[cuda, cli] feat: add NVML monitor and optimize cli

[Wangmerlyn]

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[gemini-code-assist]

Summary of Changes

Hello @Wangmerlyn, 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 significantly enhances the GPU monitoring capabilities by transitioning from unreliable nvidia-smi command parsing to a direct NVML interface via the nvidia-ml-py library. This change provides a more stable and efficient way to query GPU utilization. Concurrently, the command-line interface has been optimized for clarity and user experience, introducing specific flags for utilization thresholds while maintaining backward compatibility for existing scripts that use the older, more ambiguous --threshold flag. These updates improve both the reliability of GPU state detection and the usability of the keep-gpu tool.

Highlights

• NVML Integration: Replaced nvidia-smi parsing with direct NVML (NVIDIA Management Library) integration for more robust GPU utilization monitoring.
• CLI Refinement: Introduced explicit --busy-threshold and --util-threshold flags for GPU utilization, deprecating the generic --threshold flag.
• Backward Compatibility: Ensured that the legacy --threshold flag still functions, intelligently parsing its value as either a VRAM amount or a busy threshold.
• New Module: Added a dedicated gpu_monitor utility module to encapsulate NVML interactions, including initialization, shutdown, and error handling.

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[gemini-code-assist]

Code Review

This pull request is a great improvement, replacing the fragile nvidia-smi subprocess call with a robust, thread-safe NVML monitor using nvidia-ml-py. The new NVMLMonitor is well-designed with proper initialization and atexit cleanup. The CLI updates to handle legacy flags with deprecation warnings are also a nice touch for backward compatibility. The addition of unit tests for the new functionality is excellent. I have one suggestion in gpu_monitor.py to make the exception handling more specific, but overall this is a solid contribution.