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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

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.

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.

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