[WIP]Remove obsolete modelservice

docs/tutorial/housekeeper.md

@@ -17,15 +17,15 @@ Users can search for a model and obtain its detailed information.
 
 
 ```python
-from modelci.hub.manager import retrieve_model, retrieve_model_by_task
-from modelci.types.bo import Framework, Engine, Task
+from modelci.hub.manager import retrieve_model
+from modelci.types.models import Framework, Engine, Task
 
 # By model name and optionally filtered by model framework and(or) model engine
-model_bo = retrieve_model(
-    architecture_name='ResNet50', framework=Framework.PYTORCH, engine=Engine.TORCHSCRIPT
+ml_model = retrieve_model(
+    architecture='ResNet50', framework=Framework.PyTorch, engine=Engine.TORCHSCRIPT
 )
 # By task
-model_bo2 = retrieve_model_by_task(task=Task.IMAGE_CLASSIFICATION)
+ml_model_by_task = retrieve_model(task=Task.Image_Classification)
 ```
 
 The returned tuple contains the path where the model is cached and model meta information (e.g. model name, model framework).
@@ -40,30 +40,31 @@ You can update the model information manually using the update API.
 
 Here is an example for updating the information of a ResNet50 model. The return value of the function `update_model(model)` is a boolean that indicates the status.
 
-Since many models share a name, the function `get_models_by_name` will return a model list. You should specify the model version to get a model object. 
+Since many models share a name, the function `get_models` will return a model list. You should specify the model version to get a model object.
 
 ```python
-from modelci.persistence.service import ModelService
+from modelci.persistence.service import get_models, update_model
+from modelci.types.models import ModelUpdateSchema, Metric
+from pathlib import Path
 
-# get_models_by_name will return a list of all matched results.
+# get_models will return a list of all matched results.
 
-model = ModelService.get_models('ResNet50')[0]
-model.acc = 0.9
-model.weight.weight = bytes([123, 255])
+model = get_models(architecture='ResNet50')[0]
 
 # check if update success
-assert ModelService.update_model(model)
+assert update_model(str(model.id), ModelUpdateSchema(metric={Metric.acc: 0.9}, weight=Path('path-to-new-model-file')))
 ```
 
 
-MLModelCI allows you to get the model object by id, task and name.
+MLModelCI allows you to get the model object by id, task, architecture, framework, engine and version.
 
 ```python
-from modelci.persistence.service import ModelService
+from modelci.persistence.service import get_models, get_by_id
+from modelci.types.models import Task
 
-model_bo = ModelService.get_models('ResNet50')[0] # get model by name
-models = ModelService.get_models_by_task('image classification') # get model by task
-model_bo2 = ModelService.get_models('ResNet50')[0] # get model by id
+model_by_architecture = get_models(architecture='ResNet50')[0]  # get model by name
+model_by_task = get_models(task=Task.Image_Classification)  # get model by task
+model_by_id = get_by_id(str(model_by_architecture.id))  # get model by id
 ```
 
 Getting by name or task may return more than one model objects.
@@ -73,11 +74,11 @@ Getting by name or task may return more than one model objects.
 You can delete a model record easily using MLModelCI.
 
 ```python
-from modelci.persistence.service import ModelService
+from modelci.persistence.service import delete_model, get_models
 
-model = ModelService.get_models('ResNet50')[0]
-
-assert ModelService.delete_model_by_id(model.id) # delete the model record
+model_list = get_models(architecture='ResNet50')
+model = model_list[0]
+assert delete_model(str(model.id))  # delete the model record
 ```
 
 Currently, we only support deleting model by `model.id`.

example/resnet50_explicit_path.yml

@@ -1,5 +1,5 @@
 weight: "~/.modelci/ResNet50/pytorch-pytorch/image_classification/1.pth"
-architecture: ResNet18
+architecture: ResNet50
 framework: PYTORCH
 engine: PYTORCH
 version: 1

modelci/app/experimental/endpoints/cv_tuner.py

@@ -11,11 +11,10 @@
 
 from modelci.experimental.model.model_structure import Structure, Operation
 from modelci.hub.registrar import register_model
-from modelci.hub.manager import get_remote_model_weight
+from modelci.hub.cache_manager import get_remote_model_weight
 from modelci.hub.utils import generate_path_plain
-from modelci.persistence.service import ModelService
-from modelci.types.bo import ModelVersion, Engine, IOShape, ModelStatus
-from modelci.types.models import MLModel
+from modelci.persistence.service import get_by_id, get_models
+from modelci.types.models import MLModel, Engine, IOShape, ModelStatus
 from modelci.types.type_conversion import model_data_type_to_torch, type_to_data_type
 from modelci.utils.exceptions import ModelStructureError
 
