Kubeflow Operations Guide Josh Patterson, Michael Katzenellenbogen, Austin Harris

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Spis treści książki

• Preface
  • What Is in This Book?
  • Who Is This Book For?
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
    • Josh
    • Michael
    • Austin
• 1. Introduction to Kubeflow
  • Machine Learning on Kubernetes
    • The Evolution of Machine Learning in Enterprise
    • Its Harder Than Ever to Run Enterprise Infrastructure
    • Identifying Next-Generation Infrastructure (NGI) Core Principles
    • Kubernetes for Production Application Deployment
    • Enter: Kubeflow
    • What Problems Does Kubeflow Solve?
    • Origin of Kubeflow
    • Who Uses Kubeflow?
      • Team alignment for the line of business, DevOps, data engineering, and data science
  • Common Kubeflow Use Cases
    • Running Notebooks on GPUs
      • Advantages of notebooks on GPUs
      • Team alignment for notebooks on GPUs
    • Shared Multitenant Machine Learning Environment
      • Advantages of on-premise multitenant environment
      • Team alignment
    • Building a Transfer Learning Pipeline
      • Advantages of running computer vision pipeline on Kubeflow
      • Team alignment for computer vision pipeline
    • Deploying Models to Production for Application Integration
      • Advantages of deploying models to production on Kubeflow
      • Team alignment for model deployment
  • Components of Kubeflow
    • Machine Learning Tools
      • TensorFlow training and TFJob
      • Keras
    • Applications and Scaffolding
      • Kubeflow UI
      • Jupyter Notebooks
        • Jupyter Notebook integration with Kubeflow
      • Operators for machine learning frameworks
      • Metadata and artifacts
      • Hyperparameter tuning
      • Pipelines
        • Basic Kubeflow Pipeline concepts
    • Machine Learning Model Inference Serving with KFServing
    • Platforms and Clouds
      • Public clouds
        • Managed Kubernetes in the cloud
      • On-premise
      • Local
  • Summary
• 2. Kubeflow Architecture and Best Practices
  • Kubeflow Architecture Overview
    • Kubeflow and Kubernetes
    • Ways to Run a Job on Kubeflow
    • Machine Learning Metadata Service
    • Artifact Storage
    • Istio Operations in Kubeflow
      • Istio and KFServing
  • Kubeflow Multitenancy Architecture
    • Multitenancy and Isolation
    • Multiuser Architecture
    • Multiuser Authorization Flow
    • Kubeflow Profiles
    • Multiuser Isolation
  • Notebook Architecture
    • Notebook Server Launcher UI
    • Notebook Controller
  • Pipelines Architecture
  • Kubeflow Best Practices
    • Managing Job Dependencies
      • Building a custom notebook Docker image
    • Using GPUs
      • Using GPUs with notebooks
      • Validating that notebook code is using the GPU
    • Experiment Management
      • Install the metadata SDK
      • Basic metadata SDK usage
  • Summary
• 3. Planning a Kubeflow Installation
  • Security Planning
    • Components That Extend the Kubernetes API
    • Components Running Atop Kubernetes
    • Background and Motivation
    • Kubeflow and Deployed Applications
    • Integration
  • Users
    • Profiling Users
    • Varying Skillsets
  • Workloads
    • Cluster Utilization
    • Data Patterns
      • Dedicated versus transient
  • GPU Planning
    • Planning for GPUs
      • GPU use cases
      • GPU anti-use cases
    • Models that Benefit from GPUs
      • Distributed versus multi-GPU training
  • Infrastructure Planning
    • Kubernetes Considerations
    • On-Premise
      • The Nvidia DGX
      • Datacenter considerations
    • Cloud
    • Placement
  • Container Management
  • Serverless Container Operations with Knative
  • Sizing and Growing
    • Forecasting
    • Storage
    • Scaling
  • Summary
• 4. Installing Kubeflow On-Premise
  • Kubernetes Operations from the Command Line
    • Installing kubectl
      • Installing kubectl on macOS
      • Understanding kubectl and contexts
        • Getting the current context
        • Adding clusters to our context file
        • Switching contexts
    • Using kubectl
      • Getting running services
      • Get cluster information
      • Get currently running jobs
    • Using Docker
      • Basic Docker install
      • Basic Docker commands
      • Using Docker to build TensorFlow containers
  • Basic Install Process
    • Installing On-Premise
      • Considerations for Building Kubernetes Clusters
      • Gateway Host Access to Kubernetes Cluster
      • Active Directory Integration and User Management
        • Kubernetes, kubectl, and Active Directory
      • Kerberos Integration
      • Storage Integration
        • Thinking about Kubeflow job bandwidth
        • Common access storage patterns with Kubeflow jobs
        • Options for Kubeflow storage
        • Persistent volume claims and Kubeflow storage
      • Container Management and Artifact Repositories
        • Setting up an internal container repository
  • Accessing and Interacting with Kubeflow
    • Common Command-Line Operations
    • Accessible Web UIs
  • Installing Kubeflow
    • System Requirements
    • Set Up and Deploy
  • Summary
• 5. Running Kubeflow on Google Cloud
  • Overview of the Google Cloud Platform
    • Storage
    • Google Cloud Identity-Aware Proxy
    • Google Cloud Security and the Cloud Identity-Aware Proxy
      • Authentication
      • Authorization
    • GCP Projects for Application Deployments
    • GCP Service Accounts
    • 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
      • Set up the OAuth consent screen
      • Configuring the Credentials tab
    • Deploy Kubeflow Using the Command-Line Interface
      • Creating user credentials
