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cron.yml
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# nightly: Jenkinsfile.monai-pytorch-versions, monai-latest-image, monai-pip, monai-latest-docker, monai-notebooks
name: nightly-crons
on:
# schedule:
# - cron: "0 2 * * *" # at 02:00 UTC
# Allows you to run this workflow manually from the Actions tab
workflow_dispatch:
jobs:
cron-gpu:
if: github.repository == 'Project-MONAI/MONAI'
strategy:
matrix:
environment:
- "PT110+CUDA113"
- "PT113+CUDA118"
- "PT210+CUDA121"
- "PT240+CUDA126"
- "PTLATEST+CUDA126"
include:
# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes
- environment: PT110+CUDA113
pytorch: "torch==1.10.2 torchvision==0.11.3 --extra-index-url https://download.pytorch.org/whl/cu113"
base: "nvcr.io/nvidia/pytorch:21.06-py3" # CUDA 11.3
- environment: PT113+CUDA118
pytorch: "torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cu121"
base: "nvcr.io/nvidia/pytorch:22.10-py3" # CUDA 11.8
- environment: PT210+CUDA121
pytorch: "pytorch==2.1.0 torchvision==0.16.0 --extra-index-url https://download.pytorch.org/whl/cu121"
base: "nvcr.io/nvidia/pytorch:23.08-py3" # CUDA 12.1
- environment: PT240+CUDA126
pytorch: "pytorch==2.4.0 torchvision==0.19.0 --extra-index-url https://download.pytorch.org/whl/cu121"
base: "nvcr.io/nvidia/pytorch:24.08-py3" # CUDA 12.6
- environment: PTLATEST+CUDA126
pytorch: "-U torch torchvision --extra-index-url https://download.pytorch.org/whl/cu121"
base: "nvcr.io/nvidia/pytorch:24.10-py3" # CUDA 12.6
container:
image: ${{ matrix.base }}
options: "--gpus all"
runs-on: [self-hosted, linux, x64, common]
steps:
- uses: actions/checkout@v4
- name: apt install
run: |
apt-get update
apt-get install -y wget
- name: Install the dependencies
run: |
which python
python -m pip install --upgrade pip wheel
python -m pip uninstall -y torch torchvision
python -m pip install ${{ matrix.pytorch }}
python -m pip install -r requirements-dev.txt
python -m pip list
- name: Run tests report coverage
env:
NGC_API_KEY: ${{ secrets.NGC_API_KEY }}
NGC_ORG: ${{ secrets.NGC_ORG }}
NGC_TEAM: ${{ secrets.NGC_TEAM }}
run: |
export LAUNCH_DELAY=$[ $RANDOM % 16 * 60 ]
echo "Sleep $LAUNCH_DELAY"
sleep $LAUNCH_DELAY
nvidia-smi
export CUDA_VISIBLE_DEVICES=$(python -m tests.utils | tail -n 1)
echo $CUDA_VISIBLE_DEVICES
trap 'if pgrep python; then pkill python; fi;' ERR
python -c $'import torch\na,b=torch.zeros(1,device="cuda:0"),torch.zeros(1,device="cuda:1");\nwhile True:print(a,b)' > /dev/null &
python -c "import torch; print(torch.__version__); print('{} of GPUs available'.format(torch.cuda.device_count()))"
python -c 'import torch; print(torch.rand(5, 3, device=torch.device("cuda:0")))'
BUILD_MONAI=1 ./runtests.sh --build --coverage --unittests --disttests # unit tests with coverage report
BUILD_MONAI=1 ./runtests.sh --build --coverage --net # integration tests with coverage report
coverage xml --ignore-errors
if pgrep python; then pkill python; fi
shell: bash
- name: Upload coverage
uses: codecov/codecov-action@v5
with:
fail_ci_if_error: false
files: ./coverage.xml
cron-pt-image:
if: github.repository == 'Project-MONAI/MONAI'
strategy:
matrix:
container: ["pytorch:23.08", "pytorch:24.08", "pytorch:24.10"]
container:
image: nvcr.io/nvidia/${{ matrix.container }}-py3 # testing with the latest pytorch base image
options: "--gpus all"
runs-on: [self-hosted, linux, x64, integration]
steps:
- uses: actions/checkout@v4
- name: Install APT dependencies
run: |
apt-get update
DEBIAN_FRONTEND="noninteractive" apt-get install -y libopenslide0
