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+%global _empty_manifest_terminate_build 0
+Name:		python-pytorch-ignite
+Version:	0.4.11
+Release:	1
+Summary:	A lightweight library to help with training neural networks in PyTorch.
+License:	BSD
+URL:		https://github.com/pytorch/ignite
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/28/cf/a134fd1002371186d02de5024d53374e0a19b45ae449874bba9cafce43ad/pytorch-ignite-0.4.11.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-torch
+Requires:	python3-packaging
+
+%description
+<div align="center">
+
+<!-- ![Ignite Logo](assets/logo/ignite_logo_mixed.svg) -->
+
+<img src="https://raw.githubusercontent.com/pytorch/ignite/master/assets/logo/ignite_logo_mixed.svg" width=512>
+
+<!-- [![image](https://travis-ci.com/pytorch/ignite.svg?branch=master)](https://travis-ci.com/pytorch/ignite) -->
+
+| ![image](https://img.shields.io/badge/-Tests:-black?style=flat-square) [![image](https://github.com/pytorch/ignite/workflows/Run%20unit%20tests/badge.svg)](https://github.com/pytorch/ignite/actions) [![image](https://img.shields.io/badge/-GPU%20tests-black?style=flat-square)](https://app.circleci.com/pipelines/github/pytorch/ignite?branch=master)[![image](https://circleci.com/gh/pytorch/ignite.svg?style=svg)](https://app.circleci.com/pipelines/github/pytorch/ignite?branch=master) [![image](https://codecov.io/gh/pytorch/ignite/branch/master/graph/badge.svg)](https://codecov.io/gh/pytorch/ignite) [![image](https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pytorch.org/ignite/index.html) |
+|:---
+| ![image](https://img.shields.io/badge/-Stable%20Releases:-black?style=flat-square) [![image](https://anaconda.org/pytorch/ignite/badges/version.svg)](https://anaconda.org/pytorch/ignite) [![image](https://anaconda.org/pytorch/ignite/badges/downloads.svg)](https://anaconda.org/pytorch/ignite) [![image](https://img.shields.io/badge/dynamic/json.svg?label=PyPI&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pypi.org/project/pytorch-ignite/) [![image](https://pepy.tech/badge/pytorch-ignite)](https://pepy.tech/project/pytorch-ignite) |
+| ![image](https://img.shields.io/badge/-Nightly%20Releases:-black?style=flat-square) [![image](https://anaconda.org/pytorch-nightly/ignite/badges/version.svg)](https://anaconda.org/pytorch-nightly/ignite) [![image](https://img.shields.io/badge/PyPI-pre%20releases-brightgreen)](https://pypi.org/project/pytorch-ignite/#history)|
+| ![image](https://img.shields.io/badge/-Features:-black?style=flat-square) [![image](https://img.shields.io/badge/docker-hub-blue)](https://hub.docker.com/u/pytorchignite) [![image](https://img.shields.io/badge/Optuna-integrated-blue)](https://optuna.org) [![image](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) |
+| ![image](https://img.shields.io/badge/-Community:-black?style=flat-square) [![Twitter](https://img.shields.io/badge/news-twitter-blue)](https://twitter.com/pytorch_ignite) [![facebook](https://img.shields.io/badge/news-facebook-blue)](https://www.facebook.com/PyTorch-Ignite-Community-105837321694508) [![numfocus](https://img.shields.io/badge/NumFOCUS-affiliated%20project-green)](https://numfocus.org/sponsored-projects/affiliated-projects) [![discord](https://img.shields.io/badge/chat-discord-blue?logo=discord)](https://discord.gg/djZtm3EmKj) |
+| ![image](https://img.shields.io/badge/-Supported_PyTorch/Python_versions:-black?style=flat-square) [![link](https://img.shields.io/badge/-check_here-blue)](https://github.com/pytorch/ignite/actions?query=workflow%3A%22PyTorch+version+tests%22)|
+
+</div>
+
+## TL;DR
+
+Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
+
+<div align="center">
+
+<a href="https://colab.research.google.com/github/pytorch/ignite/blob/master/assets/tldr/teaser.ipynb">
+ <img alt="PyTorch-Ignite teaser"
+      src="https://raw.githubusercontent.com/pytorch/ignite/master/assets/tldr/pytorch-ignite-teaser.gif"
+      width=532>
+</a>
+
+_Click on the image to see complete code_
+
+</div>
+
+### Features
+
+- [Less code than pure PyTorch](https://raw.githubusercontent.com/pytorch/ignite/master/assets/ignite_vs_bare_pytorch.png)
+  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](#table-of-contents)
+- [Why Ignite?](#why-ignite)
+  - [Simplified training and validation loop](#simplified-training-and-validation-loop)
+  - [Power of Events & Handlers](#power-of-events--handlers)
+    - [Execute any number of functions whenever you wish](#execute-any-number-of-functions-whenever-you-wish)
+    - [Built-in events filtering](#built-in-events-filtering)
+    - [Stack events to share some actions](#stack-events-to-share-some-actions)
+    - [Custom events to go beyond standard events](#custom-events-to-go-beyond-standard-events)
+  - [Out-of-the-box metrics](#out-of-the-box-metrics)
+- [Installation](#installation)
+  - [Nightly releases](#nightly-releases)
+  - [Docker Images](#docker-images)
+    - [Using pre-built images](#using-pre-built-images)
+- [Getting Started](#getting-started)
+- [Documentation](#documentation)
+  - [Additional Materials](#additional-materials)
+- [Examples](#examples)
+  - [Tutorials](#tutorials)
+  - [Reproducible Training Examples](#reproducible-training-examples)
+- [Communication](#communication)
+  - [User feedback](#user-feedback)
+- [Contributing](#contributing)
+- [Projects using Ignite](#projects-using-ignite)
+- [Citing Ignite](#citing-ignite)
+- [About the team & Disclaimer](#about-the-team--disclaimer)
+
+<!-- ############################################################################################################### -->
+
+# 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.
+
+<details>
+<summary>
+Example
+</summary>
+
+```python
+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)
+```
+
+</details>
+
+## 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.
+
+### Execute any number of functions whenever you wish
+
+<details>
+<summary>
+Examples
+</summary>
+
+```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(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)
+```
+
+</details>
+
+### Built-in events filtering
+
+<details>
+<summary>
+Examples
+</summary>
+
+```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():
+    # ...
+```
+
+</details>
+
+### Stack events to share some actions
+
+<details>
+<summary>
+Examples
+</summary>
+
+Events can be stacked together to enable multiple calls:
+
+```python
+@trainer.on(Events.COMPLETED | Events.EPOCH_COMPLETED(every=10))
+def run_validation():
+    # ...
+```
+
+</details>
+
+### Custom events to go beyond standard events
+
+<details>
+<summary>
+Examples
+</summary>
+
+Custom events related to backward and optimizer step calls:
+
+```python
+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):
+    # ...
+```
+
+- Complete snippet is 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).
