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chore(ml): memory optimisations (#3934)

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This commit is contained in:
Mert
2023-08-31 19:30:53 -04:00
committed by GitHub
parent c0a48d7357
commit 41461e0d5d
8 changed files with 122 additions and 107 deletions
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9
machine-learning/Dockerfile
View File

@@ -1,4 +1,4 @@
FROM python:3.11.4-bullseye@sha256:5b401676aff858495a5c9c726c60b8b73fe52833e9e16eccdb59e93d52741727 as builder
FROM python:3.11-bookworm as builder
ENV PYTHONDONTWRITEBYTECODE=1 \
PYTHONUNBUFFERED=1 \
@@ -14,9 +14,9 @@ COPY poetry.lock pyproject.toml requirements.txt ./
RUN poetry install --sync --no-interaction --no-ansi --no-root --only main
RUN pip install --no-deps -r requirements.txt
FROM python:3.11.4-slim-bullseye@sha256:91d194f58f50594cda71dcd2e8fdefd90e7ecc57d07823813b67c8521e565dcd
FROM python:3.11-slim-bookworm
RUN apt-get update && apt-get install -y --no-install-recommends tini && rm -rf /var/lib/apt/lists/*
RUN apt-get update && apt-get install -y --no-install-recommends tini libmimalloc2.0 && rm -rf /var/lib/apt/lists/*
WORKDIR /usr/src/app
ENV NODE_ENV=production \
@@ -27,6 +27,7 @@ ENV NODE_ENV=production \
PYTHONPATH=/usr/src
COPY --from=builder /opt/venv /opt/venv
COPY start.sh log_conf.json ./
COPY app .
ENTRYPOINT ["tini", "--"]
CMD ["python", "-m", "app.main"]
CMD ["./start.sh"]

19
machine-learning/app/config.py
View File

@@ -2,6 +2,7 @@ import logging
import os
from pathlib import Path
import gunicorn
import starlette
from pydantic import BaseSettings
from rich.console import Console
@@ -56,12 +57,14 @@ LOG_LEVELS: dict[str, int] = {
settings = Settings()
log_settings = LogSettings()
console = Console(color_system="standard", no_color=log_settings.no_color)
logging.basicConfig(
format="%(message)s",
handlers=[
RichHandler(show_path=False, omit_repeated_times=False, console=console, tracebacks_suppress=[starlette])
],
)
log = logging.getLogger("uvicorn")
class CustomRichHandler(RichHandler):
def __init__(self) -> None:
console = Console(color_system="standard", no_color=log_settings.no_color)
super().__init__(
show_path=False, omit_repeated_times=False, console=console, tracebacks_suppress=[gunicorn, starlette]
)
log = logging.getLogger("gunicorn.access")
log.setLevel(LOG_LEVELS.get(log_settings.log_level.lower(), logging.INFO))

21
machine-learning/app/main.py
View File

@@ -1,11 +1,8 @@
import asyncio
import logging
import os
from concurrent.futures import ThreadPoolExecutor
from typing import Any
import orjson
import uvicorn
from fastapi import FastAPI, Form, HTTPException, UploadFile
from fastapi.responses import ORJSONResponse
from starlette.formparsers import MultiPartParser
@@ -33,7 +30,7 @@ def init_state() -> None:
)
)
# asyncio is a huge bottleneck for performance, so we use a thread pool to run blocking code
app.state.thread_pool = ThreadPoolExecutor(settings.request_threads)
app.state.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.")
@@ -73,17 +70,7 @@ async def predict(
async def run(model: InferenceModel, inputs: Any) -> Any:
if app.state.thread_pool is not None:
return await asyncio.get_running_loop().run_in_executor(app.state.thread_pool, model.predict, inputs)
if __name__ == "__main__":
is_dev = os.getenv("NODE_ENV") == "development"
uvicorn.run(
"app.main:app",
host=settings.host,
port=settings.port,
reload=is_dev,
workers=settings.workers,
log_config=None,
access_log=log.isEnabledFor(logging.INFO),
)
else:
return model.predict(inputs)

