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 the first Ray Tune HPO experiment with MLflow

Now that we have set up Ray Tune, MLflow, and created the HPO run function, we can try to run our first Ray Tune HPO experiment, as follows:

python pipeline/hpo_finetuning_model.py

After a couple of seconds, you will see the following screen, Figure 6.2, which shows that all 10 trials (that is, the values that we set for num_samples) are running concurrently:

Figure 6.2 – Ray Tune running 10 trials in parallel on a local multi-core laptop

After approximately 12–14 mins, you will see that all the trials have finished and the best hyperparameters will be printed out on the screen, as shown in the following (your results might vary due to the stochastic nature, the limited number of samples, and the use of grid search, which does not guarantee a global optimal):

Best hyperparameters found were: {'lr': 0.025639008922511797, 'batch_size': 64, 'foundation_model&apos...

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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 the first Ray Tune HPO experiment with MLflow

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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 the first Ray Tune HPO experiment with MLflow

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