userbenchmark-t4-metal.yml

name: TorchBench Userbenchmark on T4 Metal
on:
  schedule:
    - cron: '0 17 * * *' # run at 5 PM UTC
  workflow_dispatch:
    inputs:
      userbenchmark_name:
        description: "Name of the user benchmark to run"
      userbenchmark_options:
        description: "Option of the user benchmark to run"

jobs:
  run-userbenchmark:
    runs-on: [self-hosted, bm-runner]
    timeout-minutes: 1440 # 24 hours
    environment: docker-s3-upload
    env:
      CONDA_ENV_NAME: "userbenchmarks-ci"
      PLATFORM_NAME: "aws_t4_metal"
      TORCHBENCH_USERBENCHMARK_SCRIBE_GRAPHQL_ACCESS_TOKEN: ${{ secrets.TORCHBENCH_USERBENCHMARK_SCRIBE_GRAPHQL_ACCESS_TOKEN }}
      AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
      AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
      SETUP_SCRIPT: "/data/nvme/bin/setup_instance.sh"
    steps:
      - name: Checkout TorchBench
        uses: actions/checkout@v3
        with:
          path: benchmark
      - name: Create conda environment
        run: |
          python benchmark/utils/python_utils.py --create-conda-env "${CONDA_ENV_NAME}"
          sudo python benchmark/utils/cuda_utils.py --setup-cuda-softlink
      - name: Install PyTorch nightly
        run: |
          . "${SETUP_SCRIPT}" && . activate "${CONDA_ENV_NAME}"
          pushd benchmark
          # Install dependencies
          python utils/cuda_utils.py --install-torch-deps
          # check the machine is tuned
          pip install -U py-cpuinfo psutil distro boto3
          sudo ${HOME}/miniconda3/envs/${CONDA_ENV_NAME}/bin/python3 torchbenchmark/util/machine_config.py
          # Check if nightly builds are available
          NIGHTLIES=$(python torchbenchmark/util/torch_nightly.py --packages torch)
          # If failed, the script will generate empty result
          if [ -z $NIGHTLIES ]; then
              echo "Torch nightly build failed. Cancel the workflow."
              exit 1
          fi
          # Install PyTorch and torchvision nightly from pip
          python utils/cuda_utils.py --install-torch-nightly
          python utils/cuda_utils.py --check-torch-nightly-version
          # make sure pytorch+cuda works
          python -c "import torch; torch.cuda.init()"
      - name: Install TorchBench
        run: |
          set -x
          . "${SETUP_SCRIPT}" && conda activate "${CONDA_ENV_NAME}"
          pushd benchmark
          python install.py
      - name: Run user benchmark
        run: |
          set -x
          . "${SETUP_SCRIPT}" && conda activate "${CONDA_ENV_NAME}"
          # remove old results
          if [ -d benchmark-output ]; then rm -Rf benchmark-output; fi
          pushd benchmark
          if [ -d .userbenchmark ]; then rm -Rf .userbenchmark; fi
          MANUAL_WORKFLOW="${{ github.event.inputs.userbenchmark_name }}"
          if [ -z "${MANUAL_WORKFLOW}" ]; then
            # Figure out what userbenchmarks we should run, and run it
            python ./.github/scripts/userbenchmark/schedule-benchmarks.py --platform ${PLATFORM_NAME}
            if [ -d ./.userbenchmark ]; then
              cp -r ./.userbenchmark ../benchmark-output
            else
              mkdir ../benchmark-output
            fi
          else
            python run_benchmark.py "${{ github.event.inputs.userbenchmark_name }}" ${{ github.event.inputs.userbenchmark_options }}
            cp -r ./.userbenchmark/"${{ github.event.inputs.userbenchmark_name }}" ../benchmark-output
          fi
      - name: Upload artifact
        uses: actions/upload-artifact@v3
        with:
          name: TorchBench result
          path: benchmark-output/
      - name: Upload result jsons to Scribe
        run: |
          . "${SETUP_SCRIPT}" && conda activate "${CONDA_ENV_NAME}"
          conda install -y six
          pushd benchmark
          RESULTS=($(find ${PWD}/../benchmark-output -name "metrics-*.json" -maxdepth 2 | sort -r))
          echo "Uploading result jsons: ${RESULTS}"
          for r in ${RESULTS[@]}; do
            python ./scripts/userbenchmark/upload_scribe.py --userbenchmark_json "${r}" --userbenchmark_platform "${PLATFORM_NAME}"
            python ./scripts/userbenchmark/upload_s3.py --upload-file "${r}" --userbenchmark_platform "${PLATFORM_NAME}"
          done
      - name: Remove conda environment
        run: |
          conda env remove --name "${CONDA_ENV_NAME}"
      - name: Cleanup repo
        if: always()
        run: |
          sudo rm -rf benchmark || true