TorchBench Userbenchmark on T4 Metal #957
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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 utils/torch_nightly_utils.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 |