version: ${{ matrix.python-version }} - name: Install dependencies - run: | - + run: | python -m pip install --upgrade pip pip install poetry # if [ -f requirements.txt ]; then pip install -r requirements.txt; fi cd services/inference && poetry install -n --with dev - - name: Ruff quality checks - run: | - # use hf-style check - make quality - name: Test with pytest run: | make start_service_local From 0ae0d612c4c119ee3c19b678f625397771421932 Mon Sep 17 00:00:00 2001 From: "Stuart A. Siegel" Date: Thu, 1 Aug 2024 18:03:38 -0400 Subject: [PATCH 08/29] cd to correct directory --- .github/workflows/services-test.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/services-test.yml b/.github/workflows/services-test.yml index 00c60591..febe0486 100644 --- a/.github/workflows/services-test.yml +++ b/.github/workflows/services-test.yml @@ -32,6 +32,7 @@ jobs: cd services/inference && poetry install -n --with dev - name: Test with pytest run: | + cd services/inference make start_service_local sleep 20 pytest tests From bc22ac184b7c21ad8fb141353a32c4e11864a2c9 Mon Sep 17 00:00:00 2001 From: "Stuart A. Siegel" Date: Thu, 1 Aug 2024 18:15:48 -0400 Subject: [PATCH 09/29] are installs in one phase sticky in the next? --- .github/workflows/services-test.yml | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/.github/workflows/services-test.yml b/.github/workflows/services-test.yml index febe0486..be060b9b 100644 --- a/.github/workflows/services-test.yml +++ b/.github/workflows/services-test.yml @@ -24,15 +24,12 @@ jobs: uses: actions/setup-python@v3 with: python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install poetry - # if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - cd services/inference && poetry install -n --with dev - name: Test with pytest run: | + python -m pip install --upgrade pip + pip install poetry cd services/inference + poetry install -n --with dev make start_service_local sleep 20 pytest tests From d659f9b803ca614fbee79fdc973395304de17c08 Mon Sep 17 00:00:00 2001 From: "Stuart A. Siegel" Date: Thu, 1 Aug 2024 18:40:26 -0400 Subject: [PATCH 10/29] try addign explcity pandas dep --- services/inference/poetry.lock | 11 ++++++----- services/inference/pyproject.toml | 2 +- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/services/inference/poetry.lock b/services/inference/poetry.lock index a5669241..64585cb7 100644 --- a/services/inference/poetry.lock +++ b/services/inference/poetry.lock @@ -1579,6 +1579,7 @@ files = [ {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"}, {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"}, {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"}, + {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"}, {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"}, {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"}, {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"}, @@ -2300,12 +2301,12 @@ transformers = {version = ">=4.38.0", extras = ["torch"]} urllib3 = ">=1.26.19,<2" [package.extras] -all = ["tsfm_public[dev,notebooks,testing]"] -demos = ["altair (<5)", "kaleido", "plotly", "streamlit", "streamlit-aggrid", "toml", "tsfm_public[notebooks]"] -dev = ["pre-commit", "ruff (==0.4.4)", "tsfm_public[testing]"] +all = ["tsfm-public[dev,notebooks,testing]"] +demos = ["altair (<5)", "kaleido", "plotly", "streamlit", "streamlit-aggrid", "toml", "tsfm-public[notebooks]"] +dev = ["pre-commit", "ruff (==0.4.4)", "tsfm-public[testing]"] notebooks = ["datasets", "ipywidgets", "jupyter", "kaleido", "matplotlib", "plotly", "tensorboard"] ogv = ["datasets", "kaleido", "plotly"] -testing = ["parameterized", "pytest", "tsfm_public[notebooks]"] +testing = ["parameterized", "pytest", "tsfm-public[notebooks]"] [package.source] type = "git" @@ -2908,4 +2909,4 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.12" -content-hash = "0f7e44d159ca16e55b8ecba08a8b7eebff2cdf6ee010916bfbaffa1eb73b9632" +content-hash = "8e79adf80697d914193040e84e32661f7b669c6b8581bd17291f00a04b1af1cc" diff --git a/services/inference/pyproject.toml b/services/inference/pyproject.toml index 25068394..fe0b22bb 100644 --- a/services/inference/pyproject.toml +++ b/services/inference/pyproject.toml @@ -9,7 +9,7 @@ packages = [{ include = "tsfmservices/**/*.py" }] [tool.poetry.dependencies] # including 3.9 causes poetry lock to run forever python = ">=3.10,<3.12" - +pandas = "*" numpy = { version = "<2" } tsfm_public = { git = "https://github.com/IBM/tsfm.git", tag = "v0.2.5" } From 7b8b433475211d284669cfe3a6a7dc84803b000e Mon Sep 17 00:00:00 2001 From: Stuart Siegel Date: Thu, 1 Aug 2024 21:38:20 -0400 Subject: [PATCH 11/29] try a venv --- .github/workflows/services-test.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/services-test.yml b/.github/workflows/services-test.yml index be060b9b..db07a1a0 100644 --- a/.github/workflows/services-test.yml +++ b/.github/workflows/services-test.yml @@ -27,6 +27,8 @@ jobs: - name: Test with pytest run: | python -m pip install --upgrade pip + python -m venv .venv + source .venv/bin/activate pip install poetry cd services/inference poetry install -n --with dev From e8d4aca608a95e497c6c4a285a382871c56768d0 Mon Sep 17 00:00:00 2001 From: Stuart Siegel Date: Thu, 1 Aug 2024 21:38:38 -0400 Subject: [PATCH 12/29] should not need pandas to be managed here --- services/inference/poetry.lock | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/services/inference/poetry.lock b/services/inference/poetry.lock index 64585cb7..a5669241 100644 --- a/services/inference/poetry.lock +++ b/services/inference/poetry.lock @@ -1579,7 +1579,6 @@ files = [ {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"}, {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"}, {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"}, - {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"}, {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"}, {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"}, {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"}, @@ -2301,12 +2300,12 @@ transformers = {version = ">=4.38.0", extras = ["torch"]} urllib3 = ">=1.26.19,<2" [package.extras] -all = ["tsfm-public[dev,notebooks,testing]"] -demos = ["altair (<5)", "kaleido", "plotly", "streamlit", "streamlit-aggrid", "toml", "tsfm-public[notebooks]"] -dev = ["pre-commit", "ruff (==0.4.4)", "tsfm-public[testing]"] +all = ["tsfm_public[dev,notebooks,testing]"] +demos = ["altair (<5)", "kaleido", "plotly", "streamlit", "streamlit-aggrid", "toml", "tsfm_public[notebooks]"] +dev = ["pre-commit", "ruff (==0.4.4)", "tsfm_public[testing]"] notebooks = ["datasets", "ipywidgets", "jupyter", "kaleido", "matplotlib", "plotly", "tensorboard"] ogv = ["datasets", "kaleido", "plotly"] -testing = ["parameterized", "pytest", "tsfm-public[notebooks]"] +testing = ["parameterized", "pytest", "tsfm_public[notebooks]"] [package.source] type = "git" @@ -2909,4 +2908,4 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.12" -content-hash = "8e79adf80697d914193040e84e32661f7b669c6b8581bd17291f00a04b1af1cc" +content-hash = "0f7e44d159ca16e55b8ecba08a8b7eebff2cdf6ee010916bfbaffa1eb73b9632" From f5a5aabe4824705f8cf1baa2e44c1cbb3745ea4a Mon Sep 17 00:00:00 2001 From: Stuart Siegel Date: Thu, 1 Aug 2024 21:49:22 -0400 Subject: [PATCH 13/29] move install into separate stage --- .github/workflows/services-test.yml | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/.github/workflows/services-test.yml b/.github/workflows/services-test.yml index db07a1a0..7b630c63 100644 --- a/.github/workflows/services-test.yml +++ b/.github/workflows/services-test.yml @@ -24,14 +24,17 @@ jobs: uses: actions/setup-python@v3 with: python-version: ${{ matrix.python-version }} - - name: Test with pytest + - name: Install deps into a virtual enviroment run: | - python -m pip install --upgrade pip python -m venv .venv source .venv/bin/activate + python -m pip install --upgrade pip pip install poetry cd services/inference poetry install -n --with dev + - name: Test local server inference service with pytest + run: | + source .venv/bin/activate make start_service_local sleep 20 pytest tests From 7ec14b8586bcfd926e9840ee86cdd2260f707205 Mon Sep 17 00:00:00 2001 From: Stuart Siegel Date: Thu, 1 Aug 2024 21:50:09 -0400 Subject: [PATCH 14/29] assign unique name --- .github/workflows/services-test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/services-test.yml b/.github/workflows/services-test.yml index 7b630c63..9abf02d3 100644 --- a/.github/workflows/services-test.yml +++ b/.github/workflows/services-test.yml @@ -1,7 +1,7 @@ # This workflow will install Python dependencies, run tests and lint with a variety of Python versions # For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python -name: Python package +name: Service Tests on: push: From c2a29c21c1585053b7231364a7f78b7126c568dd Mon Sep 17 00:00:00 2001 From: Stuart Siegel Date: Thu, 1 Aug 2024 21:54:02 -0400 Subject: [PATCH 15/29] :bug: --- .github/workflows/services-test.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/services-test.yml b/.github/workflows/services-test.yml index 9abf02d3..d3cd3c4f 100644 --- a/.github/workflows/services-test.yml +++ b/.github/workflows/services-test.yml @@ -35,6 +35,7 @@ jobs: - name: Test local server inference service with pytest run: | source .venv/bin/activate + cd services/inference make start_service_local sleep 20 pytest tests From 582cf769a65703651bf55dadb795293d1654d7ec Mon Sep 17 00:00:00 2001 From: Stuart Siegel Date: Thu, 1 Aug 2024 21:55:43 -0400 Subject: [PATCH 16/29] rename to inference-service-test.yml --- .../{services-test.yml => inference-services-test.yml} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename .github/workflows/{services-test.yml => inference-services-test.yml} (97%) diff --git a/.github/workflows/services-test.yml b/.github/workflows/inference-services-test.yml similarity index 97% rename from .github/workflows/services-test.yml rename to .github/workflows/inference-services-test.yml index d3cd3c4f..e5c19820 100644 --- a/.github/workflows/services-test.yml +++ b/.github/workflows/inference-services-test.yml @@ -1,7 +1,7 @@ # This workflow will install Python dependencies, run tests and lint with a variety of Python versions # For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python -name: Service Tests +name: Inference Service Tests on: push: From b060ec328b53a1f00b01b180beae3ba97e93bf5d Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Fri, 2 Aug 2024 06:45:24 -0400 Subject: [PATCH 17/29] remove duplicate code --- services/inference/tsfmservices/inference/inference.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/services/inference/tsfmservices/inference/inference.py b/services/inference/tsfmservices/inference/inference.py index ad91e87c..310538d3 100644 --- a/services/inference/tsfmservices/inference/inference.py +++ b/services/inference/tsfmservices/inference/inference.py @@ -135,12 +135,6 @@ def _forecast_common( add_known_ground_truth=False, ) - # warn if future data is not provided, but is needed by the model - if preprocessor.exogenous_channel_indices and future_data is None: - raise ValueError( - "Future data should be provided for exogenous columns where the future is known (`control_columns` and `observable_columns`)" - ) - # truncate data length when exploding # context_length = model.config.context_length # if explode_forecasts and len(data) > context_length: From de5584f79de697511abde987ed0c7d1a5bf4ff02 Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Fri, 2 Aug 2024 06:51:55 -0400 Subject: [PATCH 18/29] include services in style, format --- Makefile | 2 +- services/inference/tests/test_inference.py | 10 +------- services/inference/tsfmservices/__init__.py | 23 +++++++++---------- .../inference/tsfmservices/common/util.py | 21 +++++------------ .../tsfmservices/inference/inference.py | 23 +++++-------------- .../inference/tsfmservices/inference/main.py | 7 +++--- .../tsfmservices/inference/payloads.py | 17 +++++--------- 7 files changed, 35 insertions(+), 68 deletions(-) diff --git a/Makefile b/Makefile index 571ad16d..a363f958 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,7 @@ # Adapted from HF Transformers: https://github.com/huggingface/transformers/tree/main .PHONY: