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.

Book available

Read now

Unlock full access

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 9. Case Study Using Multiple Tools

In this chapter we’re going to discuss what to do if you need to use “other” tools for your particular data science pipeline. Python has a plethora of tools for handling a wide array of data formats. RStats has a large repository of advanced math functions. Scala is the default language of big data processing engines such as Apache Spark and Apache Flink. Legacy programs that would be costly to reproduce exist in any number of languages.

A very important benefit of Kubeflow is that users no longer need to choose which language is best for their entire pipeline but can instead use the best language for each job (as long as the language and code are containerizable).

We will demonstrate these concepts through a comprehensive example denoising CT scans. Low-dose CT scans allow clinicians to use the scans as a diagnostic tool by delivering a fraction of the radiation dose—however, these scans often suffer from an increase in white noise. CT scans come in a format known as DICOM, and we’ll use a container with a specialized library called pydicom to load and process the data into a numpy matrix.

Several methods for denoising CT scans exist; however, they often focus on the mathematical justification, not the implementation. We will present an open source method that uses a singular value decomposition (SVD) to break the image into components, the “least important” of which are often the noise. We use Apache Spark with the Apache Mahout library ...