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

# 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: none

schedules:
  - cron: "0 0 * * *" # At the end of every day
    displayName: Daily midnight benchmark
    branches:
      include:
        - "master"

jobs:
  - job: benchmarks
    timeoutInMinutes: "90"
    cancelTimeoutInMinutes: "2"
    pool: azure-gpus-spot
    container:
      image: "pytorchlightning/pytorch_lightning:base-cuda-py3.7-torch1.8"
      options: "--runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all --shm-size=32g"
    workspace:
      clean: all

    steps:
      - bash: |
          # TODO: Prepare a docker image with 1.8.2 (LTS) installed and remove manual installation.
          pip install torch==1.8.2+cu102 torchvision==0.9.2+cu102 torchtext==0.9.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
          pip list
        displayName: 'Install PyTorch LTS'

      - bash: |
          python -m pytest tests/benchmarks -v --durations=0
        displayName: 'Testing: benchmarks'
        env:
          PL_RUNNING_BENCHMARKS: 1