diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml index 34102b7..d4b9e31 100644 --- a/.github/workflows/checks.yml +++ b/.github/workflows/checks.yml @@ -3,9 +3,9 @@ on: pull_request: types: [opened, synchronize, reopened, ready_for_review] paths-ignore: - - '**/*.md' - - '**/*.png' - - '**/*.json' + - "**/*.md" + - "**/*.png" + - "**/*.json" jobs: test: @@ -16,13 +16,13 @@ jobs: strategy: fail-fast: false matrix: - python-version: [3.9, 3.11] + python-version: [3.10.x, 3.11, 3.12, 3.13] poetry-version: [1.4.2] os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - - uses: actions/checkout@v2 - - uses: actions/setup-python@v2 + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Run image @@ -52,8 +52,8 @@ jobs: os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - - uses: actions/checkout@v2 - - uses: actions/setup-python@v2 + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Run image @@ -63,4 +63,4 @@ jobs: - name: Install dependencies run: make init - name: Run style checks - run: make formatting \ No newline at end of file + run: make formatting diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index f54d81b..98386c6 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -14,8 +14,8 @@ jobs: os: [ubuntu-latest] runs-on: ${{ matrix.os }} steps: - - uses: actions/checkout@v2 - - uses: actions/setup-python@v2 + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Run image @@ -27,4 +27,4 @@ jobs: PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }} run: | poetry config pypi-token.pypi $PYPI_TOKEN - poetry publish --build \ No newline at end of file + poetry publish --build diff --git a/README.md b/README.md index 10e71fb..dba04c8 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@

- modelib + modelib

A minimalist framework for online deployment of sklearn-like models @@ -14,7 +14,6 @@ - ## Installation ```bash @@ -46,6 +45,7 @@ request_model = [ {"name": "petal width (cm)", "dtype": "float64"}, ] ``` + Alternatively, you can use a pydantic model to define the request model, where the alias field is used to match the variable names with the column names in the training dataset: ```python @@ -66,7 +66,7 @@ import modelib as ml simple_runner = ml.SklearnRunner( name="my simple model", predictor=MODEL, - method_name="predict", + method_names="predict", request_model=request_model, ) ``` @@ -75,7 +75,7 @@ Another option is to use the `SklearnPipelineRunner` class which allows you to g ```python pipeline_runner = ml.SklearnPipelineRunner( - "Pipeline Model", + name="Pipeline Model", predictor=MODEL, method_names=["transform", "predict"], request_model=request_model, @@ -129,4 +129,4 @@ The response will be a JSON with the prediction: ## Contributing -If you want to contribute to the project, please read the [CONTRIBUTING.md](CONTRIBUTING.md) file for more information. \ No newline at end of file +If you want to contribute to the project, please read the [CONTRIBUTING.md](CONTRIBUTING.md) file for more information. diff --git a/Tutorial.md b/Tutorial.md new file mode 100644 index 0000000..35692d3 --- /dev/null +++ b/Tutorial.md @@ -0,0 +1,237 @@ +# Disponibilizando modelos de machine learning em APIs REST com modelib + +O uso de modelos de Machine Learning em ambientes produtivos é um motivo de atenção pois une conhecimentos das áreas de Ciência de Dados e Engenharia de Software. Conhecimentos esses que estão, muitas vezes, divididos em diferentes áreas das empresas. O Engenheiro de Machine Learning é o profissional que está nessa intersecção de conhecimentos, sendo o responsável por toda a infraestrutura que disponibiliza o trabalho das cientistas de dados (o modelo) para ser consumido pelos sistemas desenvolvidos pelas equipes de desenvolvimento. + +Neste artigo, apresentaremos uma solução para implantar modelos de machine learning numa API REST utilizando a biblioteca [modelib](https://github.com/pier-digital/modelib) que, de forma simples, suporta realizar as predições em tempo real e sob demanda, com uma interface que disponibiliza a inteligência dos modelos para outros serviços através de chamadas HTTP. + +## Definindo o modelo + +> Se você quiser fazer uma torta de maçã a partir do zero, você deve primeiro inventar o Universo. - Carl Sagan + +Antes de pensarmos em como disponibilizar um modelo, nós precisaremos (obviamente) de um modelo. Para facilitar o entendimento, utilizaremos o famoso [Iris Dataset](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html). + +Abaixo, definimos a função `create_model` como responsável por retornar um modelo treinado com parte do conjunto de dados mencionado. + +```python +def create_model(): + from sklearn.datasets import load_iris + from sklearn.ensemble import RandomForestClassifier + from sklearn.pipeline import Pipeline + from sklearn.preprocessing import StandardScaler + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True, as_frame=True) + + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + + model = Pipeline( + [ + ("scaler", StandardScaler()), + ("clf", RandomForestClassifier(random_state=42)), + ] + ).set_output(transform="pandas") + + model.fit(X_train, y_train) + + return model +``` + +Com esse modelo em mãos, podemos nos preocupar em como disponiblizá-lo. + +## Escolhendo como disponibilizar o modelo + +> Você é livre para fazer suas escolhas, mas é