{"author":"PyTorch-Ignite Team","author_email":"contact@pytorch-ignite.ai","bugtrack_url":null,"classifiers":[],"description":"

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\n\n\n\n| ![image](https://img.shields.io/badge/-Tests:-black?style=flat-square) [![image](https://github.com/pytorch/ignite/actions/workflows/unit-tests.yml/badge.svg?branch=master)](https://github.com/pytorch/ignite/actions/workflows/unit-tests.yml) [![image](https://github.com/pytorch/ignite/actions/workflows/gpu-tests.yml/badge.svg)](https://github.com/pytorch/ignite/actions/workflows/gpu-tests.yml) [![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) |\n|:---\n| ![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://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://static.pepy.tech/badge/pytorch-ignite)](https://pepy.tech/project/pytorch-ignite) ・ [![image](https://img.shields.io/badge/docker-hub-blue)](https://hub.docker.com/u/pytorchignite) |\n| ![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)|\n| ![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) [![discord](https://img.shields.io/badge/chat-discord-blue?logo=discord)](https://discord.gg/djZtm3EmKj) [![numfocus](https://img.shields.io/badge/NumFOCUS-affiliated%20project-green)](https://numfocus.org/sponsored-projects/affiliated-projects) |\n| ![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)|\n\n

\n\n## TL;DR\n\nIgnite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.\n\n

\n\n\n $\"PyTorch-Ignite$ \n\n\n_Click on the image to see complete code_\n\n

\n\n### Features\n\n- [Less code than pure PyTorch](https://raw.githubusercontent.com/pytorch/ignite/master/assets/ignite_vs_bare_pytorch.png)\n while ensuring maximum control and simplicity\n\n- Library approach and no program's control inversion - _Use ignite where and when you need_\n\n- Extensible API for metrics, experiment managers, and other components\n\n\n\n# Table of Contents\n\n- [Table of Contents](#table-of-contents)\n- [Why Ignite?](#why-ignite)\n - [Simplified training and validation loop](#simplified-training-and-validation-loop)\n - [Power of Events & Handlers](#power-of-events--handlers)\n - [Execute any number of functions whenever you wish](#execute-any-number-of-functions-whenever-you-wish)\n - [Built-in events filtering](#built-in-events-filtering)\n - [Stack events to share some actions](#stack-events-to-share-some-actions)\n - [Custom events to go beyond standard events](#custom-events-to-go-beyond-standard-events)\n - [Out-of-the-box metrics](#out-of-the-box-metrics)\n- [Installation](#installation)\n - [Nightly releases](#nightly-releases)\n - [Docker Images](#docker-images)\n - [Using pre-built images](#using-pre-built-images)\n- [Getting Started](#getting-started)\n- [Documentation](#documentation)\n - [Additional Materials](#additional-materials)\n- [Examples](#examples)\n - [Tutorials](#tutorials)\n - [Reproducible Training Examples](#reproducible-training-examples)\n- [Communication](#communication)\n - [User feedback](#user-feedback)\n- [Contributing](#contributing)\n- [Projects using Ignite](#projects-using-ignite)\n- [Citing Ignite](#citing-ignite)\n- [About the team & Disclaimer](#about-the-team--disclaimer)\n\n\n\n# Why Ignite?\n\nIgnite is a **library** that provides three high-level features:\n\n- Extremely simple engine and event system\n- Out-of-the-box metrics to easily evaluate models\n- Built-in handlers to compose training pipeline, save artifacts and log parameters and metrics\n\n## Simplified training and validation loop\n\nNo more coding `for/while` loops on epochs and iterations. Users instantiate engines and run them.\n\n