+
+</details>
+
+## 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 metrics](https://pytorch.org/ignite/metrics.html#metric-arithmetics) with ease from
+  existing ones using arithmetic operations or torch methods.
+
+<details>
+<summary>
+Example
+</summary>
+
+```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")
+```
+
+</details>
+
+<!-- ############################################################################################################### -->
+
+# Installation
+
+From [pip](https://pypi.org/project/pytorch-ignite/):
+
+```bash
+pip install pytorch-ignite
+```
+
+From [conda](https://anaconda.org/pytorch/ignite):
+
+```bash
+conda install ignite -c pytorch
+```
+
+From source:
+
+```bash
+pip install git+https://github.com/pytorch/ignite
+```
+
+## Nightly releases
+
+From pip:
+
+```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):
+
+```bash
+conda install ignite -c pytorch-nightly
+```
+
+## Docker Images
+
+### Using pre-built images
+
+Pull a pre-built docker image from [our Docker Hub](https://hub.docker.com/u/pytorchignite) and run it with docker v19.03+.
+
+```bash
+docker run --gpus all -it -v $PWD:/workspace/project --network=host --shm-size 16G pytorchignite/base:latest /bin/bash
+```
+
+<details>
+
+<summary>
+List of available pre-built images
+</summary>
+
+Base
+- `pytorchignite/base:latest` 
+- `pytorchignite/apex:latest`
+- `pytorchignite/hvd-base:latest`
+- `pytorchignite/hvd-apex:latest` 
+- `pytorchignite/msdp-apex:latest`
+
+Vision:
+- `pytorchignite/vision:latest`
+- `pytorchignite/hvd-vision:latest`
+- `pytorchignite/apex-vision:latest`
+- `pytorchignite/hvd-apex-vision:latest`
+- `pytorchignite/msdp-apex-vision:latest`
+
+NLP:
+- `pytorchignite/nlp:latest`
+- `pytorchignite/hvd-nlp:latest`
+- `pytorchignite/apex-nlp:latest` 
+- `pytorchignite/hvd-apex-nlp:latest` 
+- `pytorchignite/msdp-apex-nlp:latest`
+
+</details>
+
+For more details, see [here](docker).
+
+<!-- ############################################################################################################### -->
+
+# Getting Started
+
+Few pointers to get you started:
+
+- [Quick Start Guide: Essentials of getting a project up and running](https://pytorch.org/ignite/quickstart.html)
+- [Concepts of the library: Engine, Events & Handlers, State, Metrics](https://pytorch.org/ignite/concepts.html)
+- Full-featured template examples (coming soon)
+
+<!-- ############################################################################################################### -->
+
+# 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),
+  ["Questions on Github"](https://github.com/pytorch/ignite/issues?q=is%3Aissue+label%3Aquestion+) and
+  ["Questions on Discuss.PyTorch"](https://discuss.pytorch.org/c/ignite).
+- [Project's Roadmap](https://github.com/pytorch/ignite/wiki/Roadmap)
+
+## Additional Materials
+
+- [Distributed Training Made Easy with PyTorch-Ignite](https://labs.quansight.org/blog/2021/06/distributed-made-easy-with-ignite/)
+- [PyTorch Ecosystem Day 2021 Breakout session presentation](https://colab.research.google.com/drive/1qhUgWQ0N2U71IVShLpocyeY4AhlDCPRd)
+- [Tutorial blog post about PyTorch-Ignite](https://labs.quansight.org/blog/2020/09/pytorch-ignite/)
+- [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:
+  - [2021](https://drive.google.com/file/d/1YXrkJIepPk_KltSG1ZfWRtA5IRgPFz_U)
+  - [2019](https://drive.google.com/open?id=1bqIl-EM6GCCCoSixFZxhIbuF25F2qTZg)
+  - [2018](https://drive.google.com/open?id=1_2vzBJ0KeCjGv1srojMHiJRvceSVbVR5)
+
+<!-- ############################################################################################################### -->
+
+# Examples
+
+## Tutorials
+
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_nvidia_apex.ipynb) [Training Cycle-GAN on Horses to
+  Zebras with Nvidia/Apex](https://github.com/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_nvidia_apex.ipynb) - [ logs on W&B](https://app.wandb.ai/vfdev-5/ignite-cyclegan-apex)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_torch_cuda_amp.ipynb) [Another training Cycle-GAN on Horses to
+  Zebras with Native Torch CUDA AMP](https://github.com/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_torch_cuda_amp.ipynb) - [logs on W&B](https://app.wandb.ai/vfdev-5/ignite-cyclegan-torch-amp)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/MNIST_on_TPU.ipynb) [MNIST training on a single
+  TPU](https://github.com/pytorch/ignite/blob/master/examples/notebooks/MNIST_on_TPU.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1E9zJrptnLJ_PKhmaP5Vhb6DTVRvyrKHx) [CIFAR10 Training on multiple TPUs](https://github.com/pytorch/ignite/tree/master/examples/contrib/cifar10)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/HandlersTimeProfiler_MNIST.ipynb) [Basic example of handlers
+  time profiling on MNIST training example](https://github.com/pytorch/ignite/blob/master/examples/notebooks/HandlersTimeProfiler_MNIST.ipynb)
+
+## 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) - logs on Ignite Trains server coming soon ...
+- [Pascal VOC2012](examples/references/segmentation/pascal_voc2012) - logs on Ignite Trains server coming soon ...
+
+Features:
+
+- Distributed training: native or horovod and using [PyTorch native AMP](https://pytorch.org/docs/stable/notes/amp_examples.html)
+
+## Code-Generator application
+
+The easiest way to create your training scripts with PyTorch-Ignite:
+- https://code-generator.pytorch-ignite.ai/
+
+
+<!-- ############################################################################################################### -->
+
+# 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-Ignite Discord Server](https://discord.gg/djZtm3EmKj): to chat with the community
+
+- [GitHub Discussions](https://github.com/pytorch/ignite/discussions): general library-related discussions, ideas, Q&A, etc.
