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Pod

As explained in Distributed Machine Learning Patterns

A pod is the smallest deployable unit in Kubernetes, consisting of one or more co-located containers sharing specified resources and execution context.

A Pod is Kubernetes’ smallest deployable computing unit: one or more tightly coupled containers that share specified storage, networking, and execution context. section 8.2.1

Treat a Pod as an application-specific logical host that:

Minimal whalesay Pod

A YAML manifest can define one container using the docker/whalesay:latest image, the cowsay command, and a hello-world argument. Create it with kubectl, then inspect status and logs. section 8.2.1 section 8.7

Listing 8.26 An example Pod
apiVersion: v1
kind: Pod
metadata:
 name: whalesay
spec:
 containers:
 - name: whalesay
   image: docker/whalesay:latest
   command: [cowsay]
   args: ["hello world"]

Inspect the complete Pod manifest, including apiVersion, metadata, and the container specification. section 8.2.1

How Distributed Machine Learning Patterns covers it

How deep it goes
Defines shared context and demonstrates YAML creation, inspection, status, and logs without explaining Kubernetes internals.
Examples
Uses a whalesay container, hello-world manifest, kubectl creation, status output, logs, and raw YAML inspection.
What it assumes
Assumes access to a Kubernetes cluster, namespace, and kubectl context configured for the target environment.
What you can do after
Operate/configure a Pod by writing YAML, creating it with kubectl, and inspecting status, logs, JSON, or raw YAML.
How it connects
Connects containers with shared storage, networking, namespaces, YAML manifests, and kubectl.
Cover of Distributed Machine Learning Patterns
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