chore(ml): memory optimisations (#3934)
This commit is contained in:
Mert
GitHub
parent
c0a48d7357
commit
41461e0d5d
8 changed files with 122 additions and 107 deletions
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@ -1,4 +1,4 @@
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FROM python:3.11.4-bullseye@sha256:5b401676aff858495a5c9c726c60b8b73fe52833e9e16eccdb59e93d52741727 as builder
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FROM python:3.11-bookworm as builder
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ENV PYTHONDONTWRITEBYTECODE=1 \
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PYTHONUNBUFFERED=1 \
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@ -14,9 +14,9 @@ COPY poetry.lock pyproject.toml requirements.txt ./
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RUN poetry install --sync --no-interaction --no-ansi --no-root --only main
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RUN pip install --no-deps -r requirements.txt
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FROM python:3.11.4-slim-bullseye@sha256:91d194f58f50594cda71dcd2e8fdefd90e7ecc57d07823813b67c8521e565dcd
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FROM python:3.11-slim-bookworm
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RUN apt-get update && apt-get install -y --no-install-recommends tini && rm -rf /var/lib/apt/lists/*
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RUN apt-get update && apt-get install -y --no-install-recommends tini libmimalloc2.0 && rm -rf /var/lib/apt/lists/*
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WORKDIR /usr/src/app
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ENV NODE_ENV=production \
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@ -27,6 +27,7 @@ ENV NODE_ENV=production \
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PYTHONPATH=/usr/src
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COPY --from=builder /opt/venv /opt/venv
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COPY start.sh log_conf.json ./
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COPY app .
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ENTRYPOINT ["tini", "--"]
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CMD ["python", "-m", "app.main"]
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CMD ["./start.sh"]
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@ -2,6 +2,7 @@ import logging
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import os
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from pathlib import Path
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import gunicorn
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import starlette
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from pydantic import BaseSettings
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from rich.console import Console
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@ -56,12 +57,14 @@ LOG_LEVELS: dict[str, int] = {
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settings = Settings()
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log_settings = LogSettings()
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console = Console(color_system="standard", no_color=log_settings.no_color)
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logging.basicConfig(
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format="%(message)s",
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handlers=[
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RichHandler(show_path=False, omit_repeated_times=False, console=console, tracebacks_suppress=[starlette])
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],
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)
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log = logging.getLogger("uvicorn")
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class CustomRichHandler(RichHandler):
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def __init__(self) -> None:
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console = Console(color_system="standard", no_color=log_settings.no_color)
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super().__init__(
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show_path=False, omit_repeated_times=False, console=console, tracebacks_suppress=[gunicorn, starlette]
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)
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log = logging.getLogger("gunicorn.access")
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log.setLevel(LOG_LEVELS.get(log_settings.log_level.lower(), logging.INFO))
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@ -1,11 +1,8 @@
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import asyncio
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import logging
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import os
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from concurrent.futures import ThreadPoolExecutor
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from typing import Any
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import orjson
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import uvicorn
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from fastapi import FastAPI, Form, HTTPException, UploadFile
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from fastapi.responses import ORJSONResponse
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from starlette.formparsers import MultiPartParser
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@ -33,7 +30,7 @@ def init_state() -> None:
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)
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)
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# asyncio is a huge bottleneck for performance, so we use a thread pool to run blocking code
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app.state.thread_pool = ThreadPoolExecutor(settings.request_threads)
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app.state.thread_pool = ThreadPoolExecutor(settings.request_threads) if settings.request_threads > 0 else None
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log.info(f"Initialized request thread pool with {settings.request_threads} threads.")
