{"id":13799097,"url":"https://github.com/tensorlayer/TensorLayerX","last_synced_at":"2025-05-13T06:32:16.953Z","repository":{"id":37678566,"uuid":"434507886","full_name":"tensorlayer/TensorLayerX","owner":"tensorlayer","description":"TensorLayerX: A Unified Deep Learning and Reinforcement Learning Framework for All Hardwares, Backends and OS.","archived":false,"fork":false,"pushed_at":"2024-09-08T05:45:53.000Z","size":5728,"stargazers_count":538,"open_issues_count":15,"forks_count":44,"subscribers_count":16,"default_branch":"main","last_synced_at":"2024-10-29T15:47:59.440Z","etag":null,"topics":["deep-learning","jittor","machine-learning","mindspore","neural-network","oneflow","paddlepaddle","python","pytorch","tensorflow","tensorlayer","tensorlayerx"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tensorlayer.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.rst","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-12-03T07:41:20.000Z","updated_at":"2024-10-27T06:19:10.000Z","dependencies_parsed_at":"2024-01-22T19:33:05.325Z","dependency_job_id":"b04cba83-bd51-4c77-91f4-9284ce310ce1","html_url":"https://github.com/tensorlayer/TensorLayerX","commit_stats":{"total_commits":295,"total_committers":12,"mean_commits":"24.583333333333332","dds":0.6847457627118644,"last_synced_commit":"a49e6415ad43d5e5911ef88298d0bf06d75b4187"},"previous_names":[],"tags_count":9,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tensorlayer%2FTensorLayerX","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tensorlayer%2FTensorLayerX/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tensorlayer%2FTensorLayerX/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tensorlayer%2FTensorLayerX/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tensorlayer","download_url":"https://codeload.github.com/tensorlayer/TensorLayerX/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224161351,"owners_count":17266115,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","jittor","machine-learning","mindspore","neural-network","oneflow","paddlepaddle","python","pytorch","tensorflow","tensorlayer","tensorlayerx"],"created_at":"2024-08-04T00:00:58.787Z","updated_at":"2024-11-18T13:31:53.502Z","avatar_url":"https://github.com/tensorlayer.png","language":"Python","readme":"\u003ca href=\"https://tensorlayerx.readthedocs.io/\"\u003e\n    \u003cdiv align=\"center\"\u003e\n        \u003cimg src=\"https://git.openi.org.cn/hanjr/tensorlayerx-image/raw/branch/master/tlx-LOGO--02.jpg\" width=\"50%\" height=\"30%\"/\u003e\n    \u003c/div\u003e\n\u003c/a\u003e\n\n\u003c!--- [![PyPI Version](https://badge.fury.io/py/tensorlayer.svg)](https://pypi.org/project/tensorlayerx/) ---\u003e\n\u003c!--- ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/tensorlayer.svg)) ---\u003e\n\n![GitHub last commit (branch)](https://img.shields.io/github/last-commit/tensorlayer/tensorlayerx/main.svg)\n[![Documentation Status](https://readthedocs.org/projects/tensorlayerx/badge/)]( https://tensorlayerx.readthedocs.io/en/latest/)\n[![Build Status](https://travis-ci.org/tensorlayer/tensorlayerx.svg?branch=master)](https://travis-ci.org/tensorlayer/tensorlayerx)\n[![Downloads](http://pepy.tech/badge/tensorlayerx)](http://pepy.tech/project/tensorlayerx)\n[![Downloads](https://pepy.tech/badge/tensorlayerx/week)](https://pepy.tech/project/tensorlayerx/week)\n[![Docker Pulls](https://img.shields.io/docker/pulls/tensorlayer/tensorlayerx.svg)](https://hub.docker.com/r/tensorlayer/tensorlayerx/)\n\n[TensorLayerX](https://tensorlayerx.readthedocs.io) is a multi-backend AI framework, supports TensorFlow, Pytorch, MindSpore, PaddlePaddle, OneFlow and Jittor as the backends, allowing users to run the code on different hardware like Nvidia-GPU, Huawei-Ascend, Cambricon and more.