+
+## 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
+
+<details>
+
+<summary>
+Research papers
+</summary>
+
+- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://github.com/BlackHC/BatchBALD)
+- [A Model to Search for Synthesizable Molecules](https://github.com/john-bradshaw/molecule-chef)
+- [Localised Generative Flows](https://github.com/jrmcornish/lgf)
+- [Extracting T Cell Function and Differentiation Characteristics from the Biomedical Literature](https://github.com/hammerlab/t-cell-relation-extraction)
+- [Variational Information Distillation for Knowledge Transfer](https://github.com/amzn/xfer/tree/master/var_info_distil)
+- [XPersona: Evaluating Multilingual Personalized Chatbot](https://github.com/HLTCHKUST/Xpersona)
+- [CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images](https://github.com/ucuapps/CoronaryArteryStenosisScoreClassification)
+- [Bridging Text and Video: A Universal Multimodal Transformer for Video-Audio Scene-Aware Dialog](https://github.com/ictnlp/DSTC8-AVSD)
+- [Adversarial Decomposition of Text Representation](https://github.com/text-machine-lab/adversarial_decomposition)
+- [Uncertainty Estimation Using a Single Deep Deterministic Neural Network](https://github.com/y0ast/deterministic-uncertainty-quantification)
+- [DeepSphere: a graph-based spherical CNN](https://github.com/deepsphere/deepsphere-pytorch)
+- [Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment](https://github.com/lidq92/LinearityIQA)
+- [Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training](https://github.com/lidq92/MDTVSFA)
+- [Deep Signature Transforms](https://github.com/patrick-kidger/Deep-Signature-Transforms)
+- [Neural CDEs for Long Time-Series via the Log-ODE Method](https://github.com/jambo6/neuralCDEs-via-logODEs)
+- [Volumetric Grasping Network](https://github.com/ethz-asl/vgn)
+- [Mood Classification using Listening Data](https://github.com/fdlm/listening-moods)
+- [Deterministic Uncertainty Estimation (DUE)](https://github.com/y0ast/DUE)
+- [PyTorch-Hebbian: facilitating local learning in a deep learning framework](https://github.com/Joxis/pytorch-hebbian)
+- [Stochastic Weight Matrix-Based Regularization Methods for Deep Neural Networks](https://github.com/rpatrik96/lod-wmm-2019)
+- [Learning explanations that are hard to vary](https://github.com/gibipara92/learning-explanations-hard-to-vary)
+- [The role of disentanglement in generalisation](https://github.com/mmrl/disent-and-gen)
+- [A Probabilistic Programming Approach to Protein Structure Superposition](https://github.com/LysSanzMoreta/Theseus-PP)
+- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://github.com/auriml/Rx-thorax-automatic-captioning)
+
+</details>
+
+<details>
+
+<summary>
+Blog articles, tutorials, books
+</summary>
+
+- [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)
+- [Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt](https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition)
+- [Once Upon a Repository: How to Write Readable, Maintainable Code with PyTorch](https://towardsdatascience.com/once-upon-a-repository-how-to-write-readable-maintainable-code-with-pytorch-951f03f6a829)
+- [The Hero Rises: Build Your Own SSD](https://allegro.ai/blog/the-hero-rises-build-your-own-ssd/)
+- [Using Optuna to Optimize PyTorch Ignite Hyperparameters](https://medium.com/pytorch/using-optuna-to-optimize-pytorch-ignite-hyperparameters-626ffe6d4783)
+- [PyTorch Ignite - Classifying Tiny ImageNet with EfficientNet](https://towardsdatascience.com/pytorch-ignite-classifying-tiny-imagenet-with-efficientnet-e5b1768e5e8f)
+ 
+</details>
+
+<details>
+
+<summary>
+Toolkits
+</summary>
+
+- [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)
+- [Nussl - a flexible, object-oriented Python audio source separation library](https://github.com/nussl/nussl)
+- [PyTorch Adapt - A fully featured and modular domain adaptation library](https://github.com/KevinMusgrave/pytorch-adapt)
+- [gnina-torch: PyTorch implementation of GNINA scoring function](https://github.com/RMeli/gnina-torch)
+ 
+</details>
+
+<details>
+
+<summary>
+Others
+</summary>
+
+- [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)
+- [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/optuna/optuna-examples/blob/main/pytorch/pytorch_ignite_simple.py)
+- [Logging with ChainerUI](https://chainerui.readthedocs.io/en/latest/reference/module.html#external-library-support)
+- [FixMatch experiments in PyTorch and Ignite (CTA dataaug policy)](https://github.com/vfdev-5/FixMatch-pytorch)
+- [Kaggle Birdcall Identification Competition: 1st place solution](https://github.com/ryanwongsa/kaggle-birdsong-recognition)
+- [Logging with Aim - An open-source experiment tracker](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-pytorch-ignite)
+
+</details>
+
+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 to this list, so please send a PR with brief
+description of the project.
+
+<!-- ############################################################################################################### -->
+
+# Citing Ignite
+
+If you use PyTorch-Ignite in a scientific publication, we would appreciate citations to our project.
+
+```
+@misc{pytorch-ignite,
+  author = {V. Fomin and J. Anmol and S. Desroziers and J. Kriss and A. Tejani},
+  title = {High-level library to help with training neural networks in PyTorch},
+  year = {2020},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/pytorch/ignite}},
+}
+```
+
+<!-- ############################################################################################################### -->
+
+# About the team & Disclaimer
+
+PyTorch-Ignite is a [NumFOCUS Affiliated Project](https://www.numfocus.org/), 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"](https://pytorch-ignite.ai/about/community/#about-us)
+page for a list of core contributors. For usage questions and issues, please see the various channels
+[here](#communication). For all other questions and inquiries, please send an email
+to contact@pytorch-ignite.ai.
+
+
+%package -n python3-pytorch-ignite
+Summary:	A lightweight library to help with training neural networks in PyTorch.
+Provides:	python-pytorch-ignite
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pytorch-ignite
+<div align="center">
+
+<!-- ![Ignite Logo](assets/logo/ignite_logo_mixed.svg) -->
+
+<img src="https://raw.githubusercontent.com/pytorch/ignite/master/assets/logo/ignite_logo_mixed.svg" width=512>
+
+<!-- [![image](https://travis-ci.com/pytorch/ignite.svg?branch=master)](https://travis-ci.com/pytorch/ignite) -->
+
+| ![image](https://img.shields.io/badge/-Tests:-black?style=flat-square) [![image](https://github.com/pytorch/ignite/workflows/Run%20unit%20tests/badge.svg)](https://github.com/pytorch/ignite/actions) [![image](https://img.shields.io/badge/-GPU%20tests-black?style=flat-square)](https://app.circleci.com/pipelines/github/pytorch/ignite?branch=master)[![image](https://circleci.com/gh/pytorch/ignite.svg?style=svg)](https://app.circleci.com/pipelines/github/pytorch/ignite?branch=master) [![image](https://codecov.io/gh/pytorch/ignite/branch/master/graph/badge.svg)](https://codecov.io/gh/pytorch/ignite) [![image](https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pytorch.org/ignite/index.html) |
+|:---
+| ![image](https://img.shields.io/badge/-Stable%20Releases:-black?style=flat-square) [![image](https://anaconda.org/pytorch/ignite/badges/version.svg)](https://anaconda.org/pytorch/ignite) [![image](https://anaconda.org/pytorch/ignite/badges/downloads.svg)](https://anaconda.org/pytorch/ignite) [![image](https://img.shields.io/badge/dynamic/json.svg?label=PyPI&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pypi.org/project/pytorch-ignite/) [![image](https://pepy.tech/badge/pytorch-ignite)](https://pepy.tech/project/pytorch-ignite) |
