🇬🇧 TensorLayerX is a multi-backend AI framework, which supports TensorFlow, Pytorch, MindSpore, PaddlePaddle, OneFlow and Jittor as the backends, allowing users to run the code on different hardware like Nvidia-GPU and Huawei-Ascend. This project is maintained by researchers from Peking University, Imperial College London, Princeton, Stanford, Tsinghua, Edinburgh and Peng Cheng Lab. supported layers.

🇨🇳 TensorLayerX 是一个跨平台开发框架，支持TensorFlow, Pytorch, MindSpore, PaddlePaddle, OneFlow和Jittor，用户不需要修改任何代码即可以运行在各类操作系统和AI硬件上（如Nvidia-GPU 和 Huawei-Ascend），并支持混合框架的开发。这个项目由北京大学、鹏城实验室、爱丁堡大学、帝国理工、清华、普林斯顿、斯坦福等机构的研究人员维护。 支持列表。

GitHub项目地址：https://github.com/tensorlayer/TensorLayerX
启智平台（国内访问）：https://openi.pcl.ac.cn/OpenI/TensorLayerX

TensorLayerX has extensive documentation for both beginners and professionals.

🔥We have opened a video course for introductory learning deep learning, with example codes based on TensorLayerX.
Bilibili link

Compare with TensorLayer, TensorLayerX（TLX) is a brand new seperated project for platform-agnostic purpose.

Compare to TensorLayer version:

🔥TensorLayerX inherits the features of the previous verison, including Simplicity, Flexibility and Zero-cost Abstraction. Compare with TensorLayer, TensorLayerX supports more backends, such as TensorFlow, MindSpore, PaddlePaddle and PyTorch. It allows users to run the same code on different hardwares like Nvidia-GPU and Huawei-Ascend. In addition, more features are under development.

• Model Zoo: Build a series of model Zoos containing classic and sota models,covering CV, NLP, RL and other fields.

• Deploy: In feature, TensorLayerX will support the ONNX protocol, supporting model export, import and deployment.

• Parallel: In order to improve the efficiency of neural network model training, parallel computing is indispensable.

• TLX2ONNX ONNX Model Exporter for TensorLayerX. ✅
• Examples for tutorials✅
• GammaGL is a multi-backend graph learning library based on TensorLayerX.✅
• OpenIVA an easy-to-use product-level deployment framework✅
• TLXZoo pretrained models/backbones🚧
• TLXCV a bunch of Computer Vision applications🚧
• TLXNLP a bunch of Natural Language Processing applications🚧
• TLXRL a bunch of Reinforcement Learning applications, check RLZoo for the old version✅

More resources can be found here

Docker is an open source application container engine. In the TensorLayerX Docker Repository, different versions of TensorLayerX have been installed in docker images.

# pull from docker hub
docker pull tensorlayer/tensorlayerx:tagname

# install from pypi
pip3 install tensorlayerx 

# install from Github
pip3 install git+https://github.com/tensorlayer/tensorlayerx.git 

For more installation instructions, please refer to Installtion

You can immediately use tensorlayerx to define a model, using your favourite framework in the background, like so:

import os
os.environ['TL_BACKEND'] = 'tensorflow' # modify this line, switch to any framework easily!
#os.environ['TL_BACKEND'] = 'mindspore'
#os.environ['TL_BACKEND'] = 'paddle'
#os.environ['TL_BACKEND'] = 'torch'
import tensorlayerx as tlx
from tensorlayerx.nn import Module
from tensorlayerx.nn import Linear
class CustomModel(Module):

  def __init__(self):
      super(CustomModel, self).__init__()

      self.linear1 = Linear(out_features=800, act=tlx.ReLU, in_features=784)
      self.linear2 = Linear(out_features=800, act=tlx.ReLU, in_features=800)
      self.linear3 = Linear(out_features=10, act=None, in_features=800)

  def forward(self, x, foo=False):
      z = self.linear1(x)
      z = self.linear2(z)
      out = self.linear3(z)
      if foo:
          out = tlx.softmax(out)
      return out

MLP = CustomModel()
MLP.set_eval()

Join our community as a code contributor, find out more in our Help wanted list and Contributing guide!

• tensorlayer@gmail.com

If you find TensorLayerX useful for your project, please cite the following papers：

@article{tensorlayer2017,
    author  = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},
    journal = {ACM Multimedia},
    title   = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},
    url     = {http://tensorlayer.org},
    year    = {2017}
}

@inproceedings{tensorlayer2021,
  title={TensorLayer 3.0: A Deep Learning Library Compatible With Multiple Backends},
  author={Lai, Cheng and Han, Jiarong and Dong, Hao},
  booktitle={2021 IEEE International Conference on Multimedia \& Expo Workshops (ICMEW)},
  pages={1--3},
  year={2021},
  organization={IEEE}
}