lightning/.azure-pipelines/gpu-tests.yml

114 lines
4.1 KiB
YAML

# 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: testing
strategy:
matrix:
'PyTorch - stable':
image: "pytorchlightning/pytorch_lightning:base-cuda-py3.9-torch1.11"
# how long to run the job before automatically cancelling
timeoutInMinutes: "100"
# how much time to give 'run always even if cancelled tasks' before stopping them
cancelTimeoutInMinutes: "2"
pool: azure-jirka-spot
container:
image: $(image)
# default shm size is 64m. Increase it to avoid:
# 'Error while creating shared memory: unhandled system error, NCCL version 2.7.8'
options: "--runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all --shm-size=512m"
workspace:
clean: all
steps:
- bash: |
lspci | egrep 'VGA|3D'
whereis nvidia
nvidia-smi
which python && which pip
python --version
pip --version
pip list
displayName: 'Image info & NVIDIA'
- bash: |
python -c "fname = 'requirements/strategies.txt' ; lines = [line for line in open(fname).readlines() if 'horovod' not in line] ; open(fname, 'w').writelines(lines)"
CUDA_VERSION_MM=$(python -c "import torch ; print(''.join(map(str, torch.version.cuda.split('.')[:2])))")
pip install "bagua-cuda$CUDA_VERSION_MM>=0.9.0"
pip install -e .[strategies]
pip install --requirement requirements/devel.txt
pip list
displayName: 'Install dependencies'
- bash: |
set -e
python requirements/collect_env_details.py
python -c "import torch ; mgpu = torch.cuda.device_count() ; assert mgpu >= 2, f'GPU: {mgpu}'"
python requirements/check-avail-strategies.py
python requirements/check-avail-extras.py
displayName: 'Env details'
- bash: bash .actions/pull_legacy_checkpoints.sh
displayName: 'Get legacy checkpoints'
- bash: python -m coverage run --source pytorch_lightning -m pytest
workingDirectory: src/pytorch_lightning
displayName: 'Testing: PyTorch doctests'
- bash: python -m coverage run --source pytorch_lightning -m pytest --ignore benchmarks -v --junitxml=$(Build.StagingDirectory)/test-results.xml --durations=50
displayName: 'Testing: PyTorch standard'
workingDirectory: tests/tests_pytorch
- bash: bash run_standalone_tests.sh
workingDirectory: tests/tests_pytorch
env:
PL_USE_MOCKED_MNIST: "1"
displayName: 'Testing: PyTorch standalone tests'
- 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
workingDirectory: tests/tests_pytorch
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()
- script: |
set -e
bash run_ddp_examples.sh
bash run_pl_examples.sh --trainer.accelerator=gpu --trainer.devices=1
bash run_pl_examples.sh --trainer.accelerator=gpu --trainer.devices=2 --trainer.strategy=ddp
bash run_pl_examples.sh --trainer.accelerator=gpu --trainer.devices=2 --trainer.strategy=ddp --trainer.precision=16
workingDirectory: examples
env:
PL_USE_MOCKED_MNIST: "1"
displayName: 'Testing: PyTorch examples'
- bash: python -m pytest benchmarks -v --maxfail=2 --durations=0
workingDirectory: tests/tests_pytorch
displayName: 'Testing: PyTorch benchmarks'