quality style -check_dirs := tests tsfm_public tsfmhfdemos notebooks +check_dirs := tests tsfm_public tsfmhfdemos notebooks services # this target runs checks on all files diff --git a/services/inference/tests/test_inference.py b/services/inference/tests/test_inference.py index 5fb8ed0c..b9b5fb47 100644 --- a/services/inference/tests/test_inference.py +++ b/services/inference/tests/test_inference.py @@ -1,8 +1,5 @@ # Standard -from datetime import datetime -from io import BytesIO from typing import Any, Dict -import json # Third Party import numpy as np @@ -12,16 +9,11 @@ # First Party from tsfm_public.toolkit.util import select_by_index -from tsfm_public import TinyTimeMixerForPrediction - -from transformers import PatchTSTForPrediction, PatchTSMixerForPrediction @pytest.fixture(scope="module") def ts_data(): - dataset_path = ( - "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh1.csv" - ) + dataset_path = "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh1.csv" forecast_length = 96 context_length = 512 diff --git a/services/inference/tsfmservices/__init__.py b/services/inference/tsfmservices/__init__.py index 34dd794f..50afc3cd 100644 --- a/services/inference/tsfmservices/__init__.py +++ b/services/inference/tsfmservices/__init__.py @@ -1,17 +1,18 @@ import logging import os + TSFM_PYTHON_LOGGING_LEVEL = os.getenv("TSFM_PYTHON_LOGGING_LEVEL", "INFO") -LevelNamesMapping = dict( - INFO=logging.INFO, - WARN=logging.WARN, - WARNING=logging.WARNING, - ERROR=logging.ERROR, - CRITICAL=logging.CRITICAL, - DEBUG=logging.DEBUG, - FATAL=logging.FATAL, -) +LevelNamesMapping = { + "INFO": logging.INFO, + "WARN": logging.WARN, + "WARNING": logging.WARNING, + "ERROR": logging.ERROR, + "CRITICAL": logging.CRITICAL, + "DEBUG": logging.DEBUG, + "FATAL": logging.FATAL, +} TSFM_PYTHON_LOGGING_LEVEL = ( logging.getLevelNamesMapping()[TSFM_PYTHON_LOGGING_LEVEL] @@ -41,7 +42,5 @@ TSFM_CONFIG_FILE = os.getenv( "TSFM_CONFIG_FILE", - os.path.realpath( - os.path.join(os.path.dirname(__file__), "config", "default_config.yml") - ), + os.path.realpath(os.path.join(os.path.dirname(__file__), "config", "default_config.yml")), ) diff --git a/services/inference/tsfmservices/common/util.py b/services/inference/tsfmservices/common/util.py index bcb551ec..3e0d3341 100644 --- a/services/inference/tsfmservices/common/util.py +++ b/services/inference/tsfmservices/common/util.py @@ -11,6 +11,7 @@ # Third Party from transformers import AutoConfig, AutoModel, PretrainedConfig, PreTrainedModel + LOGGER = logging.getLogger(__file__) @@ -36,9 +37,7 @@ def register_config(model_type: str, model_config_name: str, module_path: str) - mod = importlib.import_module(module_path) conf_class = getattr(mod, model_config_name, None) except ModuleNotFoundError as exc: # modulenot found, key error ? - raise RuntimeError( - f"Could not load {model_config_name} from {module_path}" - ) from exc + raise RuntimeError(f"Could not load {model_config_name} from {module_path}") from exc if conf_class is not None: AutoConfig.register(model_type, conf_class) @@ -73,9 +72,7 @@ def load_config( conf = AutoConfig.from_pretrained(model_path) except (KeyError, ValueError) as exc: # determine error raised by autoconfig if model_type is None or model_config_name is None or module_path is None: - raise ValueError( - "model_type, model_config_name, and module_path should be specified." - ) from exc + raise ValueError("model_type, model_config_name, and module_path should be specified.") from exc register_config(model_type, model_config_name, module_path) conf = AutoConfig.from_pretrained(model_path) @@ -83,9 +80,7 @@ def load_config( return conf -def _get_model_class( - config: PretrainedConfig, module_path: Optional[str] = None -) -> type: +def _get_model_class(config: PretrainedConfig, module_path: Optional[str] = None) -> type: """Helper to find model class based on config object First the module_path will be checked if it can be loaded in the current environment. If not @@ -120,9 +115,7 @@ def _get_model_class( return model_class except AttributeError as exc: # catch specific error import error or attribute error - raise AttributeError( - "Could not load model class for architecture '{arch}'." - ) from exc + raise AttributeError("Could not load model class for architecture '{arch}'.") from exc def load_model( @@ -147,9 +140,7 @@ def load_model( """ if module_path is not None and config is None: - raise ValueError( - "Config must be provided when loading from a custom module_path" - ) + raise ValueError("Config must be provided when loading from a custom module_path") if config is not None: model_class = _get_model_class(config, module_path=module_path) diff --git a/services/inference/tsfmservices/inference/inference.py b/services/inference/tsfmservices/inference/inference.py index 310538d3..8905e279 100644 --- a/services/inference/tsfmservices/inference/inference.py +++ b/services/inference/tsfmservices/inference/inference.py @@ -2,27 +2,25 @@ import datetime import logging -from typing import Any, Dict, List from pathlib import Path +from typing import Any, Dict, List import pandas as pd from fastapi import APIRouter, HTTPException + from tsfm_public import TimeSeriesForecastingPipeline, TimeSeriesPreprocessor +from tsfmservices import TSFM_ALLOW_LOAD_FROM_HF_HUB from ..common.constants import API_VERSION from ..common.util import load_config, load_model, register_config from .payloads import ForecastingInferenceInput, PredictOutput -from tsfmservices import TSFM_ALLOW_LOAD_FROM_HF_HUB - LOGGER = logging.getLogger(__file__) class InferenceRuntime: - def __init__(self, config: Dict[str, Any] = {}): - self.config = config model_map = {} @@ -35,14 +33,11 @@ def __init__(self, config: Dict[str, Any] = {}): ) LOGGER.info(f"registered {custom_module['model_type']}") - model_map[custom_module["model_config_name"]] = custom_module[ - "module_path" - ] + model_map[custom_module["model_config_name"]] = custom_module["module_path"] self.model_to_module_map = model_map def add_routes(self, app): - self.router = APIRouter(prefix=f"/{API_VERSION}/inference", tags=["inference"]) self.router.add_api_route( "/forecasting", @@ -53,7 +48,6 @@ def add_routes(self, app): app.include_router(self.router) def load(self, model_path: str): - try: preprocessor = TimeSeriesPreprocessor.from_pretrained(model_path) LOGGER.info("Successfully loaded preprocessor") @@ -84,10 +78,7 @@ def forecast(self, input: ForecastingInferenceInput): LOGGER.exception(e) raise HTTPException(status_code=500, detail=repr(e)) - def _forecast_common( - self, input_payload: ForecastingInferenceInput - ) -> PredictOutput: - + def _forecast_common(self, input_payload: ForecastingInferenceInput) -> PredictOutput: # we need some sort of model registry # payload = input_payload.model_dump() # do we need? @@ -151,9 +142,7 @@ def _forecast_common( future_data = preprocessor.preprocess(future_data) future_data.drop(columns=input_payload.target_columns) - forecasts = forecast_pipeline( - test_data, future_time_series=future_data, inverse_scale_outputs=True - ) + forecasts = forecast_pipeline(test_data, future_time_series=future_data, inverse_scale_outputs=True) return PredictOutput( model_id=input_payload.model_id, diff --git a/services/inference/tsfmservices/inference/main.py b/services/inference/tsfmservices/inference/main.py index 3a31a1db..69a92822 100644 --- a/services/inference/tsfmservices/inference/main.py +++ b/services/inference/tsfmservices/inference/main.py @@ -1,17 +1,19 @@ """Primary entry point for inference services""" import logging -from fastapi import FastAPI + import yaml +from fastapi import FastAPI from tsfmservices import ( + TSFM_CONFIG_FILE, TSFM_PYTHON_LOGGING_FORMAT, TSFM_PYTHON_LOGGING_LEVEL, - TSFM_CONFIG_FILE, ) from tsfmservices.common.constants import API_VERSION from tsfmservices.inference import InferenceRuntime + logging.basicConfig( format=TSFM_PYTHON_LOGGING_FORMAT, level=TSFM_PYTHON_LOGGING_LEVEL, @@ -42,7 +44,6 @@ def root(): if __name__ == "__main__": - # Third Party import uvicorn diff --git a/services/inference/tsfmservices/inference/payloads.py b/services/inference/tsfmservices/inference/payloads.py index 0ecb0661..54e7751d 100644 --- a/services/inference/tsfmservices/inference/payloads.py +++ b/services/inference/tsfmservices/inference/payloads.py @@ -6,11 +6,10 @@ # Third Party from pydantic import BaseModel, ConfigDict, Field, model_validator + # WARNING: DO NOT IMPORT util here or else you'll get a circular dependency -EverythingPatternedString = Annotated[ - str, Field(min_length=0, max_length=100, pattern=".*") -] +EverythingPatternedString = Annotated[str, Field(min_length=0, max_length=100, pattern=".*")] class BaseMetadataInput(BaseModel): @@ -31,11 +30,9 @@ class BaseMetadataInput(BaseModel): min_length=0, ) freq: Optional[str] = Field( - description="""A freqency indicator for the given timestamp_column. - See https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#period-aliases for a description of the allowed values. - If not provided, we will attempt to infer it from the data.""", + description="""A freqency indicator for the given timestamp_column. See https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#period-aliases for a description of the allowed values. If not provided, we will attempt to infer it from the data.""", default=None, - pattern="\d+[B|D|W|M|Q|Y|h|min|s|ms|us|ns]|^\s*$", + pattern=r"\d+[B|D|W|M|Q|Y|h|min|s|ms|us|ns]|^\s*$", min_length=0, max_length=100, example="1h", @@ -94,7 +91,7 @@ class BaseInferenceInput(BaseModel): model_id: str = Field( description="A model identifier.", - pattern="^\S+$", + pattern=r"^\S+$", min_length=1, max_length=100, example="ibm/tinytimemixer-monash-fl_96", @@ -110,9 +107,7 @@ class ForecastingInferenceInput(BaseInferenceInput): description="Data", ) - future_data: Optional[Dict[str, List[Any]]] = Field( - description="Future data", default=None - ) + future_data: Optional[Dict[str, List[Any]]] = Field(description="Future data", default=None) @model_validator(mode="before") @classmethod From abc36c1b4ce8349bbb3012ff6faed2f0aa820e3a Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Fri, 2 Aug 2024 07:04:44 -0400 Subject: [PATCH 19/29] copyright, isort --- services/inference/tsfmservices/__init__.py | 2 ++ services/inference/tsfmservices/common/__init__.py | 2 ++ services/inference/tsfmservices/common/constants.py | 2 ++ services/inference/tsfmservices/common/util.py | 5 ++--- services/inference/tsfmservices/inference/__init__.py | 2 ++ services/inference/tsfmservices/inference/inference.py | 2 ++ services/inference/tsfmservices/inference/main.py | 2 ++ services/inference/tsfmservices/inference/payloads.py | 4 ++-- 8 files changed, 16 insertions(+), 5 deletions(-) diff --git a/services/inference/tsfmservices/__init__.py b/services/inference/tsfmservices/__init__.py index 50afc3cd..2f23ecf5 100644 --- a/services/inference/tsfmservices/__init__.py +++ b/services/inference/tsfmservices/__init__.py @@ -1,3 +1,5 @@ +# Copyright contributors to the TSFM project +# import logging import os diff --git a/services/inference/tsfmservices/common/__init__.py b/services/inference/tsfmservices/common/__init__.py index e69de29b..4f85bd0b 100644 --- a/services/inference/tsfmservices/common/__init__.py +++ b/services/inference/tsfmservices/common/__init__.py @@ -0,0 +1,2 @@ +# Copyright contributors to the TSFM project +# diff --git a/services/inference/tsfmservices/common/constants.py b/services/inference/tsfmservices/common/constants.py index 15f42cd3..219f2e4f 100644 --- a/services/inference/tsfmservices/common/constants.py +++ b/services/inference/tsfmservices/common/constants.py @@ -1,3 +1,5 @@ +# Copyright contributors to the TSFM project +# """Holds constant values used across modules.""" # What will appear in our paths for fast API version diff --git a/services/inference/tsfmservices/common/util.py b/services/inference/tsfmservices/common/util.py index 3e0d3341..192647cf 100644 --- a/services/inference/tsfmservices/common/util.py +++ b/services/inference/tsfmservices/common/util.py @@ -1,14 +1,13 @@ +# Copyright contributors to the TSFM project +# """Utilities to support custom time series models""" -# Standard import importlib import logging import