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@ -73,17 +70,7 @@ async def predict(
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async def run(model: InferenceModel, inputs: Any) -> Any:
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return await asyncio.get_running_loop().run_in_executor(app.state.thread_pool, model.predict, inputs)
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if __name__ == "__main__":
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is_dev = os.getenv("NODE_ENV") == "development"
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uvicorn.run(
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"app.main:app",
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host=settings.host,
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port=settings.port,
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reload=is_dev,
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workers=settings.workers,
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log_config=None,
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access_log=log.isEnabledFor(logging.INFO),
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)
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if app.state.thread_pool is not None:
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return await asyncio.get_running_loop().run_in_executor(app.state.thread_pool, model.predict, inputs)
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else:
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return model.predict(inputs)
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@ -53,6 +53,7 @@ class InferenceModel(ABC):
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log.debug(f"Setting intra_op_num_threads to {intra_op_num_threads}")
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self.sess_options.inter_op_num_threads = inter_op_num_threads
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self.sess_options.intra_op_num_threads = intra_op_num_threads
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self.sess_options.enable_cpu_mem_arena = False
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try:
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loader(**model_kwargs)
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17
machine-learning/log_conf.json
Normal file
17
machine-learning/log_conf.json
Normal file
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@ -0,0 +1,17 @@
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{
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"version": 1,
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"disable_existing_loggers": true,
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"formatters": { "rich": { "show_path": false, "omit_repeated_times": false } },
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"handlers": {
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"console": {
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"class": "app.config.CustomRichHandler",
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"formatter": "rich",
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"level": "INFO"
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}
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},
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"loggers": {
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"gunicorn.access": { "propagate": true },
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"gunicorn.error": { "propagate": true }
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},
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"root": { "handlers": ["console"] }
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}
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144
machine-learning/poetry.lock
generated
144
machine-learning/poetry.lock
generated
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@ -164,13 +164,13 @@ tests = ["pytest"]
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[[package]]
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name = "anyio"
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version = "3.7.1"
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version = "4.0.0"
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description = "High level compatibility layer for multiple asynchronous event loop implementations"
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optional = false
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python-versions = ">=3.7"
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python-versions = ">=3.8"
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files = [
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{file = "anyio-3.7.1-py3-none-any.whl", hash = "sha256:91dee416e570e92c64041bd18b900d1d6fa78dff7048769ce5ac5ddad004fbb5"},
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{file = "anyio-3.7.1.tar.gz", hash = "sha256:44a3c9aba0f5defa43261a8b3efb97891f2bd7d804e0e1f56419befa1adfc780"},
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{file = "anyio-4.0.0-py3-none-any.whl", hash = "sha256:cfdb2b588b9fc25ede96d8db56ed50848b0b649dca3dd1df0b11f683bb9e0b5f"},
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{file = "anyio-4.0.0.tar.gz", hash = "sha256:f7ed51751b2c2add651e5747c891b47e26d2a21be5d32d9311dfe9692f3e5d7a"},
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]
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[package.dependencies]
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@ -178,9 +178,9 @@ idna = ">=2.8"
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sniffio = ">=1.1"
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[package.extras]
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doc = ["Sphinx", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.2.2)", "sphinxcontrib-jquery"]
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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)"]
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trio = ["trio (<0.22)"]
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doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)"]
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test = ["anyio[trio]", "coverage[toml] (>=7)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
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trio = ["trio (>=0.22)"]
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[[package]]
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name = "async-timeout"
