DeepDriveSim

Deep learning-driven Adaptive Simulations

DeepDriveSim is a toolkit developed by Brookhaven National Laboratory (BNL) / RADICAL Laboratory at Rutgers University, in collaboration with Argonne National Laboratory. It implements an AI-steered ensemble simulation workflow that uses deep learning models to guide and optimize simulations in real-time.

Features

• Adaptive Simulation Management: Dynamically manages molecular simulations based on ML predictions
• Active Learning Loop: Implements simulation → training → prediction → cancellation → re-submission cycle
• Multiple Execution Backends: Supports local execution, RHAPSODY (HPC), and Dragon distributed computing
• Resource-Aware Scheduling: Automatically balances resources between simulations and training
• GPU Support: Automatic GPU detection and utilization
• Extensible Architecture: Easy to customize for different simulation types and ML models

Architecture

┌─────────────────────────────────────────────────────────────┐
│                     DDMD Manager                            │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────┐  │
│  │ Simulation  │  │  Training   │  │     Prediction      │  │
│  │   Queue     │──│   Module    │──│      Module         │  │
│  └─────────────┘  └─────────────┘  └─────────────────────┘  │
│         │                │                    │             │
│         ▼                ▼                    ▼             │
│  ┌─────────────────────────────────────────────────────┐    │
│  │              ROSE / RADICAL-AsyncFlow               │    │
│  │           (Execution Backend Abstraction)           │    │
│  └─────────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────────┘

Installation

From Source

git clone https://github.com/radical-collaboration/DeepDriveSim.git
cd DeepDriveSim
pip install -e .

With Development Dependencies

pip install -e ".[dev]"

With Documentation Dependencies

pip install -e ".[doc]"

Documentation

DeepDriveSim Documentation

Examples

See the examples/ directory for complete working examples:

• dummy_pipeline/: Standalone demo with synthetic data
• miniapps_pipeline/
  Reference scalable HPC workflow using RADICAL-Cybertools Workflow mini-apps https://github.com/radical-cybertools/workflow-mini-apps

Running Tests

# Install test dependencies
pip install -e ".[dev]"

# Run unit tests
pytest tests/unit

# Run integration tests
pytest tests/integration

# Run with coverage
pytest --cov=ddmd --cov-report=html

Development

Code Style

This project uses ruff for linting and formatting:

# Check code style
ruff check ddmd tests

# Format code
ruff format ddmd tests

Using tox

# Run all tests across Python versions
tox

# Run linting
tox -e lint

# Run formatting
tox -e format

Dependencies

• RADICAL-AsyncFlow: Async workflow orchestration
• ROSE: Machine learning integration for HPC
• PyYAML: Configuration file parsing

Citation

If you use DeepDriveSim in your research, please cite:

@inproceedings{lee2019DeepDriveSim,
  author={Lee, Hyungro and Turilli, Matteo and Jha, Shantenu and Bhowmik, Debsindhu and Ma, Heng and Ramanathan, Arvind},
  booktitle={2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS)},
  title={DeepDriveSim: Deep-Learning Driven Adaptive Molecular Simulations},
  year={2019},
  pages={12-19},
  doi={10.1109/DLS49591.2019.00007}
}

Paper: IEEE Xplore

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Brookhaven National Laboratory (BNL)
• RADICAL Laboratory at Rutgers University
• Argonne National Laboratory
• This work was supported by the DOE Office of Science