TorchBench Userbenchmark on A100 #80
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| name: TorchBench Userbenchmark on A100 | |
| on: | |
| schedule: | |
| - cron: '00 18 * * *' # run at 6:00 PM UTC, K8s containers will roll out at 12PM EST | |
| 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: [a100-runner] | |
| timeout-minutes: 1440 # 24 hours | |
| environment: docker-s3-upload | |
| env: | |
| BASE_CONDA_ENV: "torchbench" | |
| CONDA_ENV: "userbenchmark-a100" | |
| PLATFORM_NAME: "gcp_a100" | |
| 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: ${{ secrets.AWS_SECRET_ACCESS_KEY }} | |
| SETUP_SCRIPT: "/workspace/setup_instance.sh" | |
| steps: | |
| - name: Checkout TorchBench | |
| uses: actions/checkout@v3 | |
| with: | |
| path: benchmark | |
| - name: Tune Nvidia GPU | |
| run: | | |
| sudo nvidia-smi -pm 1 | |
| sudo nvidia-smi -ac 1215,1410 | |
| nvidia-smi | |
| - name: Clone and setup conda env | |
| run: | | |
| CONDA_ENV=${BASE_CONDA_ENV} . "${SETUP_SCRIPT}" | |
| conda create --name "${CONDA_ENV}" --clone "${BASE_CONDA_ENV}" | |
| - name: Install TorchBench | |
| run: | | |
| set -x | |
| . "${SETUP_SCRIPT}" | |
| pushd benchmark | |
| python install.py | |
| - name: Run user benchmark | |
| run: | | |
| set -x | |
| . "${SETUP_SCRIPT}" | |
| # remove old results | |
| if [ -d benchmark-output ]; then rm -Rf benchmark-output; fi | |
| pushd benchmark | |
| if [ -d .userbenchmark ]; then rm -Rf .userbenchmark; fi | |
| MANUAL_WORKFLOW="${{ 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 and S3 | |
| run: | | |
| . "${SETUP_SCRIPT}" | |
| pushd benchmark | |
| RESULTS=($(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 "${PLATFORM_NAME}" | |
| done | |
| - name: Clean up Conda env | |
| if: always() | |
| run: | | |
| . "${SETUP_SCRIPT}" | |
| conda deactivate && conda deactivate | |
| conda remove -n "${CONDA_ENV}" --all |