Principle:Tensorflow Serving Kubernetes Resource Deployment
| Knowledge Sources | |
|---|---|
| Domains | Kubernetes, Deployment |
| Last Updated | 2026-02-13 17:00 GMT |
Overview
A declarative deployment pattern that defines Kubernetes Deployment and Service resources to run replicated TensorFlow Serving pods with load-balanced access.
Description
Kubernetes resource deployment uses declarative YAML manifests to create:
- Deployment: Manages a set of identical pods running the TensorFlow Serving container. Specifies replica count, container image, and port configuration. Kubernetes ensures the desired number of replicas are always running.
- Service: Exposes the deployment pods via a stable network endpoint. A LoadBalancer type creates an external IP for client access. The service routes traffic to pods matching a label selector.
This pattern provides horizontal scaling (adjust replica count), self-healing (pods are restarted on failure), and load balancing (traffic distributed across replicas).
Usage
Apply Kubernetes manifests after pushing the Docker image and creating the cluster. Adjust the replica count based on expected load. Use kubectl to manage the lifecycle.
Theoretical Basis
# Abstract Kubernetes deployment structure (NOT complete manifest)
# Deployment: N replicas of serving container
# Service: LoadBalancer exposing gRPC port
#
# Traffic flow: Client -> LoadBalancer -> Service -> Pod -> tensorflow_model_server
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