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EasyTorch\n\n[![LICENSE](https://img.shields.io/github/license/cnstark/easytorch.svg)](https://github.com/cnstark/easytorch/blob/master/LICENSE)\n[![PyPI](https://img.shields.io/pypi/v/easy-torch)](https://pypi.org/project/easy-torch/)\n[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/cnstark/easytorch.svg?logo=lgtm\u0026logoWidth=18)](https://lgtm.com/projects/g/cnstark/easytorch/context:python)\n[![python lint](https://github.com/cnstark/easytorch/actions/workflows/pylint.yml/badge.svg)](https://github.com/cnstark/easytorch/blob/master/.github/workflows/pylint.yml)\n\n[English](README.md) **|** [简体中文](README_CN.md)\n\nEasyTorch 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.\n\n## :sparkles: Highlight Characteristics\n\n* :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.\n* :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.\n* :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.\n* :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.\n\n## :cd: Dependence\n\n### OS\n\n* [Linux](https://pytorch.org/get-started/locally/#linux-prerequisites)\n* [Windows](https://pytorch.org/get-started/locally/#windows-prerequisites)\n* [MacOS](https://pytorch.org/get-started/locally/#mac-prerequisites)\n\nUbuntu 16.04 and later systems are recommended.\n\n### Python\n\npython \u003e= 3.6 (recommended \u003e= 3.9)\n\n[Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Anaconda](https://www.anaconda.com/) are recommended.\n\n### PyTorch and CUDA\n\n[pytorch](https://pytorch.org/) \u003e= 1.4 (recommended \u003e= 1.9).\nTo use CUDA, please install the PyTorch package compiled with the corresponding CUDA version.\n\nNote: To use Ampere GPU, PyTorch version \u003e= 1.7 and CUDA version \u003e= 11.0.\n\n## :dart: Get Started\n\n### Installation\n\n```shell\npip install easy-torch\n```\n\n### Initialize Project\n\nTODO\n\n## :pushpin: Examples\n\n* [Linear Regression](examples/linear_regression)\n* [MNIST Digit Recognition](examples/mnist)\n* [ImageNet Image Classification](examples/imagenet)\n\n*More examples are on the way*\n\nIt is recommended to refer to the excellent open source project [BasicTS](https://github.com/zezhishao/BasicTS).\n\n## :rocket: Citations\n\n### BibTex Citations\n\nIf EasyTorch helps your research or work, please consider citing EasyTorch.\nThe BibTex reference item is as follows(requires the `url` LaTeX package).\n\n``` latex\n@misc{wang2020easytorch,\n  author =       {Yuhao Wang},\n  title =        {{EasyTorch}: Simple and powerful pytorch framework.},\n  howpublished = {\\url{https://github.com/cnstark/easytorch}},\n  year =         {2020}\n}\n```\n\n### README Badge\n\nIf your project is using EasyTorch, please consider put the EasyTorch badge [![EasyTorch](https://img.shields.io/badge/Developing%20with-EasyTorch-2077ff.svg)](https://github.com/cnstark/easytorch) add to your README.\n\n```\n[![EasyTorch](https://img.shields.io/badge/Developing%20with-EasyTorch-2077ff.svg)](https://github.com/cnstark/easytorch)\n```\n\n***(Full documentation is coming soon)***\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcnstark%2Feasytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcnstark%2Feasytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcnstark%2Feasytorch/lists"}