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chore(deps): update machine-learning (#6302)

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* chore(deps): update machine-learning

* fix typing, use new lifespan syntax

* wrap in try / finally

* move log

---------

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
Co-authored-by: mertalev <101130780+mertalev@users.noreply.github.com>
This commit is contained in:
renovate[bot] 2024-01-13 05:00:09 +00:00 • committed by GitHub
parent bd5ae9f31e
commit 20be42cec0
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12 changed files with 238 additions and 212 deletions
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43
machine-learning/app/main.py
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@ -2,11 +2,11 @@ import asyncio
import gc
import os
import signal
import sys
import threading
import time
from concurrent.futures import ThreadPoolExecutor
from typing import Any, Iterator
from contextlib import asynccontextmanager
from typing import Any, AsyncGenerator, Iterator
from zipfile import BadZipFile
import orjson
@ -26,7 +26,6 @@ from .schemas import (
)
MultiPartParser.max_file_size = 2**26 # spools to disk if payload is 64 MiB or larger
app = FastAPI()
model_cache = ModelCache(ttl=settings.model_ttl, revalidate=settings.model_ttl > 0)
thread_pool: ThreadPoolExecutor | None = None
@ -35,8 +34,8 @@ active_requests = 0
last_called: float | None = None
@app.on_event("startup")
def startup() -> None:
@asynccontextmanager
async def lifespan(_: FastAPI) -> AsyncGenerator[None, None]:
global thread_pool
log.info(
(
@ -44,21 +43,22 @@ def startup() -> None:
f"{f'after {settings.model_ttl}s of inactivity' if settings.model_ttl > 0 else 'disabled'}."
)
)
# asyncio is a huge bottleneck for performance, so we use a thread pool to run blocking code
thread_pool = ThreadPoolExecutor(settings.request_threads) if settings.request_threads > 0 else None
if settings.model_ttl > 0 and settings.model_ttl_poll_s > 0:
asyncio.ensure_future(idle_shutdown_task())
log.info(f"Initialized request thread pool with {settings.request_threads} threads.")
@app.on_event("shutdown")
def shutdown() -> None:
log.handlers.clear()
for model in model_cache.cache._cache.values():
del model
if thread_pool is not None:
thread_pool.shutdown()
gc.collect()
try:
if settings.request_threads > 0:
# asyncio is a huge bottleneck for performance, so we use a thread pool to run blocking code
thread_pool = ThreadPoolExecutor(settings.request_threads) if settings.request_threads > 0 else None
log.info(f"Initialized request thread pool with {settings.request_threads} threads.")
if settings.model_ttl > 0 and settings.model_ttl_poll_s > 0:
asyncio.ensure_future(idle_shutdown_task())
yield
finally:
log.handlers.clear()
for model in model_cache.cache._cache.values():
del model
if thread_pool is not None:
thread_pool.shutdown()
gc.collect()
def update_state() -> Iterator[None]:
@ -71,6 +71,9 @@ def update_state() -> Iterator[None]:
active_requests -= 1
app = FastAPI(lifespan=lifespan)
@app.get("/", response_model=MessageResponse)
async def root() -> dict[str, str]:
return {"message": "Immich ML"}