1
machine-learning/app/models/base.py
View File

@@ -53,6 +53,7 @@ class InferenceModel(ABC):
log.debug(f"Setting intra_op_num_threads to {intra_op_num_threads}")
self.sess_options.inter_op_num_threads = inter_op_num_threads
self.sess_options.intra_op_num_threads = intra_op_num_threads
self.sess_options.enable_cpu_mem_arena = False
try:
loader(**model_kwargs)

17
machine-learning/log_conf.json Normal file
View File

@@ -0,0 +1,17 @@
{
"version": 1,
"disable_existing_loggers": true,
"formatters": { "rich": { "show_path": false, "omit_repeated_times": false } },
"handlers": {
"console": {
"class": "app.config.CustomRichHandler",
"formatter": "rich",
"level": "INFO"
}
},
"loggers": {
"gunicorn.access": { "propagate": true },
"gunicorn.error": { "propagate": true }
},
"root": { "handlers": ["console"] }
}

144
machine-learning/poetry.lock generated
View File

@@ -164,13 +164,13 @@ tests = ["pytest"]
[[package]]
name = "anyio"
version = "3.7.1"
version = "4.0.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.7"
python-versions = ">=3.8"
files = [
{file = "anyio-3.7.1-py3-none-any.whl", hash = "sha256:91dee416e570e92c64041bd18b900d1d6fa78dff7048769ce5ac5ddad004fbb5"},
{file = "anyio-3.7.1.tar.gz", hash = "sha256:44a3c9aba0f5defa43261a8b3efb97891f2bd7d804e0e1f56419befa1adfc780"},
{file = "anyio-4.0.0-py3-none-any.whl", hash = "sha256:cfdb2b588b9fc25ede96d8db56ed50848b0b649dca3dd1df0b11f683bb9e0b5f"},
{file = "anyio-4.0.0.tar.gz", hash = "sha256:f7ed51751b2c2add651e5747c891b47e26d2a21be5d32d9311dfe9692f3e5d7a"},
]
[package.dependencies]
@@ -178,9 +178,9 @@ idna = ">=2.8"
sniffio = ">=1.1"
[package.extras]
doc = ["Sphinx", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.2.2)", "sphinxcontrib-jquery"]
test = ["anyio[trio]", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
trio = ["trio (<0.22)"]
doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)"]
test = ["anyio[trio]", "coverage[toml] (>=7)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
trio = ["trio (>=0.22)"]
[[package]]
name = "async-timeout"
@@ -874,18 +874,18 @@ test = ["anyio[trio] (>=3.2.1,<4.0.0)", "black (==23.1.0)", "coverage[toml] (>=6
[[package]]
name = "filelock"
version = "3.12.2"
version = "3.12.3"
description = "A platform independent file lock."
optional = false
python-versions = ">=3.7"
python-versions = ">=3.8"
files = [
{file = "filelock-3.12.2-py3-none-any.whl", hash = "sha256:cbb791cdea2a72f23da6ac5b5269ab0a0d161e9ef0100e653b69049a7706d1ec"},
{file = "filelock-3.12.2.tar.gz", hash = "sha256:002740518d8aa59a26b0c76e10fb8c6e15eae825d34b6fdf670333fd7b938d81"},
{file = "filelock-3.12.3-py3-none-any.whl", hash = "sha256:f067e40ccc40f2b48395a80fcbd4728262fab54e232e090a4063ab804179efeb"},
{file = "filelock-3.12.3.tar.gz", hash = "sha256:0ecc1dd2ec4672a10c8550a8182f1bd0c0a5088470ecd5a125e45f49472fac3d"},
]
[package.extras]
docs = ["furo (>=2023.5.20)", "sphinx (>=7.0.1)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"]
testing = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "diff-cover (>=7.5)", "pytest (>=7.3.1)", "pytest-cov (>=4.1)", "pytest-mock (>=3.10)", "pytest-timeout (>=2.1)"]
docs = ["furo (>=2023.7.26)", "sphinx (>=7.1.2)", "sphinx-autodoc-typehints (>=1.24)"]
testing = ["covdefaults (>=2.3)", "coverage (>=7.3)", "diff-cover (>=7.7)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)", "pytest-timeout (>=2.1)"]
[[package]]
name = "flask"
@@ -1453,17 +1453,17 @@ test = ["objgraph", "psutil"]
[[package]]
name = "gunicorn"