\nExample\n

\n\n```python\nfrom ignite.engine import Engine, Events, create_supervised_evaluator\nfrom ignite.metrics import Accuracy\n\n\n# Setup training engine:\ndef train_step(engine, batch):\n # Users can do whatever they need on a single iteration\n # Eg. forward/backward pass for any number of models, optimizers, etc\n # ...\n\ntrainer = Engine(train_step)\n\n# Setup single model evaluation engine\nevaluator = create_supervised_evaluator(model, metrics={\"accuracy\": Accuracy()})\n\ndef validation():\n state = evaluator.run(validation_data_loader)\n # print computed metrics\n print(trainer.state.epoch, state.metrics)\n\n# Run model's validation at the end of each epoch\ntrainer.add_event_handler(Events.EPOCH_COMPLETED, validation)\n\n# Start the training\ntrainer.run(training_data_loader, max_epochs=100)\n```\n\n

\n\n## Power of Events & Handlers\n\nThe 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.\n\n### Execute any number of functions whenever you wish\n\n

\nExamples\n

\n\n```python\ntrainer.add_event_handler(Events.STARTED, lambda _: print(\"Start training\"))\n\n# attach handler with args, kwargs\nmydata = [1, 2, 3, 4]\nlogger = ...\n\ndef on_training_ended(data):\n print(f\"Training is ended. mydata={data}\")\n # User can use variables from another scope\n logger.info(\"Training is ended\")\n\n\ntrainer.add_event_handler(Events.COMPLETED, on_training_ended, mydata)\n# call any number of functions on a single event\ntrainer.add_event_handler(Events.COMPLETED, lambda engine: print(engine.state.times))\n\n@trainer.on(Events.ITERATION_COMPLETED)\ndef log_something(engine):\n print(engine.state.output)\n```\n\n

\n\n### Built-in events filtering\n\n

\nExamples\n

\n\n```python\n# run the validation every 5 epochs\n@trainer.on(Events.EPOCH_COMPLETED(every=5))\ndef run_validation():\n # run validation\n\n# change some training variable once on 20th epoch\n@trainer.on(Events.EPOCH_STARTED(once=20))\ndef change_training_variable():\n # ...\n\n# Trigger handler with customly defined frequency\n@trainer.on(Events.ITERATION_COMPLETED(event_filter=first_x_iters))\ndef log_gradients():\n # ...\n```\n\n

\n\n### Stack events to share some actions\n\n

\nExamples\n

\n\nEvents can be stacked together to enable multiple calls:\n\n```python\n@trainer.on(Events.COMPLETED | Events.EPOCH_COMPLETED(every=10))\ndef run_validation():\n # ...\n```\n\n

\n\n### Custom events to go beyond standard events\n\n

\nExamples\n

\n\nCustom events related to backward and optimizer step calls:\n\n```python\nfrom ignite.engine import EventEnum\n\n\nclass BackpropEvents(EventEnum):\n BACKWARD_STARTED = 'backward_started'\n BACKWARD_COMPLETED = 'backward_completed'\n OPTIM_STEP_COMPLETED = 'optim_step_completed'\n\ndef update(engine, batch):\n # ...\n loss = criterion(y_pred, y)\n engine.fire_event(BackpropEvents.BACKWARD_STARTED)\n loss.backward()\n engine.fire_event(BackpropEvents.BACKWARD_COMPLETED)\n optimizer.step()\n engine.fire_event(BackpropEvents.OPTIM_STEP_COMPLETED)\n # ...\n\ntrainer = Engine(update)\ntrainer.register_events(*BackpropEvents)\n\n@trainer.on(BackpropEvents.BACKWARD_STARTED)\ndef function_before_backprop(engine):\n # ...\n```\n\n- Complete snippet is found [here](https://pytorch.org/ignite/faq.html#creating-custom-events-based-on-forward-backward-pass).\n- 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).\n\n

\n\n## Out-of-the-box metrics\n\n- [Metrics](https://pytorch.org/ignite/metrics.html#complete-list-of-metrics) for various tasks:\n Precision, Recall, Accuracy, Confusion Matrix, IoU etc, ~20 [regression metrics](https://pytorch.org/ignite/metrics.html#complete-list-of-metrics).\n\n- Users can also [compose their metrics](https://pytorch.org/ignite/metrics.html#metric-arithmetics) with ease from\n existing ones using arithmetic operations or torch methods.\n\n

