Skip to main content

Reinforcement Learning and 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.

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 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, matplotlib and nltk (optional). 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 tensorlayer

# for latest release candidate
pip install --pre tensorlayer

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

pip install git+https://github.com/tensorlayer/tensorlayer.git

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.

Source Distribution

tensorlayer-1.9.0.tar.gz (181.2 kB view details)

Uploaded Source

Built Distributions

tensorlayer-1.9.0-py3-none-any.whl (245.1 kB view details)

Uploaded Python 3

tensorlayer-1.9.0-py2.py3-none-any.whl (245.1 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file tensorlayer-1.9.0.tar.gz.

File metadata

  • Download URL: tensorlayer-1.9.0.tar.gz
  • Upload date:
  • Size: 181.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for tensorlayer-1.9.0.tar.gz
Algorithm Hash digest
SHA256 6dee40208fa194a575de1a8895e8c832252294c82ba4baafc90da1ad01c9d541
MD5 1d331194ddf50620836150bece26eff1
BLAKE2b-256 d38b82d4c69e388418a1c9faa9b135eb218cb780eb664e9004eb45185aaa1f04

See more details on using hashes here.

Provenance

File details

Details for the file tensorlayer-1.9.0-py3-none-any.whl.

File metadata

File hashes

Hashes for tensorlayer-1.9.0-py3-none-any.whl
Algorithm Hash digest
SHA256 20a7913abac84db6f17679586de47eaeab49a527a76410929d925815458fc053
MD5 ca54865187a595fd404e3679cdddd46e
BLAKE2b-256 2f4edbea19020665fb0b33098716e692f6795ee2e6b793b14dab7a27f94265af

See more details on using hashes here.

Provenance

File details

Details for the file tensorlayer-1.9.0-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for tensorlayer-1.9.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 bd77202e87f6cf4993e5d1067e46beda201a476ccaf75a2a62d99e680c3e5a50
MD5 3f9d33381e5a310a0f4475ebc2eb9d2e
BLAKE2b-256 b6df23aa9609129db0f58c0a0275f68d6fecd1b4e015e3c6c504f26120a2fa31

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page