- name: Install Python dependencies
run: |
which python
python -m pip install --upgrade pip wheel
python -m pip install -r requirements-dev.txt
python -m pip list
- name: Run tests report coverage
env:
NGC_API_KEY: ${{ secrets.NGC_API_KEY }}
NGC_ORG: ${{ secrets.NGC_ORG }}
NGC_TEAM: ${{ secrets.NGC_TEAM }}
run: |
export LAUNCH_DELAY=$[ $RANDOM % 16 * 60 ]
echo "Sleep $LAUNCH_DELAY"
sleep $LAUNCH_DELAY
nvidia-smi
export CUDA_VISIBLE_DEVICES=$(python -m tests.utils | tail -n 1)
echo $CUDA_VISIBLE_DEVICES
trap 'if pgrep python; then pkill python; fi;' ERR
python -c $'import torch\na,b=torch.zeros(1,device="cuda:0"),torch.zeros(1,device="cuda:1");\nwhile True:print(a,b)' > /dev/null &
python -c "import torch; print(torch.__version__); print('{} of GPUs available'.format(torch.cuda.device_count()))"
python -c 'import torch; print(torch.rand(5, 3, device=torch.device("cuda:0")))'
BUILD_MONAI=1 ./runtests.sh --build --coverage --unittests --disttests # unit tests with coverage report
BUILD_MONAI=1 ./runtests.sh --build --coverage --net # integration tests with coverage report
coverage xml --ignore-errors
if pgrep python; then pkill python; fi
shell: bash
- name: Upload coverage
uses: codecov/codecov-action@v5
with:
fail_ci_if_error: false
files: ./coverage.xml
cron-pip:
# pip install monai[all] and use it to run unit tests
if: github.repository == 'Project-MONAI/MONAI'
strategy:
matrix:
container: ["pytorch:24.10"]
container:
image: nvcr.io/nvidia/${{ matrix.container }}-py3 # testing with the latest pytorch base image
options: "--gpus all"
runs-on: [self-hosted, linux, x64, integration]
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Install the dependencies
run: |
which python
python -m pip install --upgrade pip wheel twine
python -m pip list
- name: Run tests report coverage
shell: bash
run: |
pip uninstall monai
pip list | grep -iv monai
git fetch --depth=1 origin +refs/tags/*:refs/tags/*
root_dir=$PWD
echo "$root_dir"
set -e
# build tar.gz and wheel
bash runtests.sh --clean # clear any existing dev temp files
python -m pip uninstall -y torch torchvision
python setup.py check -m -s
python setup.py sdist bdist_wheel
python -m twine check dist/*
# move packages to a temp dir
tmp_dir=$(mktemp -d)
cp dist/monai* "$tmp_dir"
rm -r build dist monai.egg-info
cd "$tmp_dir"
ls -al
# install from tar.gz
name=$(ls *.tar.gz | head -n1)
echo $name
python -m pip install $name[all]
python -c 'import monai; monai.config.print_config()' 2>&1 | grep -iv "unknown"
python -c 'import monai; print(monai.__file__)'
# run tests
cp $root_dir/requirements*.txt "$tmp_dir"
cp -r $root_dir/tests "$tmp_dir"
pwd
ls -al
export LAUNCH_DELAY=$[ $RANDOM % 16 * 60 ]
echo "Sleep $LAUNCH_DELAY"
sleep $LAUNCH_DELAY
nvidia-smi
export CUDA_VISIBLE_DEVICES=$(python -m tests.utils | tail -n 1)
echo $CUDA_VISIBLE_DEVICES
trap 'if pgrep python; then pkill python; fi;' ERR
python -c $'import torch\na,b=torch.zeros(1,device="cuda:0"),torch.zeros(1,device="cuda:1");\nwhile True:print(a,b)' > /dev/null &
python -c "import torch; print(torch.__version__); print('{} of GPUs available'.format(torch.cuda.device_count()))"
python -m pip install -r requirements-dev.txt
PYTHONPATH="$tmp_dir":$PYTHONPATH BUILD_MONAI=1 python ./tests/runner.py -p 'test_((?!integration).)' # unit tests
if pgrep python; then pkill python; fi
cron-docker:
if: github.repository == 'Project-MONAI/MONAI'
container:
image: docker://projectmonai/monai:latest # this might be slow and has the pull count limitations
options: "--gpus all"
runs-on: [self-hosted, linux, x64, integration]
steps:
