diff --git a/README.md b/README.md
index ed80bce525b..12b277c161a 100644
--- a/README.md
+++ b/README.md
@@ -43,36 +43,6 @@ _Click on the image to see complete code_
-# 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:
@@ -278,147 +248,28 @@ From [conda](https://anaconda.org/pytorch/ignite):
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
-```
-
-
-
-
-List of available pre-built images
-
-
-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`
-
-
-
-For more details, see [here](docker).
+Checkout this guide to [install PyTorch-Ignite from source or use pre-built docker images](https://pytorch-ignite.ai/how-to-guides/01-installation/).
-# 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)
+# Documentation and Getting Started
-## Additional Materials
+The website for the library containing an overview can be found at: https://pytorch-ignite.ai/. Here is the order we suggest for getting started:
-- [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)
+1. [Getting Started Guide](https://pytorch-ignite.ai/tutorials/beginner/01-getting-started/) for essentials of setting up a project.
+2. [Tutorials](https://pytorch-ignite.ai/tutorials/) for the beginner, intermediate and advanced user.
+3. [How-to Guides](https://pytorch-ignite.ai/how-to-guides/) for code recipes with minimal explanation.
+4. [Concepts](https://pytorch-ignite.ai/concepts/) to understand the essence of the library.
+5. [Stable](https://pytorch.org/ignite/) / [Development](https://pytorch.org/ignite/master/) API documentations.
+6. Previously asked questions on [Github](https://github.com/pytorch/ignite/issues?q=is%3Aissue+label%3Aquestion+) and [Discuss.PyTorch](https://discuss.pytorch.org/c/ignite).
+7. [Talks](https://pytorch-ignite.ai/talks/) for visual learners.
+8. [Project's Roadmap](https://github.com/pytorch/ignite/wiki/Roadmap) to look out for upcoming features.
-# Examples
-
-## 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_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)
-- [](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)
-- [](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)
-- [](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)
-- [](https://colab.research.google.com/drive/1E9zJrptnLJ_PKhmaP5Vhb6DTVRvyrKHx) [CIFAR10 Training on multiple TPUs](https://github.com/pytorch/ignite/tree/master/examples/contrib/cifar10)
-- [](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/
-
+The easiest way to create your training scripts with PyTorch-Ignite: https://code-generator.pytorch-ignite.ai/
@@ -428,23 +279,10 @@ The easiest way to create your training scripts with PyTorch-Ignite:
- [Discuss.PyTorch](https://discuss.pytorch.org/c/ignite), category "Ignite".
-- [PyTorch-Ignite Discord Server](https://discord.gg/djZtm3EmKj): to chat with the community
+- [PyTorch-Ignite Discord Server](https://pytorch-ignite.ai/chat): 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
@@ -457,88 +295,8 @@ As always, PRs are welcome :)
# Projects using Ignite
-
-
-
-Research papers
-
-
-- [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)
-
-
-
-
-
-
-Blog articles, tutorials, books
-
-
-- [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)
-
-
-
-
-
-
-Toolkits
-
-
-- [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)
-
-
-
-
-
-
-Others
-
-
-- [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)
-
-
-
-See other projects at ["Used by"](https://github.com/pytorch/ignite/network/dependents?package_id=UGFja2FnZS02NzI5ODEwNA%3D%3D)
+- [Ecosystem](https://pytorch-ignite.ai/ecosystem/)
+- 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
@@ -568,7 +326,7 @@ If you use PyTorch-Ignite in a scientific publication, we would appreciate citat
# 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.org/ignite/master/about.html)
+(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.