TensorLayerX: A Unified Deep Learning and Reinforcement Learning Framework for All Hardwares, Backends and OS.
Alternatives To Tensorlayerx
Project NameStarsDownloadsRepos Using ThisPackages Using ThisMost Recent CommitTotal ReleasesLatest ReleaseOpen IssuesLicenseLanguage
Tensorflow177,7583277711 hours ago46October 23, 20192,060apache-2.0C++
An Open Source Machine Learning Framework for Everyone
Transformers112,273641,8697 hours ago114July 18, 2023820apache-2.0Python
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Pytorch71,0033,3416,7287 hours ago37May 08, 202312,762otherPython
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Cs Video Courses60,215
2 days ago5
List of Computer Science courses with video lectures.
Keras59,39657815 hours ago80June 27, 202384apache-2.0Python
Deep Learning for humans
D2l Zh48,273116 days ago47December 15, 202248apache-2.0Python
9 days ago23gpl-3.0Python
Deepfakes Software For All
Tensorflow Examples42,312
a year ago218otherJupyter Notebook
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
23 days ago536gpl-3.0Python
DeepFaceLab is the leading software for creating deepfakes.
3 days ago8September 21, 2021237agpl-3.0Python
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Alternatives To Tensorlayerx
Select To Compare

Alternative Project Comparisons

GitHub last commit (branch) Documentation Status Build Status Downloads Downloads Docker Pulls

TensorLayerX 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. This project is maintained by researchers from Peking University, Peng Cheng Lab, HKUST, Imperial College London, Princeton, Oxford, Stanford, Tsinghua and Edinburgh.

Deep Learning course

We have video courses for deep learning, with example codes based on TensorLayerX.
Bilibili link (chinese)

Design Features

  • Compatibility: Support worldwide frameworks and AI chips, enabling one code runs on all platforms.

  • Model Zoo: Provide a series of applications containing classic and SOTA models, covering CV, NLP, RL and other fields.

  • Deployment: Support ONNX protocol, model export, import and deployment.

Multi-backend Design

You can immediately use TensorLayerX to define a model via Pytorch-stype, and switch to any backends easily.

import os
os.environ['TL_BACKEND'] = 'tensorflow' # modify this line, switch to any backends 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()

Quick Start

Get started with TensorLayerX quickly using the following examples:

  • 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 and mnist_mlp_custom_train.py.

  • 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.

  • MNIST GAN Training: Train a generative adversarial network (GAN) on the MNIST dataset. See the example: mnist_gan.py.

  • 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.


  • Examples for tutorials
  • GammaGL is series of graph learning algorithm
  • TLXZoo a series of pretrained backbones
  • TLXCV a series of Computer Vision applications
  • TLXNLP a series of Natural Language Processing applications
  • TLX2ONNX ONNX model exporter for TensorLayerX.
  • Paddle2TLX model code converter from PaddlePaddle to TensorLayerX.

More official resources can be found here


  • The latest TensorLayerX compatible with the following backend version
TensorLayerX TensorFlow MindSpore PaddlePaddle PyTorch OneFlow Jittor
v0.5.8 v2.4.0 v1.8.1 v2.2.0 v1.10.0 -- --
v0.5.7 v2.0.0 v1.6.1 v2.0.2 v1.10.0 -- --
  • via pip for the stable version
# install from pypi
pip3 install tensorlayerx 
  • build from source for the latest version (for advanced users)
# install from Github
pip3 install git+https://github.com/tensorlayer/tensorlayerx.git 

For more installation instructions, please refer to Installtion

  • 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


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

Getting Involved

We suggest users to report bugs using Github issues. Users can also discuss how to use TensorLayerX in the following slack channel.



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

  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)},
    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}
Popular Machine Learning Projects
Popular Deep Learning Projects
Popular Machine Learning Categories
Related Searches

Get A Weekly Email With Trending Projects For These Categories
No Spam. Unsubscribe easily at any time.
Machine Learning
Deep Learning
Neural Network