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.dev20201229.tar.gz (137.1 kB view details)

Uploaded Source

Built Distributions

pytorch_ignite-0.5.0.dev20201229-py3.7.egg (437.4 kB view details)

Uploaded Source

pytorch_ignite-0.5.0.dev20201229-py2.py3-none-any.whl (185.8 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file pytorch-ignite-0.5.0.dev20201229.tar.gz.

File metadata

  • Download URL: pytorch-ignite-0.5.0.dev20201229.tar.gz
  • Upload date:
  • Size: 137.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.0.0.post20201207 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.7.9

File hashes

Hashes for pytorch-ignite-0.5.0.dev20201229.tar.gz
Algorithm Hash digest
SHA256 83d5ab0432a9c9f402bffd7819e48260b4514a6a4d0ea01055fb74f991f47895
MD5 54dd2dafcb7d0ef1c53f98d0829c6b97
BLAKE2b-256 f8e1d35a742dd34ba19f9548391ac0b01a2bff254be78b9a9706bd1143c2ecd9

See more details on using hashes here.

File details

Details for the file pytorch_ignite-0.5.0.dev20201229-py3.7.egg.

File metadata

  • Download URL: pytorch_ignite-0.5.0.dev20201229-py3.7.egg
  • Upload date:
  • Size: 437.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.0.0.post20201207 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.7.9

File hashes

Hashes for pytorch_ignite-0.5.0.dev20201229-py3.7.egg
Algorithm Hash digest
SHA256 66e52283ba7909d0034a507bc4839c0652c763b32bf7c3ac98212e1fd7753f3e
MD5 4f0827cc7757a5d4dbdf9499aac8ed12
BLAKE2b-256 c39f70574593024ec603d3c8f46d26fbf111edf8ea2b4884f5bdcd4a65cda9be

See more details on using hashes here.

File details

Details for the file pytorch_ignite-0.5.0.dev20201229-py2.py3-none-any.whl.

File metadata

  • Download URL: pytorch_ignite-0.5.0.dev20201229-py2.py3-none-any.whl
  • Upload date:
  • Size: 185.8 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.0.0.post20201207 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.7.9

File hashes

Hashes for pytorch_ignite-0.5.0.dev20201229-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 a19d3f2e20b95dcca361528bcc115e691527bc5a1e35a10368d4036b30594599
MD5 7089a171c64f675545aa59a07eb11d75
BLAKE2b-256 1bad4ca64c959ea1591d980cbfd08c7149feb38c33c1bf4ca93b83d8963d6491

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