# ignite
**Repository Path**: simonBW/ignite
## Basic Information
- **Project Name**: ignite
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-02-15
- **Last Updated**: 2020-12-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[](https://travis-ci.org/pytorch/ignite)
[](https://github.com/pytorch/ignite/actions)
[](https://circleci.com/gh/pytorch/ignite)[](https://circleci.com/gh/pytorch/ignite)
[](https://codecov.io/gh/pytorch/ignite)
[](https://pytorch.org/ignite/index.html)
 [](https://anaconda.org/pytorch/ignite)
[](https://anaconda.org/pytorch/ignite)
[](https://pypi.org/project/pytorch-ignite/)
[](https://pepy.tech/project/pytorch-ignite)
 [](https://anaconda.org/pytorch-nightly/ignite)
[](https://pypi.org/project/pytorch-ignite/#history)
 [](https://optuna.org)
[](https://github.com/psf/black)
[](https://twitter.com/pytorch_ignite)
## TL;DR
Ignite is a high-level library to help with training neural networks in
PyTorch:
- ignite helps you write compact but full-featured training loops in a
few lines of code
- you get a training loop with metrics, early-stopping, model
checkpointing and other features without the boilerplate
Below we show a side-by-side comparison of using pure pytorch and using
ignite to create a training loop to train and validate your model with
occasional checkpointing:
[](https://raw.githubusercontent.com/pytorch/ignite/master/assets/ignite_vs_bare_pytorch.png)
As you can see, the code is more concise and readable with ignite.
Furthermore, adding additional metrics, or things like early stopping is
a breeze in ignite, but can start to rapidly increase the complexity of
your code when \"rolling your own\" training loop.
# Table of Contents
- [Installation](#installation)
- [Why Ignite?](#why-ignite)
- [Documentation](#documentation)
- [Structure](#structure)
- [Examples](#examples)
* [MNIST Example](#mnist-example)
* [Tutorials](#tutorials)
* [Distributed CIFAR10 Example](#distributed-cifar10-example)
* [Other Examples](#other-examples)
* [Reproducible Training Examples](#reproducible-training-examples)
- [Communication](#communication)
- [Contributing](#contributing)
- [Projects using Ignite](#projects-using-ignite)
- [About the team](#about-the-team)
# Installation
From [pip](https://pypi.org/project/pytorch-ignite/):
``` {.sourceCode .bash}
pip install pytorch-ignite
```
From [conda](https://anaconda.org/pytorch/ignite):
``` {.sourceCode .bash}
conda install ignite -c pytorch
```
From source:
``` {.sourceCode .bash}
pip install git+https://github.com/pytorch/ignite
```
## Nightly releases
From pip:
``` {.sourceCode .bash}
pip install --pre pytorch-ignite
```
From conda (this suggests to install [pytorch nightly
release](https://anaconda.org/pytorch-nightly/pytorch) instead of stable
version as dependency):
``` {.sourceCode .bash}
conda install ignite -c pytorch-nightly
```
# Why Ignite?
Ignite\'s high level of abstraction assumes less about the type of
network (or networks) that you are training, and we require the user to
define the closure to be run in the training and validation loop. This
level of abstraction allows for a great deal more of flexibility, such
as co-training multiple models (i.e. GANs) and computing/tracking
multiple losses and metrics in your training loop.
## 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. The first argument can be optionally `engine`, but not necessary.
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
```python
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("Training is ended. mydata={}".format(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("OK"))
@trainer.on(Events.ITERATION_COMPLETED)
def log_something(engine):
print(engine.state.output)
```
### Built-in events filtering
Examples
```python
# 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:
```python
@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:
```python
class BackpropEvents(Enum):
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):
# ...
```
- Complete snippet can be found [here](https://pytorch.org/ignite/faq.html#creating-custom-events-based-on-forward-backward-pass).
- Another use-case of custom events: [trainer for Truncated Backprop Through Time](https://pytorch.org/ignite/contrib/engines.html#ignite.contrib.engines.create_supervised_tbptt_trainer).