prisioneiro das consequências. - Pablo Neruda + +Com a crescente demanda do uso de inteligência de modelos de ML em contextos empresariais, diversas soluções foram criadas para implementar e disponibilizar as predições de tais modelos. + +Dentre as soluções mais comuns, destacam-se as seguintes ferramentas e plataformas open-source: + +- [BentoML](https://www.bentoml.com/): Uma plataforma para servir, gerenciar e implantar modelos de machine learning; +- [MLflow](https://mlflow.org/): Uma plataforma para gerenciar o ciclo de vida de modelos de machine learning; +- [Seldon](https://www.seldon.io/): Uma plataforma para implantar e gerenciar modelos de machine learning em escala; +- [Kubeflow](https://www.kubeflow.org/): Uma plataforma para implantar, gerenciar e escalar modelos de machine learning em Kubernetes; +- [FastAPI](https://fastapi.tiangolo.com/): Um framework (de alto desempenho) para construir APIs em Python; + +Num mar com tantas escolhas, que ainda incluem soluções privadas e nativas de clouds específicas, decidir qual tecnologia sua empresa adotará pode desencadear numa série de custos e limitações indesejadas. Um outro ponto importante é que, em vários casos, a escolha da tecnologia de deploy de modelos de ML pode entrar em conflito com escolhas de infraestrutura já existentes na empresa. + +## Apresentando a biblioteca modelib + +> A simplicidade é a sofisticação final. - Leonardo da Vinci + +A biblioteca funciona como uma extensão do [FastAPI](https://fastapi.tiangolo.com/), que é um framework (de alto desempenho) para construir APIs em Python. + +Um ponto importante é que o deploy de modelos com o FastAPI já foi abordado em diversos outros artigos (deixo [aqui](https://engineering.rappi.com/using-fastapi-to-deploy-machine-learning-models-cd5ed7219ea) um como exemplo). Entretanto, o principal objetivo da biblioteca é oferecer uma forma padronizada para a chamada de modelos através de uma interface simples e comum para validação dos inputs e tratamento dos outputs. + +Desta forma, não estamos preocupados sobre as escolhas de serviço de nuvem (AWS, Azure, GCP, etc), ferramenta de ambiente virtual (virtualenv, poetry, pipenv, etc), serviço de containerização (Docker, Podman, etc) e até mesmo sobre o pipeline de deploy dos modelos. + +Abaixo é apresentado o código necessário para criar um endpoint de predição numa API do FastAPI. + +```python +import modelib as ml +import pydantic + +class InputData(pydantic.BaseModel): + sepal_length: float = pydantic.Field(alias="sepal length (cm)") + sepal_width: float = pydantic.Field(alias="sepal width (cm)") + petal_length: float = pydantic.Field(alias="petal length (cm)") + petal_width: float = pydantic.Field(alias="petal width (cm)") + +simple_runner = ml.SklearnRunner( + name="my simple model", + predictor=create_model(), + method_names="predict", + request_model=InputData, +) + +app = ml.init_app(runners=[simple_runner]) +``` + +Note que é necessário criar um `Runner` a partir do modelo treinado. Além disso, precisamos definir: + +- `name`: nome que será utilizado na definição do path do endpoint gerado; +- `method_names`: nome do método do preditor que será utilizado (`predict`, `transform`, etc); +- `request_model`: modelo que define os inputs esperados pelo modelo. + +### Definindo o formato dos inputs + +Para definir o `request_model` podemos definir uma classe que define o schema esperado pelo modelo para realizar a predição. Um ponto importante é que o nome dos campos deve ser igual ao definido durante o treinamento. Caso o nome da feature contenha espações ou caracteres não suportados para nomes de variáveis no python, utilize o campo alias, conforme demonstrado abaixo: + +```python +class InputData(pydantic.BaseModel): + sepal_length: float = pydantic.Field(alias="sepal length (cm)") + sepal_width: float = pydantic.Field(alias="sepal width (cm)") + petal_length: float = pydantic.Field(alias="petal length (cm)") + petal_width: float = pydantic.Field(alias="petal width (cm)") +``` + +Existe uma segunda forma de definir o schema como uma lista de dicionários. O uso dessa segunda abordagem pode ser interessante para fluxos de deploy onde tais informações são definidas em arquivos de configuração. + +```python +features_metadata = [ + {"name": "sepal length (cm)", "dtype": "float64"}, + {"name": "sepal width (cm)", "dtype": "float64"}, + {"name": "petal length (cm)", "dtype": "float64"}, + {"name": "petal width (cm)", "dtype": "float64"}, +] + +simple_runner = ml.SklearnRunner( + ..., + request_model=features_metadata, +) +``` + +Onde é possível definir os campos: + +- `name`: nome do campo; +- `dtype`: tipo do campo, onde são aceitos os valores `float64`, `int64`, `object`, `bool` e `datetime64`; +- `optional`: booleano indicando se o campo é opcional ou não; +- `default`: valor padrão do campo; + +### Usando diferentes runners + +Por padrão, existem dois tipos de runners já implementados na biblioteca, a saber: + +- `SklearnRunner`: Executa qualquer modelo que segue a interface de um [BaseEstimator do sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html); +- `SklearnPipelineRunner`: Similar ao anterior, mas específico para a execução de [Pipelines](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html); + +No exemplo acima, utilizamos um runner do tipo `SklearnRunner` que, além