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feat(ml): model unloading (#2661)

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* model cache

* fixed revalidation when using cache namespace

* fixed ttl not being set, added lock
This commit is contained in:
Mert 2023-06-06 21:48:51 -04:00 committed by GitHub
parent 6ce35d47f5
commit d0cc231782
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5 changed files with 400 additions and 233 deletions
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84
machine-learning/app/cache.py Normal file
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@ -0,0 +1,84 @@
from aiocache.plugins import TimingPlugin, BasePlugin
from aiocache.backends.memory import SimpleMemoryCache
from aiocache.lock import OptimisticLock
from typing import Any
from models import get_model
class ModelCache:
"""Fetches a model from an in-memory cache, instantiating it if it's missing."""
def __init__(
self,
ttl: int | None = None,
revalidate: bool = False,
timeout: int | None = None,
profiling: bool = False,
):
"""
Args:
ttl: Unloads model after this duration. Disabled if None. Defaults to None.
revalidate: Resets TTL on cache hit. Useful to keep models in memory while active. Defaults to False.
timeout: Maximum allowed time for model to load. Disabled if None. Defaults to None.
profiling: Collects metrics for cache operations, adding slight overhead. Defaults to False.
"""
self.ttl = ttl
plugins = []
if revalidate:
plugins.append(RevalidationPlugin())
if profiling:
plugins.append(TimingPlugin())
self.cache = SimpleMemoryCache(
ttl=ttl, timeout=timeout, plugins=plugins, namespace=None
)
async def get_cached_model(
self, model_name: str, model_type: str, **model_kwargs
) -> Any:
"""
Args:
model_name: Name of model in the model hub used for the task.
model_type: Model type or task, which determines which model zoo is used.
Returns:
model: The requested model.
"""
key = self.cache.build_key(model_name, model_type)
model = await self.cache.get(key)
if model is None:
async with OptimisticLock(self.cache, key) as lock:
model = get_model(model_name, model_type, **model_kwargs)
await lock.cas(model, ttl=self.ttl)
return model
async def get_profiling(self) -> dict[str, float] | None:
if not hasattr(self.cache, "profiling"):
return None
return self.cache.profiling # type: ignore
class RevalidationPlugin(BasePlugin):
"""Revalidates cache item's TTL after cache hit."""
async def post_get(self, client, key, ret=None, namespace=None, **kwargs):
if ret is None:
return
if namespace is not None:
key = client.build_key(key, namespace)
if key in client._handlers:
await client.expire(key, client.ttl)
async def post_multi_get(self, client, keys, ret=None, namespace=None, **kwargs):
if ret is None:
return
for key, val in zip(keys, ret):
if namespace is not None:
key = client.build_key(key, namespace)
if val is not None and key in client._handlers:
await client.expire(key, client.ttl)

