You must also have the 375 (or later) NVidia drivers installed, this can easily be done from Ubuntu’s built in additional drivers (press windows key and search additional drivers) after you update your driver packages by: $ sudo add-apt-repository ppa:graphics-drivers/ppa Skip if not installing with GPU support Update & Install Nvidia Drivers $ (ml) $ conda install opencv Install Nvidia Toolkit 8.0 & CudNN 6.0 OpenCV is a popular computer vision package and installing with Anaconda is a breeze. (ml) $ conda install pip six libgcc swig pyopengl Install OpenCV We will install pip into our conda environment but the general rule is to always try installing a package with conda first, if that is not possible, then use pip. We will need to build additional pylons, I mean packages. $ conda create -name ml python=3.5 anacondaĪnd activate the environment $ source activate ml Now the best thing to do is to create a new isolated environment to manage package versions so that you don’t have to reinstall Anaconda if you flub your python packages. You will have to open up a new terminal to use Anaconda. $ gedit ~/.bashrcĪnd copy and paste this in the bottom export PATH="/home/ $USER/anaconda3/bin:$PATH" When Anaconda asks if you would wish prepend the Anaconda install location to your bash type in ‘yes’, but if you accidentally defaulted no by pressing enter you can. $ wget Īnd install by: $ bash Anaconda3-4.2.0-Linux-x86_64.sh I would not recommend using Python 3.6 at this time.Īnaconda Python 3.5 is probably the most common version used in python development so lets gets started by installing that. Paste each line one at a time (without the $) using Shift + Ctrl + V $ sudo apt-get install git python-dev python3-dev build-essential swig libcurl3-dev libcupti-dev golang libjpeg-turbo8-dev make tmux htop cmake zlib1g-dev libjpeg-dev xvfb libav-tools xorg-dev python-opengl libboost-all-dev libsdl2-dev apt-transport-https ca-certificates curl software-properties-common openjdk-8-jdk coreutils mercurial libav-tools libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev libsmpeg-dev libsdl1.2-dev libportmidi-dev libswscale-dev libavformat-dev libavcodec-dev libtiff5-dev libx11-6 libx11-dev fluid-soundfont-gm timgm6mb-soundfont xfonts-base xfonts-100dpi xfonts-75dpi xfonts-cyrillic fontconfig fonts-freefont-ttf 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. Anaconda just makes managing and installing your packages so much easier. Ananconda also ensures that all packages installed in a environment are optimized for performance and will manage package versions to avoid dependency conflicts. It took me a while to convert, but now Anaconda is my go to for anything Python related. This will also include building the latest version of TensorFlow from sources. This tutorial is mainly based on doing reinforcement learning and includes how to install alot of OpenAI’s software. In this tutorial I will be going through how to install various software for machine learning development using Anaconda Python and Ubuntu 16.04. I have decided to move my blog to my github page, this post will no longer be updated here.
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