Use defaults by pressing enter for all except: $ cd ~/tensorflowĬonfigure TensorFlow Installation $ cd ~/tensorflow Unless you want absolute bleeding edge I highly recommend checking-out to the latest branch rather than master. $ sudo apt-get upgrade bazel Clone Tensorflow $ cd ~

Instructions also on Bazel website $ echo 'deb stable jdk1.8' | sudo tee /etc/apt//bazel.list bashrc file: $ source ~/.bashrc Install Bazel Once you save and close the text file you can return to your original terminal and type this command to reload your. This will open your bash file in a text editor which you will scroll to the bottom and add these lines: export LD_LIBRARY_PATH='$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64' $ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* $ sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 $ sudo cp cuda/include/cudnn.h /usr/local/cuda/include To install CudNN download cudNN v6.0 for Cuda 8.0 from Nvidia website and extract into /usr/local/cuda via: $ tar -xzvf cudnn-8.0-linux-圆4-v6.0.tgz This will install cuda into: /usr/local/cuda MAKE SURE YOU SAY NO TO INSTALLING NVIDIA DRIVERS! Also make sure you select yes to creating a symbolic link to your cuda directory. To install the Nvidia Toolkit download base installation. If you experience any troubles booting linux or logging in: try disabling fast & safe boot in your bios and modifying your grub boot options to enable nomodeset. Once installed using additional drivers restart your computer. $ sudo add-apt-repository ppa:graphics-drivers/ppa You must also have the 367 (or later) NVidia drivers installed, this can easily be done from Ubuntu’s built in additional drivers after you update your driver packages. Paste each line one at a time (without the $) using Shift + Ctrl + V $ sudo apt-get install openjdk-8-jdk git python-dev python3-dev python-numpy python3-numpy python-six python3-six build-essential python-pip python3-pip python-virtualenv swig python-wheel python3-wheel libcurl3-dev libcupti-dev Update & Install Nvidia Drivers Open a terminal by pressing Ctrl + Alt + T Getting started I am going to assume you know some of the basics of using a terminal in Linux.

In order to use TensorFlow with GPU support you must have a Nvidia graphic card with a minimum compute capability of 3.0. If you prefer to build from sources using Ubuntu 14.04 please see my other tutorial. I much prefer to install Tensorflow using Anaconda Python and you can find a tutorial for that here. In this tutorial I will be going through the process of building the latest TensorFlow from sources for Ubuntu 16.04. I have decided to move my blog to my github page, this post will no longer be updated here.

Ubuntu 16.04 install cuda 9.2