Metadata-Version: 2.1
Name: pytorch-ignite
Version: 0.4.0.dev20200319
Summary: A lightweight library to help with training neural networks in PyTorch.
Home-page: https://github.com/pytorch/ignite
Author: PyTorch Core Team
Author-email: soumith@pytorch.org
License: BSD
Description: <div align="center">
        
        ![Ignite Logo](assets/ignite_logo.svg)
        
        
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        </div>
        
        ## TL;DR
        
        Ignite is a high-level library to help with training neural networks in
        PyTorch.
        
        -   ignite helps you write compact but full-featured training loops in a
            few lines of code
        -   you get a training loop with metrics, early-stopping, model
            checkpointing and other features without the boilerplate
        
        Below we show a side-by-side comparison of using pure pytorch and using
        ignite to create a training loop to train and validate your model with
        occasional checkpointing:
        
        [![image](assets/ignite_vs_bare_pytorch.png)](https://raw.githubusercontent.com/pytorch/ignite/master/assets/ignite_vs_bare_pytorch.png)
        
        As you can see, the code is more concise and readable with ignite.
        Furthermore, adding additional metrics, or things like early stopping is
        a breeze in ignite, but can start to rapidly increase the complexity of
        your code when \"rolling your own\" training loop.
        
        # Table of Contents
        - [Installation](#installation)
          * [Nightly releases](#nightly-releases)
        - [Why Ignite?](#why-ignite)
        - [Documentation](#documentation)
          * [Additional Materials](#additional-materials)
        - [Structure](#structure)
        - [Examples](#examples)
          * [MNIST Example](#mnist-example)
          * [Tutorials](#tutorials)
          * [Distributed CIFAR10 Example](#distributed-cifar10-example)
          * [Other Examples](#other-examples)
          * [Reproducible Training Examples](#reproducible-training-examples)
        - [Contributing](#contributing)
        - [Projects using Ignite](#projects-using-ignite)
        - [User feedback](#user-feedback)
        
        
        # Installation
        
        From [pip](https://pypi.org/project/pytorch-ignite/):
        
        ``` {.sourceCode .bash}
        pip install pytorch-ignite
        ```
        
        From [conda](https://anaconda.org/pytorch/ignite):
        
        ``` {.sourceCode .bash}
        conda install ignite -c pytorch
        ```
        
        From source:
        
        ``` {.sourceCode .bash}
        pip install git+https://github.com/pytorch/ignite
        ```
        
        ## Nightly releases
        
        From pip:
        
        ``` {.sourceCode .bash}
        pip install --pre pytorch-ignite
        ```
        
        From conda (this suggests to install [pytorch nightly
        release](https://anaconda.org/pytorch-nightly/pytorch) instead of stable
        version as dependency):
        
        ``` {.sourceCode .bash}
        conda install ignite -c pytorch-nightly
        ```
        
        # Why Ignite?
        
        Ignite\'s high level of abstraction assumes less about the type of
        network (or networks) that you are training, and we require the user to
        define the closure to be run in the training and validation loop. This
        level of abstraction allows for a great deal more of flexibility, such
        as co-training multiple models (i.e. GANs) and computing/tracking
        multiple losses and metrics in your training loop.
        
        Ignite also allows for multiple handlers to be attached to events, and a
        finer granularity of events in the engine loop.
        
        # Documentation
        
        API documentation and an overview of the library can be found
        [here](https://pytorch.org/ignite/index.html).
        
        ## Additional Materials
        
        - [8 Creators and Core Contributors Talk About Their Model Training Libraries From PyTorch Ecosystem](https://neptune.ai/blog/model-training-libraries-pytorch-ecosystem?utm_source=reddit&utm_medium=post&utm_campaign=blog-model-training-libraries-pytorch-ecosystem)
        - Ignite Posters from Pytorch Developer Conferences:
            - [2019](https://drive.google.com/open?id=1bqIl-EM6GCCCoSixFZxhIbuF25F2qTZg)
            - [2018](https://drive.google.com/open?id=1_2vzBJ0KeCjGv1srojMHiJRvceSVbVR5)
        
        
        
        # Structure
        
        -   **ignite**: Core of the library, contains an engine for training and
            evaluating, all of the classic machine learning metrics and a
            variety of handlers to ease the pain of training and validation of
            neural networks!
        -   **ignite.contrib**: The Contrib directory contains additional
            modules contributed by Ignite users. Modules vary from TBPTT engine,
            various optimisation parameter schedulers, logging handlers and a
            metrics module containing many regression metrics
            ([ignite.contrib.metrics.regression](https://github.com/pytorch/ignite/tree/master/ignite/contrib/metrics/regression))!
        
