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.
- 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 http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb $ sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb $ sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub $ 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 https://developer.nvidia.com/cudnn 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.