pathlib from typing import Optional, Union import transformers - -# Third Party from transformers import AutoConfig, AutoModel, PretrainedConfig, PreTrainedModel diff --git a/services/inference/tsfmservices/inference/__init__.py b/services/inference/tsfmservices/inference/__init__.py index e016c88e..6c6c3760 100644 --- a/services/inference/tsfmservices/inference/__init__.py +++ b/services/inference/tsfmservices/inference/__init__.py @@ -1 +1,3 @@ +# Copyright contributors to the TSFM project +# from .inference import InferenceRuntime diff --git a/services/inference/tsfmservices/inference/inference.py b/services/inference/tsfmservices/inference/inference.py index 8905e279..a5b987a1 100644 --- a/services/inference/tsfmservices/inference/inference.py +++ b/services/inference/tsfmservices/inference/inference.py @@ -1,3 +1,5 @@ +# Copyright contributors to the TSFM project +# """Inference Runtime""" import datetime diff --git a/services/inference/tsfmservices/inference/main.py b/services/inference/tsfmservices/inference/main.py index 69a92822..a804682a 100644 --- a/services/inference/tsfmservices/inference/main.py +++ b/services/inference/tsfmservices/inference/main.py @@ -1,3 +1,5 @@ +# Copyright contributors to the TSFM project +# """Primary entry point for inference services""" import logging diff --git a/services/inference/tsfmservices/inference/payloads.py b/services/inference/tsfmservices/inference/payloads.py index 54e7751d..36bb3385 100644 --- a/services/inference/tsfmservices/inference/payloads.py +++ b/services/inference/tsfmservices/inference/payloads.py @@ -1,9 +1,9 @@ +# Copyright contributors to the TSFM project +# """Payload definitions for inference""" -# Standard from typing import Annotated, Any, Dict, List, Optional -# Third Party from pydantic import BaseModel, ConfigDict, Field, model_validator From 4b7dd1c397fb3b714af2a105fe3aa849fe14ca81 Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Fri, 2 Aug 2024 07:06:32 -0400 Subject: [PATCH 20/29] copyright, format --- services/inference/tests/test_inference.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/services/inference/tests/test_inference.py b/services/inference/tests/test_inference.py index b9b5fb47..b2e3d463 100644 --- a/services/inference/tests/test_inference.py +++ b/services/inference/tests/test_inference.py @@ -1,13 +1,12 @@ -# Standard +# Copyright contributors to the TSFM project +# from typing import Any, Dict -# Third Party import numpy as np import pandas as pd import pytest import requests -# First Party from tsfm_public.toolkit.util import select_by_index From 6f2b45a376e331e56fdcbf94e1fbc914497af317 Mon Sep 17 00:00:00 2001 From: "Stuart A. Siegel" Date: Fri, 2 Aug 2024 13:32:53 -0400 Subject: [PATCH 21/29] flatten directory structure --- services/inference/{tsfmservices => tsfminference}/__init__.py | 0 .../{tsfmservices/common => tsfminference}/constants.py | 0 .../{tsfmservices/config => tsfminference}/default_config.yml | 0 .../{tsfmservices/inference => tsfminference}/inference.py | 0 .../{tsfmservices/common => tsfminference}/logging.conf | 0 .../{tsfmservices/inference => tsfminference}/main.py | 0 .../{tsfmservices/inference => tsfminference}/payloads.py | 0 .../inference/{tsfmservices/common => tsfminference}/util.py | 0 services/inference/tsfmservices/common/__init__.py | 2 -- services/inference/tsfmservices/inference/__init__.py | 3 --- 10 files changed, 5 deletions(-) rename services/inference/{tsfmservices => tsfminference}/__init__.py (100%) rename services/inference/{tsfmservices/common => tsfminference}/constants.py (100%) rename services/inference/{tsfmservices/config => tsfminference}/default_config.yml (100%) rename services/inference/{tsfmservices/inference => tsfminference}/inference.py (100%) rename services/inference/{tsfmservices/common => tsfminference}/logging.conf (100%) rename services/inference/{tsfmservices/inference => tsfminference}/main.py (100%) rename services/inference/{tsfmservices/inference => tsfminference}/payloads.py (100%) rename services/inference/{tsfmservices/common => tsfminference}/util.py (100%) delete mode 100644 services/inference/tsfmservices/common/__init__.py delete mode 100644 services/inference/tsfmservices/inference/__init__.py diff --git a/services/inference/tsfmservices/__init__.py b/services/inference/tsfminference/__init__.py similarity index 100% rename from services/inference/tsfmservices/__init__.py rename to services/inference/tsfminference/__init__.py diff --git a/services/inference/tsfmservices/common/constants.py b/services/inference/tsfminference/constants.py similarity index 100% rename from services/inference/tsfmservices/common/constants.py rename to services/inference/tsfminference/constants.py diff --git a/services/inference/tsfmservices/config/default_config.yml b/services/inference/tsfminference/default_config.yml similarity index 100% rename from services/inference/tsfmservices/config/default_config.yml rename to services/inference/tsfminference/default_config.yml diff --git a/services/inference/tsfmservices/inference/inference.py b/services/inference/tsfminference/inference.py similarity index 100% rename from services/inference/tsfmservices/inference/inference.py rename to services/inference/tsfminference/inference.py diff --git a/services/inference/tsfmservices/common/logging.conf b/services/inference/tsfminference/logging.conf similarity index 100% rename from services/inference/tsfmservices/common/logging.conf rename to services/inference/tsfminference/logging.conf diff --git a/services/inference/tsfmservices/inference/main.py b/services/inference/tsfminference/main.py similarity index 100% rename from services/inference/tsfmservices/inference/main.py rename to services/inference/tsfminference/main.py diff --git a/services/inference/tsfmservices/inference/payloads.py b/services/inference/tsfminference/payloads.py similarity index 100% rename from services/inference/tsfmservices/inference/payloads.py rename to services/inference/tsfminference/payloads.py diff --git a/services/inference/tsfmservices/common/util.py b/services/inference/tsfminference/util.py