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

name: Self-hosted runner (scheduled)

on:
  push:
    branches:
      - ci_*
  repository_dispatch:
  schedule:
    - cron: "0 0 * * *"

jobs:
  run_all_tests_torch_and_tf_gpu:
    runs-on: self-hosted
    steps:
    - uses: actions/checkout@v2

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

    - name: Python version
      run: |
        which python
        python --version
        pip --version        
    - name: Current dir
      run: pwd
    - run: nvidia-smi
    - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
      if: steps.cache.outputs.cache-hit != 'true'
      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/nlp        

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

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

    - name: Run examples tests on GPU
      env:
        TF_FORCE_GPU_ALLOW_GROWTH: "true"
        OMP_NUM_THREADS: 1
        RUN_SLOW: yes
        USE_CUDA: yes
      run: |
        source .env/bin/activate
        pip install -r examples/requirements.txt
        python -m pytest -n 1 --dist=loadfile -s examples