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@ -874,18 +874,18 @@ test = ["anyio[trio] (>=3.2.1,<4.0.0)", "black (==23.1.0)", "coverage[toml] (>=6
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[[package]]
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name = "filelock"
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version = "3.12.2"
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version = "3.12.3"
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description = "A platform independent file lock."
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optional = false
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python-versions = ">=3.7"
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python-versions = ">=3.8"
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files = [
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{file = "filelock-3.12.2-py3-none-any.whl", hash = "sha256:cbb791cdea2a72f23da6ac5b5269ab0a0d161e9ef0100e653b69049a7706d1ec"},
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{file = "filelock-3.12.2.tar.gz", hash = "sha256:002740518d8aa59a26b0c76e10fb8c6e15eae825d34b6fdf670333fd7b938d81"},
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{file = "filelock-3.12.3-py3-none-any.whl", hash = "sha256:f067e40ccc40f2b48395a80fcbd4728262fab54e232e090a4063ab804179efeb"},
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{file = "filelock-3.12.3.tar.gz", hash = "sha256:0ecc1dd2ec4672a10c8550a8182f1bd0c0a5088470ecd5a125e45f49472fac3d"},
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]
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[package.extras]
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docs = ["furo (>=2023.5.20)", "sphinx (>=7.0.1)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"]
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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)"]
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docs = ["furo (>=2023.7.26)", "sphinx (>=7.1.2)", "sphinx-autodoc-typehints (>=1.24)"]
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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)"]
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[[package]]
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name = "flask"
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[[package]]
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name = "gunicorn"
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version = "20.1.0"
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version = "21.2.0"
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description = "WSGI HTTP Server for UNIX"
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optional = false
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python-versions = ">=3.5"
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files = [
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{file = "gunicorn-20.1.0-py3-none-any.whl", hash = "sha256:9dcc4547dbb1cb284accfb15ab5667a0e5d1881cc443e0677b4882a4067a807e"},
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{file = "gunicorn-20.1.0.tar.gz", hash = "sha256:e0a968b5ba15f8a328fdfd7ab1fcb5af4470c28aaf7e55df02a99bc13138e6e8"},
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{file = "gunicorn-21.2.0-py3-none-any.whl", hash = "sha256:3213aa5e8c24949e792bcacfc176fef362e7aac80b76c56f6b5122bf350722f0"},
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{file = "gunicorn-21.2.0.tar.gz", hash = "sha256:88ec8bff1d634f98e61b9f65bc4bf3cd918a90806c6f5c48bc5603849ec81033"},
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]
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[package.dependencies]
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setuptools = ">=3.0"
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packaging = "*"
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[package.extras]
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eventlet = ["eventlet (>=0.24.1)"]
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@ -2619,69 +2619,61 @@ files = [
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[[package]]
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name = "pandas"
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version = "2.0.3"
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version = "2.1.0"
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description = "Powerful data structures for data analysis, time series, and statistics"
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optional = false
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python-versions = ">=3.8"
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python-versions = ">=3.9"
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files = [
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{file = "pandas-2.0.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e4c7c9f27a4185304c7caf96dc7d91bc60bc162221152de697c98eb0b2648dd8"},
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{file = "pandas-2.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f167beed68918d62bffb6ec64f2e1d8a7d297a038f86d4aed056b9493fca407f"},
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{file = "pandas-2.0.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ce0c6f76a0f1ba361551f3e6dceaff06bde7514a374aa43e33b588ec10420183"},
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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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{file = "pandas-2.0.3-cp310-cp310-win32.whl", hash = "sha256:3ef285093b4fe5058eefd756100a367f27029913760773c8bf1d2d8bebe5d210"},
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{file = "pandas-2.0.3-cp310-cp310-win_amd64.whl", hash = "sha256:9ee1a69328d5c36c98d8e74db06f4ad518a1840e8ccb94a4ba86920986bb617e"},
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{file = "pandas-2.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b084b91d8d66ab19f5bb3256cbd5ea661848338301940e17f4492b2ce0801fe8"},
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{file = "pandas-2.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:37673e3bdf1551b95bf5d4ce372b37770f9529743d2498032439371fc7b7eb26"},
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{file = "pandas-2.0.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b9cb1e14fdb546396b7e1b923ffaeeac24e4cedd14266c3497216dd4448e4f2d"},
