fork-maintenance.yml

name: Run Scheduled Events Docker

permissions:
  actions: write
  contents: write
  issues: write
  pull-requests: write

on:
  workflow_dispatch:
  schedule:
    - cron: '0 0 10 * *'

jobs:
  run-scheduled-events:
    uses: Cemberk/fork-maintenance-system/.github/workflows/fork-maintenance-action.yml@artifacts
    with:
      platform: 'gfx90a'
      upstream_repo: 'https://github.com/huggingface/transformers'
      pr_branch_prefix: 'scheduled-merge'
      requirements_command: |
        rm -rf $(pip show numpy | grep Location: | awk '{print $2}')/numpy* && 
        sudo sed -i 's/torchaudio//g' examples/pytorch/_tests_requirements.txt && 
        pip install -r examples/pytorch/_tests_requirements.txt && 
        git restore examples/pytorch/_tests_requirements.txt && 
        pip install --no-cache-dir GPUtil azureml azureml-core tokenizers ninja cerberus sympy sacremoses sacrebleu==1.5.1 sentencepiece scipy scikit-learn urllib3 && pip install huggingface_hub datasets && 
        pip install parameterized && 
        pip install -e .
      unit_test_command: folders=\$(python3 -c 'import os; workspace = \"/myworkspace\"; repo_root = os.path.join(workspace, \"tests\"); models_dir = os.path.join(repo_root, \"models\"); model_tests = os.listdir(models_dir); d1 = sorted([d for d in os.listdir(repo_root) if os.path.isdir(os.path.join(repo_root, d)) and d != \"models\"]); d2 = sorted([os.path.join(\"models\", x) for x in model_tests if os.path.isdir(os.path.join(models_dir, x))]); d = d2 + d1; print(\" \".join(d[:5]))'); echo \$folders;  for folder in \${folders[@]}; do pytest tests/\${folder} -v --make-reports=huggingface_unit_tests_\${machine_type}_run_models_gpu_\${folder} -rfEs --continue-on-collection-errors -m \"not not_device_test\" -p no:cacheprovider; done; allstats=\$(find reports -name stats.txt); for stat in \${allstats[@]}; do echo \$stat; cat \$stat; done
      performance_test_command: 'echo \"python examples/pytorch/language-modeling/run_mlm.py --model_name_or_path bert-base-uncased --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train --do_eval --output_dir /tmp/test-mlm --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --max_steps 500\"'
      docker_image: 'rocm/pytorch:latest'
      docker_options: '--device=/dev/kfd --device=/dev/dri --group-add video --shm-size 16G --network=host'
    secrets:
      GIT_TOKEN: ${{ secrets.CRED_TOKEN }}
      schedule_json: ${{ secrets.SCHEDULE_CONFIG }}