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🚀 DeepCAD RT Runners

Welcome to DeepCAD RT Runners! This project is a small UV-based solution that bundles all necessary dependencies for DeepCAD 1.2.0, along with convenient command-line scripts for configuration, training and prediction.

Key Features:

  • 🔧 No manual Python environment setup required (just uv).
  • 🖥️ Tested on Windows 11 and Debian Linux with CUDA-compatible GPUs.
  • 📊 Seamless training and prediction workflows for denoising .tif movie files.

📋 Prerequisites

Before diving in, ensure you have:

  • A system with a CUDA-compatible GPU (recommended for optimal performance).
  • uv installed.
  • Access to .tif movie files for training and testing.

🛠️ Installation

No installation needed! The project uses uvx to run commands directly from the GitHub repository. Your Python environment will be created and cached automatically.

📖 Usage

Follow these steps to train and test your models. Each step includes detailed instructions and examples.

1. 📝 Create Configuration Files

Generate local configuration files for training and testing.

Run the following command in your terminal:

uvx --from https://github.com/sommerc/deepcadrt_run.git deepcadrt-config

This creates train_config.json and test_config.json in your current directory. Customize these files as needed (e.g., adjust parameters like patch size, number of epochs or learning rate).

Example Output:

  • train_config.json: Default training settings.
  • test_config.json: Default testing settings.

2. 🎯 Train Your Model

Prepare a folder containing your .tif movie files (e.g., data/my_movies/).

Edit train_config.json to match your requirements (leave dataset_path unchanged).

Run the training command:

uvx --from https://github.com/sommerc/deepcadrt_run.git deepcadrt-train "mymovies" -c train_config.json

This will:

  • Train a DeepCAD model on your data.
  • Create a models/ folder with a subfolder named like mymovies_202310011155 (based on your data folder and current date).

Tips:

  • Ensure your .tif files are properly formatted (e.g., 3D stacks).
  • The patch size in the time dimension is smaller than the movie length
  • Monitor GPU usage during training for performance.

3. 🔮 Predict and Denoise

Use your trained model to denoise new or existing data.

Edit test_config.json as needed (leave dataset_path and denoise_model unchanged).

Run the prediction command:

uvx --from https://github.com/sommerc/deepcadrt_run.git deepcadrt-predict mymovies/ models/mymovies_202310011155 -c test_config.json

This will:

  • Apply denoising to your movies.
  • Save results in a results/ folder with a subfolder for your output.

Example:

  • Input: Noisy .tif movies.
  • Output: Denoised versions in results/. By default the model from the latest epoch is used.

❓ Troubleshooting

  • CUDA Issues: Ensure your GPU drivers are up-to-date and compatible.
  • Memory Errors: Reduce patch size or train_datasets_size in config files for large datasets.
  • Command Not Found: Verify uv is installed and in your PATH.
  • For more help, check the DeepCAD documentation or open an issue on this repo.

🤝 Contributing

We welcome contributions! Feel free to submit pull requests or report bugs via the GitHub repository.

📄 License

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