#NVIDIA CUDA TOOLKIT 9.1 HOW TO#
#NVIDIA CUDA TOOLKIT 9.1 INSTALL#
#NVIDIA CUDA TOOLKIT 9.1 UPDATE#
#NVIDIA CUDA TOOLKIT 9.1 DRIVER#

Verifying cuDNN installation: cp -r /usr/src/cudnn_samples_v7/ $HOME Goto downloaded folder and in terminal perform following: sudo dpkg -i libcudnn7_7.0.5.15-1+cuda9.1_b

#NVIDIA CUDA TOOLKIT 9.1 INSTALL#

Step 9: Install cuDNN 7.0.5:ĬuDNN v7.0.5 Runtime Library for Ubuntu16.04 (Deb)ĬuDNN v7.0.5 Developer Library for Ubuntu16.04 (Deb)ĬuDNN v7.0.5 Code Samples and User Guide for Ubuntu16.04 (Deb) Comment your linux kernel version noted in step 5. (not likely) If you got nvidia-smi is not found then you have unsupported linux kernel installed.

#NVIDIA CUDA TOOLKIT 9.1 DRIVER#

In the end of the file, add: export PATH=/usr/local/cuda-9.1/bin$Ĭtrl+x then y to save and exit source ~/.bashrcĬheck driver version probably Driver Version: 387.26 Step 8: Go to terminal and type: nano ~/.bashrc Sudo apt-get install cuda Step 7: Reboot the system to load the NVIDIA drivers. Installation Instructions: sudo apt-key adv -fetch-keys I highly recommend network installer to get updated gpu driver supported by your linux kernel. Go to and download Installer for Linux Ubuntu 16.04 x86_64 deb.

To install linux header supported by your linux kernel do following: sudo apt-get install linux-headers-$(uname -r) Step 6: Download the NVIDIA CUDA Toolkit: Sudo apt-get install python2.7-dev python3.5-dev pylint Step 5: Install linux kernel header: Required to compile from source: sudo apt-get install build-essential The x86_64 line indicates you are running on a 64-bit system which is supported by cuda 9.1 Step 4: Install Dependencies: To determine which distribution and release number you’re running, type the following at the command line: uname -m & cat /etc/*release GeForce 840M 5.0 Step 3: Verify You Have a Supported Version of Linux: If your graphics card is from NVIDIA then goto and verify if listed in CUDA enabled gpu list.

#NVIDIA CUDA TOOLKIT 9.1 UPDATE#

If you do not see any settings, update the PCI hardware database that Linux maintains by entering update-pciids (generally found in /sbin) at the command line and rerun the previous lspci command. Sudo apt-get upgrade Step 2: Verify You Have a CUDA-Capable GPU: lspci | grep -i nvidia Step 1: Update and Upgrade your system: sudo apt-get update There must be 64-bit python installed tensorflow does not work on 32-bit python installation. So, I recommend doing a fresh install of Ubuntu if you don’t have Ubuntu before starting with the tutorial. While it is technically possible to install tensorflow GPU version in a virtual machine, you cannot access the full power of your GPU via a virtual machine. In order to use the GPU version of TensorFlow, you will need an NVIDIA GPU with a compute capability > 3.0. It has both the CPU as well as GPU version available and although the CPU version works quite well, realistically, if you are going for deep learning, you will need GPU. Tensorflow is an open source software library developed and used by Google that is fairly common among students, researchers, and developers for deep learning applications such as neural networks. Just specify the correct URL for version 1.5.0 in step 11 in place of v1.4.1. Update: TensorFlow 1.5.0 has been officially released and the same process works for Tensorflow 1.5.0 as well.

#NVIDIA CUDA TOOLKIT 9.1 HOW TO#

If you want to use the official pre-built pip package instead, I recommend another post, How to install Tensorflow 1.5.0 using official pip package. At the time of writing this blog post, the latest version of tensorflow is 1.4.1.This tutorial is for building tensorflow from source.

We will also be installing CUDA Toolkit 9.1 and cuDNN 7.0.5 along with the GPU version of tensorflow 1.4.1. This is going to be a tutorial on how to install tensorflow 1.4.1 GPU version.

Nvidia cuda toolkit 9.1