+| ![image](https://img.shields.io/badge/-Nightly%20Releases:-black?style=flat-square) [![image](https://anaconda.org/pytorch-nightly/ignite/badges/version.svg)](https://anaconda.org/pytorch-nightly/ignite) [![image](https://img.shields.io/badge/PyPI-pre%20releases-brightgreen)](https://pypi.org/project/pytorch-ignite/#history)|
+| ![image](https://img.shields.io/badge/-Features:-black?style=flat-square) [![image](https://img.shields.io/badge/docker-hub-blue)](https://hub.docker.com/u/pytorchignite) [![image](https://img.shields.io/badge/Optuna-integrated-blue)](https://optuna.org) [![image](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) |
+| ![image](https://img.shields.io/badge/-Community:-black?style=flat-square) [![Twitter](https://img.shields.io/badge/news-twitter-blue)](https://twitter.com/pytorch_ignite) [![facebook](https://img.shields.io/badge/news-facebook-blue)](https://www.facebook.com/PyTorch-Ignite-Community-105837321694508) [![numfocus](https://img.shields.io/badge/NumFOCUS-affiliated%20project-green)](https://numfocus.org/sponsored-projects/affiliated-projects) [![discord](https://img.shields.io/badge/chat-discord-blue?logo=discord)](https://discord.gg/djZtm3EmKj) |
+| ![image](https://img.shields.io/badge/-Supported_PyTorch/Python_versions:-black?style=flat-square) [![link](https://img.shields.io/badge/-check_here-blue)](https://github.com/pytorch/ignite/actions?query=workflow%3A%22PyTorch+version+tests%22)|
+
+</div>
+
+## TL;DR
+
+Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
+
+<div align="center">
+
+<a href="https://colab.research.google.com/github/pytorch/ignite/blob/master/assets/tldr/teaser.ipynb">
+ <img alt="PyTorch-Ignite teaser"
+      src="https://raw.githubusercontent.com/pytorch/ignite/master/assets/tldr/pytorch-ignite-teaser.gif"
+      width=532>
+</a>
+
+_Click on the image to see complete code_
+
+</div>
+
+### Features
+
+- [Less code than pure PyTorch](https://raw.githubusercontent.com/pytorch/ignite/master/assets/ignite_vs_bare_pytorch.png)
+  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](#table-of-contents)
+- [Why Ignite?](#why-ignite)
+  - [Simplified training and validation loop](#simplified-training-and-validation-loop)
+  - [Power of Events & Handlers](#power-of-events--handlers)
+    - [Execute any number of functions whenever you wish](#execute-any-number-of-functions-whenever-you-wish)
+    - [Built-in events filtering](#built-in-events-filtering)
+    - [Stack events to share some actions](#stack-events-to-share-some-actions)
+    - [Custom events to go beyond standard events](#custom-events-to-go-beyond-standard-events)
+  - [Out-of-the-box metrics](#out-of-the-box-metrics)
+- [Installation](#installation)
+  - [Nightly releases](#nightly-releases)
+  - [Docker Images](#docker-images)
+    - [Using pre-built images](#using-pre-built-images)
+- [Getting Started](#getting-started)
+- [Documentation](#documentation)
+  - [Additional Materials](#additional-materials)
+- [Examples](#examples)
+  - [Tutorials](#tutorials)
+  - [Reproducible Training Examples](#reproducible-training-examples)
+- [Communication](#communication)
+  - [User feedback](#user-feedback)
+- [Contributing](#contributing)
+- [Projects using Ignite](#projects-using-ignite)
+- [Citing Ignite](#citing-ignite)
+- [About the team & Disclaimer](#about-the-team--disclaimer)
+
+<!-- ############################################################################################################### -->
+
+# 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.
+
+<details>
+<summary>
+Example
+</summary>
+
+```python
+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)
+```
+
+</details>
+
+## 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.
+
+### Execute any number of functions whenever you wish
+
+<details>
+<summary>
+Examples
+</summary>
+
+```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(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)
+```
+
+</details>
+
+### Built-in events filtering
+
+<details>
+<summary>
+Examples
+</summary>
+
+```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():
+    # ...
+```
+
+</details>
+
+### Stack events to share some actions
+
+<details>
+<summary>
+Examples
+</summary>
+
+Events can be stacked together to enable multiple calls:
+
+```python
+@trainer.on(Events.COMPLETED | Events.EPOCH_COMPLETED(every=10))
+def run_validation():
+    # ...
+```
+
+</details>
+
+### Custom events to go beyond standard events
+
+<details>
+<summary>
+Examples
+</summary>
+
+Custom events related to backward and optimizer step calls:
+
+```python
+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):
+    # ...
+```
+
+- Complete snippet is 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).
+
+</details>
+
+## 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 metrics](https://pytorch.org/ignite/metrics.html#metric-arithmetics) with ease from
+  existing ones using arithmetic operations or torch methods.
+
+<details>
+<summary>
+Example
+</summary>
+
+```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")
+```
+
+</details>
+
+<!-- ############################################################################################################### -->
+
+# Installation
+
+From [pip](https://pypi.org/project/pytorch-ignite/):
+
+```bash
+pip install pytorch-ignite
+```
+
+From [conda](https://anaconda.org/pytorch/ignite):
+
+```bash
+conda install ignite -c pytorch
+```
+
+From source:
+
+```bash
+pip install git+https://github.com/pytorch/ignite
+```
+
+## Nightly releases
+
+From pip:
+
+```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):
+
+```bash
+conda install ignite -c pytorch-nightly
+```
+
+## Docker Images
+
+### Using pre-built images
+
+Pull a pre-built docker image from [our Docker Hub](https://hub.docker.com/u/pytorchignite) and run it with docker v19.03+.
+
+```bash
+docker run --gpus all -it -v $PWD:/workspace/project --network=host --shm-size 16G pytorchignite/base:latest /bin/bash
+```
+
+<details>
+
+<summary>
+List of available pre-built images
+</summary>
+
+Base
+- `pytorchignite/base:latest` 
+- `pytorchignite/apex:latest`
+- `pytorchignite/hvd-base:latest`
+- `pytorchignite/hvd-apex:latest` 
+- `pytorchignite/msdp-apex:latest`
+
+Vision:
+- `pytorchignite/vision:latest`
+- `pytorchignite/hvd-vision:latest`
+- `pytorchignite/apex-vision:latest`
+- `pytorchignite/hvd-apex-vision:latest`
+- `pytorchignite/msdp-apex-vision:latest`
+
+NLP:
+- `pytorchignite/nlp:latest`
+- `pytorchignite/hvd-nlp:latest`
+- `pytorchignite/apex-nlp:latest` 
+- `pytorchignite/hvd-apex-nlp:latest` 
+- `pytorchignite/msdp-apex-nlp:latest`
+
+</details>
+
+For more details, see [here](docker).
+
+<!-- ############################################################################################################### -->
+
+# Getting Started
+
+Few pointers to get you started:
+
+- [Quick Start Guide: Essentials of getting a project up and running](https://pytorch.org/ignite/quickstart.html)
+- [Concepts of the library: Engine, Events & Handlers, State, Metrics](https://pytorch.org/ignite/concepts.html)
+- Full-featured template examples (coming soon)
+
+<!-- ############################################################################################################### -->
+
+# 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),
+  ["Questions on Github"](https://github.com/pytorch/ignite/issues?q=is%3Aissue+label%3Aquestion+) and
+  ["Questions on Discuss.PyTorch"](https://discuss.pytorch.org/c/ignite).