\nThis project is maintained by researchers from Peking University, Peng Cheng Lab, HKUST, Imperial College London, Princeton, Oxford, Stanford, Tsinghua and Edinburgh.\n\n\n- GitHub: https://github.com/tensorlayer/TensorLayerX  \n- OpenI: https://openi.pcl.ac.cn/OpenI/TensorLayerX\n- Homepage: [English](http://www.tensorlayerx.com/index_en.html?chlang=\u0026langid=2) [中文](http://tensorlayerx.com)\n- Document: https://tensorlayerx.readthedocs.io\n- Previous Project: https://github.com/tensorlayer/TensorLayer\n\n\u003c!-- # Document\nTensorLayerX has extensive documentation for both beginners and professionals. \n\n[![English Documentation](https://img.shields.io/badge/documentation-english-blue.svg)](https://tensorlayerx.readthedocs.io/en/latest/) --\u003e\n\n# Deep Learning course  \nWe have video courses for deep learning, with example codes based on TensorLayerX.  \n[Bilibili link](https://www.bilibili.com/video/BV1xB4y1h7V2?share_source=copy_web\u0026vd_source=467c17f872fcde378494433520e19999) (chinese)\n\n# Design Features\n\n\u003c!-- \u003cp align=\"center\"\u003e\u003cimg src=\"https://git.openi.org.cn/hanjr/tensorlayerx-image/raw/branch/master/version.png\" width=\"840\"\\\u003e\u003c/p\u003e --\u003e\n\n- ***Compatibility***: Support worldwide frameworks and AI chips, enabling one code runs on all platforms.\n\n- ***Model Zoo***: Provide a series of applications containing classic and SOTA models, covering CV, NLP, RL and other fields.\n\n- ***Deployment***: Support ONNX protocol, model export, import and deployment.\n\n# Multi-backend Design\n\nYou can immediately use TensorLayerX to define a model via Pytorch-stype, and switch to any backends easily.\n\n```python\nimport os\nos.environ['TL_BACKEND'] = 'tensorflow' # modify this line, switch to any backends easily!\n#os.environ['TL_BACKEND'] = 'mindspore'\n#os.environ['TL_BACKEND'] = 'paddle'\n#os.environ['TL_BACKEND'] = 'torch'\nimport tensorlayerx as tlx\nfrom tensorlayerx.nn import Module\nfrom tensorlayerx.nn import Linear\nclass CustomModel(Module):\n\n  def __init__(self):\n      super(CustomModel, self).__init__()\n\n      self.linear1 = Linear(out_features=800, act=tlx.ReLU, in_features=784)\n      self.linear2 = Linear(out_features=800, act=tlx.ReLU, in_features=800)\n      self.linear3 = Linear(out_features=10, act=None, in_features=800)\n\n  def forward(self, x, foo=False):\n      z = self.linear1(x)\n      z = self.linear2(z)\n      out = self.linear3(z)\n      if foo:\n          out = tlx.softmax(out)\n      return out\n\nMLP = CustomModel()\nMLP.set_eval()\n```\n\n# Quick Start\n\nGet started with TensorLayerX quickly using the following examples:\n\n- **MNIST Digit Recognition:** Train a simple multi-layer perceptron (MLP) model for digit recognition using the MNIST dataset. Choose between a simple training method or custom loops. See the examples: [mnist_mlp_simple_train.py](https://github.com/tensorlayer/TensorLayerX/blob/main/examples/basic_tutorials/mnist_mlp_simple_train.py) and [mnist_mlp_custom_train.py](https://github.com/tensorlayer/TensorLayerX/blob/main/examples/basic_tutorials/mnist_mlp_custom_train.py).\n\n- **CIFAR-10 Dataflow:** Learn how to create datasets, process images, and load data through DataLoader using the CIFAR-10 dataset. See the example: [cifar10_cnn.py](https://github.com/tensorlayer/TensorLayerX/blob/main/examples/basic_tutorials/cifar10_cnn.py).\n\n- **MNIST GAN Training:** Train a generative adversarial network (GAN) on the MNIST dataset. See the example: [mnist_gan.py](https://github.com/tensorlayer/TensorLayerX/blob/main/examples/basic_tutorials/mnist_gan.py).\n\n- **MNIST Mix Programming:** Mix TensorLayerX code with other deep learning libraries such as TensorFlow, PyTorch, Paddle, and MindSpore to run on the MNIST dataset. See the example: [mnist_mlp_mix_programming.py](https://github.com/tensorlayer/TensorLayerX/blob/main/examples/basic_tutorials/mnist_mlp_mix_programming.py).