Skip to main content

A lightweight library to help with training neural networks in PyTorch.

Project description

image image imageimage image image
image image image image image
image image image
image image image image Twitter
image link

TL;DR

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

PyTorch-Ignite teaser

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

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
    # E.g. 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 say, 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):
    # ...

Out-of-the-box metrics

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

Available pre-built images are :

  • pytorchignite/base:latest | pytorchignite/hvd-base:latest
  • pytorchignite/apex:latest | pytorchignite/hvd-apex:latest | pytorchignite/msdp-apex:latest
  • pytorchignite/vision:latest | pytorchignite/hvd-vision:latest
  • pytorchignite/apex-vision:latest | pytorchignite/hvd-apex-vision:latest | pytorchignite/msdp-apex-vision:latest
  • 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:

Documentation

Additional Materials

Examples

Complete list of examples can be found here.

Tutorials

Reproducible Training Examples

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:

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:

  • 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 !

Contributing

Please see the contribution guidelines for more information.

As always, PRs are welcome :)

Projects using Ignite

Research papers

Blog articles, tutorials, books

Toolkits

Others

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 in this list, so please send a PR with brief description of the project.

About the team & Disclaimer

This repository is 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.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pytorch-ignite-0.5.0.dev20210112.tar.gz (137.2 kB view details)

Uploaded Source

Built Distributions

pytorch_ignite-0.5.0.dev20210112-py3.7.egg (438.0 kB view details)

Uploaded Source

pytorch_ignite-0.5.0.dev20210112-py3-none-any.whl (186.0 kB view details)

Uploaded Python 3

File details

Details for the file pytorch-ignite-0.5.0.dev20210112.tar.gz.

File metadata

  • Download URL: pytorch-ignite-0.5.0.dev20210112.tar.gz
  • Upload date:
  • Size: 137.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.2.post20210110 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.7.9

File hashes

Hashes for pytorch-ignite-0.5.0.dev20210112.tar.gz
Algorithm Hash digest
SHA256 bf7a4859db7d8785857fe0748dccc28bbeda0a7b534a349853b0765f97c51e18
MD5 bda88889c857fd36e7b06a0046efdc5c
BLAKE2b-256 73fc2acf871f8436ae55953b231c7fed541701605cac7cc1bfccbcd7e7da27db

See more details on using hashes here.

File details

Details for the file pytorch_ignite-0.5.0.dev20210112-py3.7.egg.

File metadata

  • Download URL: pytorch_ignite-0.5.0.dev20210112-py3.7.egg
  • Upload date:
  • Size: 438.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.2.post20210110 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.7.9

File hashes

Hashes for pytorch_ignite-0.5.0.dev20210112-py3.7.egg
Algorithm Hash digest
SHA256 233a1c0cbb1d43938829d0cd57c693dffe507c5ef21c3d24280b79bc8a249fc6
MD5 4078a94762ea2bd77e268bba2320f7f3
BLAKE2b-256 7df204aa86856b66d7cf60e03afeb2d7bb7d8b85aa112cc9e47dc57c09a96b78

See more details on using hashes here.

File details

Details for the file pytorch_ignite-0.5.0.dev20210112-py3-none-any.whl.

File metadata

  • Download URL: pytorch_ignite-0.5.0.dev20210112-py3-none-any.whl
  • Upload date:
  • Size: 186.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.2.post20210110 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.7.9

File hashes

Hashes for pytorch_ignite-0.5.0.dev20210112-py3-none-any.whl
Algorithm Hash digest
SHA256 4b8763170a223e681482769309a8a20733a1187c6ffcab7a9c2b5c6b8cf4b26b
MD5 11e885453041925098ac4e81fa8507e5
BLAKE2b-256 a5d66bebb809449152bee95c1f4b8d4593f1ab48ea5830b49123b9c5823b2c9d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page