GitHub - pytorch/ignite
source link: https://github.com/pytorch/ignite
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TL;DR
Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
Features
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Less code than pure PyTorch while ensuring maximum control and simplicity
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Library approach and no program's control inversion - Use ignite where and when you need
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Extensible API for metrics, experiment managers, and other components
Table of Contents
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.
Example
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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
Examples
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Built-in events filtering
Examples
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Stack events to share some actions
Examples
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Custom events to go beyond standard events
Examples
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Out-of-the-box metrics
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Metrics for various tasks: Precision, Recall, Accuracy, Confusion Matrix, IoU etc, ~20 regression metrics.
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Users can also compose their metrics with ease from existing ones using arithmetic operations or torch methods.
Example
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Installation
From pip:
pip install pytorch-ignite
From conda:
conda install ignite -c pytorch
From source:
pip install git+https://github.com/pytorch/ignite
Nightly releases
From pip:
pip install --pre pytorch-ignite
From conda (this suggests to install pytorch nightly release instead of stable version as dependency):
conda install ignite -c pytorch-nightly
Docker Images
Using pre-built images
Pull a pre-built docker image from our Docker Hub and run it with docker v19.03+.
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
For more details, see here.
Getting Started
Few pointers to get you started:
Documentation
Additional Materials
Examples
Tutorials
Reproducible Training Examples
Inspired by torchvision/references, we provide several reproducible baselines for vision tasks:
- ImageNet - logs on Ignite Trains server coming soon ...
- Pascal VOC2012 - logs on Ignite Trains server coming soon ...
Features:
- Distributed training: native or horovod and using PyTorch native AMP
Code-Generator application
The easiest way to create your training scripts with PyTorch-Ignite:
Communication
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GitHub issues: questions, bug reports, feature requests, etc.
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Discuss.PyTorch, category "Ignite".
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PyTorch-Ignite Discord Server: to chat with the community
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GitHub Discussions: general library-related discussions, ideas, Q&A, etc.
User feedback
We have created a form for "user feedback". 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 for more information.
As always, PRs are welcome :)
Projects using Ignite
Research papers Blog articles, tutorials, books Toolkits Others
See other projects at "Used by"
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, 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" page for a list of core contributors. For usage questions and issues, please see the various channels here. For all other questions and inquiries, please send an email to [email protected].
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