forked from p32761584/tensorlayer3
216 lines
11 KiB
Markdown
216 lines
11 KiB
Markdown
<a href="https://tensorlayer3.readthedocs.io/">
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<div align="center">
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<img src="img/tl_transparent_logo.png" width="50%" height="30%"/>
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</div>
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</a>
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<!--- [](https://badge.fury.io/py/tensorlayer) --->
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<!--- ) --->
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[](https://git.openi.org.cn/TensorLayer/tensorlayer3.0)
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[](https://github.com/tensorflow/tensorflow/releases)
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[](https://tensorlayer3.readthedocs.io)
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[中文简介](https://git.openi.org.cn/TensorLayer/tensorlayer3.0/src/branch/master/tensorlayer_cn.md)
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<!--- [](https://circleci.com/gh/tensorlayer/tensorlayer/tree/master) --->
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<!--- [](https://tensorlayercn.readthedocs.io/)
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<!--- [](https://pyup.io/repos/github/tensorlayer/tensorlayer/) --->
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[TensorLayer](https://tensorlayer3.readthedocs.io) 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](https://git.openi.org.cn/TensorLayer/tensorlayer3.0/src/branch/master/examples/basic_tutorials) and [applications](https://git.openi.org.cn/TensorLayer/tensorlayer3.0/src/branch/master/examples/model_zoo). TensorLayer is awarded the 2017 Best Open Source Software by the [ACM Multimedia Society](https://twitter.com/ImperialDSI/status/923928895325442049).
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This project can also be found at [iHub](https://code.ihub.org.cn/projects/328) and [Gitee](https://gitee.com/organizations/TensorLayer).
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# News
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🔥 **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**
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🔥 Reinforcement Learning Zoo: [Low-level APIs](https://github.com/tensorlayer/tensorlayer/tree/master/examples/reinforcement_learning) for professional usage, [High-level APIs](https://github.com/tensorlayer/RLzoo) for simple usage, and a corresponding [Springer textbook](http://springer.com/gp/book/9789811540943)
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🔥 [Sipeed Maxi-EMC](https://github.com/sipeed/Maix-EMC): Run TensorLayer models on the **low-cost AI chip** (e.g., K210) (Alpha Version)
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<!-- 🔥 [NNoM](https://github.com/majianjia/nnom): Run TensorLayer quantized models on the **MCU** (e.g., STM32) (Coming Soon) -->
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# Design Features
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TensorLayer is a new deep learning library designed with simplicity, flexibility and high-performance in mind.
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- ***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](https://git.openi.org.cn/TensorLayer/tensorlayer3.0/src/branch/master/examples).
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- ***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.
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- ***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).
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TensorLayer stands at a unique spot in the TensorFlow wrappers. Other wrappers like Keras and TFLearn
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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,
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making it easy to learn while being flexible enough to cope with complex AI tasks.
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TensorLayer has a fast-growing community. It has been used by researchers and engineers all over the world, including those from Peking University,
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Imperial College London, UC Berkeley, Carnegie Mellon University, Stanford University, and companies like Google, Microsoft, Alibaba, Tencent, Xiaomi, and Bloomberg.
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# Multilingual Documents
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TensorLayer has extensive documentation for both beginners and professionals. The documentation is available in
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both English and Chinese.
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[](https://tensorlayer3.readthedocs.io/)
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[](https://tensorlayercn.readthedocs.io/)
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[](http://www.broadview.com.cn/book/5059/)
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If you want to try the experimental features on the the master branch, you can find the latest document
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[here](https://tensorlayer3.readthedocs.io/en/latest/).
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# Extensive Examples
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You can find a large collection of examples that use TensorLayer in [here](examples/) and the following space:
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<a href="https://github.com/tensorlayer/awesome-tensorlayer/blob/master/readme.md" target="\_blank">
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<div align="center">
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<img src="img/awesome-mentioned.png" width="40%"/>
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</div>
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</a>
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# Getting Start
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Comparison of TensorLayer version
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<a href="https://tensorlayer3.readthedocs.io/">
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<div align="center">
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<img src="img/tensorlayer_v.png" width="100%" height="30%"/>
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</div>
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</a>
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TensorLayer 3.0 relies on TensorFlow, numpy, and others. To use GPUs, CUDA and cuDNN are required.
