From e7397f35c9ac09e367ce6c66a6ed4a83dce07c99 Mon Sep 17 00:00:00 2001 From: Mert <101130780+mertalev@users.noreply.github.com> Date: Sun, 13 Oct 2024 18:00:21 -0400 Subject: [PATCH] chore(ml): update pydantic (#13230) * update pydantic * fix typing * remove unused import * remove unused schema --- machine-learning/app/config.py | 24 ++-- machine-learning/app/main.py | 16 ++- machine-learning/app/schemas.py | 12 +- machine-learning/app/test_main.py | 17 ++- machine-learning/poetry.lock | 196 ++++++++++++++++++++++-------- machine-learning/pyproject.toml | 3 +- 6 files changed, 186 insertions(+), 82 deletions(-) diff --git a/machine-learning/app/config.py b/machine-learning/app/config.py index 52be0a30c8..828dee15f0 100644 --- a/machine-learning/app/config.py +++ b/machine-learning/app/config.py @@ -6,7 +6,8 @@ from pathlib import Path from socket import socket from gunicorn.arbiter import Arbiter -from pydantic import BaseModel, BaseSettings +from pydantic import BaseModel +from pydantic_settings import BaseSettings, SettingsConfigDict from rich.console import Console from rich.logging import RichHandler from uvicorn import Server @@ -14,11 +15,18 @@ from uvicorn.workers import UvicornWorker class PreloadModelData(BaseModel): - clip: str | None - facial_recognition: str | None + clip: str | None = None + facial_recognition: str | None = None class Settings(BaseSettings): + model_config = SettingsConfigDict( + env_prefix="MACHINE_LEARNING_", + case_sensitive=False, + env_nested_delimiter="__", + protected_namespaces=("settings_",), + ) + cache_folder: Path = Path("/cache") model_ttl: int = 300 model_ttl_poll_s: int = 10 @@ -34,23 +42,17 @@ class Settings(BaseSettings): ann_tuning_level: int = 2 preload: PreloadModelData | None = None - class Config: - env_prefix = "MACHINE_LEARNING_" - case_sensitive = False - env_nested_delimiter = "__" - @property def device_id(self) -> str: return os.environ.get("MACHINE_LEARNING_DEVICE_ID", "0") class LogSettings(BaseSettings): + model_config = SettingsConfigDict(case_sensitive=False) + immich_log_level: str = "info" no_color: bool = False - class Config: - case_sensitive = False - _clean_name = str.maketrans(":\\/", "___", ".") diff --git a/machine-learning/app/main.py b/machine-learning/app/main.py index 000119937e..684001b875 100644 --- a/machine-learning/app/main.py +++ b/machine-learning/app/main.py @@ -12,7 +12,7 @@ from zipfile import BadZipFile import orjson from fastapi import Depends, FastAPI, File, Form, HTTPException -from fastapi.responses import ORJSONResponse +from fastapi.responses import ORJSONResponse, PlainTextResponse from onnxruntime.capi.onnxruntime_pybind11_state import InvalidProtobuf, NoSuchFile from PIL.Image import Image from pydantic import ValidationError @@ -28,14 +28,12 @@ from .schemas import ( InferenceEntries, InferenceEntry, InferenceResponse, - MessageResponse, ModelFormat, ModelIdentity, ModelTask, ModelType, PipelineRequest, T, - TextResponse, ) MultiPartParser.max_file_size = 2**26 # spools to disk if payload is 64 MiB or larger @@ -127,14 +125,14 @@ def get_entries(entries: str = Form()) -> InferenceEntries: app = FastAPI(lifespan=lifespan) -@app.get("/", response_model=MessageResponse) -async def root() -> dict[str, str]: - return {"message": "Immich ML"} +@app.get("/") +async def root() -> ORJSONResponse: + return ORJSONResponse({"message": "Immich ML"}) -@app.get("/ping", response_model=TextResponse) -def ping() -> str: - return "pong" +@app.get("/ping") +def ping() -> PlainTextResponse: + return PlainTextResponse("pong") @app.post("/predict", dependencies=[Depends(update_state)]) diff --git a/machine-learning/app/schemas.py b/machine-learning/app/schemas.py index f051db12c3..a7ce2ee60d 100644 --- a/machine-learning/app/schemas.py +++ b/machine-learning/app/schemas.py @@ -1,9 +1,9 @@ from enum import Enum -from typing import Any, Literal, Protocol, TypedDict, TypeGuard, TypeVar +from typing import Any, Literal, Protocol, TypeGuard, TypeVar import numpy as np import numpy.typing as npt -from pydantic import BaseModel +from typing_extensions import TypedDict class StrEnum(str, Enum): @@ -13,14 +13,6 @@ class StrEnum(str, Enum): return self.value -class TextResponse(BaseModel): - __root__: str - - -class MessageResponse(BaseModel): - message: str - - class BoundingBox(TypedDict): x1: int y1: int diff --git a/machine-learning/app/test_main.py b/machine-learning/app/test_main.py index ad8986d572..50ec188aa4 100644 --- a/machine-learning/app/test_main.py +++ b/machine-learning/app/test_main.py @@ -810,11 +810,26 @@ class TestLoad: mock_model.model_format = ModelFormat.ONNX +def test_root_endpoint(deployed_app: TestClient) -> None: + response = deployed_app.get("http://localhost:3003") + + body = response.json() + assert response.status_code == 200 + assert body == {"message": "Immich ML"} + + +def test_ping_endpoint(deployed_app: TestClient) -> None: + response = deployed_app.get("http://localhost:3003/ping") + + assert response.status_code == 200 + assert response.text == "pong" + + @pytest.mark.skipif( not settings.test_full, reason="More time-consuming since it deploys the app and loads models.", ) -class TestEndpoints: +class TestPredictionEndpoints: def test_clip_image_endpoint( self, pil_image: Image.Image, responses: dict[str, Any], deployed_app: TestClient ) -> None: diff --git a/machine-learning/poetry.lock b/machine-learning/poetry.lock index 2afa60e701..460151e477 100644 --- a/machine-learning/poetry.lock +++ b/machine-learning/poetry.lock @@ -40,6 +40,17 @@ develop = ["imgaug (>=0.4.0)", "pytest"] imgaug = ["imgaug (>=0.4.0)"] tests = ["pytest"] +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = 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