Skip to main content

Tools for training PyTorch models

Project description

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:

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:

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

torchninja-0.1.3.tar.gz (8.4 kB view details)

Uploaded Source

Built Distribution

torchninja-0.1.3-py3-none-any.whl (8.9 kB view details)

Uploaded Python 3

File details

Details for the file torchninja-0.1.3.tar.gz.

File metadata

  • Download URL: torchninja-0.1.3.tar.gz
  • Upload date:
  • Size: 8.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.9

File hashes

Hashes for torchninja-0.1.3.tar.gz
Algorithm Hash digest
SHA256 e20140d1a2c123c28a56a1fad58533c7dad7f6bf8614c7f7ae7b7a684c13de18
MD5 081684f13d3997b6e44d31f52697823c
BLAKE2b-256 d958e0eb97e8b4f3b15534b05361a3d9e375ce0d720dd395638cb11131238e78

See more details on using hashes here.

File details

Details for the file torchninja-0.1.3-py3-none-any.whl.

File metadata

  • Download URL: torchninja-0.1.3-py3-none-any.whl
  • Upload date:
  • Size: 8.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.9

File hashes

Hashes for torchninja-0.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 cd984f5344f0d584aa332bc4069c2f73a38854b56a2b3746fb82b51d61b04f9d
MD5 40694ee5f0e8abdf3ce429a0fd9ec7d9
BLAKE2b-256 39e26b77c972692fa3c2df1b89908bbbcf74305d6baef242306fb2c0e1d9284a

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page