similarity index 100% rename from services/inference/tsfmservices/common/util.py rename to services/inference/tsfminference/util.py diff --git a/services/inference/tsfmservices/common/__init__.py b/services/inference/tsfmservices/common/__init__.py deleted file mode 100644 index 4f85bd0b..00000000 --- a/services/inference/tsfmservices/common/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -# Copyright contributors to the TSFM project -# diff --git a/services/inference/tsfmservices/inference/__init__.py b/services/inference/tsfmservices/inference/__init__.py deleted file mode 100644 index 6c6c3760..00000000 --- a/services/inference/tsfmservices/inference/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -# Copyright contributors to the TSFM project -# -from .inference import InferenceRuntime From 19c72523292c75681fd1089cd5907bf530aa2dcc Mon Sep 17 00:00:00 2001 From: "Stuart A. Siegel" Date: Fri, 2 Aug 2024 13:33:20 -0400 Subject: [PATCH 22/29] fixes to imports, etc. --- services/inference/.dockerignore | 2 +- services/inference/Dockerfile | 28 +++++++++---------- services/inference/Makefile | 10 +++---- services/inference/README.md | 6 ++-- services/inference/pyproject.toml | 4 +-- services/inference/tsfminference/__init__.py | 16 +---------- services/inference/tsfminference/inference.py | 10 ++++--- services/inference/tsfminference/main.py | 6 ++-- services/inference/tsfminference/payloads.py | 2 +- 9 files changed, 36 insertions(+), 48 deletions(-) diff --git a/services/inference/.dockerignore b/services/inference/.dockerignore index ed4bba40..a44c199e 100644 --- a/services/inference/.dockerignore +++ b/services/inference/.dockerignore @@ -1,5 +1,5 @@ ** -!tsfmservices +!tsfminference !poetry.lock !pyproject.toml diff --git a/services/inference/Dockerfile b/services/inference/Dockerfile index 7a7f51d6..b058082a 100644 --- a/services/inference/Dockerfile +++ b/services/inference/Dockerfile @@ -10,10 +10,11 @@ RUN microdnf -y update && \ ENV POETRY_VIRTUALENVS_IN_PROJECT=1 -COPY tsfmservices* /tsfmservices/tsfmservices -COPY pyproject.toml /tsfmservices/ -COPY poetry.lock /tsfmservices/ -WORKDIR /tsfmservices +RUN mkdir /inference +COPY tsfminference/* /inference/tsfminference/ +COPY pyproject.toml /inference/ +COPY poetry.lock /inference/ +WORKDIR /inference RUN pip3.11 install poetry && poetry install FROM registry.access.redhat.com/ubi9/ubi-minimal:latest AS deploy @@ -22,20 +23,19 @@ RUN microdnf -y update && \ shadow-utils python3.11 && \ microdnf clean all -WORKDIR /tsfmservices +WORKDIR /inference -COPY --from=builder /tsfmservices/ /tsfmservices/ +COPY --from=builder /inference /inference -ENV VIRTUAL_ENV=/tsfmservices/.venv +ENV VIRTUAL_ENV=/inference/.venv ENV PATH="$VIRTUAL_ENV/bin:$PATH" ENV HF_HOME=/tmp -RUN groupadd --system tsfmservices --gid 1001 && \ - adduser --system --uid 1001 --gid 0 --groups tsfmservices \ - --create-home --home-dir /tsfmservices --shell /sbin/nologin \ - --comment "TSFMServices User" tsfmservices +RUN groupadd --system tsfminference --gid 1001 && \ + adduser --system --uid 1001 --gid 0 --groups tsfminference \ + --create-home --home-dir /inference --shell /sbin/nologin \ + --comment "tsfminference User" tsfminference -USER tsfmservices +USER tsfminference -# CMD ["python", "-m", "tsfmservices.inference.main"] -CMD ["python", "-m", "uvicorn","tsfmservices.inference.main:app", "--host", "0.0.0.0", "--port", "8000" ] \ No newline at end of file +CMD ["python", "-m", "uvicorn","tsfminference.main:app", "--host", "0.0.0.0", "--port", "8000" ] \ No newline at end of file diff --git a/services/inference/Makefile b/services/inference/Makefile index 9d515e20..26a11df7 100644 --- a/services/inference/Makefile +++ b/services/inference/Makefile @@ -1,18 +1,18 @@ CONTAINER_BUILDER ?= docker -# starts the inference service (used mainly for test cases) +# starts the tsfminference service (used mainly for test cases) start_service_local: - python -m tsfmservices.inference.main & + python -m tsfminference.main & sleep 10 stop_service_local: - pkill -f 'python.*tsfmservices.*' + pkill -f 'python.*tsfminference.*' sleep 10 image: - $(CONTAINER_BUILDER) build -t tsfmservices -f Dockerfile . + $(CONTAINER_BUILDER) build -t tsfminference -f Dockerfile . start_service_image: image - $(CONTAINER_BUILDER) run -p 8000:8000 -d --rm --name tsfmserver tsfmservices + $(CONTAINER_BUILDER) run -p 8000:8000 -d --rm --name tsfmserver tsfminference sleep 10 stop_service_image: $(CONTAINER_BUILDER) stop tsfmserver diff --git a/services/inference/README.md b/services/inference/README.md index 90683065..3f1c422a 100644 --- a/services/inference/README.md +++ b/services/inference/README.md @@ -45,11 +45,11 @@ CONTAINER_BUILDER= make image # e.g, CONTAINER_BUILDER=docker make image ``` -After a successful build you should have a local image named `tsfmservices:latest` +After a successful build you should have a local image named `tsfminference:latest` ```sh -(py311) ➜ tsfm-services git:(revised-build-system) ✗ docker images | grep tsfmservices | head -n 1 -tsfmservices latest df592dcb0533 46 seconds ago 1.49GB +(py311) ➜ tsfm-services git:(revised-build-system) ✗ docker images | grep tsfminference | head -n 1 +tsfminference latest df592dcb0533 46 seconds ago 1.49GB # some of the numeric and hash values on your machine could be different ``` diff --git a/services/inference/pyproject.toml b/services/inference/pyproject.toml index 7f10cf1f..ff0ede45 100644 --- a/services/inference/pyproject.toml +++ b/services/inference/pyproject.toml @@ -1,5 +1,5 @@ [tool.poetry] -name = "tsfmservices" +name = "tsfminference" version = "0.0.1" description = "Service layer for TSFM granite models." authors = ["IBM"] @@ -12,7 +12,7 @@ python = ">=3.10,<3.12" numpy = { version = "<2" } tsfm_public = { git = "https://github.com/IBM/tsfm.git", tag = "v0.2.5" } -# trying to pick up cpu version for inference +# trying to pick up cpu version for tsfminference # to make image smaller torch = { version = ">2,<3", source = "pytorch" } diff --git a/services/inference/tsfminference/__init__.py b/services/inference/tsfminference/__init__.py