Olympic PyTorch Documentation

Olympic implements a Keras-like API for PyTorch.

The goal of Olympic is to combine the joy of Pytorch’s dynamic graph execution with the joy of Keras’s high level abstractions for training. Concretely, Olympic contains:

1. The olympic.fit() function. This implements a very similar API to Keras’s model.fit and model.fit_generator methods in a more functional and less object-oriented fashion and spares you the effort of “hand-rolling” your own training loop.
2. Callback objects that perform functionality common to most deep learning training pipelines such as learning rate scheduling, model checkpointing and csv logging. These integrate into olympic.fit() and spare you the effort of writing boilerplate code.
3. Some helpful utility functions such as common metrics and some convenience layers from Keras that are missing in PyTorch.

Home:

• About Olympic PyTorch
• Quickstart
• Differences between Olympic and Keras
• Changelog

Features:

• fit()
  • Using your own update_fn
• evaluate()
• Callbacks
• Layers
• Metrics
  • Custom Metrics