## Out-of-the-box metrics
- [Metrics](https://pytorch.org/ignite/metrics.html#complete-list-of-metrics) for various tasks:
Precision, Recall, Accuracy, Confusion Matrix, IoU etc, ~20 [regression metrics](https://pytorch.org/ignite/contrib/metrics.html#regression-metrics).
- Users can also [compose their own metrics](https://pytorch.org/ignite/metrics.html#metric-arithmetics) with ease from
existing ones using arithmetic operations or torch methods:
```python
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")
```
# Documentation
- Stable API documentation and an overview of the library: https://pytorch.org/ignite/
- Development version API documentation: https://pytorch.org/ignite/master/
- [FAQ](https://pytorch.org/ignite/faq.html) and ["Questions on Github"](https://github.com/pytorch/ignite/issues?q=is%3Aissue+label%3Aquestion+).
- [Project's Roadmap](https://github.com/pytorch/ignite/wiki/Roadmap)
## Additional Materials
- [8 Creators and Core Contributors Talk About Their Model Training Libraries From PyTorch Ecosystem](https://neptune.ai/blog/model-training-libraries-pytorch-ecosystem?utm_source=reddit&utm_medium=post&utm_campaign=blog-model-training-libraries-pytorch-ecosystem)
- Ignite Posters from Pytorch Developer Conferences:
- [2019](https://drive.google.com/open?id=1bqIl-EM6GCCCoSixFZxhIbuF25F2qTZg)
- [2018](https://drive.google.com/open?id=1_2vzBJ0KeCjGv1srojMHiJRvceSVbVR5)
# Structure
- **ignite**: Core of the library, contains an engine for training and
evaluating, all of the classic machine learning metrics and a
variety of handlers to ease the pain of training and validation of
neural networks!
- **ignite.contrib**: The Contrib directory contains additional
modules that can require extra dependencies. Modules vary from TBPTT engine,
various optimisation parameter schedulers, logging handlers and a
metrics module containing many regression metrics
([ignite.contrib.metrics.regression](https://github.com/pytorch/ignite/tree/master/ignite/contrib/metrics/regression))!
The code in **ignite.contrib** is not as fully maintained as the core
part of the library.
# Examples
We provide several examples ported from
[pytorch/examples](https://github.com/pytorch/examples) using `ignite` to display how it helps to write compact and
full-featured training loops in a few lines of code:
## MNIST Example
Basic neural network training on MNIST dataset with/without `ignite.contrib` module:
- [MNIST with ignite.contrib TQDM/Tensorboard/Visdom
loggers](https://github.com/pytorch/ignite/tree/master/examples/contrib/mnist)
- [MNIST with native TQDM/Tensorboard/Visdom
logging](https://github.com/pytorch/ignite/tree/master/examples/mnist)
## Tutorials
- [](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/TextCNN.ipynb) [Text Classification using Convolutional Neural
Networks](https://github.com/pytorch/ignite/blob/master/examples/notebooks/TextCNN.ipynb)
- [](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/VAE.ipynb) [Variational Auto
Encoders](https://github.com/pytorch/ignite/blob/master/examples/notebooks/VAE.ipynb)
- [](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/FashionMNIST.ipynb) [Convolutional Neural Networks for Classifying Fashion-MNIST
Dataset](https://github.com/pytorch/ignite/blob/master/examples/notebooks/FashionMNIST.ipynb)
- [](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/CycleGAN.ipynb) [Training Cycle-GAN on Horses to
Zebras](https://github.com/pytorch/ignite/blob/master/examples/notebooks/CycleGAN.ipynb)
- [](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/EfficientNet_Cifar100_finetuning.ipynb) [Finetuning EfficientNet-B0 on
CIFAR100](https://github.com/pytorch/ignite/blob/master/examples/notebooks/EfficientNet_Cifar100_finetuning.ipynb)
- [](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/Cifar10_Ax_hyperparam_tuning.ipynb) [Hyperparameters tuning with
Ax](https://github.com/pytorch/ignite/blob/master/examples/notebooks/Cifar10_Ax_hyperparam_tuning.ipynb)
- [](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/FastaiLRFinder_MNIST.ipynb) [Basic example of LR finder on
MNIST](https://github.com/pytorch/ignite/blob/master/examples/notebooks/FastaiLRFinder_MNIST.ipynb)
- [](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/Cifar100_bench_amp.ipynb) [Benchmark mixed precision training on Cifar100: torch.cuda.amp vs nvidia/apex](https://github.com/pytorch/ignite/blob/master/examples/notebooks/Cifar100_bench_amp.ipynb)
## Distributed CIFAR10 Example
Training a small variant of ResNet on CIFAR10 in various configurations:
1\) single gpu, 2) single node multiple gpus, 3) multiple nodes and
multilple gpus.
