modrec-workflow/.riahub/workflows/workflow.yaml

name: RIA Hub Workflow Demo

on:
  push:
    branches:
      [main]
  pull_request:
    branches:
      [main]

jobs:
  ria-demo:
    runs-on: ubuntu-latest-2080

    steps:
      - name: Print GPU information
        run: |
          if command -v nvidia-smi &> /dev/null; then
            echo "✅ NVIDIA GPU is available"
            nvidia-smi
          else
            echo "⚠️ No NVIDIA GPU found"
          fi

      - name: Checkout code
        uses: actions/checkout@v4
        with:
          lfs: true

      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: "3.10"

      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements.txt

      - name: 1. Generate Recordings
        run: |
         mkdir -p data/recordings
         PYTHONPATH=. python scripts/dataset_building/generate_modulated_signals.py --output-dir data/recordings
         echo "recordings produced successfully"
        
      - name: Upload Recordings
        uses: actions/upload-artifact@v3
        with:
          name: recordings
          path: data/recordings/**

      - name: 2. Build HDF5 Dataset
        run: |
         mkdir -p data/dataset
         PYTHONPATH=. python scripts/dataset_building/produce_dataset.py
         echo "datasets produced successfully"
        shell: bash
      
      - name: Upload Dataset Artifacts
        uses: actions/upload-artifact@v3
        with:
          name: ria-dataset
          path: data/dataset/**


      - name: 3. Train Model
        env:
            NO_NNPACK: 1
            PYTORCH_NO_NNPACK: 1
        run: |
          PYTHONPATH=. python scripts/training/train.py 
          echo "training model"

      - name: Upload Checkpoints
        uses: actions/upload-artifact@v3
        with:
          name: ria-checkpoints
          path: checkpoint_files/inference_recognition_model.ckpt

      - name: 4. Convert to ONNX file
        run: |
          MKL_DISABLE_FAST_MM=1 PYTHONPATH=. python onnx_scripts/convert_to_onnx.py
          echo "building inference app"
          
      - name: Upload ONNX file
        uses: actions/upload-artifact@v3
        with:
          name: ria-demo-onnx
          path: onnx_files/inference_recognition_model.onnx

      - name: 5. Profile ONNX model
        run: |
          PYTHONPATH=. python onnx_scripts/profile_onnx.py
      
      - name: Upload JSON profiling data
        uses: actions/upload-artifact@v3
        with:
          name: profile-data          
          path: '**/onnxruntime_profile_*.json'
      
      - name: 6. Convert to ORT file
        run: |
          python -m onnxruntime.tools.convert_onnx_models_to_ort \
             /workspace/qoherent/modrec-workflow/onnx_files/inference_recognition_model.onnx \
            --output_dir ort_files \
            --optimization_style Fixed \
            --target_platform amd64


      - name: Upload ORT file
        uses: actions/upload-artifact@v3
        with:
          name: ria-demo-ort
          path: ort_files/inference_recognition_model.ort