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Parallel Hyperparameter Tuning

This repository provides a complete, automated workflow for GPU-accelerated hyperparameter tuning of a ResNet model using GitHub Actions powered by Machine.dev. It systematically explores combinations of key training parameters—such as learning rates and batch sizes—to identify the optimal configuration for training a ResNet model on the CIFAR-10 dataset.

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✨ Key Features

• ⚡ GPU Acceleration: Efficiently train and evaluate models using GPUs via Machine
• 🧪 Systematic Tuning: Automate the exploration of multiple hyperparameter combinations
• 📊 Performance Comparison: Automatically aggregate and compare model metrics
• 📤 Artifact Storage: Conveniently store and retrieve metrics and comparison results using GitHub Actions artifacts
• 📈 Easy Interpretation: Identify the best-performing configuration quickly with structured CSV outputs

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📁 Repository Structure

├── .github/workflows/
│   └── resnet-hyperparameter-tuning.yaml   # Workflow for hyperparameter tuning
├── train.py                                # Script to train and evaluate ResNet
├── compare_metrics.py                      # Script to aggregate and compare metrics
└── requirements.txt                        # Python dependencies

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▶️ Getting Started

1. Use This Repository as a Template

Click the Use this template button at the top of this page to create your own copy.

2. Set Up GPU Runners

Ensure your repository uses Machine GPU-powered runners. No additional configuration is required if you're already using Machine.dev.

3. Run the Workflow

• Trigger the workflow manually in GitHub Actions (workflow_dispatch)

• The workflow automatically runs training jobs for each combination of hyperparameters defined in the matrix:

  matrix:
    learning_rate: [0.001, 0.0005]
    batch_size: [32, 64]

4. Monitor and Review Results

• Training progress and metrics are logged during each workflow execution
• Performance metrics for each hyperparameter set are saved as artifacts (metrics-{learning_rate}-{batch_size})
• A final comparison of all training results is saved as model_comparison.csv in the artifact comparison-results

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🔑 Prerequisites

• GitHub account
• Access to Machine GPU-powered runners

No local installation necessary—all processes run directly within GitHub Actions.

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📄 License

This repository is available under the MIT License.

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📌 Notes

• This hyperparameter tuning template specifically targets ResNet models trained on the CIFAR-10 dataset but can easily be adapted for other models, datasets, and tasks with minimal modifications.

• This repository is currently open for use as a template. While public forks are encouraged, we are not accepting Pull Requests at this time.

For questions or concerns, please open an issue.