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Surrey Shared Containers
nlp2-docker-2024
Commits
69c599b0
Commit
69c599b0
authored
1 year ago
by
erow
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.gitlab-ci.yml
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.gitlab-ci.yml
Dockerfile
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# This file is a template, and might need editing before it works on your project.
# To contribute improvements to CI/CD templates, please follow the Development guide at:
# https://docs.gitlab.com/ee/development/cicd/templates.html
# This specific template is located at:
# https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/ci/templates/Docker.gitlab-ci.yml
# Build a Docker image with CI/CD and push to the GitLab registry.
# Docker-in-Docker documentation: https://docs.gitlab.com/ee/ci/docker/using_docker_build.html
#
# This template uses one generic job with conditional builds
# for the default branch and all other (MR) branches.
docker-build
:
# Use the official docker image.
image
:
aisurrey:latest
stage
:
build
services
:
-
docker:dind
before_script
:
-
docker login -u "$CI_REGISTRY_USER" -p "$CI_REGISTRY_PASSWORD" $CI_REGISTRY
# Default branch leaves tag empty (= latest tag)
# All other branches are tagged with the escaped branch name (commit ref slug)
script
:
-
|
if [[ "$CI_COMMIT_BRANCH" == "$CI_DEFAULT_BRANCH" ]]; then
tag=""
echo "Running on default branch '$CI_DEFAULT_BRANCH': tag = 'latest'"
else
tag=":$CI_COMMIT_REF_SLUG"
echo "Running on branch '$CI_COMMIT_BRANCH': tag = $tag"
fi
-
docker build --pull -t "$CI_REGISTRY_IMAGE${tag}" .
-
docker push "$CI_REGISTRY_IMAGE${tag}"
# Run this job in a branch where a Dockerfile exists
rules
:
-
if
:
$CI_COMMIT_BRANCH
exists
:
-
Dockerfile
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ARG
PYTORCH="2.1.1"
ARG
CUDA="12.1"
ARG
CUDNN="8"
# FROM pytorch/pytorch:1.13.1-cuda11.6-cudnn8-runtime
FROM
docker.io/pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-runtime
# To fix GPG key error when running apt-get update
# RUN rm /etc/apt/sources.list.d/cuda.list \
# && rm /etc/apt/sources.list.d/nvidia-ml.list \
# && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub \
# && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
# Install system dependencies for opencv-python
RUN
apt update
&&
apt
install
-y
libgl1 libglib2.0-0
\
&&
apt clean
# && rm -rf /var/lib/apt/lists/*
RUN
pip
install
mmcv
==
2.1.0
-f
https://download.openmmlab.com/mmcv/dist/cu121/torch2.1/index.html
\
&&
pip cache purge
# Verify the installation
RUN
python
-c
'import mmcv;print(mmcv.__version__)'
# Install FFCV
RUN
conda
install
cupy pkg-config libjpeg-turbo opencv
cudatoolkit
=
${
CUDA
}
numba
-c
pytorch
-c
conda-forge
RUN
pip
install
ffcv
\
&&
pip cache purge
# useful tools
RUN
pip
install
timm wandb imageio pandas gin-config tqdm
\
&&
pip cache purge
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