Setup from scratch

In this section, we will illustrate how to set up a deep learning environment on an AWS EC2 GPU instance g2.2xlarge running Ubuntu Server 16.04 LTS. For this example, we will use a pre-baked Amazon Machine Image (AMI) which already has a number of software packages installed—making it easier to set up an end-end deep learning system. We will use a publicly available AMI Image ami-b03ffedf, which has following pre-installed packages:

• CUDA 8.0
• Anaconda 4.20 with Python 3.0
• Keras / Theano

1. The first step to setting up the system is to set up an AWS account and spin a new EC2 GPU instance using the AWS web console as (http://console.aws.amazon.com/) shown in figure Choose EC2 AMI:

1. We pick a g2.2xlarge instance type from the next page as shown in figure Choose instance type:

1. After adding a 30 GB of storage as shown in figure Choose storage, we now launch a cluster and assign an EC2 key pair that can allow us to ssh and log in to the box using the provided key pair file:

1. Once the EC2 box is launched, next step is to install relevant software packages. To ensure proper GPU utilization, it is important to ensure graphics drivers are installed first. We will upgrade and install NVIDIA drivers as follows:

$ sudo add-apt-repository ppa:graphics-drivers/ppa -y
$ sudo apt-get update
$ sudo apt-get install -y nvidia-375 nvidia-settings

While NVIDIA drivers ensure that host GPU can now be utilized by any deep learning application, it does not provide an easy interface to application developers for easy programming on the device.

Various different software libraries exist today that help achieve this task reliably. Open Computing Language (OpenCL) and CUDA are more commonly used in industry. In this book, we use CUDA as an application programming interface for accessing NVIDIA graphics drivers. To install CUDA driver, we first SSH into the EC2 instance and download CUDA 8.0 to our $HOME folder and install from there:

$ wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb
$ sudo dpkg -i cuda-repo-ubuntu1604-8-0-local_8.0.44-1_amd64-deb
$ sudo apt-get update
$ sudo apt-get install -y cuda nvidia-cuda-toolkit

Once the installation is finished, you can run the following command to validate the installation:

$ nvidia-smi

Now your EC2 box is fully configured to be used for a deep learning development. However, for someone who is not very familiar with deep learning implementation details, building a deep learning system from scratch can be a daunting task.

To ease this development, a number of advanced deep learning software frameworks exist, such as Keras and Theano. Both of these frameworks are based on a Python development environment, hence we first install a Python distribution on the box, such as Anaconda:

$ wget https://repo.continuum.io/archive/Anaconda3-4.2.0-Linux-x86_64.sh
$ bash Anaconda3-4.2.0-Linux-x86_64.sh

Finally, Keras and Theanos are installed using Python’s package manager pip:

$ pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
$ pip install keras

Once the pip installation is completed successfully, the box is now fully set up for a deep learning development.