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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

• 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
• 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

• [](https://colab