TensorLayerX is a deep learning library designed for researchers and engineers that is compatible with multiple deep learning frameworks such as TensorFlow, MindSpore and PaddlePaddle, allowing users to run the code on different hardware like Nvidia-GPU and Huawei-Ascend. It provides popular DL and RL modules that can be easily customized and assembled for tackling real-world machine learning problems. More details can be found here. TensorLayerX will support TensorFlow, MindSpore, PaddlePaddle, and PyTorch backends in the future.

Install

TensorLayerX has some prerequisites that need to be installed first, including TensorFlow , MindSpore, PaddlePaddle,numpy and matplotlib.For GPU support CUDA and cuDNN are required.

# for last stable version
pip install --upgrade tensorlayerX

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

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

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

pip3 install git+https://github.com/tensorlayer/TensorLayerX.git

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

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

TensorLayerX is released under the Apache 2.0 license.