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 6. Artifact and Metadata Store

Machine learning typically involves dealing with a large amount of raw and intermediate (transformed) data where the ultimate goal is creating and deploying the model. In order to understand our model it is necessary to be able to explore datasets used for its creation and transformations (data lineage). The collection of these datasets and the transformation applied to them is called the metadata of our model.¹

Model metadata is critical for reproducibility in machine learning;² reproducibility is critical for reliable production deployments. Capturing the metadata allows us to understand variations when rerunning jobs or experiments. Understanding variations is necessary to iteratively develop and improve our models. It also provides a solid foundation for model comparisons. As Pete Warden defined it in this post:

To reproduce results, code, training data, and the overall platform need to be recorded accurately.

The same information is also required for other common ML operations—model comparison, reproducible model creation, etc.

There are many different options for tracking the metadata of models. Kubeflow has a built-in tool for this called Kubeflow ML Metadata.³ The goal of this tool is to help Kubeflow users understand and manage their ML workflows by tracking and managing the metadata that the workflows produce. Another tool for tracking metadata that we can integrate into our Kubeflow pipelines is MLflow Tracking. It provides API ...