https://github.com/cnstark/easytorch
Simple and powerful pytorch framework.
https://github.com/cnstark/easytorch
deep-learning pytorch
Last synced: 9 months ago
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Simple and powerful pytorch framework.
- Host: GitHub
- URL: https://github.com/cnstark/easytorch
- Owner: cnstark
- License: apache-2.0
- Created: 2020-12-27T07:40:36.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2025-05-28T08:47:43.000Z (about 1 year ago)
- Last Synced: 2025-09-23T00:19:36.615Z (10 months ago)
- Topics: deep-learning, pytorch
- Language: Python
- Homepage:
- Size: 175 KB
- Stars: 46
- Watchers: 2
- Forks: 10
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# EasyTorch
[](https://github.com/cnstark/easytorch/blob/master/LICENSE)
[](https://pypi.org/project/easy-torch/)
[](https://lgtm.com/projects/g/cnstark/easytorch/context:python)
[](https://github.com/cnstark/easytorch/blob/master/.github/workflows/pylint.yml)
[English](README.md) **|** [简体中文](README_CN.md)
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.
## :sparkles: Highlight Characteristics
* :computer: **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.
* :wrench: **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.
* :flashlight: **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.
* :page_with_curl: **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.
## :cd: Dependence
### OS
* [Linux](https://pytorch.org/get-started/locally/#linux-prerequisites)
* [Windows](https://pytorch.org/get-started/locally/#windows-prerequisites)
* [MacOS](https://pytorch.org/get-started/locally/#mac-prerequisites)
Ubuntu 16.04 and later systems are recommended.
### Python
python >= 3.6 (recommended >= 3.9)
[Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Anaconda](https://www.anaconda.com/) are recommended.
### PyTorch and CUDA
[pytorch](https://pytorch.org/) >= 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.
## :dart: Get Started
### Installation
```shell
pip install easy-torch
```
### Initialize Project
TODO
## :pushpin: Examples
* [Linear Regression](examples/linear_regression)
* [MNIST Digit Recognition](examples/mnist)
* [ImageNet Image Classification](examples/imagenet)
*More examples are on the way*
It is recommended to refer to the excellent open source project [BasicTS](https://github.com/zezhishao/BasicTS).
## :rocket: 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).
``` latex
@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 [](https://github.com/cnstark/easytorch) add to your README.
```
[](https://github.com/cnstark/easytorch)
```
***(Full documentation is coming soon)***