Home

Awesome

<div align="center"> <!-- ![Ignite Logo](assets/logo/ignite_logo_mixed.svg) --> <img src="assets/logo/ignite_logo_mixed.svg" width=512> <!-- [![image](https://travis-ci.com/pytorch/ignite.svg?branch=master)](https://travis-ci.com/pytorch/ignite) -->
image image image image image
image imageimage imageimage
image image image
image Twitter discord numfocus
image link
</div>

TL;DR

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

<div align="center"> <a href="https://colab.research.google.com/github/pytorch/ignite/blob/master/assets/tldr/teaser.ipynb"> <img alt="PyTorch-Ignite teaser" src="assets/tldr/pytorch-ignite-teaser.gif" width=532> </a>

Click on the image to see complete code

</div>

Features

<!-- ############################################################################################################### -->

Table of Contents

<!-- ############################################################################################################### -->

Why Ignite?

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

Simplified training and validation loop

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

<details> <summary> Example </summary>
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)
</details>

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

<details> <summary> Examples </summary>
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)
</details>

Built-in events filtering

<details> <summary> Examples </summary>
# 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():
    # ...
</details>

Stack events to share some actions

<details> <summary> Examples </summary>

Events can be stacked together to enable multiple calls:

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

Custom events to go beyond standard events

<details> <summary> Examples </summary>

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):
    # ...
</details>

Out-of-the-box metrics

<details> <summary> Example </summary>
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")
</details> <!-- ############################################################################################################### -->

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
<details> <summary> List of available pre-built images </summary>

Base

Vision:

NLP:

</details>

For more details, see here.

<!-- ############################################################################################################### -->

Getting Started

Few pointers to get you started:

<!-- ############################################################################################################### -->

Documentation

Additional Materials

<!-- ############################################################################################################### -->

Examples

Tutorials

Reproducible Training Examples

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

Features:

Code-Generator application

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

<!-- ############################################################################################################### -->

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:

Thank you!

<!-- ############################################################################################################### -->

Contributing

Please see the contribution guidelines for more information.

As always, PRs are welcome :)

<!-- ############################################################################################################### -->

Projects using Ignite

<details> <summary> Research papers </summary> </details> <details> <summary> Blog articles, tutorials, books </summary> </details> <details> <summary> Toolkits </summary> </details> <details> <summary> Others </summary> </details>

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.