Kubeflow Operations Guide by Harris, Austin -- Read -- Imperial Library of Trantor

Index

1. Introduction to Kubeflow

Machine Learning on Kubernetes

The Evolution of Machine Learning in the Enterprise It’s Harder Than Ever to Run Enterprise Infrastructure Identifying Next Generation Infrastructure Core Principles Enter: Kubeflow Origin of Kubeflow Who Uses Kubeflow?

Common Kubeflow Use Cases

Running Notebooks on GPUs Shared Multi-Tennant Machine Learning Environment Example: Building a Transfer Learning Pipeline Deploying Models to Production for Application Integration

Components of Kubeflow

Jupyter Notebooks Machine Learning Model Training Components Hyperparameter Tuning Pipelines Machine Learning Model Inference Serving

An Overview of Kubernetes

Core Kubenetes Concepts

Summary

2. Planning a Kubeflow Installation

Users

Profiling Users Varying Skillsets

Kubeflow Components

Components that Extend the Kubernetes API Components running atop of Kubernetes

Workloads

Cluster Utilization Data Patterns

GPU Planning

Planning for GPUs Models that Benefit from GPUs

Infrastructure Planning

Kubernetes Considerations On-Premise Cloud Placement

Container Management Security

Background & Motivation Control Plane Kubeflow and Deployed Applications Multitenancy & Isolation Integration

Sizing & Growing

Forecasting Storage Scaling

3. Running Kubeflow on Google Cloud

Installing on a Public Cloud

Managed Kubernetes in the Cloud

Overview of the Google Cloud Platform

Storage Google Cloud Security and the Cloud Identity-Aware Proxy GCP Projects for Application Deployments GCP Service Accounts Google Compute Engine Managed Kubernetes on GKE Signing Up for Google Cloud Platform

Installing the Google Cloud SDK

Update Python Download and Install Google Cloud SDK

Installing Kubeflow on Google Cloud Platform

Create a Project in the GCP Console Enabling APIs for a Project Set up OAuth for GCP Cloud IAP Deploy Kubeflow Using the Command-Line Interface Accessing the Kubeflow UI Post-Installation Understanding How the Deployment Process Works Understanding What Was Deployed on GCP

Creating Managed Kubernetes Clusters on GKE Common Operations for Google Cloud and GKE

Resizing a Cluster Deleting a Cluster