Book Image

Hands-On Deep Learning with Go

By : Gareth Seneque, Darrell Chua
Book Image

Hands-On Deep Learning with Go

By: Gareth Seneque, Darrell Chua

Overview of this book

Go is an open source programming language designed by Google for handling large-scale projects efficiently. The Go ecosystem comprises some really powerful deep learning tools such as DQN and CUDA. With this book, you'll be able to use these tools to train and deploy scalable deep learning models from scratch. This deep learning book begins by introducing you to a variety of tools and libraries available in Go. It then takes you through building neural networks, including activation functions and the learning algorithms that make neural networks tick. In addition to this, you'll learn how to build advanced architectures such as autoencoders, restricted Boltzmann machines (RBMs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. You'll also understand how you can scale model deployments on the AWS cloud infrastructure for training and inference. By the end of this book, you'll have mastered the art of building, training, and deploying deep learning models in Go to solve real-world problems.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: Deep Learning in Go, Neural Networks, and How to Train Them
6
Section 2: Implementing Deep Neural Network Architectures
11
Section 3: Pipeline, Deployment, and Beyond!

Building deployment templates

We will now put together the various templates required to deploy and train our model at scale. These templates include:

  • AWS cloud formation templates: Virtual instances and related resources
  • Kubernetes or KOPS configuration: K8s cluster management
  • Docker templates or Makefile: Create images to deploy on our K8s cluster

We are choosing a particular path here. AWS has services such as Elastic Container Service (ECS) and Elastic Kubernetes Service (EKS) that are accessible via simple API calls. Our purpose here is to engage with the nitty-gritty details, so that you can make informed choices about how to scale the deployment of your own use case. For now, you have greater control over container options and how processing is distributed, as well as how your model is called when deploying containers to a vanilla EC2 instance. These services are also...