R&R Slurm integration

[sarthakag]

Adds R&R API integration for Slurm

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[gemini-code-assist]

Summary of Changes

Hello @sarthakag, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request integrates Google Cloud's Repair and Recovery (R&R) functionality directly into the Slurm scheduler. It introduces automated detection of faulty Slurm nodes, initiates repairs via the GCP API, and manages the entire repair lifecycle. This includes tracking repair progress and dynamically updating Slurm node states to reflect successful recovery or persistent failures, thereby enhancing the resilience and self-healing capabilities of Slurm clusters on GCP.

Highlights

• New Repair Module: A new Python script, repair.py, has been introduced to manage the lifecycle of node repair operations, including initiating repairs and tracking their status.
• GCP R&R API Integration: The repair.py script directly interacts with the gcloud compute instances report-host-as-faulty API to trigger repair actions on Google Cloud instances.
• Slurm Node State Management: The slurmsync.py script is enhanced to detect Slurm nodes in a DRAIN state with specific repair reasons, trigger the R&R process, and manage their state (down, resume) based on the repair outcomes.
• Persistent Repair Tracking: Repair operations are now tracked persistently in a JSON file (repair_operations.json) to maintain state across multiple runs of the slurmsync process.

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[gemini-code-assist]

Code Review

This pull request introduces an integration with a Repair and Recovery (R&R) mechanism for Slurm nodes on GCP. A new repair.py script is added to handle the lifecycle of a node repair, from calling the GCP API to report a faulty host to monitoring the operation and bringing the node back online. The slurmsync.py script is modified to detect nodes that need repair based on their DRAIN state and reason, and to trigger the repair process. The changes look good overall, but I have a few suggestions for improving the robustness and maintainability of the new repair.py script, mainly around file handling, exception handling, and command-line argument safety. Additionally, the new functionality is not covered by unit tests, which is encouraged by the project's contribution guidelines.

[Neelabh94]

Some changes requested

[bytetwin]

Please address the comments

[sarthakag]

Addressed all comments. PTAL @bytetwin @Neelabh94