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build-tutorials.yml
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name: Build tutorials
on:
pull_request:
push:
branches:
- main
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
cancel-in-progress: true
jobs:
worker:
name: pytorch_tutorial_build_worker
strategy:
matrix:
include:
- { shard: 1, num_shards: 15, runner: "linux.16xlarge.nvidia.gpu" }
- { shard: 2, num_shards: 15, runner: "linux.g5.4xlarge.nvidia.gpu" }
- { shard: 3, num_shards: 15, runner: "linux.g5.4xlarge.nvidia.gpu" }
- { shard: 4, num_shards: 15, runner: "linux.g5.4xlarge.nvidia.gpu" }
- { shard: 5, num_shards: 15, runner: "linux.g5.4xlarge.nvidia.gpu" }
- { shard: 6, num_shards: 15, runner: "linux.g5.4xlarge.nvidia.gpu" }
- { shard: 7, num_shards: 15, runner: "linux.4xlarge.nvidia.gpu" }
- { shard: 8, num_shards: 15, runner: "linux.4xlarge.nvidia.gpu" }
- { shard: 9, num_shards: 15, runner: "linux.4xlarge.nvidia.gpu" }
- { shard: 10, num_shards: 15, runner: "linux.4xlarge.nvidia.gpu" }
- { shard: 11, num_shards: 15, runner: "linux.4xlarge.nvidia.gpu" }
- { shard: 12, num_shards: 15, runner: "linux.4xlarge.nvidia.gpu" }
- { shard: 13, num_shards: 15, runner: "linux.4xlarge.nvidia.gpu" }
- { shard: 14, num_shards: 15, runner: "linux.4xlarge.nvidia.gpu" }
- { shard: 15, num_shards: 15, runner: "linux.4xlarge.nvidia.gpu" }
fail-fast: false
runs-on: ${{ matrix.runner }}
env:
DOCKER_IMAGE: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda12.1-cudnn8-py3-gcc9"
CUDA_VERSION: "9"
steps:
- name: Setup SSH (Click me for login details)
uses: pytorch/test-infra/.github/actions/setup-ssh@main
with:
github-secret: ${{ secrets.GITHUB_TOKEN }}
instructions: |
All testing is done inside the container, to start an interactive session run:
docker exec -it $(docker container ps --format '{{.ID}}') bash
- name: Checkout Tutorials
uses: actions/checkout@v3
- name: Setup Linux
uses: pytorch/pytorch/.github/actions/setup-linux@main
- name: Install nvidia driver, nvidia-docker runtime, set GPU_FLAG
uses: pytorch/test-infra/.github/actions/setup-nvidia@main
- name: Calculate docker image
shell: bash
id: docker-image
run: |
set -ex
# for some reason, pip installs it in a different place than what is looked at in the py file
pip3 install requests==2.26
pyTorchDockerImageTag=$(python3 .jenkins/get_docker_tag.py)
echo "docker-image=${DOCKER_IMAGE}:${pyTorchDockerImageTag}" >> "${GITHUB_OUTPUT}"
- name: Pull docker image
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
with:
docker-image: ${{ steps.docker-image.outputs.docker-image }}
- name: Build
shell: bash
env:
DOCKER_IMAGE: ${{ steps.docker-image.outputs.docker-image }}
NUM_WORKERS: ${{ matrix.num_shards }}
WORKER_ID: ${{ matrix.shard }}
COMMIT_ID: ${{ github.sha }}
JOB_TYPE: worker
COMMIT_SOURCE: ${{ github.ref }}
run: |
set -ex
chmod +x ".jenkins/build.sh"
container_name=$(docker run \
${GPU_FLAG:-} \
-e WORKER_ID \
-e NUM_WORKERS \
-e COMMIT_ID \
-e JOB_TYPE \
-e COMMIT_SOURCE \
--env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
--tty \
--detach \
--user jenkins \
--shm-size=2gb \
--name="${container_name}" \
-v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
-w /var/lib/jenkins/workspace \
"${DOCKER_IMAGE}"
)
echo "rm /opt/cache/bin/*" | docker exec -u root -i "${container_name}" bash
docker exec -t "${container_name}" sh -c ".jenkins/build.sh"
- name: Teardown Linux
uses: pytorch/test-infra/.github/actions/teardown-linux@main
if: always()
manager:
name: pytorch_tutorial_build_manager
needs: worker
runs-on: [self-hosted, linux.2xlarge]
env:
DOCKER_IMAGE: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-bionic-cuda12.1-cudnn8-py3-gcc9"
CUDA_VERSION: "9"
steps:
- name: Setup SSH (Click me for login details)
uses: pytorch/test-infra/.github/actions/setup-ssh@main
with:
github-secret: ${{ secrets.GITHUB_TOKEN }}
instructions: |
All testing is done inside the container, to start an interactive session run:
docker exec -it $(docker container ps --format '{{.ID}}') bash
- name: Checkout Tutorials
uses: actions/checkout@v3
- name: Setup Linux
uses: pytorch/pytorch/.github/actions/setup-linux@main
- name: Calculate docker image
shell: bash
id: docker-image
run: |
set -ex
# for some reason, pip installs it in a different place than what is looked at in the py file
pip3 install requests==2.26
pyTorchDockerImageTag=$(python3 .jenkins/get_docker_tag.py)
echo "docker-image=${DOCKER_IMAGE}:${pyTorchDockerImageTag}" >> "${GITHUB_OUTPUT}"
- name: Pull docker image
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
with:
docker-image: ${{ steps.docker-image.outputs.docker-image }}
- name: Build
shell: bash
env:
DOCKER_IMAGE: ${{ steps.docker-image.outputs.docker-image }}
NUM_WORKERS: 15
WORKER_ID: ${{ matrix.shard }}
COMMIT_ID: ${{ github.sha }}
JOB_TYPE: manager
COMMIT_SOURCE: ${{ github.ref }}
GITHUB_PYTORCHBOT_TOKEN: ${{ secrets.PYTORCHBOT_TOKEN }}
run: |
set -ex
chmod +x ".jenkins/build.sh"
container_name=$(docker run \
${GPU_FLAG:-} \
-e WORKER_ID \
-e NUM_WORKERS \
-e COMMIT_ID \
-e JOB_TYPE \
-e COMMIT_SOURCE \
-e GITHUB_PYTORCHBOT_TOKEN \
--env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
--tty \
--detach \
--user jenkins \
--name="${container_name}" \
-v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
-w /var/lib/jenkins/workspace \
"${DOCKER_IMAGE}"
)
echo "rm /opt/cache/bin/*" | docker exec -u root -i "${container_name}" bash
docker exec -t "${container_name}" sh -c ".jenkins/build.sh"
- name: Upload docs preview
uses: seemethere/upload-artifact-s3@v5
if: ${{ github.event_name == 'pull_request' }}
with:
retention-days: 14
s3-bucket: doc-previews
if-no-files-found: error
path: docs
s3-prefix: pytorch/tutorials/${{ github.event.pull_request.number }}
- name: Teardown Linux
uses: pytorch/test-infra/.github/actions/teardown-linux@main
if: always()