Skip to main content

High Level Tensorflow Deep Learning Library for Researcher and Engineer.

Project description

TENSORLAYER-LOGO

Awesome Documentation-EN Documentation-CN Book-CN Downloads

PyPI PyPI-Prerelease Commits-Since Python TensorFlow

Travis Docker RTD-EN RTD-CN PyUP Docker-Pulls Code-Quality

JOIN-SLACK-LOGO

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides a large collection of customizable neural layers / functions that are key to build real-world AI applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society.

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](https://github.com/tensorlayer/awesome-tensorlayer).
  • 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.

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):

# for last stable version
pip install --upgrade tensorlayer

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

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

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

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 are built on top of the official TensorFlow containers:

Containers with CPU support

# 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

# 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 before submitting your PRs.

Cite

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

License

TensorLayer is released under the Apache 2.0 license.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for tensorlayer, version 2.2.3
Filename, size File type Python version Upload date Hashes
Filename, size tensorlayer-2.2.3-py2.py3-none-any.whl (363.3 kB) File type Wheel Python version py2.py3 Upload date Hashes View
Filename, size tensorlayer-2.2.3-py3-none-any.whl (363.3 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size tensorlayer-2.2.3.tar.gz (258.3 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page