torchninja

torchninja is a Python package that provides tools for training PyTorch models. It offers a flexible and easy-to-use training framework with built-in functionality for logging, metrics tracking, and model checkpointing.

Features

• Simplifies the process of training PyTorch models.
• Flexible and customizable trainer class.
• Automatic logging of training progress and metrics.
• Support for model checkpointing to save and load model states.
• Easy integration with TensorBoard for visualizing training metrics.
• Provides utility functions for common tasks in PyTorch model training.

Installation

You can install torchninja using pip:

pip install torchninja

torchninja has the following dependencies:

• torch
• torchvision
• tensorboard
• tqdm
• numpy
• matplotlib

Make sure these dependencies are installed in your environment before using torchninja.

Getting Started

To get started with torchninja, you can refer to the examples directory in the project repository. It contains example scripts that demonstrate how to use the torchninja trainer class to train a PyTorch model. You can modify these examples according to your specific use case.

Documentation

The documentation for torchninja is available on the project's GitHub repository:

• torchninja GitHub repository

The documentation provides detailed information on the usage of the torchninja package, including the trainer class, available functionality, and usage examples.

Contributing

Contributions to torchninja are welcome! If you find any issues, have suggestions for improvements, or would like to add new features, please open an issue or submit a pull request on the project's GitHub repository.

License

torchninja is released under the MIT License. See the LICENSE file for details.

Contact

For any questions or inquiries, you can reach out to the project author:

• Author: Silvan Ferreira
• Email: silvanfj@gmail.com

Feel free to contact the author for any assistance or collaboration opportunities related to torchninja.