Ignite is a high-level PyTorch library designed to simplify and streamline the training and evaluation of neural networks with flexibility and transparency.
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
Ignite is used by AI researchers and developers to reduce boilerplate code while maintaining full control over the training and validation process of neural networks in PyTorch. It is ideal for those who want an extensible, event-driven framework to manage experiments, metrics, and training loops efficiently.
Ignite is designed to complement PyTorch without taking over program control flow, allowing users to integrate it incrementally. Users should ensure compatibility with their PyTorch version and test their training loops thoroughly when using custom event handlers. The library is actively maintained with stable and nightly releases, and community support is available via Discord and Twitter.
pip install pytorch-ignite
Check compatibility with your PyTorch and Python versions
Optionally, install nightly releases for latest features
Refer to official documentation for advanced setup and usagefrom ignite.engine import Engine, Events
Import core classes to create training and evaluation engines and manage events
trainer = Engine(update_function)
Create a training engine with a user-defined update function
trainer.add_event_handler(Events.EPOCH_COMPLETED, handler_function)
Attach a handler function to be executed after each epoch
evaluator = Engine(evaluation_function)
Create an evaluation engine for model validation
from ignite.metrics import Accuracy, Loss
Import built-in metrics to evaluate model performance
accuracy = Accuracy()
Instantiate an accuracy metric to attach to an evaluator
accuracy.attach(evaluator, 'accuracy')
Attach the accuracy metric to the evaluator engine