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!

Integrating our CNN

We will now take our CNN example from an earlier chapter and make some updates that are necessary to package and deploy the network using data supplied by Pachyderm.

Creating a Docker image of our CNN

Pachyderm data pipelines are dependent on prebaked Docker images. The internet is full of Docker tutorials, so we'll keep things simple here and discuss what we need to do to take advantage of the simple deployment steps for any Go application.

Let's take a look at our Dockerfile:

FROM golang:1.12

ADD main.go /main.go

ADD cifar/ /cifar/

RUN export GOPATH=$HOME/go && cd / && go get -d -v .

And that's it! We're simply fetching the Go 1.12 image from Docker Hub and dropping our...