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chore(ml): update pydantic (#13230)

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* update pydantic

* fix typing

* remove unused import

* remove unused schema
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Mert 2024-10-13 18:00:21 -04:00 committed by GitHub
parent f29fb1655a
commit e7397f35c9
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6 changed files with 186 additions and 82 deletions
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24
machine-learning/app/config.py
View file

@ -6,7 +6,8 @@ from pathlib import Path
from socket import socket from socket import socket
from gunicorn.arbiter import Arbiter 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.console import Console
from rich.logging import RichHandler from rich.logging import RichHandler
from uvicorn import Server from uvicorn import Server
@ -14,11 +15,18 @@ from uvicorn.workers import UvicornWorker
class PreloadModelData(BaseModel): class PreloadModelData(BaseModel):
clip: str | None clip: str | None = None
facial_recognition: str | None facial_recognition: str | None = None
class Settings(BaseSettings): class Settings(BaseSettings):
model_config = SettingsConfigDict(
env_prefix="MACHINE_LEARNING_",
case_sensitive=False,
env_nested_delimiter="__",
protected_namespaces=("settings_",),
)
cache_folder: Path = Path("/cache") cache_folder: Path = Path("/cache")
model_ttl: int = 300 model_ttl: int = 300
model_ttl_poll_s: int = 10 model_ttl_poll_s: int = 10
@ -34,23 +42,17 @@ class Settings(BaseSettings):
ann_tuning_level: int = 2 ann_tuning_level: int = 2
preload: PreloadModelData | None = None preload: PreloadModelData | None = None
class Config:
env_prefix = "MACHINE_LEARNING_"
case_sensitive = False
env_nested_delimiter = "__"
@property @property
def device_id(self) -> str: def device_id(self) -> str:
return os.environ.get("MACHINE_LEARNING_DEVICE_ID", "0") return os.environ.get("MACHINE_LEARNING_DEVICE_ID", "0")
class LogSettings(BaseSettings): class LogSettings(BaseSettings):
model_config = SettingsConfigDict(case_sensitive=False)
immich_log_level: str = "info" immich_log_level: str = "info"
no_color: bool = False no_color: bool = False
class Config:
case_sensitive = False
_clean_name = str.maketrans(":\\/", "___", ".") _clean_name = str.maketrans(":\\/", "___", ".")

16
machine-learning/app/main.py
View file

@ -12,7 +12,7 @@ from zipfile import BadZipFile
import orjson import orjson
from fastapi import Depends, FastAPI, File, Form, HTTPException 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 onnxruntime.capi.onnxruntime_pybind11_state import InvalidProtobuf, NoSuchFile
from PIL.Image import Image from PIL.Image import Image
from pydantic import ValidationError from pydantic import ValidationError
@ -28,14 +28,12 @@ from .schemas import (
InferenceEntries, InferenceEntries,
InferenceEntry, InferenceEntry,
InferenceResponse, InferenceResponse,
MessageResponse,
ModelFormat, ModelFormat,
ModelIdentity, ModelIdentity,
ModelTask, ModelTask,
ModelType, ModelType,
PipelineRequest, PipelineRequest,
T, T,
TextResponse,
) )
MultiPartParser.max_file_size = 2**26 # spools to disk if payload is 64 MiB or larger 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 = FastAPI(lifespan=lifespan)
@app.get("/", response_model=MessageResponse) @app.get("/")
async def root() -> dict[str, str]: async def root() -> ORJSONResponse:
return {"message": "Immich ML"} return ORJSONResponse({"message": "Immich ML"})
@app.get("/ping", response_model=TextResponse) @app.get("/ping")
def ping() -> str: def ping() -> PlainTextResponse:
return "pong" return PlainTextResponse("pong")
@app.post("/predict", dependencies=[Depends(update_state)]) @app.post("/predict", dependencies=[Depends(update_state)])

12
machine-learning/app/schemas.py
View file

@ -1,9 +1,9 @@
from enum import Enum 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 as np
import numpy.typing as npt import numpy.typing as npt
from pydantic import BaseModel from typing_extensions import TypedDict
class StrEnum(str, Enum): class StrEnum(str, Enum):
@ -13,14 +13,6 @@ class StrEnum(str, Enum):
return self.value return self.value
class TextResponse(BaseModel):
__root__: str
class MessageResponse(BaseModel):
message: str
class BoundingBox(TypedDict): class BoundingBox(TypedDict):
x1: int x1: int
y1: int y1: int

17
machine-learning/app/test_main.py
View file

@ -810,11 +810,26 @@ class TestLoad:
mock_model.model_format = ModelFormat.ONNX 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( @pytest.mark.skipif(
not settings.test_full, not settings.test_full,
reason="More time-consuming since it deploys the app and loads models.", reason="More time-consuming since it deploys the app and loads models.",
) )
class TestEndpoints: class TestPredictionEndpoints:
def test_clip_image_endpoint( def test_clip_image_endpoint(
self, pil_image: Image.Image, responses: dict[str, Any], deployed_app: TestClient self, pil_image: Image.Image, responses: dict[str, Any], deployed_app: TestClient
) -> None: ) -> None:

196
machine-learning/poetry.lock generated
View file

@ -40,6 +40,17 @@ develop = ["imgaug (>=0.4.0)", "pytest"]
imgaug = ["imgaug (>=0.4.0)"] imgaug = ["imgaug (>=0.4.0)"]
tests = ["pytest"] 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 = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
{file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
]
[[package]] [[package]]
name = "anyio" name = "anyio"
version = "4.2.0" version = "4.2.0"
@ -2374,62 +2385,147 @@ files = [
[[package]] [[package]]
name = "pydantic" name = "pydantic"
version = "1.10.18" version = "2.9.2"
description = "Data validation and settings management using python type hints" description = "Data validation using Python type hints"
optional = false optional = false
python-versions = ">=3.7" python-versions = ">=3.8"
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[package.dependencies] [package.dependencies]
typing-extensions = ">=4.2.0" annotated-types = ">=0.6.0"
pydantic-core = "2.23.4"
typing-extensions = [
{version = ">=4.6.1", markers = "python_version < \"3.13\""},
{version = ">=4.12.2", markers = "python_version >= \"3.13\""},
]
[package.extras] [package.extras]
dotenv = ["python-dotenv (>=0.10.4)"] email = ["email-validator (>=2.0.0)"]
email = ["email-validator (>=1.0.3)"] timezone = ["tzdata"]
[[package]]
name = "pydantic-core"
version = "2.23.4"
description = "Core functionality for Pydantic validation and serialization"
optional = false
python-versions = ">=3.8"
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[[package]] [[package]]
name = "pygments" name = "pygments"
@ -3605,4 +3701,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
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3
machine-learning/pyproject.toml
View file

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