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TFJob

As explained in Distributed Machine Learning Patterns

A TFJob spec is the configuration specification for a Kubeflow TensorFlow training job, including distributed job components and replica counts.

A TFJob is a Kubernetes custom resource that specifies a distributed TensorFlow training job. Its controller watches submitted objects and creates the required training Pods, letting data science teams provide TensorFlow code without directly managing infrastructure. section 8.3.1

The specification describes replica roles, counts, container images, commands, and restart behavior. A worker configuration with two replicas produces two worker Pods running the same MNIST training container. section 8.3.1

Two-worker training job

A TFJob can use apiVersion kubeflow.org/v1, kind TFJob, and a Worker replica count of 2. The worker template runs a TensorFlow image with a Python training command, learning rate, batch size, and OnFailure restart policy. Submit it with kubectl create -f.

TFJobs also fit inside workflow automation. An Argo step can create a TFJob, wait for two workers to succeed or any worker to fail, and then pass control to model selection and serving. section 9.4.1

How Distributed Machine Learning Patterns covers it

How deep it goes
Defines custom-resource structure, replica configuration, submission, generated Pods, and workflow success conditions without covering controller internals.
Examples
Uses a two-worker MNIST job and parallel CNN, dropout, and batch-normalized training jobs inside an Argo workflow.
What it assumes
Assumes Kubernetes and TensorFlow container basics while introducing custom resources, operator reconciliation, and kubectl-based submission.
What you can do after
Operate distributed TensorFlow training by authoring TFJob YAML, submitting it with kubectl, and checking TFJobs and Pods.
How it connects
Connects Kubernetes custom resources, the training operator, Pods, Argo Workflows, persistent volumes, model selection, and KServe.
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