• News
• Design Features
• Multilingual Documents
• Extensive Examples
• Getting Start
• Performance Benchmark
• Getting Involved
• Citing TensorLayer

TensorLayer is a novel supports multiple backends deep learning and reinforcement learning library designed for researchers and engineers. It provides an extensive collection of customizable neural layers to build advanced AI models quickly, based on this, the community open-sourced mass tutorials and applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society.
This project can also be found at iHub and Gitee.

News

🔥 3.0.0 has been pre-released, it supports TensorFlow and MindSpore backends, and supports some PaddlePaddle operator backends, allowing users to run the code on different hardware like Nvidia-GPU and Huawei-Ascend. It will support TensorFlow, MindSpore, PaddlePaddle, and PyTorch backends in the future. Feel free to use it and make suggestions. We need more people to join the dev team, if you are interested, please email hao.dong@pku.edu.cn

🔥 Reinforcement Learning Zoo: Low-level APIs for professional usage, High-level APIs for simple usage, and a corresponding Springer textbook

🔥 Sipeed Maxi-EMC: Run TensorLayer models on the low-cost AI chip (e.g., K210) (Alpha Version)

Design Features

TensorLayer is a new deep learning library designed with simplicity, flexibility and high-performance in mind.

• Simplicity : TensorLayer has a high-level layer/model abstraction which is effortless to learn. You can learn how deep learning can benefit your AI tasks in minutes through the massive examples.
• Flexibility : TensorLayer APIs are transparent and flexible, inspired by the emerging PyTorch library. Compared to the Keras abstraction, TensorLayer makes it much easier to build and train complex AI models.
• Zero-cost Abstraction : Though simple to use, TensorLayer does not require you to make any compromise in the performance of TensorFlow (Check the following benchmark section for more details).

TensorLayer stands at a unique spot in the TensorFlow wrappers. Other wrappers like Keras and TFLearn
hide many powerful features of TensorFlow and provide little support for writing custom AI models. Inspired by PyTorch, TensorLayer APIs are simple, flexible and Pythonic,
making it easy to learn while being flexible enough to cope with complex AI tasks.
TensorLayer has a fast-growing community. It has been used by researchers and engineers all over the world, including those from Peking University,
Imperial College London, UC Berkeley, Carnegie Mellon University, Stanford University, and companies like Google, Microsoft, Alibaba, Tencent, Xiaomi, and Bloomberg.

Multilingual Documents

TensorLayer has extensive documentation for both beginners and professionals. The documentation is available in
both English and Chinese.

If you want to try the experimental features on the the master branch, you can find the latest document
here.

Extensive Examples

You can find a large collection of examples that use TensorLayer in here and the following space:

Getting Start

TensorLayer 3.0 relies on TensorFlow, numpy, and others. To use GPUs, CUDA and cuDNN are required.

Install TensorFlow:

pip3 install tensorflow-gpu==2.0.0-rc1 # TensorFlow GPU (version 2.0 RC1)
pip3 install tensorflow # CPU version

Install the stable release of TensorLayer, support only to TensorLayer2.2.3:

pip3 install tensorlayer

Install the unstable development version of TensorLayer, support for TensorLayer3.0:

pip3 install git+https://git.openi.org.cn/TensorLayer/tensorlayer3.0.git

If you want to install the additional dependencies, you can also run

pip3 install --upgrade tensorlayer[all]              # all additional dependencies
pip3 install --upgrade tensorlayer[extra]            # only the `extra` dependencies
pip3 install --upgrade tensorlayer[contrib_loggers]  # only the `contrib_loggers` dependencies

If you want to use mindspore backend, you should install mindspore>=1.2.1

pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.2.1/MindSpore/gpu/ubuntu_x86/cuda-10.1/mindspore_gpu-1.2.1-cp37-cp37m-linux_x86_64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple

If you want to use paddlepaddle backend, you should install paddlepaddle>=2.1.1

python -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple

If you are TensorFlow 1.X users, you can use TensorLayer 1.11.0:

# For last stable version of TensorLayer 1.X
pip3 install --upgrade tensorlayer==1.11.0

Performance Benchmark

The following table shows the training speeds of VGG16 using TensorLayer and native TensorFlow on a TITAN Xp.

Mode	Lib	Data Format	Max GPU Memory Usage(MB)	Max CPU Memory Usage(MB)	Avg CPU Memory Usage(MB)	Runtime (sec)
AutoGraph	TensorFlow 2.0	channel last	11833	2161	2136	74
	TensorLayer 2.0	channel last	11833	2187	2169	76
Graph	Keras	channel last	8677	2580	2576	101
Eager	TensorFlow 2.0	channel last	8723	2052	2024	97
	TensorLayer 2.0	channel last	8723	2010	2007	95
	TensorLayer 3.0	channel last

Getting Involved

Please read the Contributor Guideline before submitting your PRs.

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

Citing TensorLayer

If you find TensorLayer useful for your project, please cite the following paper：

@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}
}