Ai tier ga internal

[kupratyu-splunk]

Description

Related Issues

• Related to #

Type of Change

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Documentation update
• Refactoring (no functional changes)
• Performance improvement
• Test improvement
• CI/CD improvement
• Chore (dependency updates, etc.)

Changes Made

Testing Performed

• Unit tests pass (make test)
• Linting passes (make lint)
• Integration tests pass (if applicable)
• E2E tests pass (if applicable)
• Manual testing performed

Test Environment

• Kubernetes Version:
• Cloud Provider:
• Deployment Method:

Test Steps

Documentation

• Updated inline code comments
• Updated README.md (if adding features)
• Updated API documentation
• Updated deployment guides
• Updated CHANGELOG.md
• No documentation needed

Checklist

• My code follows the project's style guidelines
• I have performed a self-review of my code
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes
• Any dependent changes have been merged and published
• I have updated the Helm chart version (if applicable)
• I have updated CRD schemas (if applicable)

Breaking Changes

Impact:

Migration Path:

Screenshots/Recordings

Additional Notes

Reviewer Notes

Please pay special attention to:

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[chatgpt-codex-connector]

Codex Review Summary

This comment shows the latest Codex review activity on this pull request.

Review	Status	Commit	Review trigger
📝 Code Review	✅ Completed 2026-09-01T13:33:32.134669Z	29ca4fe	PR opened

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[chatgpt-codex-connector]

💡 Codex Review

Here are some automated review suggestions for this pull request.

Reviewed commit: 29ca4fe721

ℹ️ About Codex in GitHub

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• Open a pull request for review
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• Comment "@codex review".

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[chatgpt-codex-connector]

Do not deploy a second platform after the full-stack installer

Following this sequence creates or overwrites another AIPlatform: both supported installers already install the CR (install_ai_platform_stack calls install_ai_platform_cr in tools/cluster_setup/k0s_cluster_with_stack.sh, and OpenShift does the same in main_install). Applying the next page's example can therefore start a duplicate set of GPU/Ray workloads, or replace the installer-managed CR configuration if the same name is used. The guide should treat the installed CR as the platform to configure rather than instructing every user to deploy another one.

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[chatgpt-codex-connector]

Include the required k0s GPU scheduling rules

In the documented k0s environment, the installer taints GPU nodes with nvidia.com/gpu=true:NoSchedule (tools/cluster_setup/k0s_cluster_with_stack.sh:2845-2846), but this sample selects a GPU accelerator without defining the gpuScheduler toleration and node selector that the installer-generated CR includes. If a user applies this advertised k0s example, the Ray GPU workers cannot schedule onto the tainted GPU nodes and the platform never becomes ready; include the generated scheduling block or direct users to edit the installer-created CR.

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