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  • Introduction
  • Quickstart
  • Getting started
    • Install Feast
    • Create a feature repository
    • Deploy a feature store
    • Build a training dataset
    • Load data into the online store
    • Read features from the online store
  • Community
  • Roadmap
  • Changelog
  • Concepts
    • Overview
    • Feature view
    • Data model
    • Online store
    • Offline store
    • Provider
    • Architecture
  • Reference
    • Data sources
      • BigQuery
      • File
    • Offline stores
      • File
      • BigQuery
    • Online stores
      • SQLite
      • Redis
      • Datastore
    • Providers
      • Local
      • Google Cloud Platform
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feast CLI reference
    • Python API reference
    • Usage
  • Feast on Kubernetes
    • Getting started
      • Install Feast
        • Docker Compose
        • Kubernetes (with Helm)
        • Amazon EKS (with Terraform)
        • Azure AKS (with Helm)
        • Azure AKS (with Terraform)
        • Google Cloud GKE (with Terraform)
        • IBM Cloud Kubernetes Service (IKS) and Red Hat OpenShift (with Kustomize)
      • Connect to Feast
        • Python SDK
        • Feast CLI
      • Learn Feast
    • Concepts
      • Overview
      • Architecture
      • Entities
      • Sources
      • Feature Tables
      • Stores
    • Tutorials
      • Minimal Ride Hailing Example
    • User guide
      • Overview
      • Getting online features
      • Getting training features
      • Define and ingest features
      • Extending Feast
    • Reference
      • Configuration Reference
      • Feast and Spark
      • Metrics Reference
      • Limitations
      • API Reference
        • Go SDK
        • Java SDK
        • Core gRPC API
        • Python SDK
        • Serving gRPC API
        • gRPC Types
    • Advanced
      • Troubleshooting
      • Metrics
      • Audit Logging
      • Security
      • Upgrading Feast
  • Contributing
    • Contribution process
    • Development guide
    • Versioning policy
    • Release process
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On this page
  • Overview
  • 1. Requirements
  • 2. Preparation
  • 3. Installation
  • 4. Use Jupyter to connect to Feast
  • 5. Further Reading

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  1. Feast on Kubernetes
  2. Getting started
  3. Install Feast

Kubernetes (with Helm)

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Last updated 3 years ago

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Overview

This guide installs Feast on an existing Kubernetes cluster, and ensures the following services are running:

  • Feast Core

  • Feast Online Serving

  • Postgres

  • Redis

  • Feast Jupyter (Optional)

  • Prometheus (Optional)

1. Requirements

  1. Install and configure

  2. Install

2. Preparation

Add the Feast Helm repository and download the latest charts:

helm repo add feast-charts https://feast-helm-charts.storage.googleapis.com
helm repo update

Feast includes a Helm chart that installs all necessary components to run Feast Core, Feast Online Serving, and an example Jupyter notebook.

Feast Core requires Postgres to run, which requires a secret to be set on Kubernetes:

kubectl create secret generic feast-postgresql --from-literal=postgresql-password=password

3. Installation

Install Feast using Helm. The pods may take a few minutes to initialize.

helm install feast-release feast-charts/feast

4. Use Jupyter to connect to Feast

After all the pods are in a RUNNING state, port-forward to the Jupyter Notebook Server in the cluster:

kubectl port-forward \
$(kubectl get pod -l app=feast-jupyter -o custom-columns=:metadata.name) 8888:8888
Forwarding from 127.0.0.1:8888 -> 8888
Forwarding from [::1]:8888 -> 8888

You can now connect to the bundled Jupyter Notebook Server at localhost:8888 and follow the example Jupyter notebook.

5. Further Reading

Kubectl
Helm 3
Feast Concepts
Feast Examples/Tutorials
Feast Helm Chart Documentation
Configuring Feast components
Feast and Spark
http://localhost:8888/tree?localhost