133 lines
3.3 KiB
YAML
133 lines
3.3 KiB
YAML
name: RIA Hub Workflow Demo
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on:
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push:
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branches:
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[main]
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pull_request:
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branches:
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[main]
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jobs:
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ria-demo:
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runs-on: ubuntu-latest-2080
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env:
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RIAGIT_USERNAME: ${{ secrets.USERNAME }}
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RIAGIT_TOKEN: ${{ secrets.TOKEN }}
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steps:
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- name: Print GPU information
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run: |
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if command -v nvidia-smi &> /dev/null; then
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echo "✅ NVIDIA GPU is available"
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nvidia-smi
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else
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echo "⚠️ No NVIDIA GPU found"
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fi
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- name: Checkout code
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uses: actions/checkout@v4
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with:
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lfs: true
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: "3.10"
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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- name: 1. Generate Recordings
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run: |
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mkdir -p data/recordings
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PYTHONPATH=. python scripts/dataset_building/data_gen.py --output-dir data/recordings
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echo "recordings produced successfully"
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- name: 📦 Zip and Upload Recordings
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run: |
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echo "📦 Zipping recordings..."
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zip -qr recordings.zip data/recordings
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shell: bash
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- name: ⬆️ Upload zipped recordings
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uses: actions/upload-artifact@v3
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with:
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name: recordings
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path: recordings.zip
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- name: 2. Build HDF5 Dataset
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run: |
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mkdir -p data/dataset
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PYTHONPATH=. python scripts/dataset_building/produce_dataset.py
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echo "datasets produced successfully"
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shell: bash
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- name: 📦 Zip Dataset
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run: zip -qr dataset.zip data/dataset
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- name: 📤 Upload Zipped Dataset
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uses: actions/upload-artifact@v3
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with:
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name: dataset
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path: dataset.zip
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- name: 3. Train Model
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env:
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NO_NNPACK: 1
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PYTORCH_NO_NNPACK: 1
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run: |
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mkdir -p checkpoint_files
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PYTHONPATH=. python scripts/training/train.py
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echo "training model"
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- name: Upload Checkpoints
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uses: actions/upload-artifact@v3
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with:
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name: checkpoints
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path: checkpoint_files/*
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- name: 4. Convert to ONNX file
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env:
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NO_NNPACK: 1
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PYTORCH_NO_NNPACK: 1
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run: |
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mkdir -p onnx_files
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MKL_DISABLE_FAST_MM=1 PYTHONPATH=. python scripts/onnx/convert_to_onnx.py
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echo "building inference app"
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- name: Upload ONNX file
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uses: actions/upload-artifact@v3
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with:
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name: onnx-file
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path: onnx_files/inference_recognition_model.onnx
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- name: List checkpoint directory
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run: ls -lh onnx_files
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- name: 5. Profile ONNX model
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run: |
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PYTHONPATH=. python scrips/onnx/profile_onnx.py
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- name: Upload JSON profiling data
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uses: actions/upload-artifact@v3
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with:
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name: profile-data
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path: '**/onnxruntime_profile_*.json'
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- name: 6. Convert to ORT file
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run: |
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python -m scripts/ort/convert_to_ort.py
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- name: Upload ORT file
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uses: actions/upload-artifact@v3
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with:
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name: ort-file
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path: ort_files/inference_recognition_model.ort
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