You're reading from Practical Deep Learning at Scale with MLflow Bridge the gap between offline experimentation and online production

Product type Paperback

Published in Jul 2022

Publisher Packt

ISBN-13 9781803241333

Length 288 pages

Edition 1st Edition

Tools

MLflow

Concepts

Deep Learning

Author (1):

Yong Liu

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Table of Contents (17) Chapters

Preface

1. Section 1 - Deep Learning Challenges and MLflow Prime

2. Chapter 1: Deep Learning Life Cycle and MLOps Challenges FREE CHAPTER

3. Chapter 2: Getting Started with MLflow for Deep Learning

4. Section 2 – Tracking a Deep Learning Pipeline at Scale

5. Chapter 3: Tracking Models, Parameters, and Metrics

6. Chapter 4: Tracking Code and Data Versioning

7. Section 3 – Running Deep Learning Pipelines at Scale

8. Chapter 5: Running DL Pipelines in Different Environments

9. Chapter 6: Running Hyperparameter Tuning at Scale

10. Section 4 – Deploying a Deep Learning Pipeline at Scale

11. Chapter 7: Multi-Step Deep Learning Inference Pipeline

12. Chapter 8: Deploying a DL Inference Pipeline at Scale

13. Section 5 – Deep Learning Model Explainability at Scale

14. Chapter 9: Fundamentals of Deep Learning Explainability

15. Chapter 10: Implementing DL Explainability with MLflow

16. Other Books You May Enjoy

Chapter 5: Running DL Pipelines in Different Environments

It is critical to have the flexibility of running a deep learning (DL) pipeline in different execution environments such as local or remote, on-premises, or in the cloud. This is because, during different stages of the DL development, there may be different constraints or preferences to either improve the velocity of the development or ensure security compliance. For example, it is desirable to do small-scale model experimentation in a local or laptop environment, while for a full hyperparameter tuning, we need to run the model on a cloud-hosted GPU cluster to get a quick turn-around time. Given the diverse execution environments in both hardware and software configurations, it used to be a challenge to achieve this kind of flexibility within a single framework. MLflow provides an easy-to-use framework to run DL pipelines at scale in different environments. We will learn how to do that in this chapter.

In this chapter, we...

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You're reading from Practical Deep Learning at Scale with MLflow Bridge the gap between offline experimentation and online production

Table of Contents (17) Chapters

Chapter 5: Running DL Pipelines in Different Environments

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You're reading from Practical Deep Learning at Scale with MLflow Bridge the gap between offline experimentation and online production

Table of Contents (17) Chapters

Chapter 5: Running DL Pipelines in Different Environments

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