ColossalAI/.github/workflows/build_gpu_8.yml

name: Build on 8 GPUs

on:
  schedule:
    # run at 00:00 of every Sunday 
    - cron:  '0 0 * * *'
  workflow_dispatch:

jobs:
  build:
    name: Build and Test Colossal-AI
    if: github.repository == 'hpcaitech/ColossalAI'
    runs-on: [self-hosted, 8-gpu]
    container:
      image: hpcaitech/pytorch-cuda:1.11.0-11.3.0
      options: --gpus all --rm -v /data/scratch/cifar-10:/data/scratch/cifar-10
    timeout-minutes: 40
    steps:
      - uses: actions/checkout@v2
        with:
          repository: hpcaitech/TensorNVMe
          ssh-key: ${{ secrets.SSH_KEY_FOR_CI }}
          path: TensorNVMe
      - name: Install tensornvme
        run: |
          cd TensorNVMe
          conda install cmake
          pip install -r requirements.txt
          pip install -v .
      - uses: actions/checkout@v2
        with:
          ssh-key: ${{ secrets.SSH_KEY_FOR_CI }}
      - name: Install Colossal-AI      
        run: |
          [ ! -z "$(ls -A /github/home/cuda_ext_cache/)" ] && cp -r /github/home/cuda_ext_cache/* /__w/ColossalAI/ColossalAI/
          pip install -r requirements/requirements.txt
          pip install -v -e .
          cp -r /__w/ColossalAI/ColossalAI/build /github/home/cuda_ext_cache/
          cp /__w/ColossalAI/ColossalAI/*.so /github/home/cuda_ext_cache/
          pip install -r requirements/requirements-test.txt
      - name: Unit Testing
        run: |
          gpu_used=$(nvidia-smi -i 0 --query-gpu=memory.used --format=csv,noheader,nounits)
          [ "$gpu_used" -le "100" ] && PYTHONPATH=$PWD pytest tests
        env:
          DATA: /data/scratch/cifar-10
          LD_LIBRARY_PATH: /github/home/.tensornvme/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64