tensorlayer3/README.rst

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`TensorLayer3 <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>`__.
This project can also be found at `OpenI <https://git.openi.org.cn/TensorLayer/tensorlayer3.0>`__ and `Gitee <https://gitee.com/organizations/TensorLayer>`__.

Why another deep learning library: TensorLayer
==============================================

As deep learning practitioners, we have been looking for a library that
can address various development purposes. This library is easy to adopt
by providing diverse examples, tutorials and pre-trained models. Also,
it allow users to easily fine-tune TensorFlow; while being suitable for
production deployment. TensorLayer aims to satisfy all these purposes.
It has three key features:

-  **Simplicity** : TensorLayer lifts the low-level dataflow interface
   of TensorFlow to *high-level* layers / models. It is very easy to
   learn through the rich `example
   codes <https://github.com/tensorlayer/awesome-tensorlayer>`__
   contributed by a wide community.
-  **Flexibility** : TensorLayer APIs are transparent: it does not
   mask TensorFlow from users; but leaving massive hooks that help
   *low-level tuning* and *deep customization*.
-  **Zero-cost Abstraction** : TensorLayer can achieve the *full
   power* of TensorFlow. The following table shows the training speeds
   of classic models using TensorLayer and native TensorFlow on a Titan
   X Pascal GPU.

   +---------------+-----------------+-----------------+-----------------+
   |               | CIFAR-10        | PTB LSTM        | Word2Vec        |
   +===============+=================+=================+=================+
   | TensorLayer   | 2528 images/s   | 18063 words/s   | 58167 words/s   |
   +---------------+-----------------+-----------------+-----------------+
   | TensorFlow    | 2530 images/s   | 18075 words/s   | 58181 words/s   |
   +---------------+-----------------+-----------------+-----------------+

TensorLayer stands at a unique spot in the library landscape. Other
wrapper libraries like Keras and TFLearn also provide high-level
abstractions. They, however, often hide the underlying engine from
users, which make them hard to customize and fine-tune. On the contrary,
TensorLayer APIs are generally flexible and transparent. Users often
find it easy to start with the examples and tutorials, and then dive
into TensorFlow seamlessly. In addition, TensorLayer does not create
library lock-in through native supports for importing components from
Keras, TFSlim and TFLearn.

TensorLayer has a fast growing usage among top researchers and
engineers, from universities like Imperial College London, UC Berkeley,
Carnegie Mellon University, Stanford University, and University of
Technology of Compiegne (UTC), and companies like Google, Microsoft,
Alibaba, Tencent, Xiaomi, and Bloomberg.

Install
=======

TensorLayer has pre-requisites including TensorFlow, numpy, and others. For GPU support, CUDA and cuDNN are required.
The simplest way to install TensorLayer is to use the Python Package Index (PyPI):

.. code:: bash

    # for last stable version
    pip install --upgrade tensorlayer3

    # for latest release candidate
    pip install --upgrade --pre tensorlayer3

    # if you want to install the additional dependencies, you can also run
    pip install --upgrade tensorlayer3[all]              # all additional dependencies
    pip install --upgrade tensorlayer3[extra]            # only the `extra` dependencies
    pip install --upgrade tensorlayer3[contrib_loggers]  # only the `contrib_loggers` dependencies

Alternatively, you can install the latest or development version by directly pulling from github:

.. code:: bash

    pip install https://github.com/tensorlayer/tensorlayer/archive/master.zip
    # or
    # pip install https://github.com/tensorlayer/tensorlayer/archive/<branch-name>.zip

Using Docker - a ready-to-use environment
-----------------------------------------

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/>`__:

Containers with CPU support
~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: bash

    # for CPU version and Python 2
    docker pull tensorlayer/tensorlayer:latest
    docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest

    # for CPU version and Python 3
    docker pull tensorlayer/tensorlayer:latest-py3
    docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-py3

Containers with GPU support
~~~~~~~~~~~~~~~~~~~~~~~~~~~

NVIDIA-Docker is required for these containers to work: `Project
Link <https://github.com/NVIDIA/nvidia-docker>`__

.. code:: bash

    # for GPU version and Python 2
    docker pull tensorlayer/tensorlayer:latest-gpu
    nvidia-docker run -it --rm -p 8888:88888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-gpu

    # for GPU version and Python 3
    docker pull tensorlayer/tensorlayer:latest-gpu-py3
    nvidia-docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-gpu-py3

Contribute
==========

Please read the `Contributor
Guideline <https://github.com/tensorlayer/tensorlayer/blob/master/CONTRIBUTING.md>`__
before submitting your PRs.

Cite
====

If you find this project useful, we would be grateful if you cite the
TensorLayer 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}
    }

License
=======

TensorLayer is released under the Apache 2.0 license.


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