fork-maintenance-docker.yml

name: Run Scheduled Events Docker last

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

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

jobs:
  run-scheduled-events:
    runs-on: self-hosted
    container: 
      image: rocm/pytorch:latest
      env:
        NODE_ENV: development
      ports:
        - 80
      volumes:
        - my_docker_volume:/volume_mount
      options: --device=/dev/kfd --device=/dev/dri --group-add video --ipc=host --shm-size 16G
    steps:
      - name: Fork Maintenance System
        uses: Cemberk/Fork-Maintenance-System@docker 
        with:
          github_token: ${{ secrets.CRED_TOKEN }}
          upstream_repo: "https://github.com/huggingface/transformers"
          schedule_json: |
            {
              "upstream_main_branch": "main",
              "upstream_release_branch": "v4.43-release",
              "downstream_main_branch": "upstream_sync",
              "downstream_testing_branch": "rocm6.3_testing_rel4.43_testing",
              "downstream_develop_branch": "develop",
              "commits": [
                "731f0308bb0a2796e28b68664f4ed050eab6dbfd",
                "0c1b311bff616a4faf214461584a758fbab14a6f",
                "5b2d4fec5abb8ffe755fe66ce0856fc066c561a6"
              ]
            }
          pr_branch_prefix: "scheduled-merge"
          requirements_command: >
            ls;
            pwd;
            sudo sed -i 's$torchaudio$$g' examples/pytorch/_tests_requirements.txt;
            pip install -r 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<2" ;
            pip install huggingface_hub datasets ;
            pip install parameterized;
          unit_test_command: >
            cd tests; folders=($(python3 -c 'import os; tests = os.getcwd(); model_tests = os.listdir(os.path.join(tests, "models")); d1 = sorted(list(filter(os.path.isdir, os.listdir(tests)))); d2 = sorted(list(filter(os.path.isdir, [f"models/{x}" for x in model_tests]))); d1.remove("models"); d = d2 + d1; print("\n".join(d))')); cd ..; for folder in "${folders[@]}"; do pytest tests/${folder} -v -rfEs --make-reports="huggingface_unit_tests_run_models_gpu_${folder}" -p no:faulthandler --continue-on-collection-errors -m "not not_device_test" -p no:cacheprovider || true; 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"