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Install TensorFlow:
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```bash
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pip3 install tensorflow-gpu==2.0.0-rc1 # TensorFlow GPU (version 2.0 RC1)
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pip3 install tensorflow # CPU version
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```
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Install the stable release of TensorLayer3:
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```bash
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pip3 install tensorlayer3
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```
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Install the stable release of TensorLayer2.x:
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```bast
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pip3 install tensorlayer
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```
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Install the unstable development version of TensorLayer3:
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```bash
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pip3 install git+https://git.openi.org.cn/TensorLayer/tensorlayer3.0.git
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```
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If you want to install the additional dependencies, you can also run
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```bash
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pip3 install --upgrade tensorlayer[all] # all additional dependencies
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pip3 install --upgrade tensorlayer[extra] # only the `extra` dependencies
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pip3 install --upgrade tensorlayer[contrib_loggers] # only the `contrib_loggers` dependencies
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```
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If you want to use mindspore backend, you should install mindspore>=1.2.1
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```bash
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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
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```
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If you want to use paddlepaddle backend, you should install paddlepaddle>=2.1.1
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```bash
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python -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
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```
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If you are TensorFlow 1.X users, you can use TensorLayer 1.11.0:
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```bash
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# For last stable version of TensorLayer 1.X
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pip3 install --upgrade tensorlayer==1.11.0
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```
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<!---
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## Using Docker
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The [TensorLayer containers](https://hub.docker.com/r/tensorlayer/tensorlayer/) are built on top of the official [TensorFlow containers](https://hub.docker.com/r/tensorflow/tensorflow/):
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### Containers with CPU support
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```bash
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# for CPU version and Python 2
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docker pull tensorlayer/tensorlayer:latest
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docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest
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# for CPU version and Python 3
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docker pull tensorlayer/tensorlayer:latest-py3
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docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-py3
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```
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### Containers with GPU support
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NVIDIA-Docker is required for these containers to work: [Project Link](https://github.com/NVIDIA/nvidia-docker)
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```bash
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# for GPU version and Python 2
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docker pull tensorlayer/tensorlayer:latest-gpu
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nvidia-docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-gpu
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# for GPU version and Python 3
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docker pull tensorlayer/tensorlayer:latest-gpu-py3
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nvidia-docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-gpu-py3
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```
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--->
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# Performance Benchmark
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The following table shows the training speeds of [VGG16](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) using TensorLayer and native TensorFlow on a TITAN Xp.
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| Mode | Lib | Data Format | Max GPU Memory Usage(MB) |Max CPU Memory Usage(MB) | Avg CPU Memory Usage(MB) | Runtime (sec) |
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| :-------: | :-------------: | :-----------: | :-----------------: | :-----------------: | :-----------------: | :-----------: |
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| AutoGraph | TensorFlow 2.0 | channel last | 11833 | 2161 | 2136 | 74 |
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| | TensorLayer 2.0 | channel last | 11833 | 2187 | 2169 | 76 |
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| Graph | Keras | channel last | 8677 | 2580 | 2576 | 101 |
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| Eager | TensorFlow 2.0 | channel last | 8723 | 2052 | 2024 | 97 |
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| | TensorLayer 2.0 | channel last | 8723 | 2010 | 2007 | 95 |
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| | TensorLayer 3.0 | channel last | | | | |
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# Getting Involved
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Please read the [Contributor Guideline](CONTRIBUTING.md) before submitting your PRs.
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We suggest users to report bugs using Github issues. Users can also discuss how to use TensorLayer in the following slack channel.
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<br/>
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<a href="https://join.slack.com/t/tensorlayer/shared_invite/enQtODk1NTQ5NTY1OTM5LTQyMGZhN2UzZDBhM2I3YjYzZDBkNGExYzcyZDNmOGQzNmYzNjc3ZjE3MzhiMjlkMmNiMmM3Nzc4ZDY2YmNkMTY" target="\_blank">
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<div align="center">
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<img src="img/join_slack.png" width="40%"/>
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</div>
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</a>
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<br/>
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# Citing TensorLayer
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If you find TensorLayer useful for your project, please cite the following papers：
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```
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@article{tensorlayer2017,
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author = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},
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journal = {ACM Multimedia},
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title = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},
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url = {http://tensorlayer.org},
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year = {2017}
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}
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@inproceedings{tensorlayer2021,
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title={Tensorlayer 3.0: A Deep Learning Library Compatible With Multiple Backends},
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author={Lai, Cheng and Han, Jiarong and Dong, Hao},
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booktitle={2021 IEEE International Conference on Multimedia \& Expo Workshops (ICMEW)},
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pages={1--3},
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year={2021},
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organization={IEEE}
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}
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```
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