\nExample\n

\n\n```python\nprecision = Precision(average=False)\nrecall = Recall(average=False)\nF1_per_class = (precision * recall * 2 / (precision + recall))\nF1_mean = F1_per_class.mean() # torch mean method\nF1_mean.attach(engine, \"F1\")\n```\n\n

\n\n\n\n# Installation\n\nFrom [pip](https://pypi.org/project/pytorch-ignite/):\n\n```bash\npip install pytorch-ignite\n```\n\nFrom [conda](https://anaconda.org/pytorch/ignite):\n\n```bash\nconda install ignite -c pytorch\n```\n\nFrom source:\n\n```bash\npip install git+https://github.com/pytorch/ignite\n```\n\n## Nightly releases\n\nFrom pip:\n\n```bash\npip install --pre pytorch-ignite\n```\n\nFrom conda (this suggests to install [pytorch nightly release](https://anaconda.org/pytorch-nightly/pytorch) instead of stable\nversion as dependency):\n\n```bash\nconda install ignite -c pytorch-nightly\n```\n\n## Docker Images\n\n### Using pre-built images\n\nPull a pre-built docker image from [our Docker Hub](https://hub.docker.com/u/pytorchignite) and run it with docker v19.03+.\n\n```bash\ndocker run --gpus all -it -v $PWD:/workspace/project --network=host --shm-size 16G pytorchignite/base:latest /bin/bash\n```\n\n

\n\n

\nList of available pre-built images\n

\n\nBase\n\n- `pytorchignite/base:latest`\n- `pytorchignite/apex:latest`\n- `pytorchignite/hvd-base:latest`\n- `pytorchignite/hvd-apex:latest`\n- `pytorchignite/msdp-apex:latest`\n\nVision:\n\n- `pytorchignite/vision:latest`\n- `pytorchignite/hvd-vision:latest`\n- `pytorchignite/apex-vision:latest`\n- `pytorchignite/hvd-apex-vision:latest`\n- `pytorchignite/msdp-apex-vision:latest`\n\nNLP:\n\n- `pytorchignite/nlp:latest`\n- `pytorchignite/hvd-nlp:latest`\n- `pytorchignite/apex-nlp:latest`\n- `pytorchignite/hvd-apex-nlp:latest`\n- `pytorchignite/msdp-apex-nlp:latest`\n\n