+- [Project's Roadmap](https://github.com/pytorch/ignite/wiki/Roadmap)
+
+## Additional Materials
+
+- [Distributed Training Made Easy with PyTorch-Ignite](https://labs.quansight.org/blog/2021/06/distributed-made-easy-with-ignite/)
+- [PyTorch Ecosystem Day 2021 Breakout session presentation](https://colab.research.google.com/drive/1qhUgWQ0N2U71IVShLpocyeY4AhlDCPRd)
+- [Tutorial blog post about PyTorch-Ignite](https://labs.quansight.org/blog/2020/09/pytorch-ignite/)
+- [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:
+  - [2021](https://drive.google.com/file/d/1YXrkJIepPk_KltSG1ZfWRtA5IRgPFz_U)
+  - [2019](https://drive.google.com/open?id=1bqIl-EM6GCCCoSixFZxhIbuF25F2qTZg)
+  - [2018](https://drive.google.com/open?id=1_2vzBJ0KeCjGv1srojMHiJRvceSVbVR5)
+
+<!-- ############################################################################################################### -->
+
+# Examples
+
+## Tutorials
+
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_nvidia_apex.ipynb) [Training Cycle-GAN on Horses to
+  Zebras with Nvidia/Apex](https://github.com/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_nvidia_apex.ipynb) - [ logs on W&B](https://app.wandb.ai/vfdev-5/ignite-cyclegan-apex)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_torch_cuda_amp.ipynb) [Another training Cycle-GAN on Horses to
+  Zebras with Native Torch CUDA AMP](https://github.com/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_torch_cuda_amp.ipynb) - [logs on W&B](https://app.wandb.ai/vfdev-5/ignite-cyclegan-torch-amp)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/MNIST_on_TPU.ipynb) [MNIST training on a single
+  TPU](https://github.com/pytorch/ignite/blob/master/examples/notebooks/MNIST_on_TPU.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1E9zJrptnLJ_PKhmaP5Vhb6DTVRvyrKHx) [CIFAR10 Training on multiple TPUs](https://github.com/pytorch/ignite/tree/master/examples/contrib/cifar10)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/HandlersTimeProfiler_MNIST.ipynb) [Basic example of handlers
+  time profiling on MNIST training example](https://github.com/pytorch/ignite/blob/master/examples/notebooks/HandlersTimeProfiler_MNIST.ipynb)
+
+## 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) - logs on Ignite Trains server coming soon ...
+- [Pascal VOC2012](examples/references/segmentation/pascal_voc2012) - logs on Ignite Trains server coming soon ...
+
+Features:
+
+- Distributed training: native or horovod and using [PyTorch native AMP](https://pytorch.org/docs/stable/notes/amp_examples.html)
+
+## Code-Generator application
+
+The easiest way to create your training scripts with PyTorch-Ignite:
+- https://code-generator.pytorch-ignite.ai/
+
+
+<!-- ############################################################################################################### -->
+
+# 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-Ignite Discord Server](https://discord.gg/djZtm3EmKj): to chat with the community
+
+- [GitHub Discussions](https://github.com/pytorch/ignite/discussions): general library-related discussions, ideas, Q&A, etc.
+
+## 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
+
+<details>
+
+<summary>
+Research papers
+</summary>
+
+- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://github.com/BlackHC/BatchBALD)
+- [A Model to Search for Synthesizable Molecules](https://github.com/john-bradshaw/molecule-chef)
+- [Localised Generative Flows](https://github.com/jrmcornish/lgf)
+- [Extracting T Cell Function and Differentiation Characteristics from the Biomedical Literature](https://github.com/hammerlab/t-cell-relation-extraction)
+- [Variational Information Distillation for Knowledge Transfer](https://github.com/amzn/xfer/tree/master/var_info_distil)
+- [XPersona: Evaluating Multilingual Personalized Chatbot](https://github.com/HLTCHKUST/Xpersona)
+- [CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images](https://github.com/ucuapps/CoronaryArteryStenosisScoreClassification)
+- [Bridging Text and Video: A Universal Multimodal Transformer for Video-Audio Scene-Aware Dialog](https://github.com/ictnlp/DSTC8-AVSD)
+- [Adversarial Decomposition of Text Representation](https://github.com/text-machine-lab/adversarial_decomposition)
+- [Uncertainty Estimation Using a Single Deep Deterministic Neural Network](https://github.com/y0ast/deterministic-uncertainty-quantification)
+- [DeepSphere: a graph-based spherical CNN](https://github.com/deepsphere/deepsphere-pytorch)
+- [Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment](https://github.com/lidq92/LinearityIQA)
+- [Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training](https://github.com/lidq92/MDTVSFA)
+- [Deep Signature Transforms](https://github.com/patrick-kidger/Deep-Signature-Transforms)
+- [Neural CDEs for Long Time-Series via the Log-ODE Method](https://github.com/jambo6/neuralCDEs-via-logODEs)
+- [Volumetric Grasping Network](https://github.com/ethz-asl/vgn)
+- [Mood Classification using Listening Data](https://github.com/fdlm/listening-moods)
+- [Deterministic Uncertainty Estimation (DUE)](https://github.com/y0ast/DUE)
+- [PyTorch-Hebbian: facilitating local learning in a deep learning framework](https://github.com/Joxis/pytorch-hebbian)
+- [Stochastic Weight Matrix-Based Regularization Methods for Deep Neural Networks](https://github.com/rpatrik96/lod-wmm-2019)
+- [Learning explanations that are hard to vary](https://github.com/gibipara92/learning-explanations-hard-to-vary)
+- [The role of disentanglement in generalisation](https://github.com/mmrl/disent-and-gen)
+- [A Probabilistic Programming Approach to Protein Structure Superposition](https://github.com/LysSanzMoreta/Theseus-PP)
+- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://github.com/auriml/Rx-thorax-automatic-captioning)
+
+</details>
+
+<details>
+
+<summary>
+Blog articles, tutorials, books
+</summary>
+
+- [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)
+- [Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt](https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition)
+- [Once Upon a Repository: How to Write Readable, Maintainable Code with PyTorch](https://towardsdatascience.com/once-upon-a-repository-how-to-write-readable-maintainable-code-with-pytorch-951f03f6a829)
+- [The Hero Rises: Build Your Own SSD](https://allegro.ai/blog/the-hero-rises-build-your-own-ssd/)
+- [Using Optuna to Optimize PyTorch Ignite Hyperparameters](https://medium.com/pytorch/using-optuna-to-optimize-pytorch-ignite-hyperparameters-626ffe6d4783)
+- [PyTorch Ignite - Classifying Tiny ImageNet with EfficientNet](https://towardsdatascience.com/pytorch-ignite-classifying-tiny-imagenet-with-efficientnet-e5b1768e5e8f)
+ 
+</details>
+
+<details>
+
+<summary>
+Toolkits
+</summary>
+
+- [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)
+- [Nussl - a flexible, object-oriented Python audio source separation library](https://github.com/nussl/nussl)
+- [PyTorch Adapt - A fully featured and modular domain adaptation library](https://github.com/KevinMusgrave/pytorch-adapt)
+- [gnina-torch: PyTorch implementation of GNINA scoring function](https://github.com/RMeli/gnina-torch)
+ 
+</details>
+
+<details>
+
+<summary>
+Others
+</summary>
+
+- [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)
+- [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/optuna/optuna-examples/blob/main/pytorch/pytorch_ignite_simple.py)
+- [Logging with ChainerUI](https://chainerui.readthedocs.io/en/latest/reference/module.html#external-library-support)
+- [FixMatch experiments in PyTorch and Ignite (CTA dataaug policy)](https://github.com/vfdev-5/FixMatch-pytorch)
+- [Kaggle Birdcall Identification Competition: 1st place solution](https://github.com/ryanwongsa/kaggle-birdsong-recognition)
+- [Logging with Aim - An open-source experiment tracker](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-pytorch-ignite)
+
+</details>
+
+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 to this list, so please send a PR with brief
+description of the project.
