Installation¶
TensorLayerX has some prerequisites that need to be installed first, including TensorFlow , MindSpore, PaddlePaddle, PyTorch, numpy and matplotlib. For GPU support CUDA and cuDNN are required.
If you run into any trouble, please check the TensorFlow installation instructions, MindSpore installation instructions, PaddlePaddle installation instructions, PyTorch installation instructions, which cover installing the TensorFlow for a range of operating systems including Mac OX, Linux and Windows, or ask for help on tensorlayer@gmail.com or FAQ.
Install Backend¶
TensorLayerX supports multiple deep learning backends, default TensorFlow as backend also supports MindSpore, PaddlePaddle and PyTorch.
pip3 install tensorflow-gpu==2.0.0-beta1 # specific version (YOU SHOULD INSTALL THIS ONE NOW)
pip3 install tensorflow-gpu # GPU version
pip3 install tensorflow # CPU version
The installation instructions of TensorFlow are written to be very detailed on TensorFlow website. However, there are something need to be considered. For example, TensorFlow officially supports GPU acceleration for Linux, Mac OX and Windows at present. For ARM processor architecture, you need to install TensorFlow from source.
If you want to use mindspore backend, you should install mindspore==1.6.1.
pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.1/MindSpore/gpu/x86_64/cuda-10.1/mindspore_gpu-1.6.1-cp37-cp37m-linux_x86_64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple
If you want to use paddlepaddle backend, you should install paddlepaddle>=2.1.1
python -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
If you want to use PyTorch backend, you should install PyTorch>=1.8.0
pip3 install torch==1.8.2+cu102 torchvision==0.9.2+cu102 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
Install TensorLayerX¶
For stable version:
pip3 install tensorlayerx
pip install tensorlayerx -i https://pypi.tuna.tsinghua.edu.cn/simple (faster in China)
For latest version, please install from github.
pip3 install git+https://github.com/tensorlayer/TensorLayerX.git
For developers, you should clone the folder to your local machine and put it along with your project scripts.
git clone https://github.com/tensorlayer/TensorLayerX.git
Alternatively, you can build from the source.
# First clone the repository and change the current directory to the newly cloned repository
git clone https://github.com/tensorlayer/TensorLayerX.git
cd tensorlayer
# Install virtualenv if necessary
sudo pip3 install virtualenv
# Then create a virtualenv called `venv`
virtualenv venv
# Activate the virtualenv
## Linux:
source venv/bin/activate
## Windows:
venv\Scripts\activate.bat
# basic installation
pip3 install .
# ============= IF TENSORFLOW IS NOT ALREADY INSTALLED ============= #
# for a machine **without** an NVIDIA GPU
pip3 install -e ".[all_cpu_dev]"
# for a machine **with** an NVIDIA GPU
pip3 install -e ".[all_gpu_dev]"
If you want install TensorLayer 2.X, It does not support multiple backends.
[stable version] pip3 install tensorlayer==2.x.x
If you want install TensorLayer 1.X, the simplest way to install TensorLayer 1.X is as follow. It will also install the numpy and matplotlib automatically.
[stable version] pip3 install tensorlayer==1.x.x
However, if you want to modify or extend TensorLayer 1.X, you can download the repository from Github and install it as follow.
cd to the root of the git tree
pip3 install -e .
This command will run the setup.py
to install TensorLayerX. The -e
reflects
editable, then you can edit the source code in tensorlayer
folder, and import
the edited
TensorLayerX.
GPU support¶
Thanks to NVIDIA supports, training a fully connected network on a GPU, which may be 10 to 20 times faster than training them on a CPU. For convolutional network, may have 50 times faster. This requires an NVIDIA GPU with CUDA and cuDNN support.
CUDA¶
The TensorFlow website also teach how to install the CUDA and cuDNN, please see TensorFlow GPU Support.
Download and install the latest CUDA is available from NVIDIA website:
If CUDA is set up correctly, the following command should print some GPU information on the terminal:
python -c "import tensorflow"
cuDNN¶
Apart from CUDA, NVIDIA also provides a library for common neural network operations that especially speeds up Convolutional Neural Networks (CNNs). Again, it can be obtained from NVIDIA after registering as a developer (it take a while):
Download and install the latest cuDNN is available from NVIDIA website:
To install it, copy the *.h
files to /usr/local/cuda/include
and the
lib*
files to /usr/local/cuda/lib64
.
Windows User¶
TensorLayer is built on the top of Python-version TensorFlow, so please install Python first. Note:We highly recommend installing Anaconda. The lowest version requirements of Python is py36.
GPU support¶
Thanks to NVIDIA supports, training a fully connected network on a GPU, which may be 10 to 20 times faster than training them on a CPU. For convolutional network, may have 50 times faster. This requires an NVIDIA GPU with CUDA and cuDNN support.
1. Installing Microsoft Visual Studio¶
You should preinstall Microsoft Visual Studio (VS) before installing CUDA. The lowest version requirements is VS2010. We recommend installing VS2015 or VS2013. CUDA7.5 supports VS2010, VS2012 and VS2013. CUDA8.0 also supports VS2015.
2. Installing CUDA¶
Download and install the latest CUDA is available from NVIDIA website:
We do not recommend modifying the default installation directory.
3. Installing cuDNN¶
The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. Download and extract the latest cuDNN is available from NVIDIA website:
After extracting cuDNN, you will get three folders (bin, lib, include). Then these folders should be copied to CUDA installation. (The default installation directory is C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0)