\n\nFor more details, see [here](docker).\n\n\n\n# Getting Started\n\nFew pointers to get you started:\n\n- [Quick Start Guide: Essentials of getting a project up and running](https://pytorch-ignite.ai/tutorials/beginner/01-getting-started/)\n- [Concepts of the library: Engine, Events & Handlers, State, Metrics](https://pytorch-ignite.ai/concepts/)\n- Full-featured template examples (coming soon)\n\n\n\n# Documentation\n\n- Stable API documentation and an overview of the library: https://pytorch.org/ignite/\n- Development version API documentation: https://pytorch.org/ignite/master/\n- [FAQ](https://pytorch.org/ignite/faq.html),\n [\"Questions on Github\"](https://github.com/pytorch/ignite/issues?q=is%3Aissue+label%3Aquestion+) and\n [\"Questions on Discuss.PyTorch\"](https://discuss.pytorch.org/c/ignite).\n- [Project's Roadmap](https://github.com/pytorch/ignite/wiki/Roadmap)\n\n## Additional Materials\n\n- [Distributed Training Made Easy with PyTorch-Ignite](https://labs.quansight.org/blog/2021/06/distributed-made-easy-with-ignite/)\n- [PyTorch Ecosystem Day 2021 Breakout session presentation](https://colab.research.google.com/drive/1qhUgWQ0N2U71IVShLpocyeY4AhlDCPRd)\n- [Tutorial blog post about PyTorch-Ignite](https://labs.quansight.org/blog/2020/09/pytorch-ignite/)\n- [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)\n- Ignite Posters from Pytorch Developer Conferences:\n - [2021](https://drive.google.com/file/d/1YXrkJIepPk_KltSG1ZfWRtA5IRgPFz_U)\n - [2019](https://drive.google.com/open?id=1bqIl-EM6GCCCoSixFZxhIbuF25F2qTZg)\n - [2018](https://drive.google.com/open?id=1_2vzBJ0KeCjGv1srojMHiJRvceSVbVR5)\n\n\n\n# Examples\n\n## Tutorials\n\n- [![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\n Networks](https://github.com/pytorch/ignite/blob/master/examples/notebooks/TextCNN.ipynb)\n- [![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\n Encoders](https://github.com/pytorch/ignite/blob/master/examples/notebooks/VAE.ipynb)\n- [![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\n Dataset](https://github.com/pytorch/ignite/blob/master/examples/notebooks/FashionMNIST.ipynb)\n- [![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\n 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)\n- [![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\n 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)\n- [![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\n CIFAR100](https://github.com/pytorch/ignite/blob/master/examples/notebooks/EfficientNet_Cifar100_finetuning.ipynb)\n- [![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\n Ax](https://github.com/pytorch/ignite/blob/master/examples/notebooks/Cifar10_Ax_hyperparam_tuning.ipynb)\n- [![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\n MNIST](https://github.com/pytorch/ignite/blob/master/examples/notebooks/FastaiLRFinder_MNIST.ipynb)\n- [![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:\n torch.cuda.amp vs nvidia/apex](https://github.com/pytorch/ignite/blob/master/examples/notebooks/Cifar100_bench_amp.ipynb)\n- [![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\n TPU](https://github.com/pytorch/ignite/blob/master/examples/notebooks/MNIST_on_TPU.ipynb)\n- [![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/cifar10)\n- [![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\n time profiling on MNIST training example](https://github.com/pytorch/ignite/blob/master/examples/notebooks/HandlersTimeProfiler_MNIST.ipynb)\n\n## Reproducible Training Examples\n\nInspired by [torchvision/references](https://github.com/pytorch/vision/tree/master/references),\nwe provide several reproducible baselines for vision tasks:\n\n- [ImageNet](examples/references/classification/imagenet) - logs on Ignite Trains server coming soon ...\n- [Pascal VOC2012](examples/references/segmentation/pascal_voc2012) - logs on Ignite Trains server coming soon ...\n\nFeatures:\n\n- Distributed training: native or horovod and using [PyTorch native AMP](https://pytorch.org/docs/stable/notes/amp_examples.html)\n\n## Code-Generator application\n\nThe easiest way to create your training scripts with PyTorch-Ignite:\n\n- https://code-generator.pytorch-ignite.ai/\n\n\n\n# Communication\n\n- [GitHub issues](https://github.com/pytorch/ignite/issues): questions, bug reports, feature requests, etc.\n\n- [Discuss.PyTorch](https://discuss.pytorch.org/c/ignite), category \"Ignite\".\n\n- [PyTorch-Ignite Discord Server](https://discord.gg/djZtm3EmKj): to chat with the community\n\n- [GitHub Discussions](https://github.com/pytorch/ignite/discussions): general library-related discussions, ideas, Q&A, etc.\n\n## User feedback\n\nWe have created a form for [\"user feedback\"](https://github.com/pytorch/ignite/issues/new/choose). We\nappreciate any type of feedback, and this is how we would like to see our\ncommunity:\n\n- If you like the project and want to say thanks, this the right\n place.\n- If you do not like something, please, share it with us, and we can\n see how to improve it.\n\nThank you!\n\n\n\n# Contributing\n\nPlease see the [contribution guidelines](https://github.com/pytorch/ignite/blob/master/CONTRIBUTING.md) for more information.\n\nAs always, PRs are welcome :)\n\n\n\n# Projects using Ignite\n\n

