run file from Nvidia website ( download the. Scp -pr dir_name install the Nvidia Toolkit download base installation. refer to the following scp command)įile Transfer: getting files to/from your Ubuntu server (If you need to use command line to transfer files from your clienet computer to your server. How to uninstall CUDA Toolkit and cuDNN under Linux?() ( pdf)) ( Note: If you have older version of CUDA and cuDNN installed, check the post for uninstallation. You can use the following command to get various diagnostics of the GTX 1080. 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. You can use the command below to reboot the server from command line. Once installed the driver restart your computer. ( Note: use the following command if you encounter this error “ sudo: add-apt-repository: command not found”) $ sudo apt-get install software-properties-common (you can check the latest drivers version according to your GPU info from The NVIDIA downloads page, for example, mine is 375.) $ 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. You can reconnect your monitor after you successfully install the NVIDIA drivers. Otherwise, it may cause trouble when you reboot your server after you install your NVIDIA drivers. Note that if you have a monitor connected to your server, be sure to disconnect it before you start to install the NVIDIA drivers. 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 build-essential python-pip python3-pip python-virtualenv swig python-wheel libcurl3-dev (Because it is Ubuntu Server 16.04, need to install those required packages below, if you are on Ubuntu Desktop 16.04, most of the libraries below already come with the OS installation.) Open a terminal by pressing Ctrl + Alt + T. (Check this post for commonly used Linux commands.) 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. TensorFlow now supports using Cuda 8.0 & CuDNN 5.1 so you can use the pip’s from their website for a much easier install. In this tutorial I will be going through the process of building the latest TensorFlow from sources for Ubuntu Server 16.04. I installed GPU TensorFlow from source on Ubuntu Server 16.04 LTS with CUDA 8 and a GeForce GTX 1080 GPU, but it should work for Ubuntu Desktop 16.04 LTS.
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