version = "20.1.0"
version = "21.2.0"
description = "WSGI HTTP Server for UNIX"
optional = false
python-versions = ">=3.5"
files = [
{file = "gunicorn-20.1.0-py3-none-any.whl", hash = "sha256:9dcc4547dbb1cb284accfb15ab5667a0e5d1881cc443e0677b4882a4067a807e"},
{file = "gunicorn-20.1.0.tar.gz", hash = "sha256:e0a968b5ba15f8a328fdfd7ab1fcb5af4470c28aaf7e55df02a99bc13138e6e8"},
{file = "gunicorn-21.2.0-py3-none-any.whl", hash = "sha256:3213aa5e8c24949e792bcacfc176fef362e7aac80b76c56f6b5122bf350722f0"},
{file = "gunicorn-21.2.0.tar.gz", hash = "sha256:88ec8bff1d634f98e61b9f65bc4bf3cd918a90806c6f5c48bc5603849ec81033"},
]
[package.dependencies]
setuptools = ">=3.0"
packaging = "*"
[package.extras]
eventlet = ["eventlet (>=0.24.1)"]
@@ -2619,69 +2619,61 @@ files = [
[[package]]
name = "pandas"
version = "2.0.3"
version = "2.1.0"
description = "Powerful data structures for data analysis, time series, and statistics"
optional = false
python-versions = ">=3.8"
python-versions = ">=3.9"
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{file = "pandas-2.0.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba619e410a21d8c387a1ea6e8a0e49bb42216474436245718d7f2e88a2f8d7c0"},
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[package.dependencies]
numpy = [
{version = ">=1.21.0", markers = "python_version >= \"3.10\""},
{version = ">=1.23.2", markers = "python_version >= \"3.11\""},
]
numpy = {version = ">=1.23.2", markers = "python_version >= \"3.11\""}
python-dateutil = ">=2.8.2"
pytz = ">=2020.1"
tzdata = ">=2022.1"
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clipboard = ["PyQt5 (>=5.15.1)", "qtpy (>=2.2.0)"]
compression = ["brotlipy (>=0.7.0)", "python-snappy (>=0.6.0)", "zstandard (>=0.15.2)"]
computation = ["scipy (>=1.7.1)", "xarray (>=0.21.0)"]
excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.0.7)", "pyxlsb (>=1.0.8)", "xlrd (>=2.0.1)", "xlsxwriter (>=1.4.3)"]
all = ["PyQt5 (>=5.15.6)", "SQLAlchemy (>=1.4.36)", "beautifulsoup4 (>=4.11.1)", "bottleneck (>=1.3.4)", "dataframe-api-compat (>=0.1.7)", "fastparquet (>=0.8.1)", "fsspec (>=2022.05.0)", "gcsfs (>=2022.05.0)", "html5lib (>=1.1)", "hypothesis (>=6.46.1)", "jinja2 (>=3.1.2)", "lxml (>=4.8.0)", "matplotlib (>=3.6.1)", "numba (>=0.55.2)", "numexpr (>=2.8.0)", "odfpy (>=1.4.1)", "openpyxl (>=3.0.10)", "pandas-gbq (>=0.17.5)", "psycopg2 (>=2.9.3)", "pyarrow (>=7.0.0)", "pymysql (>=1.0.2)", "pyreadstat (>=1.1.5)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)", "pyxlsb (>=1.0.9)", "qtpy (>=2.2.0)", "s3fs (>=2022.05.0)", "scipy (>=1.8.1)", "tables (>=3.7.0)", "tabulate (>=0.8.10)", "xarray (>=2022.03.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.3)", "zstandard (>=0.17.0)"]
aws = ["s3fs (>=2022.05.0)"]
clipboard = ["PyQt5 (>=5.15.6)", "qtpy (>=2.2.0)"]
compression = ["zstandard (>=0.17.0)"]
computation = ["scipy (>=1.8.1)", "xarray (>=2022.03.0)"]
consortium-standard = ["dataframe-api-compat (>=0.1.7)"]
excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.0.10)", "pyxlsb (>=1.0.9)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.3)"]
feather = ["pyarrow (>=7.0.0)"]
fss = ["fsspec (>=2021.07.0)"]
gcp = ["gcsfs (>=2021.07.0)", "pandas-gbq (>=0.15.0)"]
hdf5 = ["tables (>=3.6.1)"]
html = ["beautifulsoup4 (>=4.9.3)", "html5lib (>=1.1)", "lxml (>=4.6.3)"]
mysql = ["SQLAlchemy (>=1.4.16)", "pymysql (>=1.0.2)"]
output-formatting = ["jinja2 (>=3.0.0)", "tabulate (>=0.8.9)"]
fss = ["fsspec (>=2022.05.0)"]
gcp = ["gcsfs (>=2022.05.0)", "pandas-gbq (>=0.17.5)"]