+
+<!-- ############################################################################################################### -->
+
+# Citing Ignite
+
+If you use PyTorch-Ignite in a scientific publication, we would appreciate citations to our project.
+
+```
+@misc{pytorch-ignite,
+  author = {V. Fomin and J. Anmol and S. Desroziers and J. Kriss and A. Tejani},
+  title = {High-level library to help with training neural networks in PyTorch},
+  year = {2020},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/pytorch/ignite}},
+}
+```
+
+<!-- ############################################################################################################### -->
+
+# About the team & Disclaimer
+
+PyTorch-Ignite is a [NumFOCUS Affiliated Project](https://www.numfocus.org/), 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"](https://pytorch-ignite.ai/about/community/#about-us)
+page for a list of core contributors. For usage questions and issues, please see the various channels
+[here](#communication). For all other questions and inquiries, please send an email
+to contact@pytorch-ignite.ai.
+
+
+%package help
+Summary:	Development documents and examples for pytorch-ignite
+Provides:	python3-pytorch-ignite-doc
+%description help
+<div align="center">
+
+<!-- ![Ignite Logo](assets/logo/ignite_logo_mixed.svg) -->
+
+<img src="https://raw.githubusercontent.com/pytorch/ignite/master/assets/logo/ignite_logo_mixed.svg" width=512>
+
+<!-- [![image](https://travis-ci.com/pytorch/ignite.svg?branch=master)](https://travis-ci.com/pytorch/ignite) -->
+
+| ![image](https://img.shields.io/badge/-Tests:-black?style=flat-square) [![image](https://github.com/pytorch/ignite/workflows/Run%20unit%20tests/badge.svg)](https://github.com/pytorch/ignite/actions) [![image](https://img.shields.io/badge/-GPU%20tests-black?style=flat-square)](https://app.circleci.com/pipelines/github/pytorch/ignite?branch=master)[![image](https://circleci.com/gh/pytorch/ignite.svg?style=svg)](https://app.circleci.com/pipelines/github/pytorch/ignite?branch=master) [![image](https://codecov.io/gh/pytorch/ignite/branch/master/graph/badge.svg)](https://codecov.io/gh/pytorch/ignite) [![image](https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pytorch.org/ignite/index.html) |
+|:---
+| ![image](https://img.shields.io/badge/-Stable%20Releases:-black?style=flat-square) [![image](https://anaconda.org/pytorch/ignite/badges/version.svg)](https://anaconda.org/pytorch/ignite) [![image](https://anaconda.org/pytorch/ignite/badges/downloads.svg)](https://anaconda.org/pytorch/ignite) [![image](https://img.shields.io/badge/dynamic/json.svg?label=PyPI&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pypi.org/project/pytorch-ignite/) [![image](https://pepy.tech/badge/pytorch-ignite)](https://pepy.tech/project/pytorch-ignite) |
+| ![image](https://img.shields.io/badge/-Nightly%20Releases:-black?style=flat-square) [![image](https://anaconda.org/pytorch-nightly/ignite/badges/version.svg)](https://anaconda.org/pytorch-nightly/ignite) [![image](https://img.shields.io/badge/PyPI-pre%20releases-brightgreen)](https://pypi.org/project/pytorch-ignite/#history)|
+| ![image](https://img.shields.io/badge/-Features:-black?style=flat-square) [![image](https://img.shields.io/badge/docker-hub-blue)](https://hub.docker.com/u/pytorchignite) [![image](https://img.shields.io/badge/Optuna-integrated-blue)](https://optuna.org) [![image](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) |
+| ![image](https://img.shields.io/badge/-Community:-black?style=flat-square) [![Twitter](https://img.shields.io/badge/news-twitter-blue)](https://twitter.com/pytorch_ignite) [![facebook](https://img.shields.io/badge/news-facebook-blue)](https://www.facebook.com/PyTorch-Ignite-Community-105837321694508) [![numfocus](https://img.shields.io/badge/NumFOCUS-affiliated%20project-green)](https://numfocus.org/sponsored-projects/affiliated-projects) [![discord](https://img.shields.io/badge/chat-discord-blue?logo=discord)](https://discord.gg/djZtm3EmKj) |
+| ![image](https://img.shields.io/badge/-Supported_PyTorch/Python_versions:-black?style=flat-square) [![link](https://img.shields.io/badge/-check_here-blue)](https://github.com/pytorch/ignite/actions?query=workflow%3A%22PyTorch+version+tests%22)|
+
+</div>
+
+## TL;DR
+
+Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
+
+<div align="center">
+
+<a href="https://colab.research.google.com/github/pytorch/ignite/blob/master/assets/tldr/teaser.ipynb">
+ <img alt="PyTorch-Ignite teaser"
+      src="https://raw.githubusercontent.com/pytorch/ignite/master/assets/tldr/pytorch-ignite-teaser.gif"
+      width=532>
+</a>
+
+_Click on the image to see complete code_
+
+</div>
+
+### Features
+
+- [Less code than pure PyTorch](https://raw.githubusercontent.com/pytorch/ignite/master/assets/ignite_vs_bare_pytorch.png)
+  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](#table-of-contents)
+- [Why Ignite?](#why-ignite)
+  - [Simplified training and validation loop](#simplified-training-and-validation-loop)
+  - [Power of Events & Handlers](#power-of-events--handlers)
+    - [Execute any number of functions whenever you wish](#execute-any-number-of-functions-whenever-you-wish)
+    - [Built-in events filtering](#built-in-events-filtering)
+    - [Stack events to share some actions](#stack-events-to-share-some-actions)
+    - [Custom events to go beyond standard events](#custom-events-to-go-beyond-standard-events)
+  - [Out-of-the-box metrics](#out-of-the-box-metrics)
+- [Installation](#installation)
+  - [Nightly releases](#nightly-releases)
+  - [Docker Images](#docker-images)
+    - [Using pre-built images](#using-pre-built-images)
+- [Getting Started](#getting-started)
+- [Documentation](#documentation)
+  - [Additional Materials](#additional-materials)
+- [Examples](#examples)
+  - [Tutorials](#tutorials)
+  - [Reproducible Training Examples](#reproducible-training-examples)
+- [Communication](#communication)
+  - [User feedback](#user-feedback)
+- [Contributing](#contributing)
+- [Projects using Ignite](#projects-using-ignite)
+- [Citing Ignite](#citing-ignite)
+- [About the team & Disclaimer](#about-the-team--disclaimer)
+
+<!-- ############################################################################################################### -->
+
+# 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.