@@ -52,7 +51,7 @@ def update_finetune_model_as_new(id: str, updated_layer: Structure, dry_run: boo
     """
     if len(updated_layer.layer.items()) == 0:
         return True
-    model = ModelService.get_model_by_id(id)
+    model = get_by_id(id)
     if model.engine != Engine.PYTORCH:
         raise ValueError(f'model {id} is not supported for editing. '
                          f'Currently only support model with engine=PYTORCH')
@@ -117,23 +116,23 @@ def update_finetune_model_as_new(id: str, updated_layer: Structure, dry_run: boo
     if not isinstance(output_tensors, (list, tuple)):
         output_tensors = (output_tensors,)
     for output_tensor in output_tensors:
-        output_shape = IOShape(shape=[bs, *output_tensor.shape[1:]], dtype=type_to_data_type(output_tensor.dtype))
+        output_shape = IOShape(name="output", shape=[bs, *output_tensor.shape[1:]], dtype=type_to_data_type(output_tensor.dtype))
         output_shapes.append(output_shape)
 
     if not dry_run:
         # TODO return validation result for dry_run mode
         # TODO apply Semantic Versioning https://semver.org/
         # TODO reslove duplicate model version problem in a more efficient way
-        version = ModelVersion(model.version.ver + 1)
-        previous_models = ModelService.get_models(
+        version = model.version + 1
+        previous_models = get_models(
             architecture=model.architecture,
             task=model.task,
             framework=model.framework,
             engine=Engine.NONE
         )
         if len(previous_models):
-            last_version = max(previous_models, key=lambda k: k.version.ver).version.ver
-            version = ModelVersion(last_version + 1)
+            last_version = max(previous_models, key=lambda k: k.version.ver).version
+            version = last_version + 1
 
         saved_path = generate_path_plain(
             architecture=model.architecture,
@@ -143,32 +142,32 @@ def update_finetune_model_as_new(id: str, updated_layer: Structure, dry_run: boo
             version=version
         )
         saved_path.parent.mkdir(parents=True, exist_ok=True)
-        torch.save(model,saved_path.with_suffix('.pt') )
+        torch.save(net, saved_path.with_suffix('.pt'))
         mlmodelin = MLModel(
             dataset='',
             metric={key: 0 for key in model.metric.keys()},
             task=model.task,
             inputs=model.inputs,
             outputs=output_shapes,
-            architecture=model.name,
+            architecture=model.architecture,
             framework=model.framework,
             engine=Engine.NONE,
             model_status=[ModelStatus.DRAFT],
             parent_model_id=model.id,
             version=version,
-            weight=saved_path
+            weight=saved_path.with_suffix('.pt')
         )
         register_model(
             mlmodelin,
             convert=False, profile=False
         )
 
-        model_bo = ModelService.get_models(
+        ml_model = get_models(
             architecture=model.architecture,
             task=model.task,
             framework=model.framework,
             engine=Engine.NONE,
             version=version
         )[0]
 
-        return {'id': model_bo.id}
+        return {'id': str(ml_model.id)}

modelci/app/experimental/endpoints/model_structure.py

@@ -7,13 +7,7 @@
 
 ML model structure related API
 """
-import torch
 from fastapi import APIRouter
-from modelci.hub.manager import get_remote_model_weight
-
-from modelci.types.bo import Engine
-
-from modelci.persistence.service import ModelService
 
 from modelci.experimental.model.model_structure import Structure
 

modelci/app/experimental/endpoints/trainer.py

@@ -7,7 +7,7 @@
 """
 from typing import List
 
-from fastapi import APIRouter
+from fastapi import APIRouter, BackgroundTasks
 from starlette.responses import JSONResponse
 
 from modelci.experimental.curd.model_train import save, get_by_id, get_all, delete_by_id, delete_all
@@ -18,13 +18,14 @@
 
 
 @router.post('/', status_code=201)
-def create_training_job(training_job: TrainingJobIn):
+def create_training_job(training_job: TrainingJobIn, background_tasks: BackgroundTasks):
     """
     Create a training job data object, and save it into the database. Then, submit the created training job
     (with job ID generated by database) to the training job coordinator.
     TODO return training job as soon as created
 
     Args:
+        background_tasks:
         training_job (TrainingJobIn): Training job to be submitted.
 
     Returns:
@@ -34,8 +35,7 @@ def create_training_job(training_job: TrainingJobIn):
     if id_ is not None:
         training_job = get_by_id(id_)
         trainer = PyTorchTrainer.from_training_job(training_job)
-        trainer.start()
-        trainer.join()
+        background_tasks.add_task(lambda trainer: trainer.start(), trainer)
     return {'id': str(id_)}
 
 

modelci/app/v1/endpoints/model.py

@@ -6,7 +6,6 @@
 Date: 6/20/2020
 """
 import asyncio
-import http
 import json
 import shutil
 from pathlib import Path
@@ -15,10 +14,10 @@
 from fastapi import APIRouter, File, UploadFile, Depends
 from fastapi.exceptions import RequestValidationError, HTTPException
 from pydantic.error_wrappers import ErrorWrapper
-from starlette.responses import JSONResponse
+from starlette.responses import JSONResponse, Response
 
 from modelci.hub.registrar import register_model
-from modelci.persistence.service_ import get_by_id, get_models, update_model, delete_model, exists_by_id
+from modelci.persistence.service import get_by_id, get_models, update_model, delete_model, exists_by_id
 from modelci.types.models import MLModel, BaseMLModel, ModelUpdateSchema, Framework, Engine, Task
 
 router = APIRouter()
@@ -52,7 +51,7 @@ def update(id: str, schema: ModelUpdateSchema):
     return update_model(id, schema)
 
