119 lines
4.1 KiB
YAML
119 lines
4.1 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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# how long to run the job before automatically cancelling
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timeoutInMinutes: 45
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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: gridai-spot-pool
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#strategy:
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# matrix:
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# PT16:
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# torch.version: '1.6'
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# python.version: '3.7'
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# ToDo: this need to have installed docker in the base image...
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#container: pytorchlightning/pytorch_lightning:base-cuda-py3.7-torch1.6
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#container: "pytorchlightning/pytorch_lightning:base-cuda-py$[ variables['python.version'] ]-torch1.6"
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container:
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# base ML image: mcr.microsoft.com/azureml/openmpi3.1.2-cuda10.2-cudnn8-ubuntu18.04
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image: "pytorchlightning/pytorch_lightning:base-cuda-py3.8-torch1.6"
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#endpoint: azureContainerRegistryConnection
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options: "--runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all"
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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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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/extra.txt' ; lines = [line for line in open(fname).readlines() if 'horovod' not in line] ; open(fname, 'w').writelines(lines)"
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pip install fairscale>=0.3.4
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pip install . --requirement requirements/devel.txt
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pip list
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displayName: 'Install dependencies'
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- bash: |
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python tests/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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displayName: 'Env details'
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- bash: |
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wget https://pl-public-data.s3.amazonaws.com/legacy/checkpoints.zip -P legacy/
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unzip -o legacy/checkpoints.zip -d legacy/
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ls -l legacy/checkpoints/
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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 -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/special_tests.sh
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displayName: 'Testing: special'
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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 $(python.version)'
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condition: succeededOrFailed()
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- task: PublishCodeCoverageResults@1
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displayName: 'Publish coverage report'
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inputs:
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codeCoverageTool: 'cobertura'
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summaryFileLocation: 'coverage.xml'
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reportDirectory: '$(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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- bash: |
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python -m pytest benchmarks -v --maxfail=2 --durations=0
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displayName: 'Testing: benchmarks'
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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-args.sh --trainer.gpus 1 --trainer.max_epochs 1 --data.batch_size 64 --trainer.limit_train_batches 5 --trainer.limit_val_batches 3
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bash pl_examples/run_ddp-examples.sh --trainer.max_epochs 1 --data.batch_size 32 --trainer.limit_train_batches 2 --trainer.limit_val_batches 2
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env:
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PL_USE_MOCKED_MNIST: "1"
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displayName: 'Examples'
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