Skip to main content

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

Project description

Ignite

https://travis-ci.org/pytorch/ignite.svg?branch=master https://codecov.io/gh/pytorch/ignite/branch/master/graph/badge.svg https://pepy.tech/badge/pytorch-ignite https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v

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:

assets/ignite_vs_bare_pytorch.png

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.

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.

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

Please check out the examples to see how to use ignite to train various types of networks, as well as how to use visdom or tensorboardX for training visualizations.

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 :)

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-0.3.0.dev20190928.tar.gz (51.7 kB view details)

Uploaded Source

Built Distribution

pytorch_ignite-0.3.0.dev20190928-py2.py3-none-any.whl (84.6 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file pytorch-ignite-0.3.0.dev20190928.tar.gz.

File metadata

  • Download URL: pytorch-ignite-0.3.0.dev20190928.tar.gz
  • Upload date:
  • Size: 51.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.9

File hashes

Hashes for pytorch-ignite-0.3.0.dev20190928.tar.gz
Algorithm Hash digest
SHA256 44c09b7f16d14bf0508273e56fcd8aad0d0294e5421c08586099409556574c33
MD5 5dd9a16c092a40a900baef8547d21426
BLAKE2b-256 d5546aaa44f4ed3ae00d67b45d59cb019cafddf7e11fbb5915866e2eae34b283

See more details on using hashes here.

File details

Details for the file pytorch_ignite-0.3.0.dev20190928-py2.py3-none-any.whl.

File metadata

  • Download URL: pytorch_ignite-0.3.0.dev20190928-py2.py3-none-any.whl
  • Upload date:
  • Size: 84.6 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.9

File hashes

Hashes for pytorch_ignite-0.3.0.dev20190928-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 dfff7e8552e6128f692df44d3bddae80e4cb1c5c6800db6d2be2be020f10e828
MD5 71cb8fdb26f81443ab3d5cf735bab726
BLAKE2b-256 4814427051f9d3b3dae5578cfd18573c1f9ea17a1f2deba754bdc00da39d9bc6

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