- [CIFAR10](https://github.com/pytorch/ignite/tree/master/examples/contrib/cifar10)
## Other Examples
- [DCGAN](https://github.com/pytorch/ignite/tree/master/examples/gan)
- [Reinforcement
Learning](https://github.com/pytorch/ignite/tree/master/examples/reinforcement_learning)
- [Fast Neural
Style](https://github.com/pytorch/ignite/tree/master/examples/fast_neural_style)
## Reproducible Training Examples
Inspired by
[torchvision/references](https://github.com/pytorch/vision/tree/master/references),
we provide several reproducible baselines for vision tasks:
- [ImageNet](examples/references/classification/imagenet)
- [Pascal VOC2012](examples/references/segmentation/pascal_voc2012)
Features:
- Distributed training with mixed precision by
[nvidia/apex](https://github.com/NVIDIA/apex/)
- Experiments tracking with [MLflow](https://mlflow.org/) or
[Polyaxon](https://polyaxon.com/)
# Communication
- [GitHub issues](https://github.com/pytorch/ignite/issues): questions, bug reports, feature requests, etc.
- [Discuss.PyTorch](https://discuss.pytorch.org/c/ignite), category "Ignite".
- [PyTorch Slack](https://pytorch.slack.com) at #pytorch-ignite channel. [Request access](https://bit.ly/ptslack).
## User feedback
We have created a form for [\"user
feedback\"](https://github.com/pytorch/ignite/issues/new/choose). 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](https://github.com/pytorch/ignite/blob/master/CONTRIBUTING.md)
for more information.
As always, PRs are welcome :)
# Projects using Ignite
- [State-of-the-Art Conversational AI with Transfer
Learning](https://github.com/huggingface/transfer-learning-conv-ai)
- [Tutorial on Transfer Learning in NLP held at NAACL
2019](https://github.com/huggingface/naacl_transfer_learning_tutorial)
- [Implementation of \"Attention is All You Need\"
paper](https://github.com/akurniawan/pytorch-transformer)
- [Implementation of DropBlock: A regularization method for
convolutional networks in
PyTorch](https://github.com/miguelvr/dropblock)
- [Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by
Packt](https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition)
- [Kaggle Kuzushiji Recognition: 2nd place
solution](https://github.com/lopuhin/kaggle-kuzushiji-2019)
- [Unsupervised Data Augmentation experiments in
PyTorch](https://github.com/vfdev-5/UDA-pytorch)
- [Hyperparameters tuning with
Optuna](https://github.com/pfnet/optuna/blob/master/examples/pytorch_ignite_simple.py)
- [Project MONAI -
AI Toolkit for Healthcare Imaging
](https://github.com/Project-MONAI/MONAI)
- [DeepSeismic - Deep Learning for Seismic Imaging and Interpretation](https://github.com/microsoft/seismic-deeplearning)
See other projects at [\"Used
by\"](https://github.com/pytorch/ignite/network/dependents?package_id=UGFja2FnZS02NzI5ODEwNA%3D%3D)
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
Project is currently maintained by a team of volunteers.
See the ["About us"](https://pytorch.org/ignite/master/about.html) page for a list of core contributors.