        The code in **ignite.contrib** is not as fully maintained as the core
        part of the library. It may change or be removed at any time without
        notice.
        
        # Examples
        
        We provide several examples ported from
        [pytorch/examples](https://github.com/pytorch/examples) using `ignite` to display how it helps to write compact and
        full-featured training loops in a few lines of code:
        
        ## MNIST Example
        
        Basic neural network training on MNIST dataset with/without `ignite.contrib` module:
        
        -   [MNIST with ignite.contrib TQDM/Tensorboard/Visdom
            loggers](https://github.com/pytorch/ignite/tree/master/examples/contrib/mnist)
        -   [MNIST with native TQDM/Tensorboard/Visdom
            logging](https://github.com/pytorch/ignite/tree/master/examples/mnist)
        
        ## Tutorials
        
        -   [![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.ipynb)  [Training Cycle-GAN on Horses to
            Zebras](https://github.com/pytorch/ignite/blob/master/examples/notebooks/CycleGAN.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/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) 
        
        ## Distributed CIFAR10 Example
        
        Training a small variant of ResNet on CIFAR10 in various configurations:
        1\) single gpu, 2) single node multiple gpus, 3) multiple nodes and
        multilple gpus.
        
        -   [CIFAR10](https://github.com/pytorch/ignite/tree/master/examples/contrib/cifar10)
        
        ## Other Examples
        
        -   [DCGAN](https://github.com/pytorch/ignite/tree/master/examples/gan)
        -   [Reinforcement
            Learning](https://github.com/pytorch/ignite/tree/master/examples/reinforcement_learning)
        -   [Fast Neural
            Style](https://github.com/pytorch/ignite/tree/master/examples/fast_neural_style)
        
        ## Reproducible Training Examples
        
        Inspired by
        [torchvision/references](https://github.com/pytorch/vision/tree/master/references),
        we provide several reproducible baselines for vision tasks:
        
        -   [ImageNet](examples/references/classification/imagenet)
        -   [Pascal VOC2012](examples/references/segmentation/pascal_voc2012)
        
        Features:
        
        -   Distributed training with mixed precision by
            [nvidia/apex](https://github.com/NVIDIA/apex/)
        -   Experiments tracking with [MLflow](https://mlflow.org/) or
            [Polyaxon](https://polyaxon.com/)
        
        # Contributing
        
        We appreciate all contributions. If you are planning to contribute back
        bug-fixes, please do so without any further discussion. If you plan to
        contribute new features, utility functions or extensions, please first
        open an issue and discuss the feature with us.
        
        Please see the [contribution
        guidelines](https://github.com/pytorch/ignite/blob/master/CONTRIBUTING.md)
        for more information.
        
        As always, PRs are welcome :)
        
        # Projects using Ignite
        
        -   [State-of-the-Art Conversational AI with Transfer
            Learning](https://github.com/huggingface/transfer-learning-conv-ai)
        -   [Tutorial on Transfer Learning in NLP held at NAACL
            2019](https://github.com/huggingface/naacl_transfer_learning_tutorial)
        -   [Implementation of \"Attention is All You Need\"
            paper](https://github.com/akurniawan/pytorch-transformer)
        -   [Implementation of DropBlock: A regularization method for
            convolutional networks in
            PyTorch](https://github.com/miguelvr/dropblock)
        -   [Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by
            Packt](https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition)
        -   [Kaggle Kuzushiji Recognition: 2nd place
            solution](https://github.com/lopuhin/kaggle-kuzushiji-2019)
        -   [Unsupervised Data Augmentation experiments in
            PyTorch](https://github.com/vfdev-5/UDA-pytorch)
        -   [Hyperparameters tuning with
            Optuna](https://github.com/pfnet/optuna/blob/master/examples/pytorch_ignite_simple.py)
        -   [Project MONAI -
            AI Toolkit for Healthcare Imaging
            ](https://github.com/Project-MONAI/MONAI)
        
        See other projects at [\"Used
        by\"](https://github.com/pytorch/ignite/network/dependents?package_id=UGFja2FnZS02NzI5ODEwNA%3D%3D)
        
        If your project implements a paper, represents other use-cases not
        covered in our official tutorials, Kaggle competition\'s code or just
        your code presents interesting results and uses Ignite. We would like to
        add your project in this list, so please send a PR with brief
        description of the project.
        
        # 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 !
        
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