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

Creating HPO-ready DL models with Ray Tune and MLflow

To use Ray Tune with MLflow for HPO, let's use the fine-tuning step in our DL pipeline example from Chapter 5, Running DL Pipelines in Different Environments, to see what needs to be set up and what code changes we need to make. Before we start, first, let's review a few key concepts that are specifically relevant to our usage of Ray Tune:

Objective function: An objective function can be either to minimize or maximize some metric values for a given configuration of hyperparameters. For example, in the DL model training and fine-tuning scenarios, we would like to maximize the F1-score for the accuracy of an NLP text classifier. This objective function needs to be wrapped as a trainable function, where Ray Tune can do HPO. In the following section, we will illustrate how to wrap our NLP text sentiment model.
Function-based APIs and class-based APIs: A function-based API allows a user to insert Ray Tune statements...

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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

Creating HPO-ready DL models with Ray Tune and MLflow

Authors (1)

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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

Creating HPO-ready DL models with Ray Tune and MLflow

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