🇬🇧 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

Document

TensorLayerX has extensive documentation for both beginners and professionals.

DeepLearning course

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

Design Features

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.

Resources

• 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

Quick Start

Installation

Via docker

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

Via pip

# install from pypi
pip3 install tensorlayerx 

Build from source

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

For more installation instructions, please refer to Installtion

Define a model

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()

Contributing

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

Contact

• tensorlayer@gmail.com

Citation

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}
}