EasyTorch

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EasyTorch is an open source neural network framework based on PyTorch, which encapsulates common functions in PyTorch projects to help users quickly build deep learning projects.

✨ Highlight Characteristics

• 💻 Minimum Code. EasyTorch encapsulates the general neural network training pipeline. Users only need to implement key codes such as Dataset, Model, and training/inference to build deep learning projects.
• 🔧 Everything Based on Config. Users control the training mode and hyperparameters through the config file. EasyTorch automatically generates a unique result storage directory according to the MD5 of the config file content, which help users to adjust hyperparameters more conveniently.
• 🔦 Support All Devices. EasyTorch supports CPU, GPU and GPU distributed training (single node multiple GPUs and multiple nodes). Users can use it by setting parameters without modifying any code.
• 📃 Save Training Log. Support logging log system and Tensorboard, and encapsulate it as a unified interface, users can save customized training logs by calling simple interfaces.

💿 Dependence

OS

• Linux
• Windows
• MacOS

Ubuntu 16.04 and later systems are recommended.

Python

python >= 3.6 (recommended >= 3.9)

Miniconda or Anaconda are recommended.

PyTorch and CUDA

pytorch >= 1.4 (recommended >= 1.9). To use CUDA, please install the PyTorch package compiled with the corresponding CUDA version.

Note: To use Ampere GPU, PyTorch version >= 1.7 and CUDA version >= 11.0.

🎯 Get Started

Installation

pip install easy-torch

Initialize Project

TODO

📌 Examples

• Linear Regression
• MNIST Digit Recognition
• ImageNet Image Classification

More examples are on the way

It is recommended to refer to the excellent open source project BasicTS.

🚀 Citations

BibTex Citations

If EasyTorch helps your research or work, please consider citing EasyTorch. The BibTex reference item is as follows(requires the url LaTeX package).

@misc{wang2020easytorch,
  author =       {Yuhao Wang},
  title =        {{EasyTorch}: Simple and powerful pytorch framework.},
  howpublished = {\url{https://github.com/cnstark/easytorch}},
  year =         {2020}
}

README Badge

If your project is using EasyTorch, please consider put the EasyTorch badge add to your README.

[![EasyTorch](https://img.shields.io/badge/Developing%20with-EasyTorch-2077ff.svg)](https://github.com/cnstark/easytorch)

(Full documentation is coming soon)