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{file = "pandas-2.0.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d9cd88488cceb7635aebb84809d087468eb33551097d600c6dad13602029c2df"},
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{file = "pandas-2.0.3-cp311-cp311-win32.whl", hash = "sha256:694888a81198786f0e164ee3a581df7d505024fbb1f15202fc7db88a71d84ebd"},
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{file = "pandas-2.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:6a21ab5c89dcbd57f78d0ae16630b090eec626360085a4148693def5452d8a6b"},
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{file = "pandas-2.0.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:9e4da0d45e7f34c069fe4d522359df7d23badf83abc1d1cef398895822d11061"},
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{file = "pandas-2.0.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:32fca2ee1b0d93dd71d979726b12b61faa06aeb93cf77468776287f41ff8fdc5"},
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{file = "pandas-2.0.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:258d3624b3ae734490e4d63c430256e716f488c4fcb7c8e9bde2d3aa46c29089"},
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{file = "pandas-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9eae3dc34fa1aa7772dd3fc60270d13ced7346fcbcfee017d3132ec625e23bb0"},
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{file = "pandas-2.0.3-cp38-cp38-win32.whl", hash = "sha256:f3421a7afb1a43f7e38e82e844e2bca9a6d793d66c1a7f9f0ff39a795bbc5e02"},
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{file = "pandas-2.0.3-cp38-cp38-win_amd64.whl", hash = "sha256:69d7f3884c95da3a31ef82b7618af5710dba95bb885ffab339aad925c3e8ce78"},
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{file = "pandas-2.0.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5247fb1ba347c1261cbbf0fcfba4a3121fbb4029d95d9ef4dc45406620b25c8b"},
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{file = "pandas-2.0.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:81af086f4543c9d8bb128328b5d32e9986e0c84d3ee673a2ac6fb57fd14f755e"},
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{file = "pandas-2.0.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1994c789bf12a7c5098277fb43836ce090f1073858c10f9220998ac74f37c69b"},
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{file = "pandas-2.0.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ec591c48e29226bcbb316e0c1e9423622bc7a4eaf1ef7c3c9fa1a3981f89641"},
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{file = "pandas-2.0.3-cp39-cp39-win32.whl", hash = "sha256:04dbdbaf2e4d46ca8da896e1805bc04eb85caa9a82e259e8eed00254d5e0c682"},
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{file = "pandas-2.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:1168574b036cd8b93abc746171c9b4f1b83467438a5e45909fed645cf8692dbc"},
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{file = "pandas-2.0.3.tar.gz", hash = "sha256:c02f372a88e0d17f36d3093a644c73cfc1788e876a7c4bcb4020a77512e2043c"},
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{file = "pandas-2.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d4f38e4fedeba580285eaac7ede4f686c6701a9e618d8a857b138a126d067f2f"},
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{file = "pandas-2.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:38f74ef7ebc0ffb43b3d633e23d74882bce7e27bfa09607f3c5d3e03ffd9a4a5"},
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{file = "pandas-2.1.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:cda72cc8c4761c8f1d97b169661f23a86b16fdb240bdc341173aee17e4d6cedd"},
|
||||
{file = "pandas-2.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d97daeac0db8c993420b10da4f5f5b39b01fc9ca689a17844e07c0a35ac96b4b"},
|
||||
{file = "pandas-2.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d8c58b1113892e0c8078f006a167cc210a92bdae23322bb4614f2f0b7a4b510f"},
|
||||
{file = "pandas-2.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:629124923bcf798965b054a540f9ccdfd60f71361255c81fa1ecd94a904b9dd3"},
|
||||
{file = "pandas-2.1.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:70cf866af3ab346a10debba8ea78077cf3a8cd14bd5e4bed3d41555a3280041c"},
|
||||
{file = "pandas-2.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:d53c8c1001f6a192ff1de1efe03b31a423d0eee2e9e855e69d004308e046e694"},
|
||||
{file = "pandas-2.1.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:86f100b3876b8c6d1a2c66207288ead435dc71041ee4aea789e55ef0e06408cb"},
|
||||
{file = "pandas-2.1.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:28f330845ad21c11db51e02d8d69acc9035edfd1116926ff7245c7215db57957"},
|
||||
{file = "pandas-2.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b9a6ccf0963db88f9b12df6720e55f337447aea217f426a22d71f4213a3099a6"},
|
||||
{file = "pandas-2.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d99e678180bc59b0c9443314297bddce4ad35727a1a2656dbe585fd78710b3b9"},
|
||||
{file = "pandas-2.1.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:b31da36d376d50a1a492efb18097b9101bdbd8b3fbb3f49006e02d4495d4c644"},
|
||||
{file = "pandas-2.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:0164b85937707ec7f70b34a6c3a578dbf0f50787f910f21ca3b26a7fd3363437"},
|
||||
{file = "pandas-2.1.0.tar.gz", hash = "sha256:62c24c7fc59e42b775ce0679cfa7b14a5f9bfb7643cfbe708c960699e05fb918"},
|
||||
]
|
||||
|
||||
[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"
|
||||
|
||||
[package.extras]
|
||||
all = ["PyQt5 (>=5.15.1)", "SQLAlchemy (>=1.4.16)", "beautifulsoup4 (>=4.9.3)", "bottleneck (>=1.3.2)", "brotlipy (>=0.7.0)", "fastparquet (>=0.6.3)", "fsspec (>=2021.07.0)", "gcsfs (>=2021.07.0)", "html5lib (>=1.1)", "hypothesis (>=6.34.2)", "jinja2 (>=3.0.0)", "lxml (>=4.6.3)", "matplotlib (>=3.6.1)", "numba (>=0.53.1)", "numexpr (>=2.7.3)", "odfpy (>=1.4.1)", "openpyxl (>=3.0.7)", "pandas-gbq (>=0.15.0)", "psycopg2 (>=2.8.6)", "pyarrow (>=7.0.0)", "pymysql (>=1.0.2)", "pyreadstat (>=1.1.2)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)", "python-snappy (>=0.6.0)", "pyxlsb (>=1.0.8)", "qtpy (>=2.2.0)", "s3fs (>=2021.08.0)", "scipy (>=1.7.1)", "tables (>=3.6.1)", "tabulate (>=0.8.9)", "xarray (>=0.21.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=1.4.3)", "zstandard (>=0.15.2)"]
|
||||
aws = ["s3fs (>=2021.08.0)"]
|
||||
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"
|
||||
|
|
|
|||
|
|
@ -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
13
machine-learning/start.sh
Executable 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
|
||||
Loading…
Reference in a new issue