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MLOps with Red Hat OpenShift

You're reading from   MLOps with Red Hat OpenShift A cloud-native approach to machine learning operations

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781805120230
Length 238 pages
Edition 1st Edition
Tools
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Authors (2):
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Ross Brigoli Ross Brigoli
Author Profile Icon Ross Brigoli
Ross Brigoli
Faisal Masood Faisal Masood
Author Profile Icon Faisal Masood
Faisal Masood
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Toc

Table of Contents (13) Chapters Close

Preface 1. Part 1: Introduction FREE CHAPTER
2. Chapter 1: Introduction to MLOps and OpenShift 3. Part 2: Provisioning and Configuration
4. Chapter 2: Provisioning an MLOps Platform in the Cloud 5. Chapter 3: Building Machine Learning Models with OpenShift 6. Part 3: Operating ML Workloads
7. Chapter 4: Managing a Model Training Workflow 8. Chapter 5: Deploying ML Models as a Service 9. Chapter 6: Operating ML Workloads 10. Chapter 7: Building a Face Detector Using the Red Hat ML Platform 11. Index 12. Other Books You May Enjoy

Enabling GPU support

First, you need to start provisioning nodes with a GPU. Like MinIO, this is a one-time activity that will be executed by the platform engineering team. The entire process of enabling the GPU can be automated for your OpenShift clusters. Let’s learn how to provision the machines with a GPU in our cluster.

OpenShift enables you to use machine sets to provision nodes – nodes where your container images would run. To enable GPU support for the Jupyter environment that you created earlier, you need to provision nodes with a GPU. Once the nodes with the GPU have been provisioned, RHODS will automatically detect them and allow you to use the Jupyter environment with GPU support.

For the ROSA cluster, you can use the Red Hat cloud console to provision new machines. OpenShift can scale out machines so that it provisions the hardware as needed. You can also choose to use the machine on spot instances to further reduce your bill. Let’s see how to...

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