Kubernetes object template

As explained in Machine Learning Platform Engineering

A Kubernetes object template is a parameterized YAML definition of a Kubernetes object with customizable properties.

A Kubernetes object template is a parameterized YAML definition of a Kubernetes object. Helm charts use templates to package applications while keeping properties such as container images, CPU, memory, service types, and ports customizable through a values.yaml file. section 3.3.6

Templates support maintainable application deployment by separating structure from deployment-specific values:

• Define individual Kubernetes objects in template files. section 3.3.6
• Place customizable properties in values.yaml, including images, resource requests, service types, and ports. section 3.3.6
• Combine templates and values into a Helm chart that can be installed, upgraded, and managed as one application. section 3.3.6

Figure 3.9 The service_template.yaml file defines the K8s service, and the type of service and port are obtained from values.yaml. The template and values form the core of a Helm chart.

Use the service template and values relationship to see how configurable values populate a Kubernetes object. section 3.3.6

How Machine Learning Platform Engineering covers it

How deep it goes

Defines templates, values.yaml customization, and chart packaging but does not detail template syntax or rendering internals.

Examples

Uses a service template whose type and port come from values.yaml, plus Redis and hello-joker Helm deployments.

What you can do after

Apply it via Helm to customize, install, upgrade, and manage Kubernetes applications.

How it connects

Connects Kubernetes objects with Helm charts and values files.

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Related concepts

• values.yaml fileconfigured by · A values.yaml file is a Helm configuration file containing parameter values used to customize chart templates and their deployed resources.
• Helm charthas part · A Helm chart is a versioned package of templated Kubernetes resource definitions, configuration values, and metadata for deploying an application or service.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Platform Engineering. The section links point to the parts of the book it draws on.