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You're reading from Hands-On Deep Learning with Go A practical guide to building and implementing neural network models using Go

Product type Paperback

Published in Aug 2019

Publisher Packt

ISBN-13 9781789340990

Length 242 pages

Edition 1st Edition

Languages

Tools

CUDA

Concepts

Deep Learning

Authors (2):

Darrell Chua

Gareth Seneque

View More author details

Table of Contents (15) Chapters

Preface

1. Section 1: Deep Learning in Go, Neural Networks, and How to Train Them

2. Introduction to Deep Learning in Go FREE CHAPTER

3. What Is a Neural Network and How Do I Train One?

4. Beyond Basic Neural Networks - Autoencoders and RBMs

5. CUDA - GPU-Accelerated Training

6. Section 2: Implementing Deep Neural Network Architectures

7. Next Word Prediction with Recurrent Neural Networks

8. Object Recognition with Convolutional Neural Networks

9. Maze Solving with Deep Q-Networks

10. Generative Models with Variational Autoencoders

11. Section 3: Pipeline, Deployment, and Beyond!

12. Building a Deep Learning Pipeline

13. Scaling Deployment

14. Other Books You May Enjoy

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Running a model on a K8s cluster

You can now edit the vars.sh file we created earlier and set the appropriate values using your favorite command-line text editor. You will also need to create the bucket where k8s stores cluster information.

Once you have done this, you can bring up your Kubernetes cluster:

source vars.sh
./cluster-up.sh

KOPS is now interacting with Kubernetes via kubectl to spin up the AWS resources that will run your cluster and then configure K8s itself on these same resources. You will need to verify that your cluster has been brought up successfully before proceeding:

kops validate cluster
Validating cluster hodlgo.k8s.local

INSTANCE GROUPS
NAME ROLE MACHINETYPE MIN MAX SUBNETS
master-ap-southeast-2a Master c4.large 1 1 ap-southeast-2
nodes Node t2.medium 2 2 ap-southeast-2

NODE STATUS
NAME ROLE READY
ip-172-20-35-114.ec2.internal node True
ip-172-20-49-22.ec2.internal...

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Authors (2)

Seneque

Gareth Seneque is a machine learning engineer with 11 years' experience of building and deploying systems at scale in the finance and media industries. He became interested in deep learning in 2014 and is currently building a search platform within his organization, using neuro-linguistic programming and other machine learning techniques to generate content metadata and drive recommendations. He has contributed to a number of open source projects, including CoREBench and Gorgonia. He also has extensive experience with modern DevOps practices, using AWS, Docker, and Kubernetes to effectively distribute the processing of machine learning workloads.

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Chua

Darrell Chua is a senior data scientist with more than 10 years' experience. He has developed models of varying complexity, from building credit scorecards with logistic regression to creating image classification models for trading cards. He has spent the majority of his time working with in fintech companies, trying to bring machine learning technologies into the world of finance. He has been programming in Go for several years and has been working on deep learning models for even longer. Among his achievements is the creation of numerous business intelligence and data science pipelines that enable the delivery of a top-of-the-line automated underwriting system, producing near-instant approval decisions.

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