\n\n\n# Resources\n\n- [Examples](https://github.com/tensorlayer/TensorLayerX/tree/main/examples) for tutorials\n- [GammaGL](https://github.com/BUPT-GAMMA/GammaGL) is series of graph learning algorithm\n- [TLXZoo](https://github.com/tensorlayer/TLXZoo) a series of pretrained backbones\n- [TLXCV](https://github.com/tensorlayer/TLXCV) a series of Computer Vision applications\n- [TLXNLP](https://github.com/tensorlayer/TLXNLP) a series of Natural Language Processing applications\n- [TLX2ONNX](https://github.com/tensorlayer/TLX2ONNX/) ONNX model exporter for TensorLayerX.\n- [Paddle2TLX](https://github.com/tensorlayer/paddle2tlx) model code converter from PaddlePaddle to TensorLayerX.  \n\nMore official resources can be found [here](https://github.com/tensorlayer)\n\n\n# Installation\n\n- The latest TensorLayerX compatible with the following backend version\n\n| TensorLayerX | TensorFlow | MindSpore | PaddlePaddle | PyTorch | OneFlow | Jittor|\n| :-----:| :----: | :----: |:-----:|:----:|:----:|:----:|\n|  v0.5.8  | v2.4.0 | v1.8.1 | v2.2.0 | v1.10.0 | -- | v1.3.8.5 |\n| v0.5.7 | v2.0.0 | v1.6.1 | v2.0.2 | v1.10.0 | -- | -- |\n\n- via pip for the stable version\n```bash\n# install from pypi\npip3 install tensorlayerx \n```\n\n- build from source for the latest version (for advanced users)\n```bash\n# install from Github\npip3 install git+https://github.com/tensorlayer/tensorlayerx.git \n```\nFor more installation instructions, please refer to [Installtion](https://tensorlayerx.readthedocs.io/en/latest/user/installation.html)\n\n\n- via docker\n\nDocker is an open source application container engine. In the [TensorLayerX Docker Repository](https://hub.docker.com/repository/docker/tensorlayer/tensorlayerx), \ndifferent versions of TensorLayerX have been installed in docker images.\n\n```bash\n# pull from docker hub\ndocker pull tensorlayer/tensorlayerx:tagname\n```\n\n# Contributing\nJoin our community as a code contributor, find out more in our [Help wanted list](https://github.com/tensorlayer/TensorLayerX/issues/5) and [Contributing](https://tensorlayerx.readthedocs.io/en/latest/user/contributing.html) guide!\n\n\n# Getting Involved\n\nWe suggest users to report bugs using Github issues. Users can also discuss how to use TensorLayerX in the following slack channel.\n\n\u003cbr/\u003e\n\n\u003ca href=\"https://join.slack.com/t/tensorlayer/shared_invite/enQtODk1NTQ5NTY1OTM5LTQyMGZhN2UzZDBhM2I3YjYzZDBkNGExYzcyZDNmOGQzNmYzNjc3ZjE3MzhiMjlkMmNiMmM3Nzc4ZDY2YmNkMTY\" target=\"\\_blank\"\u003e\n\t\u003cdiv align=\"center\"\u003e\n\t\t\u003cimg src=\"https://github.com/tensorlayer/TensorLayer/blob/bdc2c14ff9ed9bd3ec7004d625e15683df7b530d/img/join_slack.png?raw=true\" width=\"40%\"/\u003e\n\t\u003c/div\u003e\n\u003c/a\u003e\n\n# Contact\n - tensorlayer@gmail.com\n\n# Citation\n\nIf you find TensorLayerX useful for your project, please cite the following papers：\n\n```\n@inproceedings{tensorlayer2021,\n  title={TensorLayer 3.0: A Deep Learning Library Compatible With Multiple Backends},\n  author={Lai, Cheng and Han, Jiarong and Dong, Hao},\n  booktitle={2021 IEEE International Conference on Multimedia \\\u0026 Expo Workshops (ICMEW)},\n  pages={1--3},\n  year={2021},\n  organization={IEEE}\n}\n@article{tensorlayer2017,\n    author  = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},\n    journal = {ACM Multimedia},\n    title   = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},\n    url     = {http://tensorlayer.org},\n    year    = {2017}\n} \n```\n\n\n\n\n","funding_links":[],"categories":["Libraries","其他_机器学习与深度学习","三、高效开发工具与库"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftensorlayer%2FTensorLayerX","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftensorlayer%2FTensorLayerX","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftensorlayer%2FTensorLayerX/lists"}