+
+<details>
+<summary>
+Example
+</summary>
+
+```python
+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)
+```
+
+</details>
+
+## 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.
+
+### Execute any number of functions whenever you wish
+
+<details>
+<summary>
+Examples
+</summary>
+
+```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(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)
+```
+
+</details>
+
+### Built-in events filtering
+
+<details>
+<summary>
+Examples
+</summary>
+
+```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():
+    # ...
+```
+
+</details>
+
+### Stack events to share some actions
+
+<details>
+<summary>
+Examples
+</summary>
+
+Events can be stacked together to enable multiple calls:
+
+```python
+@trainer.on(Events.COMPLETED | Events.EPOCH_COMPLETED(every=10))
+def run_validation():
+    # ...
+```
+
+</details>
+
+### Custom events to go beyond standard events
+
+<details>
+<summary>
+Examples
+</summary>
+
+Custom events related to backward and optimizer step calls:
+
+```python
+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):
+    # ...
+```
+
+- Complete snippet is 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).
+
+</details>
+
+## 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 metrics](https://pytorch.org/ignite/metrics.html#metric-arithmetics) with ease from
+  existing ones using arithmetic operations or torch methods.
+
+<details>
+<summary>
+Example
+</summary>
+
+```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")
+```
+
+</details>
+
+<!-- ############################################################################################################### -->
+
+# Installation
+
+From [pip](https://pypi.org/project/pytorch-ignite/):
+
+```bash
+pip install pytorch-ignite
+```
+
+From [conda](https://anaconda.org/pytorch/ignite):
+
+```bash
+conda install ignite -c pytorch
+```
+
+From source:
+
+```bash
+pip install git+https://github.com/pytorch/ignite
+```
+
+## Nightly releases
+
+From pip:
+
+```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):
+
+```bash
+conda install ignite -c pytorch-nightly
+```
+
+## Docker Images
+
+### Using pre-built images
+
+Pull a pre-built docker image from [our Docker Hub](https://hub.docker.com/u/pytorchignite) and run it with docker v19.03+.
+
+```bash
+docker run --gpus all -it -v $PWD:/workspace/project --network=host --shm-size 16G pytorchignite/base:latest /bin/bash
+```
+
+<details>
+
+<summary>
+List of available pre-built images
+</summary>
+
+Base
+- `pytorchignite/base:latest` 
+- `pytorchignite/apex:latest`
+- `pytorchignite/hvd-base:latest`
+- `pytorchignite/hvd-apex:latest` 
+- `pytorchignite/msdp-apex:latest`
+
+Vision:
+- `pytorchignite/vision:latest`
+- `pytorchignite/hvd-vision:latest`
+- `pytorchignite/apex-vision:latest`
+- `pytorchignite/hvd-apex-vision:latest`
+- `pytorchignite/msdp-apex-vision:latest`
+
+NLP:
+- `pytorchignite/nlp:latest`
+- `pytorchignite/hvd-nlp:latest`
+- `pytorchignite/apex-nlp:latest` 
+- `pytorchignite/hvd-apex-nlp:latest` 
+- `pytorchignite/msdp-apex-nlp:latest`
+
+</details>
+
+For more details, see [here](docker).
+
+<!-- ############################################################################################################### -->
+
+# Getting Started
+
+Few pointers to get you started:
+
+- [Quick Start Guide: Essentials of getting a project up and running](https://pytorch.org/ignite/quickstart.html)
+- [Concepts of the library: Engine, Events & Handlers, State, Metrics](https://pytorch.org/ignite/concepts.html)
+- Full-featured template examples (coming soon)
+
+<!-- ############################################################################################################### -->
+
+# 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),
+  ["Questions on Github"](https://github.com/pytorch/ignite/issues?q=is%3Aissue+label%3Aquestion+) and
+  ["Questions on Discuss.PyTorch"](https://discuss.pytorch.org/c/ignite).
+- [Project's Roadmap](https://github.com/pytorch/ignite/wiki/Roadmap)
+
+## Additional Materials
+
+- [Distributed Training Made Easy with PyTorch-Ignite](https://labs.quansight.org/blog/2021/06/distributed-made-easy-with-ignite/)
+- [PyTorch Ecosystem Day 2021 Breakout session presentation](https://colab.research.google.com/drive/1qhUgWQ0N2U71IVShLpocyeY4AhlDCPRd)
+- [Tutorial blog post about PyTorch-Ignite](https://labs.quansight.org/blog/2020/09/pytorch-ignite/)
+- [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:
+  - [2021](https://drive.google.com/file/d/1YXrkJIepPk_KltSG1ZfWRtA5IRgPFz_U)
+  - [2019](https://drive.google.com/open?id=1bqIl-EM6GCCCoSixFZxhIbuF25F2qTZg)
+  - [2018](https://drive.google.com/open?id=1_2vzBJ0KeCjGv1srojMHiJRvceSVbVR5)
+
+<!-- ############################################################################################################### -->
+
+# Examples
+
+## Tutorials
+
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_nvidia_apex.ipynb) [Training Cycle-GAN on Horses to
+  Zebras with Nvidia/Apex](https://github.com/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_nvidia_apex.ipynb) - [ logs on W&B](https://app.wandb.ai/vfdev-5/ignite-cyclegan-apex)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_torch_cuda_amp.ipynb) [Another training Cycle-GAN on Horses to
+  Zebras with Native Torch CUDA AMP](https://github.com/pytorch/ignite/blob/master/examples/notebooks/CycleGAN_with_torch_cuda_amp.ipynb) - [logs on W&B](https://app.wandb.ai/vfdev-5/ignite-cyclegan-torch-amp)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/MNIST_on_TPU.ipynb) [MNIST training on a single
+  TPU](https://github.com/pytorch/ignite/blob/master/examples/notebooks/MNIST_on_TPU.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1E9zJrptnLJ_PKhmaP5Vhb6DTVRvyrKHx) [CIFAR10 Training on multiple TPUs](https://github.com/pytorch/ignite/tree/master/examples/contrib/cifar10)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/HandlersTimeProfiler_MNIST.ipynb) [Basic example of handlers
+  time profiling on MNIST training example](https://github.com/pytorch/ignite/blob/master/examples/notebooks/HandlersTimeProfiler_MNIST.ipynb)
+
+## 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) - logs on Ignite Trains server coming soon ...