- name: Run tests report coverage
# The docker image process has done the compilation.
# BUILD_MONAI=1 is necessary for triggering the USE_COMPILED flag.
env:
NGC_API_KEY: ${{ secrets.NGC_API_KEY }}
NGC_ORG: ${{ secrets.NGC_ORG }}
NGC_TEAM: ${{ secrets.NGC_TEAM }}
run: |
cd /opt/monai
nvidia-smi
export CUDA_VISIBLE_DEVICES=$(python -m tests.utils | tail -n 1)
echo $CUDA_VISIBLE_DEVICES
trap 'if pgrep python; then pkill python; fi;' ERR
python -c $'import torch\na,b=torch.zeros(1,device="cuda:0"),torch.zeros(1,device="cuda:1");\nwhile True:print(a,b)' > /dev/null &
python -c "import torch; print(torch.__version__); print('{} of GPUs available'.format(torch.cuda.device_count()))"
python -c 'import torch; print(torch.rand(5,3, device=torch.device("cuda:0")))'
ngc --version
BUILD_MONAI=1 ./runtests.sh --build --coverage --pytype --unittests --disttests # unit tests with pytype checks, coverage report
BUILD_MONAI=1 ./runtests.sh --build --coverage --net # integration tests with coverage report
coverage xml --ignore-errors
if pgrep python; then pkill python; fi
shell: bash
- name: Upload coverage
uses: codecov/codecov-action@v5
with:
fail_ci_if_error: false
files: ./coverage.xml
cron-tutorial-notebooks:
if: github.repository == 'Project-MONAI/MONAI'
needs: cron-gpu # so that monai itself is verified first
container:
image: nvcr.io/nvidia/pytorch:24.10-py3 # testing with the latest pytorch base image
options: "--gpus all --ipc=host"
runs-on: [self-hosted, linux, x64, integration]
steps:
- uses: actions/checkout@v4
- name: Install MONAI
id: monai-install
run: |
which python
python -m pip install --upgrade pip wheel
python -m pip install -r requirements-dev.txt
BUILD_MONAI=1 python setup.py develop # install monai
nvidia-smi
export CUDA_VISIBLE_DEVICES=$(python -m tests.utils | tail -n 1)
echo $CUDA_VISIBLE_DEVICES
echo "devices=$CUDA_VISIBLE_DEVICES" >> $GITHUB_OUTPUT
- name: Checkout tutorials and install their requirements
run: |
cd /opt
git clone --depth 1 --branch main --single-branch https://github.com/Project-MONAI/tutorials.git # latest commit of main branch
cd tutorials
python -m pip install -r requirements.txt
- name: Run tutorial notebooks
timeout-minutes: 150
run: |
export CUDA_VISIBLE_DEVICES=${{ steps.monai-install.outputs.devices }}
echo $CUDA_VISIBLE_DEVICES
trap 'if pgrep python; then pkill python; fi;' ERR
python -c $'import torch\na,b=torch.zeros(1,device="cuda:0"),torch.zeros(1,device="cuda:1");\nwhile True:print(a,b)' > /dev/null &
cd /opt/tutorials
python -c 'import monai; monai.config.print_debug_info()'
$(pwd)/runner.sh
python -c 'import monai; monai.config.print_debug_info()'
if pgrep python; then pkill python; fi
shell: bash