do `request_model` definido, também informamos os valores dos parâmetros a seguir: + +- `name`: Nome do runner; +- `predictor`: Modelo treinado; +- `method_name`: Nome do método do modelo que será utilizado para realizar a predição; + +Caso o modelo seja um pipeline, podemos utilizar o runner `SklearnPipelineRunner` que ao invés de receber apenas um valor no campo `method_name`, recebe uma lista de strings com os nomes dos métodos que serão executados em sequência no campo `method_names`. + +```python +pipeline_runner = ml.SklearnPipelineRunner( + "Pipeline Model", + predictor=create_model(), + method_names=["transform", "predict"], + request_model=request_model, +) +``` + +A vantagem de utilizar um pipeline `SklearnPipelineRunner` é que recebemos a predição do modelo juntamente com o resultado das transformações realizadas no input em cada etapa do pipeline. + +Além disso, é possível definir runners customizados, bastando para isso criar uma classe que herda de `modelib.BaseRunner` e implementar o método `get_runner_func` que deve retornar uma função que recebe um input e retorna um output. + +```python +class CustomRunner(ml.BaseRunner): + def get_runner_func(self): + def runner_func(input_data): + # Implementação do runner + return output_data + return runner_func +``` + +### Inicializando a aplicação + +Por fim, para inicializar a aplicação, basta chamar a função `init_app` passando uma lista de runners como argumento. + +```python +app = ml.init_app(runners=[simple_runner, pipeline_runner]) +``` + +Caso você queira utilizar uma aplicação já existente, basta chamar a função `init_app` passando a aplicação como argumento. + +```python +import fastapi + +app = fastapi.FastAPI() + +app = ml.init_app(app=app, runners=[simple_runner, pipeline_runner]) +``` + +Após definir os runners e inicializar a aplicação, basta subir a aplicação utilizando o comando `uvicorn` ou `gunicorn` e a aplicação estará pronta para receber requisições. + +```bash +uvicorn :app --reload +``` + +### Realizando predições + +Após subir a aplicação, a mesma estará pronta para receber requisições. Para realizar uma predição, basta enviar uma requisição do tipo POST para o endpoint do runner desejado com um payload contendo os inputs esperados pelo modelo. + +```json +{ + "sepal length (cm)": 5.1, + "sepal width (cm)": 3.5, + "petal length (cm)": 1.4, + "petal width (cm)": 0.2 +} +``` + +Que para o exemplo acima, o endpoint gerado será `/my-simple-model` e a resposta será algo como: + +```json +{ + "result": 0 +} +``` + +Já para o runner do tipo `SklearnPipelineRunner`, a resposta será algo como: + +```json +{ + "result": 0, + "steps": { + "scaler": [ + { + "sepal length (cm)": -7.081194586015879, + "sepal width (cm)": -6.845571885453045, + "petal length (cm)": -2.135591504400147, + "petal width (cm)": -1.5795728805764124 + } + ], + "clf": [0] + } +} +``` + +## Conclusão + +Neste artigo, apresentamos a biblioteca modelib que oferece uma forma padronizada para a chamada de modelos de machine learning através de uma interface simples e comum para validação dos inputs e tratamento dos outputs. A biblioteca é uma extensão do [FastAPI](https://fastapi.tiangolo.com/), que é um framework (de alto desempenho) para construir APIs em Python. + +O principal objetivo da biblioteca é fornecer uma forma simples que se integre tanto em infraestruturas já existentes como em novos projetos, permitindo que cientistas de dados e engenheiros de machine learning possam disponibilizar seus modelos de forma rápida e padronizada. diff --git a/example.py b/example.py index 910ed27..99dcbd3 100644 --- a/example.py +++ b/example.py @@ -46,12 +46,12 @@ class InputData(pydantic.BaseModel): simple_runner = ml.SklearnRunner( name="my simple model", predictor=MODEL, - method_name="predict", + method_names="predict", request_model=InputData, # OR request_model=features_metadata ) pipeline_runner = ml.SklearnPipelineRunner( - "Pipeline Model", + name="Pipeline Model", predictor=MODEL, method_names=["transform", "predict"], request_model=InputData, diff --git a/logo.png b/images/logo.png similarity index 100% rename from logo.png rename to images/logo.png diff --git a/modelib/__init__.py b/modelib/__init__.py index dfc3260..abf225b 100644 --- a/modelib/__init__.py +++ b/modelib/__init__.py @@ -1,5 +1,14 @@ from modelib.server.app import init_app from modelib.runners.base import BaseRunner from modelib.runners.sklearn import SklearnRunner, SklearnPipelineRunner +from modelib.core import exceptions, schemas, endpoint_factory -__all__ = ["init_app", "BaseRunner", "SklearnRunner", "SklearnPipelineRunner"] +__all__ = [ + "init_app", + "BaseRunner", + "SklearnRunner", + "SklearnPipelineRunner", + "exceptions", + "schemas", + "endpoint_factory", +] diff --git a/modelib/core/endpoint_factory.py b/modelib/core/endpoint_factory.py index de8dafa..bf5fc03 100644 --- a/modelib/core/endpoint_factory.py +++ b/modelib/core/endpoint_factory.py @@ -3,28 +3,26 @@ import fastapi -from modelib.core import schemas -from modelib.runners.base import EndpointMetadataManager, BaseRunner +from modelib.runners.base import BaseRunner def create_runner_endpoint( app: fastapi.FastAPI, - runner_func: typing.Callable, - endpoint_metadata_manager: EndpointMetadataManager, + runner: BaseRunner, **kwargs, ) -> fastapi.FastAPI: - path = f"/{endpoint_metadata_manager.slug}" + path = f"/{runner.slug}" route_kwargs = { - "name": endpoint_metadata_manager.name, + "name": runner.name, "methods": ["POST"], - "response_model": endpoint_metadata_manager.response_model, + "response_model": runner.response_model, } route_kwargs.update(kwargs) app.add_api_route( path, - runner_func, + runner.get_runner_func(), **route_kwargs, ) @@ -34,15 +32,6 @@ def create_runner_endpoint( def create_runners_router( runners: typing.List[BaseRunner], **runners_router_kwargs ) -> fastapi.APIRouter: - responses = runners_router_kwargs.pop("responses", {}) - if 500 not in responses: - responses[500] = { - "model": schemas.JsonApiErrorModel, - "description": "Inference Internal Server Error", - } - - runners_router_kwargs["responses"] = responses - runners_router_kwargs["tags"] = runners_router_kwargs.get("tags", ["runners"]) router = fastapi.APIRouter(**runners_router_kwargs) @@ -50,8 +39,7 @@ def create_runners_router( for runner in runners: router = create_runner_endpoint( router, - runner_func=runner.get_runner_func(), - endpoint_metadata_manager=runner.endpoint_metadata_manager, + runner=runner, ) return router diff --git a/modelib/core/exceptions.py b/modelib/core/exceptions.py index ef44c77..522c892 100644 --- a/modelib/core/exceptions.py +++ b/modelib/core/exceptions.py @@ -1,118 +1,41 @@ -import traceback - -from fastapi import FastAPI, Request, status -from fastapi.exceptions import RequestValidationError +import fastapi from fastapi.responses import JSONResponse -from starlette.exceptions import HTTPException as StarletteHTTPException +import traceback -class JsonAPIException(Exception): - def __init__( - self, - status_code, - title, - detail=None, - headers=None, - type_="JsonAPIException", - kind="json-api-exception", - ): - self.status_code = status_code - self.title = title - self.detail = detail - self.headers = headers or {} - if not isinstance(detail, dict): - self.detail = {"message": detail} +def parse_exception(exception: Exception) -> dict: + data = { + "message": str(exception), + "type": type(exception).__name__, + "traceback": None, + } - self.detail["type"] = type_ - self.detail["kind"] = kind + try: + data["traceback"] = "".join( + traceback.format_exception(None, exception, exception.__traceback__) + ).strip() - super().__init__(self.detail or self.title) + except Exception: + pass - def to_dict(self): - return { - "status": self.status_code, - "title": self.title, - "detail": self.detail, - } + return data - def content(self): - return {"errors": [self.to_dict()]} - def to_json_response(self): +async def internal_exception_handler(request: fastapi.Request, exc: Exception): + if isinstance(exc, fastapi.HTTPException): return JSONResponse( - status_code=self.status_code, - content=self.content(), - headers=self.headers, + status_code=exc.status_code, + content=exc.detail, ) - -async def custom_http_exception_handler(request: Request, exc: StarletteHTTPException): - response = JSONResponse( - status_code=exc.status_code, - content={ - "errors": [ - { - "status": exc.status_code, - "title": "HTTP Exception", - "detail": { - "message": exc.detail, - "type": "HTTPException", - "kind": "http-exception", - }, - } - ] - }, - ) - - return response - - -async def request_validation_exception_handler( - request: Request, exc: RequestValidationError -): - response = JSONResponse( - status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, - content={"errors": exc.errors()}, - ) - - return response - - -async def json_api_exception_handler(request: Request, exc: JsonAPIException): - response = exc.to_json_response() - - return response - - -async def generic_exception_handler(request: Request, exc: Exception): - try: - traceback_info = traceback.format_exception(exc) - except Exception: - traceback_info = "Unknown" - - response = JSONResponse( + return JSONResponse( status_code=500, content={ - "errors": [ - { - "status": 500, - "title": "Internal Server Error", - "detail": { - "message": str(exc), - "type": type(exc).__name__, - "kind": "unknown-generic-exception", - "traceback": traceback_info, - }, - } - ] + "title": "Internal server error", + "error": parse_exception(exc), }, ) - return response - -def init_app(app: FastAPI): - app.exception_handler(StarletteHTTPException)(custom_http_exception_handler) - app.exception_handler(RequestValidationError)(request_validation_exception_handler) - app.exception_handler(JsonAPIException)(json_api_exception_handler) - app.exception_handler(Exception)(generic_exception_handler) +def init_app(app: fastapi.FastAPI): + app.exception_handler(500)(internal_exception_handler) diff --git a/modelib/core/schemas.py b/modelib/core/schemas.py index 4be7242..b3c9e2b 100644 --- a/modelib/core/schemas.py +++ b/modelib/core/schemas.py @@ -1,7 +1,5 @@ import typing -from enum import Enum - import pydantic MAP_PANDAS_DTYPE_TO_PYDANTIC = { @@ -27,13 +25,34 @@ def __call__(self, value: typing.Any) -> typing.Any: return value +class ResultResponseModel(pydantic.BaseModel): + result: typing.Any + + +class ResultResponseWithStepsModel(ResultResponseModel): + steps: typing.Dict[str, typing.Any] + + +class FeatureMetadataSchema(pydantic.BaseModel): + name: str + dtype: typing.Literal["object", "int64", "float64", "datetime64", "bool"] + alias: typing.Optional[str] = None + default: typing.Optional[typing.Any] = None + optional: typing.Optional[bool] = False + description: typing.Optional[str] = None + + +class HealthCheckStausSchema(pydantic.BaseModel): + status: str + + def pydantic_model_from_list_of_dicts(name, fields) -> typing.Type[pydantic.BaseModel]: """Create pydantic model from list of dicts""" fields_dict = {} fields_dict["model_config"] = pydantic.ConfigDict(protected_namespaces=()) for i, field in enumerate(fields): - field_name = field.get("alias", field.get("name", f"feat_{i}")) + field_name = field.get("name") fields_dict[field_name] = pydantic_field_from_dict(field) return pydantic.create_model( @@ -59,66 +78,13 @@ def pydantic_field_from_dict(field_dict: dict) -> pydantic.Field: pydantic.BeforeValidator(NullToDefaultValidator(default)), ] - alias = field_dict.get("name") - - return (dtype, pydantic.Field(default, alias=alias, json_schema_extra=field_dict)) - - -class ResultResponseModel(pydantic.BaseModel): - result: typing.Any - - -class ResultResponseWithStepsModel(ResultResponseModel): - steps: typing.Dict[str, typing.Any] - - -class JsonApiErrorResponseModel(pydantic.BaseModel): - status: int - title: str - detail: str - - -class JsonApiErrorModel(pydantic.BaseModel): - errors: typing.List[JsonApiErrorResponseModel] - - -class BaseEnum(str, Enum): - def __eq__(self, __x: object) -> bool: - return super().__eq__(__x) or str(self) == __x - - def __str__(self) -> str: - return str(self.value) - - def __hash__(self): - return hash(self.value) - - def __repr__(self): - return self.value + field_alias = field_dict.pop("alias", None) + kwargs = { + "json_schema_extra": field_dict, + } -class DType(BaseEnum): - t_object = "object" - t_int64 = "int64" - t_float64 = "float64" - t_datetime64 = "datetime64" - t_bool = "bool" + if field_alias: + kwargs["alias"] = field_alias - -class FeatureMetadataSchema(pydantic.BaseModel): - name: str - dtype: DType - alias: typing.Optional[str] = None - default: typing.Optional[typing.Any] = None - optional: bool = False - description: typing.Optional[str] = None - indexes: typing.Optional[typing.List[str]] = [] - - -class DeploymentMetadataSchema(pydantic.BaseModel): - version: str - features: typing.List[FeatureMetadataSchema] - runners: typing.List[typing.Dict[str, typing.Any]] - - -class HealthCheckStausSchema(pydantic.BaseModel): - status: str + return (dtype, pydantic.Field(default, **kwargs)) diff --git a/modelib/runners/base.py b/modelib/runners/base.py index 62ff4e1..7bc167b 100644 --- a/modelib/runners/base.py +++ b/modelib/runners/base.py @@ -12,29 +12,19 @@ def remove_unset_features(features: typing.List[dict]) -> typing.List[dict]: ] -class EndpointMetadataManager: +class BaseRunner: def __init__( self, name: str, request_model: typing.Union[typing.Type[pydantic.BaseModel], typing.List[dict]], response_model: typing.Type[pydantic.BaseModel] = schemas.ResultResponseModel, + by_alias: bool = True, **kwargs, ): self._name = name - if isinstance(request_model, list): - request_model = remove_unset_features(request_model) - self._request_model = schemas.pydantic_model_from_list_of_dicts( - name, request_model - ) - elif issubclass(request_model, pydantic.BaseModel): - self._request_model = request_model - else: - raise ValueError("request_model must be a pydantic.BaseModel subclass") - - if not issubclass(response_model, pydantic.BaseModel): - raise ValueError("response_model must be a pydantic.BaseModel subclass") - - self._response_model = response_model + self.request_model = request_model + self.response_model = response_model + self._by_alias = by_alias @property def name(self) -> str: @@ -49,14 +39,32 @@ def request_model(self) -> typing.Type[pydantic.BaseModel]: return self._request_model @property - def response_model(self) -> typing.Type[pydantic.BaseModel]: - return self._response_model + def by_alias(self) -> bool: + return self._by_alias + @request_model.setter + def request_model( + self, value: typing.Union[typing.Type[pydantic.BaseModel], typing.List[dict]] + ): + if isinstance(value, list): + value = remove_unset_features(value) + self._request_model = schemas.pydantic_model_from_list_of_dicts( + self.name, value + ) + elif issubclass(value, pydantic.BaseModel): + self._request_model = value + else: + raise ValueError("request_model must be a pydantic.BaseModel subclass") -class BaseRunner: @property - def endpoint_metadata_manager(self) -> EndpointMetadataManager: - raise NotImplementedError + def response_model(self) -> typing.Type[pydantic.BaseModel]: + return self._response_model + + @response_model.setter + def response_model(self, value: typing.Type[pydantic.BaseModel]): + if not issubclass(value, pydantic.BaseModel): + raise ValueError("response_model must be a pydantic.BaseModel subclass") + self._response_model = value def get_runner_func(self) -> typing.Callable: raise NotImplementedError diff --git a/modelib/runners/sklearn.py b/modelib/runners/sklearn.py index 220eeab..83ceaf4 100644 --- a/modelib/runners/sklearn.py +++ b/modelib/runners/sklearn.py @@ -1,143 +1,129 @@ import typing -import traceback - import numpy as np import pandas as pd import pydantic from modelib.core import exceptions, schemas -from .base import EndpointMetadataManager, BaseRunner -from sklearn.pipeline import Pipeline +from .base import BaseRunner from sklearn.base import BaseEstimator +import fastapi +from abc import abstractmethod +import logging class SklearnBaseRunner(BaseRunner): def __init__( self, predictor: BaseEstimator, + method_names: typing.Union[str, typing.List[str]], + logger: logging.Logger = logging.getLogger("uvicorn.error"), **kwargs, ): - if not isinstance(predictor, BaseEstimator): - raise ValueError( - f"Predictor must be an instance of sklearn.base.BaseEstimator, got {type(predictor)}" - ) + self._method_names = ( + [method_names] if isinstance(method_names, str) else method_names + ) + self._predictor = predictor + self.validate() + self._logger = logger - self._predictor = predictor.set_output(transform="pandas") - self._endpoint_metadata_manager = EndpointMetadataManager(**kwargs) + super().