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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.
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:
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.
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
The TensorLayer containers are built on top of the official TensorFlow containers:
# 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
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
Please read the Contributor Guideline before submitting your PRs.
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}
}
TensorLayer is released under the Apache 2.0 license.
TensorLayerX是一款兼容多深度学习框架后端的深度学习库, 可以使用TensorFlow、MindSpore、PaddlePaddle、PyTorch作为后端计算引擎进行模型训练、推理。
Python other
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