      • Create required environment variables
      • Set up kfctl
      • Set up environment variables for kfctl
        • Setting the ZONE environment variable
        • Setting the PROJECT environment variable
        • Set GCloud configuration variables
        • Setting the CONFIG environment variable
        • Setting the Kubeflow deployment environment variables
      • Kubeflow deployment with kfctl
      • Confirm Kubeflow deployment
    • Accessing the Kubeflow UI Post-Installation
      • Getting the ingress URI for your deployment
  • Summary
• 6. Running Kubeflow on Amazon Web Services
  • Overview of Amazon Web Services
    • Storage
    • Amazon Storage Pricing
    • Amazon Cloud Security
    • AWS Compute Services
    • Managed Kubernetes on EKS
  • Signing Up for Amazon Web Services
  • Installing the AWS CLI
    • Update Python
    • Install the AWS CLI
      • Configuring AWS CLI
  • Kubeflow on Amazon Web Services
    • Installing kubectl
    • Install the eksctl CLI for Amazon EKS
    • Install AWS IAM Authenticator
    • Install jq
  • Using Managed Kubernetes on Amazon EKS
    • Create an EKS Service Role
    • Create an AWS VPC
    • Creating EKS Clusters
    • Deploying an EKS Cluster with eksctl
  • Understanding the Deployment Process
    • Kubeflow Configuration and Deployment
      • Download and configure kfctl
      • Deploy Kubeflow to EKS
      • Confirm EKS Deployment
    • Customize the Kubeflow Deployment
    • Customize Authentication
    • Resizing EKS Clusters
    • Deleting EKS Clusters
    • Adding Logging
    • Troubleshooting Deployments
  • Summary
• 7. Running Kubeflow on Azure
  • Overview of the Azure Cloud Platform
    • Key Azure Components
    • Storage on Azure
      • File storage
      • Disk storage
      • Blob Storage
      • Azure Data Lake Storage Gen2
      • Archive storage
      • Avere vFXT
    • The Azure Security Model
      • Authentication and authorization
    • Service Accounts
    • Resources and Resource Groups
    • Azure Virtual Machines
    • Containers and Managed Azure Kubernetes Services
  • The Azure CLI
    • Installing the Azure CLI
      • macOS install
      • Windows install
      • Debian and Ubuntu (x86_64)
  • Installing Kubeflow on Azure Kubernetes
    • Azure Login and Configuration
    • Create an AKS Cluster for Kubeflow
      • Creating an Azure resource group
      • Creating an AKS cluster for Kubeflow
    • Kubeflow Installation
      • Get Azure credentials
      • Download, install, and configure kfctl
      • Set up environment variables for kfctl
        • Setting the CONFIG environment variable
        • Setting the Kubeflow deployment environment variables
      • Kubeflow deployment with kfctl
      • Confirm Kubeflow deployment
  • Authorizing Network Access to Deployment
  • Summary
• 8. Model Serving and Integration
  • Basic Concepts of Model Management
    • Understanding Training Models Versus Model Inference
    • Building an Intuition for Model Integration
    • Scaling Model Inference Throughput
      • Developing example inference-per-second forecasts
    • Model Management
  • Introduction to KFServing
    • Advantages of Using KFServing
    • Core Concepts in KFServing
      • InferenceService
      • Endpoint
      • Predictor
      • Explainer
      • Transformers
      • Leveraging canarying with KFServing
      • Outlier detection
      • Concept drift
    • Supported Pre-Built Model Servers
      • InferenceService and storage provider support
        • Google Cloud Storage
        • S3-compatible object storage
        • Azure Blob Storage
        • Local container filesystem
        • Persistent volume claim
    • KFServing Security Model
  • Managing Models with KFServing
    • Installing KFServing on a Kubernetes Cluster
      • Installing KFServing standalone on Minikube
    • Deploying a Model on KFServing
      • Deploying a Python TensorFlow model as an InferenceService
      • Deploy InferenceService with custom model serving strategy
    • Managing Model Traffic with Canarying
    • Deploying a Custom Transformer
    • Roll Back a Deployed Model
    • Removing a Deployed Model
  • Summary
• A. Infrastructure Concepts
  • Public Key Infrastructure
  • Authentication
    • Kubeflow and Authentication
  • Authorization
    • Authorization and Role-Based Access Control
  • Lightweight Directory Access Protocol
  • Kerberos
  • Transport Layer Security
  • X.509 Cert
  • Webhook
  • Active Directory
  • Identity Providers
  • Identity-Aware Proxy (IAP)
    • IAP and Google Cloud Platform
  • OAuth
  • OpenID Connect
  • End-User Authentication with JWT
  • Simple and Protected GSS_API Negotiation Mechanism
  • Dex: A Federated OpenID Connect Provider
    • Dex and Kerberos
  • Service Accounts
  • The Control Plane
    • Options for Securing the Control Plane
• B. An Overview of Kubernetes
  • Core Kubernetes Concepts
    • Pod
    • Object Spec and Status
    • Describing a Kubernetes Object
    • Submitting Containers to Kubernetes
    • Kubernetes Resource Model
    • Custom Resources, Controllers, and Operators
    • Custom Controllers
    • Custom Resource Definition
• C. Istio Operations and Kubeflow
  • Service Mesh Management with Istio
    • Istio Architecture
      • Control plane
      • Data plane
    • Traffic Management
      • Virtual services
      • Destination rules
      • Gateways
    • Istio Security Architecture
      • Policies
      • Istio identity
      • Istio authentication
    • Istio Authorization and Role-Based Access Control
      • Authorization policies
      • ServiceRole
      • ServiceRoleBinding
• Index

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