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 remote code in GitHub locally

Now, let's see how we run remote code from a GitHub repository on a local execution environment. This allows us to precisely run a specific version that has been checked into the GitHub repository using the commit hash. Let's use the same example as before by running a single download_data step of the DL pipeline that we have been using in this chapter. In the command line prompt, run the following command:

mlflow run https://github.com/PacktPublishing/Practical-Deep-Learning-at-Scale-with-MLFlow#chapter05 -v 26119e984e52dadd04b99e6f7e95f8dda8b59238  --experiment-name='dl_model_chapter05' -P pipeline_steps='download_data'

Notice the difference between this command line and the one in the previous section. Instead of a dot to refer to a local copy of the code, we are pointing to a remote GitHub repository (https://github.com/PacktPublishing/Practical-Deep-Learning-at-Scale-with-MLFlow) and the folder...

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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 remote code in GitHub locally

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 remote code in GitHub locally

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