hdf5 = ["tables (>=3.7.0)"]
html = ["beautifulsoup4 (>=4.11.1)", "html5lib (>=1.1)", "lxml (>=4.8.0)"]
mysql = ["SQLAlchemy (>=1.4.36)", "pymysql (>=1.0.2)"]
output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.8.10)"]
parquet = ["pyarrow (>=7.0.0)"]
performance = ["bottleneck (>=1.3.2)", "numba (>=0.53.1)", "numexpr (>=2.7.1)"]
performance = ["bottleneck (>=1.3.4)", "numba (>=0.55.2)", "numexpr (>=2.8.0)"]
plot = ["matplotlib (>=3.6.1)"]
postgresql = ["SQLAlchemy (>=1.4.16)", "psycopg2 (>=2.8.6)"]
spss = ["pyreadstat (>=1.1.2)"]
sql-other = ["SQLAlchemy (>=1.4.16)"]
test = ["hypothesis (>=6.34.2)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
xml = ["lxml (>=4.6.3)"]
postgresql = ["SQLAlchemy (>=1.4.36)", "psycopg2 (>=2.9.3)"]
spss = ["pyreadstat (>=1.1.5)"]
sql-other = ["SQLAlchemy (>=1.4.36)"]
test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
xml = ["lxml (>=4.8.0)"]
[[package]]
name = "pathspec"
@@ -3877,13 +3869,13 @@ files = [
[[package]]
name = "tifffile"
version = "2023.8.25"
version = "2023.8.30"
description = "Read and write TIFF files"
optional = false
python-versions = ">=3.9"
files = [
{file = "tifffile-2023.8.25-py3-none-any.whl", hash = "sha256:40318485b59e9acb62e7139f22bd46e6760f92daea562b79900bfce3ee2613b7"},
{file = "tifffile-2023.8.25.tar.gz", hash = "sha256:0a3ebcdfe71eb61a487dd22eaf21ed8962c511e6eb692153c7ac15f81798dfa4"},
{file = "tifffile-2023.8.30-py3-none-any.whl", hash = "sha256:62364eef35a6fdcc7bc2ad6f97dd270f577efb01b31260ff800af76a66c1e145"},
{file = "tifffile-2023.8.30.tar.gz", hash = "sha256:6a8c53b012a286b75d09a1498ab32f202f24cc6270a105b5d5911dc4426f162a"},
]
[package.dependencies]
@@ -3894,13 +3886,13 @@ all = ["defusedxml", "fsspec", "imagecodecs (>=2023.8.12)", "lxml", "matplotlib"
[[package]]
name = "timm"
version = "0.9.5"
version = "0.9.6"
description = "PyTorch Image Models"
optional = false
python-versions = ">=3.7"
files = [
{file = "timm-0.9.5-py3-none-any.whl", hash = "sha256:6e70af3a347bddb4167db46c3252a83c59165332ecf6b3df480d49c22866fa46"},
{file = "timm-0.9.5.tar.gz", hash = "sha256:669835f0030cfb2412c464b7b563bb240d4d41a141226afbbf1b457e4f18cff1"},
{file = "timm-0.9.6-py3-none-any.whl", hash = "sha256:7549a924b86a6151d4083a880c27ae86ce729e1b5c8c6099657217d0a0526a4e"},
{file = "timm-0.9.6.tar.gz", hash = "sha256:6c3c0451b69431de0290eed5662e66b134caf916f1cb9b4aa3b9a13c3d61fd03"},
]
[package.dependencies]
@@ -4126,13 +4118,13 @@ telegram = ["requests"]
[[package]]
name = "transformers"
version = "4.32.0"
version = "4.32.1"
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
optional = false
python-versions = ">=3.8.0"
files = [
{file = "transformers-4.32.0-py3-none-any.whl", hash = "sha256:32d8adf0ed76285508e7fd66657b4448ec1f882599ae6bf6f9c36bd7bf798402"},
{file = "transformers-4.32.0.tar.gz", hash = "sha256:ca510f9688d2fe7347abbbfbd13f2f6dcd3c8349870c8d0ed98beed5f579b354"},
{file = "transformers-4.32.1-py3-none-any.whl", hash = "sha256:b930d3dbd907a3f300cf49e54d63a56f8a0ab16b01a2c2a61ecff37c6de1da08"},
{file = "transformers-4.32.1.tar.gz", hash = "sha256:1edc8ae1de357d97c3d36b04412aa63d55e6fc0c4b39b419a7d380ed947d2252"},
]
[package.dependencies]
@@ -4693,4 +4685,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
[metadata]
lock-version = "2.0"
python-versions = "^3.11"
content-hash = "6d200d3ea1ccf9fb89f44043e3e0845e70f19aac374b96227559375f44508dc5"
content-hash = "4e97a32e7525cfedbf23892b8c1191b3fe7b4d09b9f043cdb285ed9772862d67"

