transformers/.github/workflows/self-push.yml

name: Self-hosted runner (push)

on: 
  push:
    branches:
      - master
    paths: 
      - "src/**"
      - "tests/**"
      - ".github/**"
  # pull_request:
  repository_dispatch:


jobs:
  run_tests_torch_and_tf_gpu:
    runs-on: [self-hosted, single-gpu]
    steps:
    - uses: actions/checkout@v2
    - name: Python version
      run: |
        which python
        python --version
        pip --version        
    - name: Current dir
      run: pwd
    - run: nvidia-smi

    - name: Loading cache.
      uses: actions/cache@v2
      id: cache
      with:
        path: .env
        key: v0-tests_tf_torch_gpu-${{ hashFiles('setup.py') }}

    - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
      run: |
        python -m venv .env
        source .env/bin/activate
        which python
        python --version
        pip --version        
    - name: Install dependencies
      run: |
        source .env/bin/activate
        pip install --upgrade pip
        pip install torch!=1.6.0
        pip install .[sklearn,testing,onnxruntime]
        pip install git+https://github.com/huggingface/datasets        

    - name: Are GPUs recognized by our DL frameworks
      run: |
        source .env/bin/activate
        python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
        python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"        

    - name: Run all non-slow tests on GPU
      env:
        TF_FORCE_GPU_ALLOW_GROWTH: "true"
        # TF_GPU_MEMORY_LIMIT: 4096
        OMP_NUM_THREADS: 1
        USE_CUDA: yes
      run: |
        source .env/bin/activate
        python -m pytest -n 2 --dist=loadfile -s ./tests/        

  run_tests_torch_and_tf_multiple_gpu:
    runs-on: [self-hosted, multi-gpu]
    steps:
      - uses: actions/checkout@v2
      - name: Python version
        run: |
          which python
          python --version
          pip --version          
      - name: Current dir
        run: pwd
      - run: nvidia-smi

      - name: Loading cache.
        uses: actions/cache@v2
        id: cache
        with:
          path: .env
          key: v0-tests_tf_torch_multiple_gpu-${{ hashFiles('setup.py') }}

      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
        run: |
          python -m venv .env
          source .env/bin/activate
          which python
          python --version
          pip --version          
      - name: Install dependencies
        run: |
          source .env/bin/activate
          pip install --upgrade pip
          pip install torch!=1.6.0
          pip install .[sklearn,testing,onnxruntime]
          pip install git+https://github.com/huggingface/datasets          

      - name: Are GPUs recognized by our DL frameworks
        run: |
          source .env/bin/activate
          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"          

      - name: Run all non-slow tests on GPU
        env:
          TF_FORCE_GPU_ALLOW_GROWTH: "true"
          # TF_GPU_MEMORY_LIMIT: 4096
          OMP_NUM_THREADS: 1
          USE_CUDA: yes
        run: |
          source .env/bin/activate
          python -m pytest -n 2 --dist=loadfile -s ./tests/