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TL;DR

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

Click on the image to see complete code

Features

Less code than pure PyTorch while ensuring maximum control and simplicity
Library approach and no program's control inversion - Use ignite where and when you need
Extensible API for metrics, experiment managers, and other components

Table of Contents
Why Ignite?
Installation
- Nightly releases
- Docker Images
  - Using pre-built images
Getting Started
Documentation
- Additional Materials
Examples
- Tutorials
- Reproducible Training Examples
Communication
- User feedback
Contributing
Projects using Ignite
Citing Ignite
About the team & Disclaimer

Why Ignite?

Ignite is a library that provides three high-level features:

Extremely simple engine and event system
Out-of-the-box metrics to easily evaluate models
Built-in handlers to compose training pipeline, save artifacts and log parameters and metrics

Simplified training and validation loop

No more coding for/while loops on epochs and iterations. Users instantiate engines and run them.

Example

from ignite.engine import Engine, Events, create_supervised_evaluator
from ignite.metrics import Accuracy


# Setup training engine:
def train_step(engine, batch):
    # Users can do whatever they need on a single iteration
    # Eg. forward/backward pass for any number of models, optimizers, etc
    # ...

trainer = Engine(train_step)

# Setup single model evaluation engine
evaluator = create_supervised_evaluator(model, metrics={"accuracy": Accuracy()})

def validation():
    state = evaluator.run(validation_data_loader)
    # print computed metrics
    print(trainer.state.epoch, state.metrics)

# Run model's validation at the end of each epoch
trainer.add_event_handler(Events.EPOCH_COMPLETED, validation)

# Start the training
trainer.run(training_data_loader, max_epochs=100)

Power of Events & Handlers

The cool thing with handlers is that they offer unparalleled flexibility (compared to, for example, callbacks). Handlers can be any function: e.g. lambda, simple function, class method, etc. Thus, we do not require to inherit from an interface and override its abstract methods which could unnecessarily bulk up your code and its complexity.

Execute any number of functions whenever you wish

Examples

trainer.add_event_handler(Events.STARTED, lambda _: print("Start training"))

# attach handler with args, kwargs
mydata = [1, 2, 3, 4]
logger = ...

def on_training_ended(data):
    print(f"Training is ended. mydata={data}")
    # User can use variables from another scope
    logger.info("Training is ended")


trainer.add_event_handler(Events.COMPLETED, on_training_ended, mydata)
# call any number of functions on a single event
trainer.add_event_handler(Events.COMPLETED, lambda engine: print(engine.state.times))

@trainer.on(Events.ITERATION_COMPLETED)
def log_something(engine):
    print(engine.state.output)

Built-in events filtering

Examples

# run the validation every 5 epochs
@trainer.on(Events.EPOCH_COMPLETED(every=5))
def run_validation():
    # run validation

# change some training variable once on 20th epoch
@trainer.on(Events.EPOCH_STARTED(once=20))
def change_training_variable():
    # ...

# Trigger handler with customly defined frequency
@trainer.on(Events.ITERATION_COMPLETED(event_filter=first_x_iters))
def log_gradients():
    # ...

Stack events to share some actions

Examples

Events can be stacked together to enable multiple calls:

@trainer.on(Events.COMPLETED | Events.EPOCH_COMPLETED(every=10))
def run_validation():
    # ...

Custom events to go beyond standard events

Examples

Custom events related to backward and optimizer step calls:

from ignite.engine import EventEnum


class BackpropEvents(EventEnum):
    BACKWARD_STARTED = 'backward_started'
    BACKWARD_COMPLETED = 'backward_completed'
    OPTIM_STEP_COMPLETED = 'optim_step_completed'

def update(engine, batch):
    # ...
    loss = criterion(y_pred, y)
    engine.fire_event(BackpropEvents.BACKWARD_STARTED)
    loss.backward()
    engine.fire_event(BackpropEvents.BACKWARD_COMPLETED)
    optimizer.step()
    engine.fire_event(BackpropEvents.OPTIM_STEP_COMPLETED)
    # ...

trainer = Engine(update)
trainer.register_events(*BackpropEvents)

@trainer.on(BackpropEvents.BACKWARD_STARTED)
def function_before_backprop(engine):
    # ...

Complete snippet is found here.
Another use-case of custom events: trainer for Truncated Backprop Through Time.

