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
    env:
      RIAGIT_USERNAME: ${{ secrets.USERNAME }}
      RIAGIT_TOKEN: ${{ secrets.TOKEN }}
    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/data_gen.py --output-dir data/recordings
         echo "recordings produced successfully"

      - name: 📦 Zip and Upload Recordings
        run: |
          echo "📦 Zipping recordings..."
          zip -qr recordings.zip data/recordings
        shell: bash

      - name: ⬆️ Upload zipped recordings
        uses: actions/upload-artifact@v3
        with:
          name: recordings
          path: recordings.zip

      - 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: 📦 Zip Dataset
        run: zip -qr dataset.zip data/dataset

      - name: 📤 Upload Zipped Dataset
        uses: actions/upload-artifact@v3
        with:
          name: dataset
          path: dataset.zip

      - name: 3. Train Model
        env:
            NO_NNPACK: 1
            PYTORCH_NO_NNPACK: 1
        run: |
          mkdir -p checkpoint_files
          PYTHONPATH=. python scripts/training/train.py 
          echo "training model"
    
      - name: 4. Plot Model
        env:
            NO_NNPACK: 1
            PYTORCH_NO_NNPACK: 1
        run: |
          PYTHONPATH=. python scripts/training/plot_data.py
      

      - name: Upload Checkpoints
        uses: actions/upload-artifact@v3
        with:
          name: checkpoints
          path: checkpoint_files/*


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

      - name: List checkpoint directory
        run: ls -lh onnx_files


      - name: 5. Profile ONNX model
        run: |
          PYTHONPATH=. python scripts/onnx/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 scripts/ort/convert_to_ort.py


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