__init__(**kwargs) @property - def predictor(self) -> BaseEstimator: - return self._predictor + def method_names(self) -> typing.List[str]: + return self._method_names @property - def endpoint_metadata_manager(self) -> EndpointMetadataManager: - return self._endpoint_metadata_manager + def predictor(self) -> typing.Callable: + return self._predictor + + @abstractmethod + def validate(self) -> None: + pass - def execute(self, input_df: pd.DataFrame) -> typing.Any: - raise NotImplementedError + @abstractmethod + def execute(self, input_df: pd.DataFrame) -> dict: + pass def get_runner_func(self) -> typing.Callable: - def runner_func(data: self.endpoint_metadata_manager.request_model): + def runner_func(data: self.request_model): + payload = None try: + payload = data.model_dump(by_alias=self.by_alias) + input_df = ( - pd.DataFrame(data.model_dump(by_alias=True), index=[0]) + pd.DataFrame(payload, index=[0]) if isinstance(data, pydantic.BaseModel) else data ) - return self.execute(input_df) + result = self.execute(input_df) + + self._logger.info( + { + "msg": "Successfully executed runner", + "runner": self.name, + "payload": payload, + "result": result, + }, + ) + + return result except Exception as ex: - if isinstance(ex, exceptions.JsonAPIException): + self._logger.error( + { + "msg": "Error during runner execution", + "runner": self.name, + "payload": payload, + }, + exc_info=True, + ) + if isinstance(ex, fastapi.HTTPException): raise ex - raise exceptions.JsonAPIException( + raise fastapi.HTTPException( status_code=500, - title="Inference Internal Server Error", detail={ - "error": str(ex), - "traceback": str(traceback.format_exc()), - "runner": self.endpoint_metadata_manager.name, + "runner": self.name, + **exceptions.parse_exception(ex), }, - ) from ex + ) - runner_func.__name__ = self.endpoint_metadata_manager.name + runner_func.__name__ = self.name return runner_func class SklearnRunner(SklearnBaseRunner): - def __init__( - self, - name: str, - predictor: BaseEstimator, - method_name: str, - request_model: typing.Union[typing.Type[pydantic.BaseModel], typing.List[dict]], - **kwargs, - ): - super().__init__( - predictor=predictor, name=name, request_model=request_model, **kwargs - ) + def validate(self) -> None: + if len(self.method_names) != 1: + raise ValueError("SklearnExecutor only supports one method") - if not hasattr(predictor, method_name): - raise ValueError(f"Predictor does not have method {method_name}") + if not hasattr(self.predictor, self.method_names[0]): + raise ValueError(f"Predictor does not have method {self.method_names[0]}") - self._method_name = method_name + def execute(self, input_df) -> dict: + predictor_method = getattr(self.predictor, self.method_names[0]) - @property - def method_name(self) -> str: - return self._method_name - - @property - def method(self) -> typing.Callable: - return getattr(self.predictor, self.method_name) - - def execute(self, input_df: pd.DataFrame) -> typing.Any: - return {"result": self.method(input_df).tolist()[0]} + return {"result": predictor_method(input_df).tolist()[0]} class SklearnPipelineRunner(SklearnBaseRunner): - def __init__( - self, - name: str, - predictor: Pipeline, - method_names: typing.List[str], - request_model: typing.Union[typing.Type[pydantic.BaseModel], typing.List[dict]], - **kwargs, - ): - kwargs["response_model"] = kwargs.get( - "response_model", schemas.ResultResponseWithStepsModel - ) - - super().__init__( - name=name, - predictor=predictor, - request_model=request_model, - **kwargs, - ) - - method_names = [method_names] if isinstance(method_names, str) else method_names - - if not hasattr(predictor, "steps"): + def validate(self) -> None: + if not hasattr(self.predictor, "steps"): raise ValueError("Predictor does not have steps") - if len(predictor.steps) != len(method_names): + if len(self.predictor.steps) != len(self.method_names): raise ValueError( - f"Predictor does not have the same number of steps ({len(predictor.steps)}) as method names ({len(method_names)})" + f"Predictor does not have the same number of steps ({len(self.predictor.steps)}) as method names ({len(self.method_names)})" ) - for i, method_name in enumerate(method_names): - if not hasattr(predictor.steps[i][1], method_name): + for i, method_name in enumerate(self.method_names): + if not hasattr(self.predictor.steps[i][1], method_name): raise ValueError( - f"Predictor does not have method {method_name} in step {predictor.steps[i][0]}" + f"Predictor does not have method {method_name} in step {self.predictor.steps[i][0]}" ) - self._method_names = method_names - - @property - def method_names(self) -> typing.List[str]: - return self._method_names - - def execute(self, input_df: pd.DataFrame) -> typing.Any: + def execute(self, input_df: pd.DataFrame) -> dict: step_outputs = {} previous_step_output = input_df.copy() for i, method_name in enumerate(self.method_names): @@ -147,20 +133,15 @@ def execute(self, input_df: pd.DataFrame) -> typing.Any: previous_step_output ) except Exception as ex: - raise exceptions.JsonAPIException( + raise fastapi.HTTPException( status_code=500, - title="Inference Internal Server Error - Pipeline Step", detail={ - "error": str(ex), - "traceback": str(traceback.format_exc()), - "runner": self.endpoint_metadata_manager.name, + "runner": self.name, "step": step_name, "method": method_name, - "step_outputs": step_outputs, - "previous_step_output_type": type(previous_step_output), - "previous_step_output": str(previous_step_output), + **exceptions.parse_exception(ex), }, - ) from ex + ) if isinstance(previous_step_output, pd.DataFrame): step_outputs[step_name] = previous_step_output.to_dict(orient="records") @@ -181,3 +162,11 @@ def execute(self, input_df: pd.DataFrame) -> typing.Any: "result": step_outputs[step_name][0], "steps": step_outputs, } + + @property + def response_model(self) -> typing.Type[pydantic.BaseModel]: + return schemas.ResultResponseWithStepsModel + + @response_model.setter + def response_model(self, value: typing.Type[pydantic.BaseModel]): + pass diff --git a/modelib/server/infrastructure.py b/modelib/server/infrastructure.py index d91330c..12ade29 100644 --- a/modelib/server/infrastructure.py +++ b/modelib/server/infrastructure.py @@ -1,9 +1,8 @@ -from fastapi import APIRouter -from prometheus_fastapi_instrumentator import Instrumentator +import fastapi from modelib.core import schemas -router = APIRouter(prefix="", tags=["Infrastructure"]) +router = fastapi.APIRouter(prefix="", tags=["Infrastructure"]) @router.get("/healthz", response_model=schemas.HealthCheckStausSchema) @@ -24,15 +23,5 @@ async def livez(): return schemas.HealthCheckStausSchema(status="ok") -def init_app(app): - Instrumentator( - excluded_handlers=[ - "/metrics", - "/docs*", - "/livez", - "/readyz", - "/healthz", - "openapi.json", - ] - ).instrument(app).expose(app, tags=["Infrastructure"]) +def init_app(app: fastapi.FastAPI): app.include_router(router) diff --git a/poetry.lock b/poetry.lock index 78a2751..928ddf4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,61 +1,58 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. 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"^0.26.0" +httpx = "^0.27.2" +scipy = "^1.15.1" +uvicorn = "^0.34.0" [tool.pytest.ini_options] addopts = "-vv --junitxml=pytest.xml -p no:warnings --cov-report term-missing:skip-covered --cov=modelib" diff --git a/tests/test_core/test_exceptions.py b/tests/test_core/test_exceptions.py new file mode 100644 index 0000000..529bcd5 --- /dev/null +++ b/tests/test_core/test_exceptions.py @@ -0,0 +1,13 @@ +from modelib.core.exceptions import parse_exception + + +def test_create_jsonapi_exception(): + try: + raise ValueError("Test exception") + except Exception as ex: + parsed_exception = parse_exception(ex) + + assert parsed_exception["type"] == "ValueError" + assert parsed_exception["message"] == "Test exception" + assert parsed_exception["traceback"] is not None + assert "ValueError: Test exception" in parsed_exception["traceback"] diff --git a/tests/test_core/test_schemas.py b/tests/test_core/test_schemas.py index 944aecf..997d34a 100644 --- a/tests/test_core/test_schemas.py +++ b/tests/test_core/test_schemas.py @@ -10,11 +10,11 @@ ( { "name": "test", - "dtype": schemas.DType.t_object, + "dtype": "object", }, { "name": "test", - "dtype": schemas.DType.t_object, + "dtype": "object", "alias": None, "default": None, "optional": False, @@ -23,13 +23,13 @@ ( { "name": "test", - "dtype": schemas.DType.t_object, + "dtype": "object", "alias": "test_alias", "default": "test_default", }, { "name": "test", - "dtype": schemas.DType.t_object, + "dtype": "object", "alias": "test_alias", "default": "test_default", "optional": True, @@ -91,52 +91,73 @@ def test_create_model(): assert issubclass(model, pydantic.BaseModel) all_default = model(bank_number=1) + assert all_default.model_dump() == { "bank_number": 1, - "plan_type_num": 1.0, - "user_age": 21, - "latest_market_value_cents": 250000.0, - "score_emailage": 500, + "sm_safe_claim_score_contracts_v2:contract_active_days": 60, + "sm_safe_claim_score_contracts_v2:graph_suspect_fraud_rate": -1, + "sm_safe_claim_score_contracts_v2:latest_market_value_cents": 250000.0, + "sm_safe_claim_score_contracts_v2:plan_type_num": 1.0, + "sm_safe_claim_score_contracts_v2:score_emailage": 500, + "sm_safe_claim_score_contracts_v2:user_age": 21, + } + + assert all_default.model_dump(by_alias=True) == { + "bank_number": 1, "contract_active_days": 60, "graph_suspect_fraud_rate": -1, + "latest_market_value_cents": 250000.0, + "plan_type_num": 1.0, + "score_emailage": 500, + "user_age": 21, } partial_default = model( **{ "bank_number": 1, - "sm_safe_claim_score_contracts_v2:plan_type_num": 0.8, - "sm_safe_claim_score_contracts_v2:user_age": 25, + "plan_type_num": 0.8, + "user_age": 25, } ) assert partial_default.model_dump() == { "bank_number": 