Out-of-the-box metrics

Metrics for various tasks: Precision, Recall, Accuracy, Confusion Matrix, IoU etc, ~20 regression metrics.
Users can also compose their metrics with ease from existing ones using arithmetic operations or torch methods.

Example

precision = Precision(average=False)
recall = Recall(average=False)
F1_per_class = (precision * recall * 2 / (precision + recall))
F1_mean = F1_per_class.mean()  # torch mean method
F1_mean.attach(engine, "F1")

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

Docker Images

Using pre-built images

Pull a pre-built docker image from our Docker Hub and run it with docker v19.03+.

docker run --gpus all -it -v $PWD:/workspace/project --network=host --shm-size 16G pytorchignite/base:latest /bin/bash

List of available pre-built images

Base

pytorchignite/base:latest
pytorchignite/apex:latest
pytorchignite/hvd-base:latest
pytorchignite/hvd-apex:latest
pytorchignite/msdp-apex:latest

Vision:

pytorchignite/vision:latest
pytorchignite/hvd-vision:latest
pytorchignite/apex-vision:latest
pytorchignite/hvd-apex-vision:latest
pytorchignite/msdp-apex-vision:latest

NLP:

pytorchignite/nlp:latest
pytorchignite/hvd-nlp:latest
pytorchignite/apex-nlp:latest
pytorchignite/hvd-apex-nlp:latest
pytorchignite/msdp-apex-nlp:latest

For more details, see here.

Getting Started

Few pointers to get you started:

Quick Start Guide: Essentials of getting a project up and running
Concepts of the library: Engine, Events & Handlers, State, Metrics
Full-featured template examples (coming soon)

Documentation

Stable API documentation and an overview of the library: https://pytorch.org/ignite/
Development version API documentation: https://pytorch.org/ignite/master/
FAQ, "Questions on Github" and "Questions on Discuss.PyTorch".
Project's Roadmap

Additional Materials

Distributed Training Made Easy with PyTorch-Ignite
PyTorch Ecosystem Day 2021 Breakout session presentation
Tutorial blog post about PyTorch-Ignite
8 Creators and Core Contributors Talk About Their Model Training Libraries From PyTorch Ecosystem
Ignite Posters from Pytorch Developer Conferences:
- 2021
- 2019
- 2018

Examples

Tutorials

Reproducible Training Examples

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

ImageNet - logs on Ignite Trains server coming soon ...
Pascal VOC2012 - logs on Ignite Trains server coming soon ...

Features:

Distributed training: native or horovod and using PyTorch native AMP

Code-Generator application

The easiest way to create your training scripts with PyTorch-Ignite:

https://code-generator.pytorch-ignite.ai/

Communication

GitHub issues: questions, bug reports, feature requests, etc.
Discuss.PyTorch, category "Ignite".
PyTorch-Ignite Discord Server: to chat with the community
GitHub Discussions: general library-related discussions, ideas, Q&A, etc.

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

Please see the contribution guidelines for more information.

As always, PRs are welcome :)

Projects using Ignite

Research papers

Blog articles, tutorials, books

Toolkits

Others

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 to this list, so please send a PR with brief description of the project.

Citing Ignite

If you use PyTorch-Ignite in a scientific publication, we would appreciate citations to our project.

@misc{pytorch-ignite,
  author = {V. Fomin and J. Anmol and S. Desroziers and J. Kriss and A. Tejani},
  title = {High-level library to help with training neural networks in PyTorch},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/pytorch/ignite}},
}

About the team & Disclaimer

PyTorch-Ignite is a NumFOCUS Affiliated Project, operated and maintained by volunteers in the PyTorch community in their capacities as individuals (and not as representatives of their employers). See the "About us" page for a list of core contributors. For usage questions and issues, please see the various channels here. For all other questions and inquiries, please send an email to contact@pytorch-ignite.ai.

TL;DR

Features

Table of Contents

Why Ignite?

Simplified training and validation loop

Power of Events & Handlers

Execute any number of functions whenever you wish

Built-in events filtering

Stack events to share some actions

Custom events to go beyond standard events

Out-of-the-box metrics

Installation

Nightly releases

Docker Images

Using pre-built images

Getting Started

Documentation

Additional Materials

Examples

Tutorials

Reproducible Training Examples

Code-Generator application

Communication

User feedback

Contributing

Projects using Ignite

Citing Ignite

About the team & Disclaimer

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pytorch/ignite

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