Book Image

Practical Deep Learning at Scale with MLflow

By : Yong Liu
5 (1)
Book Image

Practical Deep Learning at Scale with MLflow

5 (1)
By: Yong Liu

Overview of this book

The book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas. From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You’ll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you’ll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox. By the end of this book, you’ll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Share Your Thoughts
1
Section 1 - Deep Learning Challenges and MLflow Prime
Section 1 - Deep Learning Challenges and MLflow Prime
Free Chapter
2
Chapter 1: Deep Learning Life Cycle and MLOps Challenges
Chapter 1: Deep Learning Life Cycle and MLOps Challenges
Technical requirements
Understanding the DL life cycle and MLOps challenges
Understanding DL data challenges
Understanding DL model challenges
Understanding DL code challenges
Understanding DL explainability challenges
Summary
Further reading
3
Chapter 2: Getting Started with MLflow for Deep Learning
Chapter 2: Getting Started with MLflow for Deep Learning
Technical requirements
Setting up MLflow
Implementing our first DL experiment with MLflow autologging
Exploring MLflow's components and usage patterns
Summary
Further reading
4
Section 2 –
Tracking a Deep Learning Pipeline at Scale
Section 2 –
Tracking a Deep Learning Pipeline at Scale
5
Chapter 3: Tracking Models, Parameters, and Metrics
Chapter 3: Tracking Models, Parameters, and Metrics
Technical requirements
Setting up a full-fledged local MLflow tracking server
Tracking model provenance
Tracking model metrics
Tracking model parameters
Summary
Further reading
6
Chapter 4: Tracking Code and Data Versioning
Chapter 4: Tracking Code and Data Versioning
Technical requirements
Tracking notebook and pipeline versioning
Tracking locally, privately built Python libraries
Tracking data versioning in Delta Lake
Summary
Further reading
7
Section 3 –
Running Deep Learning Pipelines at Scale
Section 3 –
Running Deep Learning Pipelines at Scale
8
Chapter 5: Running DL Pipelines in Different Environments
Chapter 5: Running DL Pipelines in Different Environments
Technical requirements
An overview of different execution scenarios and environments
Running locally with local code
Running remote code in GitHub locally
Running local code remotely in the cloud
Running remotely in the cloud with remote code in GitHub
Summary
Further reading
9
Chapter 6: Running Hyperparameter Tuning at Scale
Chapter 6: Running Hyperparameter Tuning at Scale
Technical requirements
Understanding automatic HPO for DL pipelines
Creating HPO-ready DL models with Ray Tune and MLflow
Running the first Ray Tune HPO experiment with MLflow
Running HPO with Ray Tune using Optuna and HyperBand
Summary
Further reading
10
Section 4 –
Deploying a Deep Learning Pipeline at Scale
Section 4 –
Deploying a Deep Learning Pipeline at Scale
11
Chapter 7: Multi-Step Deep Learning Inference Pipeline
Chapter 7: Multi-Step Deep Learning Inference Pipeline
Technical requirements
Understanding patterns of DL inference pipelines
Implementing a custom MLflow Python model
Implementing preprocessing and postprocessing steps in a DL inference pipeline
Implementing an inference pipeline as a new entry point in the main MLproject
Summary
Further reading
12
Chapter 8: Deploying a DL Inference Pipeline at Scale
Chapter 8: Deploying a DL Inference Pipeline at Scale
Technical requirements
Understanding different deployment tools and host environments
Deploying locally for batch and web service inference
Deploying using Ray Serve and MLflow deployment plugins
Deploying to AWS SageMaker – a complete end-to-end guide
Summary
Further reading
13
Section 5 – Deep Learning Model Explainability at Scale
Section 5 – Deep Learning Model Explainability at Scale
14
Chapter 9: Fundamentals of Deep Learning Explainability
Chapter 9: Fundamentals of Deep Learning Explainability
Technical requirements
Understanding the categories and audience of explainability
Exploring the SHAP Explainability toolbox
Exploring the Transformers Interpret toolbox
Summary
Further reading
15
Chapter 10: Implementing DL Explainability with MLflow
Chapter 10: Implementing DL Explainability with MLflow
Technical requirements
Understanding current MLflow explainability integration
Implementing a SHAP explanation using the MLflow artifact logging API
Implementing a SHAP explainer using the MLflow pyfunc API
Summary
Further reading
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16
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Creating HPO-ready DL models with Ray Tune and MLflow

To use Ray Tune with MLflow for HPO, let's use the fine-tuning step in our DL pipeline example from Chapter 5, Running DL Pipelines in Different Environments, to see what needs to be set up and what code changes we need to make. Before we start, first, let's review a few key concepts that are specifically relevant to our usage of Ray Tune:

  • Objective function: An objective function can be either to minimize or maximize some metric values for a given configuration of hyperparameters. For example, in the DL model training and fine-tuning scenarios, we would like to maximize the F1-score for the accuracy of an NLP text classifier. This objective function needs to be wrapped as a trainable function, where Ray Tune can do HPO. In the following section, we will illustrate how to wrap our NLP text sentiment model.
  • Function-based APIs and class-based APIs: A function-based API allows a user to insert Ray Tune statements...
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