1, + "sm_safe_claim_score_contracts_v2:contract_active_days": 60, + "sm_safe_claim_score_contracts_v2:graph_suspect_fraud_rate": -1, + "sm_safe_claim_score_contracts_v2:latest_market_value_cents": 250000.0, + "sm_safe_claim_score_contracts_v2:plan_type_num": 0.8, + "sm_safe_claim_score_contracts_v2:user_age": 25, + "sm_safe_claim_score_contracts_v2:score_emailage": 500, + } + + assert partial_default.model_dump(by_alias=True) == { + "bank_number": 1, + "contract_active_days": 60, + "graph_suspect_fraud_rate": -1, + "latest_market_value_cents": 250000.0, "plan_type_num": 0.8, "user_age": 25, - "latest_market_value_cents": 250000.0, "score_emailage": 500, - "contract_active_days": 60, - "graph_suspect_fraud_rate": -1, } all_non_default = model( **{ "bank_number": 1, - "sm_safe_claim_score_contracts_v2:plan_type_num": 0.8, - "sm_safe_claim_score_contracts_v2:user_age": 25, - "sm_safe_claim_score_contracts_v2:latest_market_value_cents": 10000.0, - "sm_safe_claim_score_contracts_v2:score_emailage": 10, - "sm_safe_claim_score_contracts_v2:contract_active_days": 40, - "sm_safe_claim_score_contracts_v2:graph_suspect_fraud_rate": -1, + "plan_type_num": 0.8, + "user_age": 25, + "latest_market_value_cents": 10000.0, + "score_emailage": 10, + "contract_active_days": 40, + "graph_suspect_fraud_rate": -1, } ) assert all_non_default.model_dump() == { "bank_number": 1, - "plan_type_num": 0.8, - "user_age": 25, - "latest_market_value_cents": 10000.0, - "score_emailage": 10, - "contract_active_days": 40, - "graph_suspect_fraud_rate": -1, + "sm_safe_claim_score_contracts_v2:plan_type_num": 0.8, + "sm_safe_claim_score_contracts_v2:user_age": 25, + "sm_safe_claim_score_contracts_v2:latest_market_value_cents": 10000.0, + "sm_safe_claim_score_contracts_v2:score_emailage": 10, + "sm_safe_claim_score_contracts_v2:contract_active_days": 40, + "sm_safe_claim_score_contracts_v2:graph_suspect_fraud_rate": -1, } diff --git a/tests/test_example.py b/tests/test_example.py index 1f29f3e..b2571bf 100644 --- a/tests/test_example.py +++ b/tests/test_example.py @@ -60,7 +60,7 @@ def test_example(): ml.SklearnRunner( name="my simple model", predictor=example.MODEL, - method_name="predict", + method_names="predict", request_model=example.InputData, ), "/my-simple-model", @@ -70,7 +70,7 @@ def test_example(): ml.SklearnRunner( name="my 232, simple model", predictor=example.MODEL, - method_name="predict", + method_names="predict", request_model=example.features_metadata, ), "/my-232-simple-model", @@ -78,7 +78,7 @@ def test_example(): ), ( ml.SklearnPipelineRunner( - "Pipeline Model", + name="Pipeline Model", predictor=example.MODEL, method_names=["transform", "predict"], request_model=example.InputData, @@ -101,7 +101,7 @@ def test_example(): ), ( ml.SklearnPipelineRunner( - "My SUPER Pipeline Model", + name="My SUPER Pipeline Model", predictor=example.MODEL, method_names=["transform", "predict"], request_model=example.features_metadata, diff --git a/tests/test_server/test_infrastructure_endpoints.py b/tests/test_server/test_infrastructure_endpoints.py index e13b7bb..574c774 100644 --- a/tests/test_server/test_infrastructure_endpoints.py +++ b/tests/test_server/test_infrastructure_endpoints.py @@ -28,9 +28,3 @@ def test_livez(client): response = client.get("/livez") assert response.status_code == 200 assert response.json() == {"status": "ok"} - - -def test_metrics(client): - response = client.get("/metrics") - assert response.status_code == 200 - assert response.text.startswith("# HELP") diff --git a/tests/test_sklearn_runner.py b/tests/test_sklearn_runner.py new file mode 100644 index 0000000..5dd87ab --- /dev/null +++ b/tests/test_sklearn_runner.py @@ -0,0 +1,67 @@ +from modelib.runners.sklearn import SklearnRunner +import example +import pandas as pd +import pytest +import fastapi + + +@pytest.fixture +def input_example() -> dict: + return { + "sepal length (cm)": 0, + "sepal width (cm)": 0, + "petal length (cm)": 0, + "petal width (cm)": 0, + } + + +@pytest.fixture +def input_example_df(input_example) -> pd.DataFrame: + return pd.DataFrame( + input_example, + index=[0], + ) + + +def test_create_sklearn_runner(input_example_df): + runner = SklearnRunner( + name="my simple model", + predictor=example.create_model(), + method_names="predict", + request_model=example.features_metadata, + ) + assert runner is not None + assert isinstance(runner, SklearnRunner) + + assert runner.name == "my simple model" + assert runner.slug == "my-simple-model" + + assert runner.execute(input_example_df) == {"result": 0} + + runner_func = runner.get_runner_func() + + assert runner_func is not None + assert callable(runner_func) + assert runner_func(runner.request_model(**input_example_df)) == {"result": 0} + + +def test_sklearn_runner_with_error(): + runner = SklearnRunner( + name="my simple model", + predictor=example.create_model(), + method_names="predict", + request_model=example.features_metadata, + ) + + runner_func = runner.get_runner_func() + + with pytest.raises(fastapi.HTTPException) as ex: + runner_func(None) + + assert ex.value.status_code == 500 + assert ex.value.detail["runner"] == "my simple model" + assert ( + ex.value.detail["message"] == "'NoneType' object has no attribute 'model_dump'" + ) + assert ex.value.detail["type"] == "AttributeError" + assert ex.value.detail["traceback"] is not None