index 2f23ecf5..4312d1c0 100644 --- a/services/inference/tsfminference/__init__.py +++ b/services/inference/tsfminference/__init__.py @@ -26,23 +26,9 @@ "%(levelname)s:p-%(process)d:t-%(thread)d:%(filename)s:%(funcName)s:%(message)s", ) -# Do we have redis? - -try: - # Third Party - import redis - import rq - - HAVE_REDIS_BACKEND = True -except ImportError: - HAVE_REDIS_BACKEND = False - -TSFM_USE_KFTO_ASYNC_BACKEND = int(os.getenv("TSFM_USE_KFTO_ASYNC_BACKEND", "0")) == 1 -USE_REDIS_ASYNC_BACKEND = HAVE_REDIS_BACKEND and not TSFM_USE_KFTO_ASYNC_BACKEND - TSFM_ALLOW_LOAD_FROM_HF_HUB = int(os.getenv("TSFM_ALLOW_LOAD_FROM_HF_HUB", "1")) == 1 TSFM_CONFIG_FILE = os.getenv( "TSFM_CONFIG_FILE", - os.path.realpath(os.path.join(os.path.dirname(__file__), "config", "default_config.yml")), + os.path.realpath(os.path.join(os.path.dirname(__file__), "default_config.yml")), ) diff --git a/services/inference/tsfminference/inference.py b/services/inference/tsfminference/inference.py index a5b987a1..a60f6cd7 100644 --- a/services/inference/tsfminference/inference.py +++ b/services/inference/tsfminference/inference.py @@ -1,6 +1,8 @@ # Copyright contributors to the TSFM project # -"""Inference Runtime""" +"""Tsfminference Runtime""" + +print("In module __package__, __name__ ==", __package__, __name__) import datetime import logging @@ -11,11 +13,11 @@ from fastapi import APIRouter, HTTPException from tsfm_public import TimeSeriesForecastingPipeline, TimeSeriesPreprocessor -from tsfmservices import TSFM_ALLOW_LOAD_FROM_HF_HUB -from ..common.constants import API_VERSION -from ..common.util import load_config, load_model, register_config +from . import TSFM_ALLOW_LOAD_FROM_HF_HUB +from .constants import API_VERSION from .payloads import ForecastingInferenceInput, PredictOutput +from .util import load_config, load_model, register_config LOGGER = logging.getLogger(__file__) diff --git a/services/inference/tsfminference/main.py b/services/inference/tsfminference/main.py index a804682a..d7d559b2 100644 --- a/services/inference/tsfminference/main.py +++ b/services/inference/tsfminference/main.py @@ -7,13 +7,13 @@ import yaml from fastapi import FastAPI -from tsfmservices import ( +from . import ( TSFM_CONFIG_FILE, TSFM_PYTHON_LOGGING_FORMAT, TSFM_PYTHON_LOGGING_LEVEL, ) -from tsfmservices.common.constants import API_VERSION -from tsfmservices.inference import InferenceRuntime +from .constants import API_VERSION +from .inference import InferenceRuntime logging.basicConfig( diff --git a/services/inference/tsfminference/payloads.py b/services/inference/tsfminference/payloads.py index 36bb3385..8d212bc3 100644 --- a/services/inference/tsfminference/payloads.py +++ b/services/inference/tsfminference/payloads.py @@ -1,6 +1,6 @@ # Copyright contributors to the TSFM project # -"""Payload definitions for inference""" +"""Payload definitions for tsfminference""" from typing import Annotated, Any, Dict, List, Optional From 9f1e3edaf2f1cde627c03d5a2967ea83d3abe6e2 Mon Sep 17 00:00:00 2001 From: "Stuart A. Siegel" Date: Fri, 2 Aug 2024 13:34:31 -0400 Subject: [PATCH 23/29] comments --- .github/workflows/inference-services-test.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/inference-services-test.yml b/.github/workflows/inference-services-test.yml index e5c19820..8bbbeb8c 100644 --- a/.github/workflows/inference-services-test.yml +++ b/.github/workflows/inference-services-test.yml @@ -1,7 +1,7 @@ # This workflow will install Python dependencies, run tests and lint with a variety of Python versions # For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python -name: Inference Service Tests +name: Tsfminference Service Tests on: push: @@ -32,7 +32,7 @@ jobs: pip install poetry cd services/inference poetry install -n --with dev - - name: Test local server inference service with pytest + - name: Test local server tsfminference service with pytest run: | source .venv/bin/activate cd services/inference From f2b0de1457e027946d6f89b34a48bdf752516caa Mon Sep 17 00:00:00 2001 From: "Stuart A. Siegel" Date: Fri, 2 Aug 2024 13:44:53 -0400 Subject: [PATCH 24/29] fix ruff failed check --- services/inference/tsfminference/inference.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/services/inference/tsfminference/inference.py b/services/inference/tsfminference/inference.py index a60f6cd7..6de4554e 100644 --- a/services/inference/tsfminference/inference.py +++ b/services/inference/tsfminference/inference.py @@ -2,8 +2,6 @@ # """Tsfminference Runtime""" -print("In module __package__, __name__ ==", __package__, __name__) - import datetime import logging from pathlib import Path From 88a17488589349009253086d99c81e9b166311d0 Mon Sep 17 00:00:00 2001 From: "Stuart A. Siegel" Date: Fri, 2 Aug 2024 13:57:48 -0400 Subject: [PATCH 25/29] fix dir name --- services/inference/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/services/inference/pyproject.toml b/services/inference/pyproject.toml index ff0ede45..354d3783 100644 --- a/services/inference/pyproject.toml +++ b/services/inference/pyproject.toml @@ -4,7 +4,7 @@ version = "0.0.1" description = "Service layer for TSFM granite models." authors = ["IBM"] license = "https://github.com/ibm-granite/granite-tsfm/blob/main/LICENSE" -packages = [{ include = "tsfmservices/**/*.py" }] +packages = [{ include = "tsfminference/**/*.py" }] [tool.poetry.dependencies] # including 3.9 causes poetry lock to run forever From 3a8265ebafc7efc2ee56c0ff98471d04de9d3b9a Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Fri, 2 Aug 2024 15:14:37 -0400 Subject: [PATCH 26/29] update readme --- README.md | 6 +++--- services/inference/README.md | 5 +++-- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 06b3d32d..77e55d6b 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ # TSFM: Time Series Foundation Models -Public notebooks and utilities for working with Time Series Foundation Models (TSFM) +Public notebooks, utilities, and serving components for working with Time Series Foundation Models (TSFM). The core TSFM time series models have been made available on Hugging Face -- details can be found -[here](wiki.md). +[here](wiki.md). Information on the services component can be found [here](services/inference/README.md). # Python Version @@ -31,7 +31,7 @@ pip install ".