TensorLayer has some prerequisites that need to be installed first, including TensorFlow , numpy and matplotlib. For GPU support CUDA and cuDNN are required.
If you run into any trouble, please check the TensorFlow installation instructions which cover installing the TensorFlow for a range of operating systems including Mac OX, Linux and Windows, or ask for help on firstname.lastname@example.org or FQA.
Step 1 : Install dependencies¶
TensorLayer is build on the top of Python-version TensorFlow, so please install Python first.
We highly recommend python3 instead of python2 for the sake of future.
pip command for installing additional modules is recommended.
Besides, a virtual environment
virtualenv can help you to manage python packages.
Take Python3 on Ubuntu for example, to install Python includes
pip, run the following commands:
sudo apt-get install python3 sudo apt-get install python3-pip sudo pip3 install virtualenv
To build a virtual environment and install dependencies into it, run the following commands: (You can also skip to Step 3, automatically install the prerequisites by TensorLayer)
virtualenv env env/bin/pip install matplotlib env/bin/pip install numpy env/bin/pip install scipy env/bin/pip install scikit-image
To check the installed packages, run the following command:
After that, you can run python script by using the virtual python as follow.
Step 2 : TensorFlow¶
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.
Step 3 : TensorLayer¶
The simplest way to install TensorLayer is as follow, it will also install the numpy and matplotlib automatically.
[stable version] pip install tensorlayer [master version] pip install git+https://github.com/zsdonghao/tensorlayer.git
However, if you want to modify or extend TensorLayer, you can download the repository from Github and install it as follow.
cd to the root of the git tree pip install -e .
This command will run the
setup.py to install TensorLayer. The
editable, then you can edit the source code in
tensorlayer folder, and
import the edited
Step 4 : 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.
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"
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