Local GPU

Windows

This article describes how to detect whether your graphics card uses an NVIDIA® GPU:

http://nvidia.custhelp.com/app/answers/detail/a_id/2040/~/identifying-the-graphics-card-model-and-device-id-in-a-pc

Once you’ve confirmed that you have an NVIDIA® GPU, the following article describes how to install required software components including the CUDA Toolkit v9.0, required NVIDIA® drivers, and cuDNN v7.0:

https://www.tensorflow.org/install/install_windows#requirements_to_run_tensorflow_with_gpu_support

Note that the documentation on installation of the last component (cuDNN v7.0) is a bit sparse. Once you join the NVIDIA® developer program and download the zip file containing cuDNN you need to extract the zip file and add the location where you extracted it to your system PATH.

Ubuntu

This article describes how to install required software components including the CUDA Toolkit v9.0, required NVIDIA® drivers, and cuDNN v7.0:

https://www.tensorflow.org/install/install_linux#nvidia_requirements_to_run_tensorflow_with_gpu_support

The specifics of installing required software differ by Linux version so please review the NVIDIA® documentation carefully to ensure you install everything correctly.

The following section provides as example of the installation commands you might use on Ubuntu 16.04.

Ubuntu 16.04 Example

First, install the CUDA Toolkit v9.0:

sudo apt-get install linux-headers-$(uname -r)
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt-get update
sudo apt-get install sudo apt-get install cuda-9-0

Next, install cuDNN v7.0. You can download cuDNN v7.0 from here: https://developer.nvidia.com/cudnn (note that you will need to sign up for the NVIDIA® developer program and perform the download from within an authenticated web browser session).

You should download the Debian package (.deb) for Ubuntu 16.04 cuDNN 7.0 Runtime LIbrary for CUDA 9.0 (the correct download link is underlined):

Note that it’s important to download cuDNN v7.0 for CUDA 9.0 (rather than CUDA 9.1, which may be the choice initially presented) as that is what TensorFlow v1.5 is built against.

You can then install cuDNN v7.0 via the following command

sudo dpkg -i libcudnn7_7.0.5.15-1+cuda9.0_amd64.deb

Finally, install the NVIDIA CUDA Profile Tools:

sudo apt-get install libcupti-dev

export CUDA_HOME=/usr/local/cuda
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${CUDA_HOME}/lib64 
PATH=${CUDA_HOME}/bin:${PATH} 
export PATH

rsession-ld-library-path=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64

Sys.setenv(CUDA_HOME="/usr/local/cuda")
Sys.setenv(PATH=paste(Sys.getenv("PATH"), "/usr/local/cuda/bin", sep = ":"))

Mac OS X

As of version 1.2 of TensorFlow, GPU support is no longer available on Mac OS X. If you want to use a GPU on Mac OS X you will need to install TensorFlow v1.1 as follows:

library(tensorflow)
install_tensorflow(version = "1.1-gpu")

However, before you install you should ensure that you have an NVIDIA® GPU and that you have the required CUDA libraries on your system.

While some older Macs include NVIDIA® GPU’s, most Macs (especially newer ones) do not, so you should check the type of graphics card you have in your Mac before proceeding.

Here is a list of Mac systems which include built in NVIDIA GPU’s:

https://support.apple.com/en-us/HT204349

You can check which graphics card your Mac has via the System Report button found within the About This Mac dialog:

The MacBook Pro system displayed above does not have an NVIDIA® GPU installed (rather it has an Intel Iris Pro).

If you do have an NVIDIA® GPU, the following article describes how to install the base CUDA libraries:

http://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/index.html

You also need to intall the cuDNN library 5.1 library for OS X from here:

https://developer.nvidia.com/cudnn

After installing these components, you need to ensure that both CUDA and cuDNN are available to your R session via the DYLD_LIBRARY_PATH. This typically involves setting environment variables in your .bash_profile as described in the NVIDIA documentation for CUDA and cuDNN.

Note that environment variables set in .bash_profile will not be available by default to OS X desktop applications like R GUI and RStudio. To use CUDA within those environments you should start the application from a system terminal as follows:

open -a R         # R GUI
open -a RStudio   # RStudio

Single User

In a single-user desktop environment you can install TensorFlow with GPU support via:

library(tensorflow)
install_tensorflow(version = "gpu")

If this version doesn’t load successfully you should review the prerequisites above and ensure that you’ve provided definitions of CUDA environment variables as recommended above.

See the main installation article for details on other available options (e.g. virtualenv vs. conda installation, installing development versions, etc.).

Multiple Users

In a multi-user server environment you may want to install a system-wide version of TensorFlow with GPU support so all users can share the same configuration. To do this, start by following the directions for native pip installation of the GPU version of TensorFlow here:

https://www.tensorflow.org/install/install_linux#InstallingNativePip

There are some components of TensorFlow (e.g. the Keras library) which have dependencies on additional Python packages.

You can install Keras and it’s optional dependencies with the following command (ensuring you have the correct privilege to write to system library locations as required via sudo, etc.):

pip install keras h5py pyyaml requests Pillow scipy

If you have any trouble with locating the system-wide version of TensorFlow from within R please see the section on locating TensorFlow.