131
machine-learning/app/main.py
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@ -1,5 +1,7 @@
import os import os
from typing import Any from typing import Any
from cache import ModelCache
from schemas import ( from schemas import (
EmbeddingResponse, EmbeddingResponse,
FaceResponse, FaceResponse,
@ -9,16 +11,11 @@ from schemas import (
TextResponse, TextResponse,
VisionModelRequest, VisionModelRequest,
) )
import cv2 as cv
import uvicorn import uvicorn
from insightface.app import FaceAnalysis
from transformers import pipeline
from sentence_transformers import SentenceTransformer
from transformers import Pipeline
from PIL import Image from PIL import Image
from fastapi import FastAPI from fastapi import FastAPI, HTTPException
from models import get_model, run_classification, run_facial_recognition
classification_model = os.getenv( classification_model = os.getenv(
"MACHINE_LEARNING_CLASSIFICATION_MODEL", "microsoft/resnet-50" "MACHINE_LEARNING_CLASSIFICATION_MODEL", "microsoft/resnet-50"
@ -29,21 +26,20 @@ facial_recognition_model = os.getenv(
"MACHINE_LEARNING_FACIAL_RECOGNITION_MODEL", "buffalo_l" "MACHINE_LEARNING_FACIAL_RECOGNITION_MODEL", "buffalo_l"
) )
min_face_score = float(os.getenv("MACHINE_LEARNING_MIN_FACE_SCORE", 0.7))
min_tag_score = float(os.getenv("MACHINE_LEARNING_MIN_TAG_SCORE", 0.9)) min_tag_score = float(os.getenv("MACHINE_LEARNING_MIN_TAG_SCORE", 0.9))
eager_startup = ( eager_startup = (
os.getenv("MACHINE_LEARNING_EAGER_STARTUP", "true") == "true" os.getenv("MACHINE_LEARNING_EAGER_STARTUP", "true") == "true"
) # loads all models at startup ) # loads all models at startup
model_ttl = int(os.getenv("MACHINE_LEARNING_MODEL_TTL", 300))
cache_folder = os.getenv("MACHINE_LEARNING_CACHE_FOLDER", "/cache") _model_cache = None
_model_cache = {}
app = FastAPI() app = FastAPI()
@app.on_event("startup") @app.on_event("startup")
async def startup_event() -> None: async def startup_event() -> None:
global _model_cache
_model_cache = ModelCache(ttl=model_ttl, revalidate=True)
models = [ models = [
(classification_model, "image-classification"), (classification_model, "image-classification"),
(clip_image_model, "clip"), (clip_image_model, "clip"),
@ -54,9 +50,9 @@ async def startup_event() -> None:
# Get all models # Get all models
for model_name, model_type in models: for model_name, model_type in models:
if eager_startup: if eager_startup:
get_cached_model(model_name, model_type) await _model_cache.get_cached_model(model_name, model_type)
else: else:
_get_model(model_name, model_type) get_model(model_name, model_type)
@app.get("/", response_model=MessageResponse) @app.get("/", response_model=MessageResponse)
@ -70,10 +66,14 @@ def ping() -> str:
@app.post("/image-classifier/tag-image", response_model=TagResponse, status_code=200) @app.post("/image-classifier/tag-image", response_model=TagResponse, status_code=200)
def image_classification(payload: VisionModelRequest) -> list[str]: async def image_classification(payload: VisionModelRequest) -> list[str]:
model = get_cached_model(classification_model, "image-classification") if _model_cache is None:
assetPath = payload.image_path raise HTTPException(status_code=500, detail="Unable to load model.")
labels = run_engine(model, assetPath)
model = await _model_cache.get_cached_model(
classification_model, "image-classification"
)
labels = run_classification(model, payload.image_path, min_tag_score)
return labels return labels
@ -82,10 +82,14 @@ def image_classification(payload: VisionModelRequest) -> list[str]:
response_model=EmbeddingResponse, response_model=EmbeddingResponse,
status_code=200, status_code=200,
) )
def clip_encode_image(payload: VisionModelRequest) -> list[float]: async def clip_encode_image(payload: VisionModelRequest) -> list[float]:
model = get_cached_model(clip_image_model, "clip") if _model_cache is None:
raise HTTPException(status_code=500, detail="Unable to load model.")
model = await _model_cache.get_cached_model(clip_image_model, "clip")
image = Image.open(payload.image_path) image = Image.open(payload.image_path)
return model.encode(image).tolist() embedding = model.encode(image).tolist()
return embedding
@app.post( @app.post(
@ -93,82 +97,27 @@ def clip_encode_image(payload: VisionModelRequest) -> list[float]:
response_model=EmbeddingResponse, response_model=EmbeddingResponse,
status_code=200, status_code=200,
) )
def clip_encode_text(payload: TextModelRequest) -> list[float]: async def clip_encode_text(payload: TextModelRequest) -> list[float]:
model = get_cached_model(clip_text_model, "clip") if _model_cache is None:
return model.encode(payload.text).tolist() raise HTTPException(status_code=500, detail="Unable to load model.")
model = await _model_cache.get_cached_model(clip_text_model, "clip")
embedding = model.encode(payload.text).tolist()
return embedding
@app.post( @app.post(
"/facial-recognition/detect-faces", response_model=FaceResponse, status_code=200 "/facial-recognition/detect-faces", response_model=FaceResponse, status_code=200
) )
def facial_recognition(payload: VisionModelRequest) -> list[dict[str, Any]]: async def facial_recognition(payload: VisionModelRequest) -> list[dict[str, Any]]:
model = get_cached_model(facial_recognition_model, "facial-recognition") if _model_cache is None:
img = cv.imread(payload.image_path) raise HTTPException(status_code=500, detail="Unable to load model.")
height, width, _ = img.shape
results = []
faces = model.get(img)
for face in faces: model = await _model_cache.get_cached_model(
if face.det_score < min_face_score: facial_recognition_model, "facial-recognition"
continue )
x1, y1, x2, y2 = face.bbox faces = run_facial_recognition(model, payload.image_path)
return faces
results.append(
{
"imageWidth": width,
"imageHeight": height,
"boundingBox": {
"x1": round(x1),
"y1": round(y1),
"x2": round(x2),
"y2": round(y2),
},
"score": face.det_score.item(),
"embedding": face.normed_embedding.tolist(),
}
)
return results
def run_engine(engine: Pipeline, path: str) -> list[str]:
result: list[str] = []
predictions: list[dict[str, Any]] = engine(path) # type: ignore
for pred in predictions:
tags = pred["label"].split(", ")
if pred["score"] > min_tag_score:
result = [*result, *tags]
if len(result) > 1:
result = list(set(result))
return result
def get_cached_model(model, task) -> Any:
global _model_cache
key = "|".join([model, str(task)])
if key not in _model_cache:
model = _get_model(model, task)
_model_cache[key] = model
return _model_cache[key]
def _get_model(model, task) -> Any:
match task:
case "facial-recognition":
model = FaceAnalysis(
name=model,
root=cache_folder,
allowed_modules=["detection", "recognition"],
)
model.prepare(ctx_id=0, det_size=(640, 640))
case "clip":
model = SentenceTransformer(model, cache_folder=cache_folder)
case _:
model = pipeline(model=model, task=task)
return model
if __name__ == "__main__": if __name__ == "__main__":

