book

Kubeflow for Machine Learning

by Trevor Grant, Holden Karau, Boris Lublinsky, Richard Liu, Ilan Filonenko

October 2020

Intermediate to advanced

261 pages

6h 19m

English

O'Reilly Media, Inc.

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Our Assumption About YouYour Responsibility as a PractitionerConventions Used in This BookCode ExamplesUsing Code ExamplesO’Reilly Online LearningHow to Contact the AuthorsHow to Contact UsAcknowledgmentsGrievances
Model Development Life CycleWhere Does Kubeflow Fit In?Why Containerize?Why Kubernetes?Kubeflow’s Design and Core ComponentsData Exploration with NotebooksData/Feature PreparationTrainingHyperparameter TuningModel ValidationInference/PredictionPipelinesComponent OverviewAlternatives to KubeflowClipper (RiseLabs)MLflow (Databricks)OthersIntroducing Our Case StudiesModified National Institute of Standards and TechnologyMailing List DataProduct RecommenderCT ScansConclusion
Getting Set Up with KubeflowInstalling Kubeflow and Its DependenciesSetting Up Local KubernetesSetting Up Your Kubeflow Development EnvironmentCreating Our First Kubeflow ProjectTraining and Deploying a ModelTraining and Monitoring ProgressTest QueryGoing Beyond a Local DeploymentConclusion
Getting Around the Central DashboardNotebooks (JupyterHub)Training OperatorsKubeflow PipelinesHyperparameter TuningModel InferenceMetadataComponent SummarySupport ComponentsMinIOIstioKnativeApache SparkKubeflow Multiuser IsolationConclusion
Getting Started with PipelinesExploring the Prepackaged Sample PipelinesBuilding a Simple Pipeline in PythonStoring Data Between StepsIntroduction to Kubeflow Pipelines ComponentsArgo: the Foundation of PipelinesWhat Kubeflow Pipelines Adds to Argo WorkflowBuilding a Pipeline Using Existing ImagesKubeflow Pipeline ComponentsAdvanced Topics in PipelinesConditional Execution of Pipeline StagesRunning Pipelines on ScheduleConclusion
Deciding on the Correct ToolingLocal Data and Feature PreparationFetching the DataData Cleaning: Filtering Out the JunkFormatting the DataFeature PreparationCustom ContainersDistributed ToolingTensorFlow ExtendedDistributed Data Using Apache SparkDistributed Feature Preparation Using Apache SparkPutting It Together in a PipelineUsing an Entire Notebook as a Data Preparation Pipeline StageConclusion
Kubeflow ML MetadataProgrammatic QueryKubeflow Metadata UIUsing MLflow’s Metadata Tools with KubeflowCreating and Deploying an MLflow Tracking ServerLogging Data on RunsUsing the MLflow UIConclusion
Building a Recommender with TensorFlowGetting StartedStarting a New Notebook SessionTensorFlow TrainingDeploying a TensorFlow Training JobDistributed TrainingUsing GPUsUsing Other Frameworks for Distributed TrainingTraining a Model Using Scikit-LearnStarting a New Notebook SessionData PreparationScikit-Learn TrainingExplaining the ModelExporting ModelIntegration into PipelinesConclusion
Model ServingModel Serving RequirementsModel MonitoringModel Accuracy, Drift, and ExplainabilityModel Monitoring RequirementsModel UpdatingModel Updating RequirementsSummary of Inference RequirementsModel Inference in KubeflowTensorFlow ServingReviewSeldon CoreDesigning a Seldon Inference GraphTesting Your ModelServing RequestsMonitoring Your ModelsReviewKFServingServerless and the Service PlaneData PlaneExample WalkthroughPeeling Back the Underlying InfrastructureReviewConclusion

The Denoising CT Scans ExampleData Prep with PythonDS-SVD with Apache SparkVisualizationThe CT Scan Denoising PipelineSharing the PipelineConclusion
AutoML: An OverviewHyperparameter Tuning with Kubeflow KatibKatib ConceptsInstalling KatibRunning Your First Katib ExperimentPrepping Your Training CodeConfiguring an ExperimentRunning the ExperimentKatib User InterfaceTuning Distributed Training JobsNeural Architecture SearchAdvantages of Katib over Other FrameworksConclusion
Google CloudTPU-Accelerated InstancesDataflow for TFX
Building Streaming Applications Leveraging Model ServingStream Processing Engines and LibrariesIntroducing CloudflowBuilding Batch Applications Leveraging Model Serving

Content preview from Kubeflow for Machine Learning

Chapter 3. Kubeflow Design: Beyond the Basics

You made it through two chapters. Well done. So far you have decided to learn Kubeflow and worked through a simple example. Now we want to take a step back and look at each component in detail. Figure 3-1 shows the main Kubeflow components and the role they play in the overall architecture.

Essentially, we’ll look at the core elements that make up our example deployment as well as the supporting pieces. In the chapters that follow, we will dig into each of these sections in greater depth.

That said, let’s get started.

Getting Around the Central Dashboard

Your main interface to Kubeflow is the central dashboard (see Figure 3-2), which allows you to access the majority of Kubeflow components. Depending on your Kubernetes provider, it might take up to half an hour to have your ingress become available.

Note

While it is meant to be automatic, if you don’t have a namespace created for your work, follow Kubeflow’s “Manual profile creation” instructions.

From the home page of the central dashboard you can access Kubeflow’s Pipelines, Notebooks, Katib (hyperparameter tuning), and the artifact store. We will cover the design of these components and how to use them next. ...