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 local code remotely in the cloud

In previous chapters, we ran all our code in a local laptop environment, and limited our DL fine-tuning step to only three epochs due to the limited power of a laptop. This serves the purpose of getting the code running and testing quickly in a local environment but does not serve to build an actual high-performance DL model. We really need to run the fine-tuning step in a remote GPU cluster. Ideally, we should only change some configuration and still issue the MLflow run command line in a local laptop console, but the actual pipeline will be submitted to a remote cluster in the cloud. Let's see how we can do this for our DL pipeline.

Let's start with submitting code to run in a Databricks server. There are three prerequisites:

An Enterprise Databricks server: You need to have access to an Enterprise-licensed Databricks server or a free trial version of the Databricks server (https://docs.databricks.com/getting-started/try...

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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 local code remotely in the cloud

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 local code remotely in the cloud

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