Building a common DL environment

Our main goal to achieve by the end of this chapter is to standardize the toolsets to work together and achieve consistently accurate results.

In the process of building applications using DL algorithms that can also scale for production, it's very important to have the right kind of setup, whether local or on the cloud, to make things work end to end. So, in this chapter, we will learn how to set up a DL environment that we will be using to run all the experiments and finally take the AI models into production.

First, we will discuss the major components required to code, build, and deploy the DL models, then various ways to do this, and finally, look at a few code snippets that will help to automate the whole process.

The following is the list of required components that we need to build DL applications:

• Ubuntu 16.04 or greater
• Anaconda Package
• Python 2.x/3.x
• TensorFlow/Keras DL packages
• CUDA for GPU support
• Gunicorn for deployment at scale

Get focused and into the code!

We'll start by setting up your local DL environment. Much of the work that you'll do can be done on local machines. But with large datasets and complex model architectures, processing time slows down dramatically. This is why we are also setting up a DL environment in the cloud, because the processing time for these complex and repetitive calculations just becomes too long to be able to efficiently get things done otherwise.

We will work straight through the preceding list, and by the end (and with the help of a bit of automated script), you'll have everything set up!