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 remotely in the cloud with remote code in GitHub

The most reliable way to reproduce a DL pipeline is to point to a specific version of the project code in GitHub and then run it in the cloud without invoking any local resources. This way, we know the exact version of the code as well as using the same running environment defined in the project. Let's see how this works with our DL pipeline.

As a prerequisite and a reminder, the following three environment variables need to be set up before you issue the MLflow run command to complete this section of the learning:

export MLFLOW_TRACKING_URI=databricks
export DATABRICKS_TOKEN=[databricks_token]
export DATABRICKS_HOST='https://[your databricks host name/'

We already know how to set up these environment variables from the last section. There is potentially one more setup needed, which is to allow your Databricks server to access your GitHub repository if it is non-public (see the following GitHub Token...

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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 remotely in the cloud with remote code in GitHub

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 remotely in the cloud with remote code in GitHub

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