\n\n

\nResearch papers\n

\n\n- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://github.com/BlackHC/BatchBALD)\n- [A Model to Search for Synthesizable Molecules](https://github.com/john-bradshaw/molecule-chef)\n- [Localised Generative Flows](https://github.com/jrmcornish/lgf)\n- [Extracting T Cell Function and Differentiation Characteristics from the Biomedical Literature](https://github.com/hammerlab/t-cell-relation-extraction)\n- [Variational Information Distillation for Knowledge Transfer](https://github.com/amzn/xfer/tree/master/var_info_distil)\n- [XPersona: Evaluating Multilingual Personalized Chatbot](https://github.com/HLTCHKUST/Xpersona)\n- [CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images](https://github.com/ucuapps/CoronaryArteryStenosisScoreClassification)\n- [Bridging Text and Video: A Universal Multimodal Transformer for Video-Audio Scene-Aware Dialog](https://github.com/ictnlp/DSTC8-AVSD)\n- [Adversarial Decomposition of Text Representation](https://github.com/text-machine-lab/adversarial_decomposition)\n- [Uncertainty Estimation Using a Single Deep Deterministic Neural Network](https://github.com/y0ast/deterministic-uncertainty-quantification)\n- [DeepSphere: a graph-based spherical CNN](https://github.com/deepsphere/deepsphere-pytorch)\n- [Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment](https://github.com/lidq92/LinearityIQA)\n- [Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training](https://github.com/lidq92/MDTVSFA)\n- [Deep Signature Transforms](https://github.com/patrick-kidger/Deep-Signature-Transforms)\n- [Neural CDEs for Long Time-Series via the Log-ODE Method](https://github.com/jambo6/neuralCDEs-via-logODEs)\n- [Volumetric Grasping Network](https://github.com/ethz-asl/vgn)\n- [Mood Classification using Listening Data](https://github.com/fdlm/listening-moods)\n- [Deterministic Uncertainty Estimation (DUE)](https://github.com/y0ast/DUE)\n- [PyTorch-Hebbian: facilitating local learning in a deep learning framework](https://github.com/Joxis/pytorch-hebbian)\n- [Stochastic Weight Matrix-Based Regularization Methods for Deep Neural Networks](https://github.com/rpatrik96/lod-wmm-2019)\n- [Learning explanations that are hard to vary](https://github.com/gibipara92/learning-explanations-hard-to-vary)\n- [The role of disentanglement in generalisation](https://github.com/mmrl/disent-and-gen)\n- [A Probabilistic Programming Approach to Protein Structure Superposition](https://github.com/LysSanzMoreta/Theseus-PP)\n- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://github.com/auriml/Rx-thorax-automatic-captioning)\n\n

\n\n

\nBlog articles, tutorials, books\n

\n\n- [State-of-the-Art Conversational AI with Transfer Learning](https://github.com/huggingface/transfer-learning-conv-ai)\n- [Tutorial on Transfer Learning in NLP held at NAACL 2019](https://github.com/huggingface/naacl_transfer_learning_tutorial)\n- [Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt](https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition)\n- [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)\n- [The Hero Rises: Build Your Own SSD](https://allegro.ai/blog/the-hero-rises-build-your-own-ssd/)\n- [Using Optuna to Optimize PyTorch Ignite Hyperparameters](https://medium.com/pytorch/using-optuna-to-optimize-pytorch-ignite-hyperparameters-626ffe6d4783)\n- [PyTorch Ignite - Classifying Tiny ImageNet with EfficientNet](https://towardsdatascience.com/pytorch-ignite-classifying-tiny-imagenet-with-efficientnet-e5b1768e5e8f)\n\n

\n\n

\nToolkits\n

\n\n- [Project MONAI - AI Toolkit for Healthcare Imaging](https://github.com/Project-MONAI/MONAI)\n- [DeepSeismic - Deep Learning for Seismic Imaging and Interpretation](https://github.com/microsoft/seismic-deeplearning)\n- [Nussl - a flexible, object-oriented Python audio source separation library](https://github.com/nussl/nussl)\n- [PyTorch Adapt - A fully featured and modular domain adaptation library](https://github.com/KevinMusgrave/pytorch-adapt)\n- [gnina-torch: PyTorch implementation of GNINA scoring function](https://github.com/RMeli/gnina-torch)\n\n

