Machine Learning is not anymore a technology of the future, it’s a present-day miracle. We are using benefits of machine learning every now and then, maybe knowingly or maybe unknowingly. It has become a part of our daily life.

Today we are going to demonstrate a basic setup which will be required to run a machine learning application on your system.

Hardware Specification:

  • Processor: AMD Ryzen 1700X
  • Motherboard: Gigabyte
  • RAM: Corsair Vengeance LPX 16GB DDR4 2400MHz
  • GPU: nVidia GeForce GTX 1070 Ti
  • Hard Disk: 128GB SSD (PCIe)
  • SMPS: Coolermaster 650W
  • Operating System: Ubuntu 18.04 LTS 64-bit

In order to have a running setup for the machine learning & data science application, please follow the steps mentioned below.

  • Install latest Graphics driver
    $ sudo add-apt-repository ppa:graphics-drivers
    $ sudo apt update
    $ sudo apt upgrade -y
    $ sudo apt install nvidia-396 -y
  • Install CUDA 9.0 libraries
    $ sudo apt install build-essential
    $ wget
    $ sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
    $ sudo apt-key adv --fetch-keys
    $ sudo apt update
    $ sudo apt install cuda-9-0
  • Install nVidia cuDNN for CUDA 9.0
    cuDNN is a proprietary package from NVIDIA, which provides support for GPU accelerated libraries for deep neural networks. In order to install cuDNN, you have to signup at and download the required package for CUDA 9.0 from here. Once the package is downloaded use following command to install it:

    $ sudo dkpg -i <<package-name>>.deb
  • Create basic development environment
    $ sudo apt install python-pip python-virtualenv
    $ virtualenv venv
    $ source venv/bin/active
    $ pip install tensorflow-gpu keras

This should give us a decent setup to start with a GPU accelerated machine learning application built on top of Tensorflow and Keras. Please feel free to get back to us with any queries or issues.