123 lines
4.4 KiB
YAML
123 lines
4.4 KiB
YAML
# Python package
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# Create and test a Python package on multiple Python versions.
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# Add steps that analyze code, save the dist with the build record, publish to a PyPI-compatible index, and more:
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# https://docs.microsoft.com/azure/devops/pipelines/languages/python
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trigger:
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tags:
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include:
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- '*'
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branches:
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include:
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- "master"
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- "release/*"
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- "refs/tags/*"
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pr:
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- "master"
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- "release/*"
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jobs:
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- job: pytest
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strategy:
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matrix:
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'PyTorch - LTS':
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image: "pytorchlightning/pytorch_lightning:base-cuda-py3.7-torch1.8"
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'PyTorch - stable':
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image: "pytorchlightning/pytorch_lightning:base-cuda-py3.9-torch1.11"
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# how long to run the job before automatically cancelling
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timeoutInMinutes: "100"
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# how much time to give 'run always even if cancelled tasks' before stopping them
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cancelTimeoutInMinutes: "2"
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pool: azure-jirka-spot
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container:
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image: $(image)
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# default shm size is 64m. Increase it to avoid:
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# 'Error while creating shared memory: unhandled system error, NCCL version 2.7.8'
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options: "--runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all --shm-size=512m"
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workspace:
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clean: all
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steps:
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- bash: |
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lspci | egrep 'VGA|3D'
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whereis nvidia
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nvidia-smi
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which python && which pip
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python --version
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pip --version
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pip list
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displayName: 'Image info & NVIDIA'
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- bash: |
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python -c "fname = 'requirements/strategies.txt' ; lines = [line for line in open(fname).readlines() if 'horovod' not in line] ; open(fname, 'w').writelines(lines)"
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CUDA_VERSION_MM=$(python -c "import torch ; print(''.join(map(str, torch.version.cuda.split('.')[:2])))")
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pip install "bagua-cuda$CUDA_VERSION_MM>=0.9.0"
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pip install . --requirement requirements/devel.txt
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pip install . --requirement requirements/strategies.txt
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pip list
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displayName: 'Install dependencies'
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- bash: |
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set -e
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python requirements/collect_env_details.py
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python -c "import torch ; mgpu = torch.cuda.device_count() ; assert mgpu >= 2, f'GPU: {mgpu}'"
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python requirements/check-avail-strategies.py
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python requirements/check-avail-extras.py
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displayName: 'Env details'
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- bash: bash .actions/pull_legacy_checkpoints.sh
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displayName: 'Get legacy checkpoints'
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- bash: |
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python -m coverage run --source pytorch_lightning -m pytest pytorch_lightning tests --ignore tests/benchmarks -v --junitxml=$(Build.StagingDirectory)/test-results.xml --durations=50
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displayName: 'Testing: standard'
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- bash: |
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bash tests/standalone_tests.sh
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env:
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PL_USE_MOCKED_MNIST: "1"
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displayName: 'Testing: standalone'
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- bash: |
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python -m coverage report
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python -m coverage xml
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python -m coverage html
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python -m codecov --token=$(CODECOV_TOKEN) --commit=$(Build.SourceVersion) --flags=gpu,pytest --name="GPU-coverage" --env=linux,azure
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ls -l
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displayName: 'Statistics'
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- task: PublishTestResults@2
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displayName: 'Publish test results'
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inputs:
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testResultsFiles: '$(Build.StagingDirectory)/test-results.xml'
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testRunTitle: '$(Agent.OS) - $(Build.DefinitionName) - Python $(python.version)'
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condition: succeededOrFailed()
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# todo: re-enable after schema check pass, also atm it seems does not have any effect
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#- task: PublishCodeCoverageResults@2
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# displayName: 'Publish coverage report'
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# inputs:
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# codeCoverageTool: 'Cobertura'
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# summaryFileLocation: 'coverage.xml'
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# reportDirectory: '$(Build.SourcesDirectory)/htmlcov'
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# testRunTitle: '$(Agent.OS) - $(Build.BuildNumber)[$(Agent.JobName)] - Python $(python.version)'
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# condition: succeededOrFailed()
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- script: |
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set -e
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python -m pytest pl_examples -v --maxfail=2 --durations=0
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bash pl_examples/run_examples.sh --trainer.accelerator=gpu --trainer.devices=1
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bash pl_examples/run_examples.sh --trainer.accelerator=gpu --trainer.devices=2 --trainer.strategy=ddp
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bash pl_examples/run_examples.sh --trainer.accelerator=gpu --trainer.devices=2 --trainer.strategy=ddp --trainer.precision=16
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env:
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PL_USE_MOCKED_MNIST: "1"
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displayName: 'Testing: examples'
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- bash: |
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python -m pytest tests/benchmarks -v --maxfail=2 --durations=0
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displayName: 'Testing: benchmarks'
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