\n\n

\nOthers\n

\n\n- [Implementation of \"Attention is All You Need\" paper](https://github.com/akurniawan/pytorch-transformer)\n- [Implementation of DropBlock: A regularization method for convolutional networks in PyTorch](https://github.com/miguelvr/dropblock)\n- [Kaggle Kuzushiji Recognition: 2nd place solution](https://github.com/lopuhin/kaggle-kuzushiji-2019)\n- [Unsupervised Data Augmentation experiments in PyTorch](https://github.com/vfdev-5/UDA-pytorch)\n- [Hyperparameters tuning with Optuna](https://github.com/optuna/optuna-examples/blob/main/pytorch/pytorch_ignite_simple.py)\n- [Logging with ChainerUI](https://chainerui.readthedocs.io/en/latest/reference/module.html#external-library-support)\n- [FixMatch experiments in PyTorch and Ignite (CTA dataaug policy)](https://github.com/vfdev-5/FixMatch-pytorch)\n- [Kaggle Birdcall Identification Competition: 1st place solution](https://github.com/ryanwongsa/kaggle-birdsong-recognition)\n- [Logging with Aim - An open-source experiment tracker](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-pytorch-ignite)\n\n

\n\nSee other projects at [\"Used by\"](https://github.com/pytorch/ignite/network/dependents?package_id=UGFja2FnZS02NzI5ODEwNA%3D%3D)\n\nIf your project implements a paper, represents other use-cases not\ncovered in our official tutorials, Kaggle competition's code, or just\nyour code presents interesting results and uses Ignite. We would like to\nadd your project to this list, so please send a PR with brief\ndescription of the project.\n\n\n\n# Citing Ignite\n\nIf you use PyTorch-Ignite in a scientific publication, we would appreciate citations to our project.\n\n```\n@misc{pytorch-ignite,\n author = {V. Fomin and J. Anmol and S. Desroziers and J. Kriss and A. Tejani},\n title = {High-level library to help with training neural networks in PyTorch},\n year = {2020},\n publisher = {GitHub},\n journal = {GitHub repository},\n howpublished = {\\url{https://github.com/pytorch/ignite}},\n}\n```\n\n\n\n# About the team & Disclaimer\n\nPyTorch-Ignite is a [NumFOCUS Affiliated Project](https://www.numfocus.org/), operated and maintained by volunteers in the PyTorch community in their capacities as individuals\n(and not as representatives of their employers). See the [\"About us\"](https://pytorch-ignite.ai/about/community/#about-us)\npage for a list of core contributors. For usage questions and issues, please see the various channels\n[here](#communication). For all other questions and inquiries, please send an email\nto contact@pytorch-ignite.ai.\n","description_content_type":"text/markdown","docs_url":null,"download_url":null,"downloads":{"last_day":-1,"last_month":-1,"last_week":-1},"dynamic":["Author","Author-Email","Description","Description-Content-Type","Home-Page","License","Requires-Dist","Summary"],"home_page":"https://github.com/pytorch/ignite","keywords":null,"license":"BSD","license_expression":null,"license_files":null,"maintainer":null,"maintainer_email":null,"name":"pytorch-ignite","package_url":"https://pypi.org/project/pytorch-ignite/","platform":null,"project_url":"https://pypi.org/project/pytorch-ignite/","project_urls":{"Homepage":"https://github.com/pytorch/ignite"},"provides_extra":null,"release_url":"https://pypi.org/project/pytorch-ignite/0.5.2/","requires_dist":["torch<3,>=1.3","packaging"],"requires_python":null,"summary":"A lightweight library to help with training neural networks in PyTorch.","version":"0.5.2","yanked":false,"yanked_reason":null}

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torch	false

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