+- [Pascal VOC2012](examples/references/segmentation/pascal_voc2012) - logs on Ignite Trains server coming soon ...
+
+Features:
+
+- Distributed training: native or horovod and using [PyTorch native AMP](https://pytorch.org/docs/stable/notes/amp_examples.html)
+
+## Code-Generator application
+
+The easiest way to create your training scripts with PyTorch-Ignite:
+- https://code-generator.pytorch-ignite.ai/
+
+
+<!-- ############################################################################################################### -->
+
+# 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-Ignite Discord Server](https://discord.gg/djZtm3EmKj): to chat with the community
+
+- [GitHub Discussions](https://github.com/pytorch/ignite/discussions): general library-related discussions, ideas, Q&A, etc.
+
+## 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
+
+<details>
+
+<summary>
+Research papers
+</summary>
+
+- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://github.com/BlackHC/BatchBALD)
+- [A Model to Search for Synthesizable Molecules](https://github.com/john-bradshaw/molecule-chef)
+- [Localised Generative Flows](https://github.com/jrmcornish/lgf)
+- [Extracting T Cell Function and Differentiation Characteristics from the Biomedical Literature](https://github.com/hammerlab/t-cell-relation-extraction)
+- [Variational Information Distillation for Knowledge Transfer](https://github.com/amzn/xfer/tree/master/var_info_distil)
+- [XPersona: Evaluating Multilingual Personalized Chatbot](https://github.com/HLTCHKUST/Xpersona)
+- [CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images](https://github.com/ucuapps/CoronaryArteryStenosisScoreClassification)
+- [Bridging Text and Video: A Universal Multimodal Transformer for Video-Audio Scene-Aware Dialog](https://github.com/ictnlp/DSTC8-AVSD)
+- [Adversarial Decomposition of Text Representation](https://github.com/text-machine-lab/adversarial_decomposition)
+- [Uncertainty Estimation Using a Single Deep Deterministic Neural Network](https://github.com/y0ast/deterministic-uncertainty-quantification)
+- [DeepSphere: a graph-based spherical CNN](https://github.com/deepsphere/deepsphere-pytorch)
+- [Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment](https://github.com/lidq92/LinearityIQA)
+- [Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training](https://github.com/lidq92/MDTVSFA)
+- [Deep Signature Transforms](https://github.com/patrick-kidger/Deep-Signature-Transforms)
+- [Neural CDEs for Long Time-Series via the Log-ODE Method](https://github.com/jambo6/neuralCDEs-via-logODEs)
+- [Volumetric Grasping Network](https://github.com/ethz-asl/vgn)
+- [Mood Classification using Listening Data](https://github.com/fdlm/listening-moods)
+- [Deterministic Uncertainty Estimation (DUE)](https://github.com/y0ast/DUE)
+- [PyTorch-Hebbian: facilitating local learning in a deep learning framework](https://github.com/Joxis/pytorch-hebbian)
+- [Stochastic Weight Matrix-Based Regularization Methods for Deep Neural Networks](https://github.com/rpatrik96/lod-wmm-2019)
+- [Learning explanations that are hard to vary](https://github.com/gibipara92/learning-explanations-hard-to-vary)
+- [The role of disentanglement in generalisation](https://github.com/mmrl/disent-and-gen)
+- [A Probabilistic Programming Approach to Protein Structure Superposition](https://github.com/LysSanzMoreta/Theseus-PP)
+- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://github.com/auriml/Rx-thorax-automatic-captioning)
+
+</details>
+
+<details>
+
+<summary>
+Blog articles, tutorials, books
+</summary>
+
+- [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)
+- [Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt](https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition)
+- [Once Upon a Repository: How to Write Readable, Maintainable Code with PyTorch](https://towardsdatascience.com/once-upon-a-repository-how-to-write-readable-maintainable-code-with-pytorch-951f03f6a829)
+- [The Hero Rises: Build Your Own SSD](https://allegro.ai/blog/the-hero-rises-build-your-own-ssd/)
+- [Using Optuna to Optimize PyTorch Ignite Hyperparameters](https://medium.com/pytorch/using-optuna-to-optimize-pytorch-ignite-hyperparameters-626ffe6d4783)
+- [PyTorch Ignite - Classifying Tiny ImageNet with EfficientNet](https://towardsdatascience.com/pytorch-ignite-classifying-tiny-imagenet-with-efficientnet-e5b1768e5e8f)
+ 
+</details>
+
+<details>
+
+<summary>
+Toolkits
+</summary>
+
+- [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)
+- [Nussl - a flexible, object-oriented Python audio source separation library](https://github.com/nussl/nussl)
+- [PyTorch Adapt - A fully featured and modular domain adaptation library](https://github.com/KevinMusgrave/pytorch-adapt)
+- [gnina-torch: PyTorch implementation of GNINA scoring function](https://github.com/RMeli/gnina-torch)
+ 
+</details>
+
+<details>
+
+<summary>
+Others
+</summary>
+
+- [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)
+- [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/optuna/optuna-examples/blob/main/pytorch/pytorch_ignite_simple.py)
+- [Logging with ChainerUI](https://chainerui.readthedocs.io/en/latest/reference/module.html#external-library-support)
+- [FixMatch experiments in PyTorch and Ignite (CTA dataaug policy)](https://github.com/vfdev-5/FixMatch-pytorch)
+- [Kaggle Birdcall Identification Competition: 1st place solution](https://github.com/ryanwongsa/kaggle-birdsong-recognition)
+- [Logging with Aim - An open-source experiment tracker](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-pytorch-ignite)
+
+</details>
+
+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 to this list, so please send a PR with brief
+description of the project.
+
+<!-- ############################################################################################################### -->
+
+# Citing Ignite
+
+If you use PyTorch-Ignite in a scientific publication, we would appreciate citations to our project.
+
+```
+@misc{pytorch-ignite,
+  author = {V. Fomin and J. Anmol and S. Desroziers and J. Kriss and A. Tejani},
+  title = {High-level library to help with training neural networks in PyTorch},
+  year = {2020},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/pytorch/ignite}},
+}
+```
+
+<!-- ############################################################################################################### -->
+
+# About the team & Disclaimer
+
+PyTorch-Ignite is a [NumFOCUS Affiliated Project](https://www.numfocus.org/), 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"](https://pytorch-ignite.ai/about/community/#about-us)
+page for a list of core contributors. For usage questions and issues, please see the various channels
+[here](#communication). For all other questions and inquiries, please send an email
+to contact@pytorch-ignite.ai.
+
+
+%prep
+%autosetup -n pytorch-ignite-0.4.11
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-pytorch-ignite -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.11-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..c0ffcee
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+72d67c8e549ff5f2c41026e04654ec22  pytorch-ignite-0.4.11.tar.gz