117
machine-learning/app/models.py Normal file
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@ -0,0 +1,117 @@
import torch
from insightface.app import FaceAnalysis
from pathlib import Path
import os
from transformers import pipeline, Pipeline
from sentence_transformers import SentenceTransformer
from typing import Any
import cv2 as cv
cache_folder = os.getenv("MACHINE_LEARNING_CACHE_FOLDER", "/cache")
device = "cuda" if torch.cuda.is_available() else "cpu"
def get_model(model_name: str, model_type: str, **model_kwargs):
"""
Instantiates the specified model.
Args:
model_name: Name of model in the model hub used for the task.
model_type: Model type or task, which determines which model zoo is used.
`facial-recognition` uses Insightface, while all other models use the HF Model Hub.
Options:
`image-classification`, `clip`,`facial-recognition`, `tokenizer`, `processor`
Returns:
model: The requested model.
"""
cache_dir = _get_cache_dir(model_name, model_type)
match model_type:
case "facial-recognition":
model = _load_facial_recognition(
model_name, cache_dir=cache_dir, **model_kwargs
)
case "clip":
model = SentenceTransformer(
model_name, cache_folder=cache_dir, **model_kwargs
)
case _:
model = pipeline(
model_type,
model_name,
model_kwargs={"cache_dir": cache_dir, **model_kwargs},
)
return model
def run_classification(
model: Pipeline, image_path: str, min_score: float | None = None
):
predictions: list[dict[str, Any]] = model(image_path) # type: ignore
result = {
tag
for pred in predictions
for tag in pred["label"].split(", ")
if min_score is None or pred["score"] >= min_score
}
return list(result)
def run_facial_recognition(
model: FaceAnalysis, image_path: str
) -> list[dict[str, Any]]:
img = cv.imread(image_path)
height, width, _ = img.shape
results = []
faces = model.get(img)
for face in faces:
x1, y1, x2, y2 = face.bbox
results.append(
{
"imageWidth": width,
"imageHeight": height,
"boundingBox": {
"x1": round(x1),
"y1": round(y1),
"x2": round(x2),
"y2": round(y2),
},
"score": face.det_score.item(),
"embedding": face.normed_embedding.tolist(),
}
)
return results
def _load_facial_recognition(
model_name: str,
min_face_score: float | None = None,
cache_dir: Path | str | None = None,
**model_kwargs,
):
if cache_dir is None:
cache_dir = _get_cache_dir(model_name, "facial-recognition")
if isinstance(cache_dir, Path):
cache_dir = cache_dir.as_posix()
if min_face_score is None:
min_face_score = float(os.getenv("MACHINE_LEARNING_MIN_FACE_SCORE", 0.7))
model = FaceAnalysis(
name=model_name,
root=cache_dir,
allowed_modules=["detection", "recognition"],
**model_kwargs,
)
model.prepare(ctx_id=0, det_thresh=min_face_score, det_size=(640, 640))
return model
def _get_cache_dir(model_name: str, model_type: str) -> Path:
return Path(cache_folder, device, model_type, model_name)

