Missing table-driven tests and deterministic curve generation in Flux plugin

[gojogourav]

What happened?

Currently, the TestFlux table-driven tests in the Flux plugin only verify the final generated Kubernetes objects and container properties.

They do not assert against internal state changes (specifically runtime.Info mutations) or explicitly check for expected errors during the EnforceMLPolicy and Build phases. Because the plugin relies heavily on mutating this internal state, this lack of test coverage creates a blind spot where silent regressions in state management could occur without failing the test suite.

What did you expect to happen?

The test suite should be expanded to explicitly assert against wantInfo, wantMLPolicyError, and wantBuildError.

By adding these fields to the table-driven test cases, we can strictly verify that EnforceMLPolicy and Build correctly mutate the internal runtime.Info state and bubble up the appropriate error values under various conditions.

Environment

Kubernetes version:

$ kubectl version

Kubeflow Trainer version:

$ kubectl get pods -n kubeflow-system -l app.kubernetes.io/name=kubeflow-trainer -o jsonpath="{.items[*].spec.containers[*].image}"

Kubeflow Python SDK version:

$ pip show kubeflow

Impacted by this bug?

Give it a 👍 We prioritize the issues with most 👍

[github-actions]

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[gojogourav]

What happened?

Currently, the TestFlux table-driven tests in the Flux plugin only verify the final generated Kubernetes objects and container properties.

They do not assert against internal state changes (specifically runtime.Info mutations) or explicitly check for expected errors during the EnforceMLPolicy and Build phases. Because the plugin relies heavily on mutating this internal state, this lack of test coverage creates a blind spot where silent regressions in state management could occur without failing the test suite.

What did you expect to happen?

The test suite should be expanded to explicitly assert against wantInfo, wantMLPolicyError, and wantBuildError.

By adding these fields to the table-driven test cases, we can strictly verify that EnforceMLPolicy and Build correctly mutate the internal runtime.Info state and bubble up the appropriate error values under various conditions.

Environment

Kubernetes version:

$ kubectl version

Kubeflow Trainer version:

$ kubectl get pods -n kubeflow-system -l app.kubernetes.io/name=kubeflow-trainer -o jsonpath="{.items[].spec.containers[].image}"

Kubeflow Python SDK version:

$ pip show kubeflow

Impacted by this bug?

Give it a 👍 We prioritize the issues with most 👍

[gojogourav]

#3410