 
-@router.delete('/{id}', status_code=http.HTTPStatus.NO_CONTENT)
+@router.delete('/{id}', status_code=204, response_class=Response)
 def delete(id: str):
     if not exists_by_id(id):
         raise HTTPException(

modelci/app/v1/endpoints/visualizer.py

@@ -8,8 +8,9 @@
 """
 
 from fastapi import APIRouter
-from modelci.persistence.service import ModelService
-from modelci.types.bo.model_objects import Engine
+
+from modelci.persistence.service import get_by_id
+from modelci.types.models import Engine
 from torchviz import make_dot
 import torch
 
@@ -18,11 +19,11 @@
 
 @router.get('/{id}')
 def generate_model_graph(*, id: str):  # noqa
-    model_bo = ModelService.get_model_by_id(id)
+    ml_model = get_by_id(id)
     dot_graph = ''
-    if model_bo.engine == Engine.PYTORCH:
-        pytorch_model = torch.load(model_bo.saved_path)
-        sample_data = torch.zeros(1, *model_bo.inputs[0].shape[1:], dtype=torch.float, requires_grad=False)
+    if ml_model.engine == Engine.PYTORCH:
+        pytorch_model = torch.load(ml_model.saved_path)
+        sample_data = torch.zeros(1, *ml_model.inputs[0].shape[1:], dtype=torch.float, requires_grad=False)
         out = pytorch_model(sample_data)
         dot_graph = make_dot(out, params=dict(list(pytorch_model.named_parameters()) + [('x', sample_data)]))
 

modelci/cli/modelhub.py

@@ -20,7 +20,7 @@
 import typer
 import yaml
 from pydantic import ValidationError
-import modelci.persistence.service_ as ModelDB
+import modelci.persistence.service as ModelDB
 from modelci.hub.converter import generate_model_family
 
 from modelci.config import app_settings

modelci/controller/executor.py

@@ -13,16 +13,17 @@
 
 from modelci.hub.profiler import Profiler
 from modelci.metrics.benchmark.metric import BaseModelInspector
-from modelci.persistence.service import ModelService
-from modelci.types.bo import ModelBO, Status
+from modelci.persistence.service import update_model, register_dynamic_profiling_result
+from modelci.types.bo import Status
+from modelci.types.models import MLModel, ModelUpdateSchema
 
 
 class Job(object):
     def __init__(
             self,
             client: BaseModelInspector,
             device: str,
-            model_info: ModelBO,
+            model_info: MLModel,
             container_name: str = None
     ):
         self.client = client
@@ -81,16 +82,14 @@ def run(self) -> None:
             else:
                 container_name = job.container_name
             # change model status
-            job.model.status = Status.RUNNING
-            ModelService.update_model(job.model)
+            update_model(str(job.model.id), ModelUpdateSchema(model_status=Status.RUNNING))
 
             profiler = Profiler(model_info=job.model, server_name=container_name, inspector=job.client)
             dpr = profiler.diagnose(device=job.device)
-            ModelService.append_dynamic_profiling_result(job.model.id, dynamic_result=dpr)
+            register_dynamic_profiling_result(str(job.model.id), dynamic_result=dpr)
 
             # set model status to pass
-            job.model.status = Status.PASS
-            ModelService.update_model(job.model)
+            update_model(str(job.model.id), ModelUpdateSchema(model_status=Status.PASS))
 
             if job.container_name is None:
                 # get holding container

modelci/experimental/curd/model_train.py

@@ -12,8 +12,8 @@
 from modelci.config import db_settings
 from modelci.experimental.model.model_train import TrainingJob, TrainingJobIn, TrainingJobUpdate
 from modelci.experimental.mongo_client import MongoClient
+from modelci.persistence import service
 from modelci.persistence.exceptions import ServiceException
-from modelci.persistence.model_dao import ModelDAO
 
 _db = MongoClient()[db_settings.mongo_db]
 _collection = _db['training_job']
@@ -42,7 +42,7 @@ def get_all() -> List[TrainingJob]:
 
 def save(training_job_in: TrainingJobIn) -> str:
     model_id = training_job_in.model
-    if not ModelDAO.exists_by_id(ObjectId(model_id)):
+    if not service.exists_by_id(str(model_id)):
         raise ServiceException(f'Model with ID {model_id} not exist.')
 
     training_job = TrainingJob(**training_job_in.dict(exclude_none=True))
@@ -55,7 +55,7 @@ def update(training_job: TrainingJobUpdate) -> int:
         raise ValueError(f'id {training_job.id} not found.')
 
     # check model ID
-    if training_job.model and not ModelDAO.exists_by_id(ObjectId(training_job.model)):
+    if training_job.model and not service.exists_by_id(str(training_job.model)):
         raise ServiceException(f'Model with ID {training_job.model} not exist.')
 
     # save update