PyTorch-Ignite PyTorch-Ignite

How-to Guides

Below are step by step guides on understanding the concepts and how-to tips, and best practices of PyTorch-Ignite.

1. How to install PyTorch-Ignite

Install PyTorch-Ignite from pip, conda, source or use pre-built docker images

2. How to convert pure PyTorch code to Ignite

In this guide, we will show how PyTorch code components can be converted into compact and flexible PyTorch-Ignite code.

3. How to do time profiling

Learn how to get the time breakdown for individual epochs during training, individual events, all handlers corresponding to an event, individual handlers, data loading and data processing using Engine’s State, BasicTimeProfiler and HandlersTimeProfiler.

4. How to use FastaiLRFinder with Ignite

This how-to guide demonstrates how we can leverage the FastaiLRFinder handler to find an optimal learning rate to train our model on. We will compare the results produced with and without using the handler for better understanding.

5. How to effectively increase batch size on limited compute

To effectively increase the batch size on limited GPU resources, follow this simple best practice.

6. How to work with data iterators

When the data provider for training or validation is an iterator (infinite or finite with known or unknown size), here are some basic examples of how to setup trainer or evaluator.

7. How to do Cross Validation in Ignite

This how-to guide demonstrates how we can do Cross Validation using the k-fold technique with PyTorch-Ignite and save the best results.

Cross Validation is useful for tuning model parameters or when the available data is insufficient to properly test