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

Running HPO with Ray Tune using Optuna and HyperBand

Now, let's do some experiments with different search algorithms and schedulers. Given that Optuna is such a great TPE-based search algorithm, and ASHA is a great scheduler that does asynchronous parallel trials with early termination of the unpromising ones, it would be interesting to see how many changes we need to do to make this work.

It turns out the change is very minimal based on what we have already done in the previous section. Here, we will illustrate the four main changes:

Install the Optuna package. This can be done by running the following command:
```
pip install optuna==2.10.0
```

This will install Optuna in the same virtual environment that we had before. If you have already run pip install -r requirements.text, then Optuna has already been installed and you can skip this step.

Import the relevant Ray Tune modules that integrate with Optuna and the ASHA scheduler (here, we use the HyperBand implementation...

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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

Running HPO with Ray Tune using Optuna and HyperBand

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

Running HPO with Ray Tune using Optuna and HyperBand

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