3
machine-learning/pyproject.toml
View File

@@ -33,13 +33,13 @@ open-clip-torch = "^2.20.0"
python-multipart = "^0.0.6"
orjson = "^3.9.5"
safetensors = "0.3.2"
gunicorn = "^21.1.0"
[tool.poetry.group.dev.dependencies]
mypy = "^1.3.0"
black = "^23.3.0"
pytest = "^7.3.1"
locust = "^2.15.1"
gunicorn = "^20.1.0"
httpx = "^0.24.1"
pytest-asyncio = "^0.21.0"
pytest-cov = "^4.1.0"
@@ -74,6 +74,7 @@ warn_untyped_fields = true
module = [
"huggingface_hub",
"transformers",
"gunicorn",
"cv2",
"insightface.model_zoo",
"insightface.utils.face_align",

13
machine-learning/start.sh Executable file
View File

@@ -0,0 +1,13 @@
#!/usr/bin/env sh
export LD_PRELOAD="/usr/lib/$(arch)-linux-gnu/libmimalloc.so.2"
: "${MACHINE_LEARNING_HOST:=0.0.0.0}"
: "${MACHINE_LEARNING_PORT:=3003}"
: "${MACHINE_LEARNING_WORKERS:=1}"
gunicorn app.main:app \
-k uvicorn.workers.UvicornWorker \
-w $MACHINE_LEARNING_WORKERS \
-b $MACHINE_LEARNING_HOST:$MACHINE_LEARNING_PORT \
--log-config-json log_conf.json