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Code Releases Activity

Feat/ml image optimisations (#1916)

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* Use multi stage build to slim down ML image size

* Use gunicorn as WSGI server in ML image

* Configure gunicorn server for ML use case

* Use requirements.txt file to install python dependencies in ML image

* Make ML listen IP configurable

* Revert "Use requirements.txt file to install python dependencies in ML image"

This reverts commit 32e706c7f3.

* Separate out pip installs in ML builder image
This commit is contained in:
Olly Welch 2023-03-03 22:45:20 +00:00 committed by GitHub
parent 8708867c1c
commit d5d0624311
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2 changed files with 45 additions and 10 deletions
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26
machine-learning/Dockerfile
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@ -1,19 +1,25 @@
FROM python:3.10
FROM python:3.10 as builder
ENV PYTHONDONTWRITEBYTECODE=1 \
PYTHONUNBUFFERED=1 \
PIP_NO_CACHE_DIR=true
RUN python -m venv /opt/venv
RUN /opt/venv/bin/pip install --pre torch -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
RUN /opt/venv/bin/pip install transformers tqdm numpy scikit-learn scipy nltk sentencepiece flask Pillow gunicorn
RUN /opt/venv/bin/pip install --no-deps sentence-transformers
FROM python:3.10-slim
COPY --from=builder /opt/venv /opt/venv
ENV TRANSFORMERS_CACHE=/cache \
PYTHONDONTWRITEBYTECODE=1 \
PYTHONUNBUFFERED=1 \
PIP_NO_CACHE_DIR=true
PATH="/opt/venv/bin:$PATH"
WORKDIR /usr/src/app
RUN python -m venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"
RUN pip install --pre torch -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
RUN pip install transformers tqdm numpy scikit-learn scipy nltk sentencepiece flask Pillow
RUN pip install --no-deps sentence-transformers
COPY . .
CMD ["python", "src/main.py"]
CMD ["gunicorn", "src.main:server"]

29
machine-learning/gunicorn.conf.py Normal file
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@ -0,0 +1,29 @@
"""
Gunicorn configuration options.
https://docs.gunicorn.org/en/stable/settings.html
"""
import os
# Set the bind address based on the env
port = os.getenv("MACHINE_LEARNING_PORT") or "3003"
listen_ip = os.getenv("MACHINE_LEARNING_IP") or "0.0.0.0"
bind = [f"{listen_ip}:{port}"]
# Preload the Flask app / models etc. before starting the server
preload_app = True
# Logging settings - log to stdout and set log level
accesslog = "-"
loglevel = os.getenv("MACHINE_LEARNING_LOG_LEVEL") or "info"
# Worker settings
# ----------------------
# It is important these are chosen carefully as per
# https://pythonspeed.com/articles/gunicorn-in-docker/
# Otherwise we get workers failing to respond to heartbeat checks,
# especially as requests take a long time to complete.
workers = 2
threads = 4
worker_tmp_dir = "/dev/shm"
timeout = 60