Skip to main content

A lightweight library to help with training neural networks in PyTorch.

Project description

Ignite Logo

image image imageimage image image

image image image image image

image image image

image image image

TL;DR

Ignite is a high-level library to help with training neural networks in PyTorch.

  • ignite helps you write compact but full-featured training loops in a few lines of code
  • you get a training loop with metrics, early-stopping, model checkpointing and other features without the boilerplate

Below we show a side-by-side comparison of using pure pytorch and using ignite to create a training loop to train and validate your model with occasional checkpointing:

image

As you can see, the code is more concise and readable with ignite. Furthermore, adding additional metrics, or things like early stopping is a breeze in ignite, but can start to rapidly increase the complexity of your code when "rolling your own" training loop.

Table of Contents

Installation

From pip:

pip install pytorch-ignite

From conda:

conda install ignite -c pytorch

From source:

pip install git+https://github.com/pytorch/ignite

Nightly releases

From pip:

pip install --pre pytorch-ignite

From conda (this suggests to install pytorch nightly release instead of stable version as dependency):

conda install ignite -c pytorch-nightly

Why Ignite?

Ignite's high level of abstraction assumes less about the type of network (or networks) that you are training, and we require the user to define the closure to be run in the training and validation loop. This level of abstraction allows for a great deal more of flexibility, such as co-training multiple models (i.e. GANs) and computing/tracking multiple losses and metrics in your training loop.

Ignite also allows for multiple handlers to be attached to events, and a finer granularity of events in the engine loop.

Documentation

API documentation and an overview of the library can be found here.

Additional Materials

Structure

  • ignite: Core of the library, contains an engine for training and evaluating, all of the classic machine learning metrics and a variety of handlers to ease the pain of training and validation of neural networks!
  • ignite.contrib: The Contrib directory contains additional modules contributed by Ignite users. Modules vary from TBPTT engine, various optimisation parameter schedulers, logging handlers and a metrics module containing many regression metrics (ignite.contrib.metrics.regression)!

The code in ignite.contrib is not as fully maintained as the core part of the library. It may change or be removed at any time without notice.

Examples

We provide several examples ported from pytorch/examples using ignite to display how it helps to write compact and full-featured training loops in a few lines of code:

MNIST Example

Basic neural network training on MNIST dataset with/without ignite.contrib module:

Tutorials

Distributed CIFAR10 Example

Training a small variant of ResNet on CIFAR10 in various configurations: 1) single gpu, 2) single node multiple gpus, 3) multiple nodes and multilple gpus.

Other Examples

Reproducible Training Examples

Inspired by torchvision/references, we provide several reproducible baselines for vision tasks:

Features:

Communication

User feedback

We have created a form for "user feedback". We appreciate any type of feedback and this is how we would like to see our community:

  • If you like the project and want to say thanks, this the right place.
  • If you do not like something, please, share it with us and we can see how to improve it.

Thank you !

Contributing

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.

Please see the contribution guidelines for more information.

As always, PRs are welcome :)

Projects using Ignite

See other projects at "Used by"

If your project implements a paper, represents other use-cases not covered in our official tutorials, Kaggle competition's code or just your code presents interesting results and uses Ignite. We would like to add your project in this list, so please send a PR with brief description of the project.

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

pytorch-ignite-0.4.0.dev20200330.tar.gz (86.5 kB view details)

Uploaded Source

Built Distribution

pytorch_ignite-0.4.0.dev20200330-py2.py3-none-any.whl (124.1 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file pytorch-ignite-0.4.0.dev20200330.tar.gz.

File metadata

  • Download URL: pytorch-ignite-0.4.0.dev20200330.tar.gz
  • Upload date:
  • Size: 86.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3 requests-toolbelt/0.9.1 tqdm/4.44.1 CPython/3.7.7

File hashes

Hashes for pytorch-ignite-0.4.0.dev20200330.tar.gz
Algorithm Hash digest
SHA256 a7ff358cc3f39b91370099e92defa515139ff2eb05419668b171868ae9b95d5c
MD5 7d7782366a702ff2c34082f55009f70c
BLAKE2b-256 69373d0b3a2e719e7fa37dae73ae9b0b9465c28c9499fc6bcde99373f633079f

See more details on using hashes here.

File details

Details for the file pytorch_ignite-0.4.0.dev20200330-py2.py3-none-any.whl.

File metadata

  • Download URL: pytorch_ignite-0.4.0.dev20200330-py2.py3-none-any.whl
  • Upload date:
  • Size: 124.1 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3 requests-toolbelt/0.9.1 tqdm/4.44.1 CPython/3.7.7

File hashes

Hashes for pytorch_ignite-0.4.0.dev20200330-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 34f17e16d831c29dca188e952bc501180bb7d02085ac61aca2b05711621924e2
MD5 f41a10cf59afe668e1ee8ed341f09d1e
BLAKE2b-256 e297fa86924f31f27bc5cfe9d3b691c6aef2c8e1440664212bbb4233ffc7df00

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