book

Practical Deep Learning at Scale with MLflow

Name: Practical Deep Learning at Scale with MLflow
Author: Yong Liu
ISBN: 9781803241333

by Yong Liu

July 2022

Intermediate to advanced

288 pages

6h 22m

English

Packt Publishing

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Practical Deep Learning at Scale with MLflow
ForewordContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchShare Your Thoughts
Section 1 - Deep Learning Challenges and MLflow Prime
Chapter 1: Deep Learning Life Cycle and MLOps Challenges
Technical requirements Understanding the DL life cycle and MLOps challengesImplementing a basic DL sentiment classifierUnderstanding DL's full life cycle developmentUnderstanding MLOps challengesUnderstanding DL data challengesUnderstanding DL model challengesUnderstanding DL code challengesUnderstanding DL explainability challengesSummaryFurther reading
Chapter 2: Getting Started with MLflow for Deep Learning
Technical requirements Setting up MLflowSetting up MLflow locally using minicondaSetting up MLflow to interact with a remote MLflow serverImplementing our first DL experiment with MLflow autologgingExploring MLflow's components and usage patternsExploring experiments and runs in MLflowExploring MLflow models and their usagesExploring MLflow code tracking and its usagesSummaryFurther reading
Section 2 – Tracking a Deep Learning Pipeline at Scale
Chapter 3: Tracking Models, Parameters, and Metrics
Technical requirementsSetting up a full-fledged local MLflow tracking serverTracking model provenanceUnderstanding the open provenance tracking frameworkImplementing MLflow model trackingTracking model metrics Tracking model parametersSummaryFurther reading
Chapter 4: Tracking Code and Data Versioning
Technical requirements Tracking notebook and pipeline versioningPipeline trackingTracking locally, privately built Python librariesTracking data versioning in Delta LakeAn example of tracking data using MLflowSummaryFurther reading
Section 3 – Running Deep Learning Pipelines at Scale
Chapter 5: Running DL Pipelines in Different Environments
Technical requirementsAn overview of different execution scenarios and environmentsRunning locally with local codeRunning remote code in GitHub locallyRunning local code remotely in the cloudRunning remotely in the cloud with remote code in GitHubSummaryFurther reading

Chapter 6: Running Hyperparameter Tuning at Scale
Technical requirements Understanding automatic HPO for DL pipelinesTypes of hyperparameters and their challengesHow HPO works and which ones to chooseCreating HPO-ready DL models with Ray Tune and MLflow Setting up Ray Tune and MLflow Creating the Ray Tune trainable for the DL modelCreating the Ray Tune HPO run functionRunning the first Ray Tune HPO experiment with MLflowRunning HPO with Ray Tune using Optuna and HyperBandSummaryFurther reading
Section 4 – Deploying a Deep Learning Pipeline at Scale
Chapter 7: Multi-Step Deep Learning Inference Pipeline
Technical requirementsUnderstanding patterns of DL inference pipelinesUnderstanding the MLflow Model Python Function APIImplementing a custom MLflow Python model Implementing preprocessing and postprocessing steps in a DL inference pipelineImplementing language detection preprocessing logicImplementing caching preprocessing and postprocessing logicImplementing response composition postprocessing logicImplementing an inference pipeline as a new entry point in the main MLprojectSummaryFurther reading
Chapter 8: Deploying a DL Inference Pipeline at Scale
Technical requirements Understanding different deployment tools and host environmentsDeploying locally for batch and web service inference Batch inferenceModel as a web serviceDeploying using Ray Serve and MLflow deployment pluginsDeploying to AWS SageMaker – a complete end-to-end guideStep 1: Build a local SageMaker Docker imageStep 2: Add additional model artifacts layers onto the SageMaker Docker imageStep 3: Test local deployment with the newly built SageMaker Docker imageStep 4: Push the SageMaker Docker image to AWS Elastic Container RegistryStep 5: Deploy the inference pipeline model to create a SageMaker endpointStep 6: Query the SageMaker endpoint for online inferenceSummaryFurther reading
Section 5 – Deep Learning Model Explainability at Scale
Chapter 9: Fundamentals of Deep Learning Explainability
Technical requirementsUnderstanding the categories and audience of explainabilityAudience: who needs to knowStage: when to provide an explanation in the DL life cycleScope: which prediction needs explanationInput data format: what is the format of the input dataOutput data format: what is the format of the output explanationProblem type: what is the machine learning problem typeObjectives type: what is the motivation or goal to explainMethod type: what is the specific post-hoc explanation method usedExploring the SHAP Explainability toolboxExploring the Transformers Interpret toolboxSummaryFurther reading
Chapter 10: Implementing DL Explainability with MLflow
Technical requirementsUnderstanding current MLflow explainability integrationImplementing a SHAP explanation using the MLflow artifact logging APIImplementing a SHAP explainer using the MLflow pyfunc APICreating and logging an MLflow pyfunc explainerDeploying an MLflow pyfunc explainer for an EaaSUsing an MLflow pyfunc explainer for batch explanationSummaryFurther reading
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Content preview from Practical Deep Learning at Scale with MLflow

Chapter 6: Running Hyperparameter Tuning at Scale

Hyperparameter tuning or hyperparameter optimization (HPO) is a procedure that finds the best possible deep neural network structures, types of pretrained models, and model training process within a reasonable computing resource constraint and time frame. Here, hyperparameter refers to parameters that cannot be changed or learned during the ML training process, such as the number of layers inside a deep neural network, the choice of a pretrained language model, or the learning rate, batch size, and optimizer of the training process. In this chapter, we will use HPO as a shorthand to refer to the process of hyperparameter tuning and optimization. HPO is a critical step for producing a high-performance ...

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Practical Deep Learning at Scale with MLflow

by Yong Liu

Chapter 6: Running Hyperparameter Tuning at Scale

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