# Python package # Create and test a Python package on multiple Python versions. # Add steps that analyze code, save the dist with the build record, publish to a PyPI-compatible index, and more: # https://docs.microsoft.com/azure/devops/pipelines/languages/python trigger: tags: include: - '*' branches: include: - "master" - "release/*" - "refs/tags/*" pr: - "master" - "release/*" jobs: - job: pytest # how long to run the job before automatically cancelling timeoutInMinutes: "45" # how much time to give 'run always even if cancelled tasks' before stopping them cancelTimeoutInMinutes: "2" pool: gridai-spot-pool # ToDo: this need to have installed docker in the base image... container: # base ML image: mcr.microsoft.com/azureml/openmpi3.1.2-cuda10.2-cudnn8-ubuntu18.04 # run on torch 1.8 as it's the LTS version image: "pytorchlightning/pytorch_lightning:base-cuda-py3.7-torch1.8" # default shm size is 64m. Increase it to avoid: # 'Error while creating shared memory: unhandled system error, NCCL version 2.7.8' options: "--runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all --shm-size=512m" workspace: clean: all steps: - bash: | lspci | egrep 'VGA|3D' whereis nvidia nvidia-smi python --version pip --version pip list displayName: 'Image info & NVIDIA' - bash: | python -c "fname = 'requirements/extra.txt' ; lines = [line for line in open(fname).readlines() if 'horovod' not in line] ; open(fname, 'w').writelines(lines)" pip install fairscale>=0.3.4 pip install "deepspeed>=0.4.3, !=0.4.4" # FIXME: bug with 0.4.4 pip install . --requirement requirements/devel.txt pip list displayName: 'Install dependencies' - bash: | python requirements/collect_env_details.py python -c "import torch ; mgpu = torch.cuda.device_count() ; assert mgpu >= 2, f'GPU: {mgpu}'" displayName: 'Env details' - bash: | wget https://pl-public-data.s3.amazonaws.com/legacy/checkpoints.zip -P legacy/ unzip -o legacy/checkpoints.zip -d legacy/ ls -l legacy/checkpoints/ displayName: 'Get legacy checkpoints' - bash: | python -m coverage run --source pytorch_lightning -m pytest pytorch_lightning tests -v --junitxml=$(Build.StagingDirectory)/test-results.xml --durations=50 displayName: 'Testing: standard' - bash: | bash tests/special_tests.sh env: PL_USE_MOCKED_MNIST: "1" displayName: 'Testing: special' - bash: | python -m coverage report python -m coverage xml python -m coverage html python -m codecov --token=$(CODECOV_TOKEN) --commit=$(Build.SourceVersion) --flags=gpu,pytest --name="GPU-coverage" --env=linux,azure ls -l displayName: 'Statistics' - task: PublishTestResults@2 displayName: 'Publish test results' inputs: testResultsFiles: '$(Build.StagingDirectory)/test-results.xml' testRunTitle: '$(Agent.OS) - $(Build.DefinitionName) - Python $(python.version)' condition: succeededOrFailed() # todo: re-enable after schema check pass, also atm it seems does not have any effect #- task: PublishCodeCoverageResults@2 # displayName: 'Publish coverage report' # inputs: # codeCoverageTool: 'Cobertura' # summaryFileLocation: 'coverage.xml' # reportDirectory: '$(Build.SourcesDirectory)/htmlcov' # testRunTitle: '$(Agent.OS) - $(Build.BuildNumber)[$(Agent.JobName)] - Python $(python.version)' # condition: succeededOrFailed() - script: | set -e python -m pytest pl_examples -v --maxfail=2 --durations=0 bash pl_examples/run_examples.sh --trainer.gpus=1 bash pl_examples/run_examples.sh --trainer.gpus=2 --trainer.accelerator=ddp bash pl_examples/run_examples.sh --trainer.gpus=2 --trainer.accelerator=ddp --trainer.precision=16 env: PL_USE_MOCKED_MNIST: "1" displayName: 'Testing: examples' - bash: | python -m pytest benchmarks -v --maxfail=2 --durations=0 displayName: 'Testing: benchmarks'