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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

• 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?
  • 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

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

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

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)

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.

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)

# 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():
    # ...

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

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):
    # ...

• 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.

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")

From pip:

pip install pytorch-ignite

From conda:

conda install ignite -c pytorch

From source:

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

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

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

For more details, see here.

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)

• 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

• 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

• Text Classification using Convolutional Neural Networks
• Variational Auto Encoders
• Convolutional Neural Networks for Classifying Fashion-MNIST Dataset
• Training Cycle-GAN on Horses to Zebras with Nvidia/Apex - logs on W&B
• Another training Cycle-GAN on Horses to Zebras with Native Torch CUDA AMP - logs on W&B
• Finetuning EfficientNet-B0 on CIFAR100
• Hyperparameters tuning with Ax
• Basic example of LR finder on MNIST
• Benchmark mixed precision training on Cifar100: torch.cuda.amp vs nvidia/apex
• MNIST training on a single TPU
• CIFAR10 Training on multiple TPUs
• Basic example of handlers time profiling on MNIST training example

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

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

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

• 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.

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!

Please see the contribution guidelines for more information.

As always, PRs are welcome :)

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.

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}},
}

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.