Principle:Tensorflow Serving Kubernetes Cluster Creation

Knowledge Sources	GKE Documentation TF Serving Kubernetes
Domains	Cloud_Infrastructure, Kubernetes
Last Updated	2026-02-13 17:00 GMT

Overview

A cloud infrastructure provisioning process that creates a managed Kubernetes cluster with compute nodes for hosting TensorFlow Serving containers.

Description

Kubernetes cluster creation provisions the compute infrastructure needed to run TensorFlow Serving at scale. The official tutorial uses Google Kubernetes Engine (GKE), but the deployment manifests are compatible with any Kubernetes cluster (EKS, AKS, self-managed).

The process involves:

Authentication: Log in to the cloud provider
Cluster creation: Provision nodes with sufficient resources
Credential setup: Configure local kubectl to connect to the cluster

Node sizing should account for model memory requirements, batch sizes, and expected concurrent requests.

Usage

Create a cluster before deploying Kubernetes resources. The cluster should have enough nodes to handle the expected inference load with redundancy.

Theoretical Basis

# Abstract cluster creation (NOT real implementation)
authenticate(project="my-project")
cluster = create_cluster(
    name="serving-cluster",
    num_nodes=5,
    machine_type="n1-standard-4"
)
configure_kubectl(cluster)

Related Pages

Implemented By

Implementation:Tensorflow_Serving_GKE_Cluster_Setup

Page Connections

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Principle

Implementation

Heuristic

Environment