[notebooks]" ## 📗 Google Colab Run the TTM tutorial in Google Colab, and quickly build a forecasting application with pre-trained TSFM models. -- [TTM Colab Tutorial](https://colab.research.google.com/github/IBM/tsfm/blob/tutorial/notebooks/tutorial/ttm_tutorial.ipynb) +- [TTM Colab Tutorial](https://colab.research.google.com/github/IBM/tsfm/blob/main/notebooks/tutorial/ttm_tutorial.ipynb) ## 💻 Demos Installation The demo presented at NeurIPS 2023 is available in `tsfmhfdemos`. This demo requires you to have pre-trained and finetuned models in place (we plan to release these at later date). To install the requirements use `pip`: diff --git a/services/inference/README.md b/services/inference/README.md index 3f1c422a..7c8b9e74 100644 --- a/services/inference/README.md +++ b/services/inference/README.md @@ -1,7 +1,8 @@ # TSFM Services -This component provides basic RESTful services for the IBM tsfm-granite -class of timeseries foundation models. At present it can serve the following models: + + +This component provides RESTful services for the tsfm-granite class of timeseries foundation models. At present it can serve the following models: * https://huggingface.co/ibm-granite/granite-timeseries-ttm-v1 * https://huggingface.co/ibm-granite/granite-timeseries-patchtst From 83f1e62b836382b04f2e46bca02d97d59259428e Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Fri, 2 Aug 2024 15:32:06 -0400 Subject: [PATCH 27/29] minor update --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 77e55d6b..0cbad39d 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ pip install ".[notebooks]" - Getting started with `PatchTSMixer` [[Try it out]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/patch_tsmixer_getting_started.ipynb) - Transfer learning with `PatchTSMixer` [[Try it out]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/patch_tsmixer_transfer.ipynb) - Transfer learning with `PatchTST` [[Try it out]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/patch_tst_transfer.ipynb) -- Getting started with `TinyTimeMixer (TTM)` [Try it out](notebooks/hfdemo/ttm_getting_started.ipynb) +- Getting started with `TinyTimeMixer (TTM)` [[Try it out]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb) ## 📗 Google Colab Run the TTM tutorial in Google Colab, and quickly build a forecasting application with pre-trained TSFM models. @@ -46,7 +46,7 @@ Before opening a new issue, please search for similar issues. It's possible that # Notice -The intention of this repository is to make it easier to use and demonstrate IBM Research TSFM components that have been made available in the [Hugging Face transformers library](https://huggingface.co/docs/transformers/main/en/index). As we continue to develop these capabilities we will update the code here. +The intention of this repository is to make it easier to use and demonstrate Granite TimeSeries components that have been made available in the [Hugging Face transformers library](https://huggingface.co/docs/transformers/main/en/index). As we continue to develop these capabilities we will update the code here. IBM Public Repository Disclosure: All content in this repository including code has been provided by IBM under the associated open source software license and IBM is under no obligation to provide enhancements, updates, or support. IBM developers produced this code as an open source project (not as an IBM product), and IBM makes no assertions as to the level of quality nor security, and will not be maintaining this code going forward. From b23f803769f130e38066f61c2dbdf0a23706f07b Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Fri, 2 Aug 2024 15:33:12 -0400 Subject: [PATCH 28/29] minor update --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 0cbad39d..b18c6e0c 100644 --- a/README.md +++ b/README.md @@ -34,7 +34,7 @@ Run the TTM tutorial in Google Colab, and quickly build a forecasting applicatio - [TTM Colab Tutorial](https://colab.research.google.com/github/IBM/tsfm/blob/main/notebooks/tutorial/ttm_tutorial.ipynb) ## 💻 Demos Installation -The demo presented at NeurIPS 2023 is available in `tsfmhfdemos`. This demo requires you to have pre-trained and finetuned models in place (we plan to release these at later date). To install the requirements use `pip`: +The demo presented at NeurIPS 2023 is available in `tsfmhfdemos`. This demo requires you to have pre-trained and finetuned models in place (we plan to release these at a later date). To install the requirements use `pip`: ```bash pip install ".[demos]" From df23b5b7b6fb91efe44b9f2641967655a6351a1d Mon Sep 17 00:00:00 2001 From: "Stuart A. Siegel" Date: Mon, 5 Aug 2024 11:20:53 -0400 Subject: [PATCH 29/29] root redirects to doc page --- services/inference/README.md | 2 +- services/inference/tsfminference/main.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/services/inference/README.md b/services/inference/README.md index 7c8b9e74..679d992d 100644 --- a/services/inference/README.md +++ b/services/inference/README.md @@ -66,7 +66,7 @@ make test_image make start_local_server ``` -Then open your browser to http://127.0.0.1:8000/docs, click on the `/openapi.json` link at the top of this page. +Then open your browser to http://127.0.0.1:8000 To stop the server run: diff --git a/services/inference/tsfminference/main.py b/services/inference/tsfminference/main.py index d7d559b2..d36c289a 100644 --- a/services/inference/tsfminference/main.py +++ b/services/inference/tsfminference/main.py @@ -4,8 +4,10 @@ import logging +import starlette.status as status import yaml from fastapi import FastAPI +from fastapi.responses import RedirectResponse from . import ( TSFM_CONFIG_FILE, @@ -40,9 +42,7 @@ @app.get("/") def root(): - # Endpoint at the root URL ("/") returns a welcome message with a clickable link - message = "Welcome. Go to /docs to access the API documentation." - return {"message": message} + return RedirectResponse(url="/docs", status_code=status.HTTP_302_FOUND) if __name__ == "__main__":