298
machine-learning/poetry.lock generated
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@ -1,5 +1,21 @@
# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. # This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
[[package]]
name = "aiocache"
version = "0.12.1"
description = "multi backend asyncio cache"
optional = false
python-versions = "*"
files = [
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]
[package.extras]
memcached = ["aiomcache (>=0.5.2)"]
msgpack = ["msgpack (>=0.5.5)"]
redis = ["redis (>=4.2.0)"]
[[package]] [[package]]
name = "albumentations" name = "albumentations"
version = "1.3.0" version = "1.3.0"
@ -789,61 +805,61 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
[[package]] [[package]]
name = "markupsafe" name = "markupsafe"
version = "2.1.2" version = "2.1.3"
description = "Safely add untrusted strings to HTML/XML markup." description = "Safely add untrusted strings to HTML/XML markup."
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] ]
[[package]] [[package]]
@ -2441,4 +2457,4 @@ files = [
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = "^3.11" python-versions = "^3.11"
content-hash = "5c63ad195f75d0d66874c22860de716df81aceb5a3c6bf65081bedd871f9733b" content-hash = "0c347e972e004807bb2cd1f50f60ccf8bb32496575f813f48745f24389ab1a2c"

3
machine-learning/pyproject.toml
View File

@ -10,7 +10,7 @@ packages = [{include = "app"}]
python = "^3.11" python = "^3.11"
torch = [ torch = [
{markers = "platform_machine == 'arm64' or platform_machine == 'aarch64'", version = "=2.0.1", source = "pypi"}, {markers = "platform_machine == 'arm64' or platform_machine == 'aarch64'", version = "=2.0.1", source = "pypi"},
{markers = "platform_machine == 'amd64' or platform_machine == 'x86_64'", version = "=2.0.1+cpu", source = "pytorch-cpu"} {markers = "platform_machine == 'amd64' or platform_machine == 'x86_64'", version = "=2.0.1", source = "pytorch-cpu"}
] ]
transformers = "^4.29.2" transformers = "^4.29.2"
sentence-transformers = "^2.2.2" sentence-transformers = "^2.2.2"
@ -21,6 +21,7 @@ pillow = "^9.5.0"
fastapi = "^0.95.2" fastapi = "^0.95.2"
uvicorn = {extras = ["standard"], version = "^0.22.0"} uvicorn = {extras = ["standard"], version = "^0.22.0"}
pydantic = "^1.10.8" pydantic = "^1.10.8"
aiocache = "^0.12.1"
[tool.poetry.group.dev.dependencies] [tool.poetry.group.dev.dependencies]
mypy = "^1.3.0" mypy = "^1.3.0"