Mai/.planning/phases/03-resource-management/03-01-SUMMARY.md

phase, plan, subsystem, tags, requires, provides, affects, tech-stack, key-files, key-decisions, patterns-established, duration, completed

phase	plan	subsystem	tags	requires	provides	affects	tech-stack	key-files	key-decisions	patterns-established	duration	completed
03-resource-management	01	resource-management	pynvml gpu-monitoring resource-detection performance-optimization	phase provides 02-safety Security assessment and sandboxing infrastructure	Enhanced ResourceMonitor with pynvml GPU detection Precise NVIDIA GPU VRAM monitoring capabilities Graceful fallback for non-NVIDIA GPUs and CPU-only systems Optimized resource monitoring with caching	03-02 03-03 03-04	added patterns pynvml>=11.0.0 GPU detection with fallback resource monitoring caching performance optimization	created modified pyproject.toml src/models/resource_monitor.py	Use pynvml for precise NVIDIA GPU monitoring Implement graceful fallback to gpu-tracker for AMD/Intel GPUs Add caching to avoid repeated pynvml initialization overhead Track pynvml failures to skip repeated failed attempts	Pattern 1: GPU detection with primary library (pynvml) and fallback (gpu-tracker) Pattern 2: Resource monitoring with performance caching Pattern 3: Graceful degradation when GPU unavailable	8min	2026-01-27

Phase 3 Plan 1: Enhanced GPU Detection Summary

Enhanced ResourceMonitor with pynvml support for precise NVIDIA GPU VRAM tracking and graceful fallback across different hardware configurations.

Performance

• Duration: 8 min
• Started: 2026-01-27T23:13:14Z
• Completed: 2026-01-27T23:21:29Z
• Tasks: 2
• Files modified: 2

Accomplishments

• Added pynvml>=11.0.0 dependency to pyproject.toml for NVIDIA GPU support
• Enhanced ResourceMonitor with comprehensive GPU detection using pynvml as primary library
• Implemented detailed GPU metrics: total/used/free VRAM, utilization, temperature
• Added graceful fallback to gpu-tracker for AMD/Intel GPUs or when pynvml fails
• Optimized performance with caching and failure tracking to reduce overhead from ~1000ms to ~50ms
• Maintained backward compatibility with existing gpu_vram_gb field
• Enhanced get_current_resources() to return 9 GPU-related metrics
• Added proper pynvml initialization and shutdown with error handling

Task Commits

1. Task 1: Add pynvml dependency - e202375 (feat)
2. Task 2: Enhance ResourceMonitor with pynvml - 8cf9e9a (feat)
3. Task 2 optimization - 0ad2b39 (perf)

Plan metadata: (included in task commits)

Files Created/Modified

• pyproject.toml - Added pynvml>=11.0.0 dependency for NVIDIA GPU monitoring
• src/models/resource_monitor.py - Enhanced with pynvml GPU detection, caching, and performance optimizations (368 lines)

Decisions Made

• Primary library choice: Selected pynvml as primary GPU detection library for NVIDIA GPUs due to its precision and official NVIDIA support
• Fallback strategy: Implemented gpu-tracker as fallback for AMD/Intel GPUs and when pynvml initialization fails
• Performance optimization: Added caching mechanism to avoid repeated pynvml initialization overhead which can be expensive
• Failure tracking: Added pynvml failure flag to skip repeated initialization attempts after first failure
• Backward compatibility: Maintained existing gpu_vram_gb field to ensure no breaking changes for existing code

Deviations from Plan

None - plan executed exactly as written with additional performance optimizations to meet the < 1% CPU overhead requirement.

Issues Encountered

• Performance issue: Initial implementation had ~1000ms overhead due to psutil.cpu_percent(interval=1.0) blocking for 1 second
  • Resolution: Reduced interval to 0.05s and added GPU info caching to achieve ~50ms average call time
• pynvml initialization overhead: Repeated pynvml initialization failures caused performance degradation
  • Resolution: Added failure tracking flag to skip repeated pynvml attempts after first failure

User Setup Required

None - no external service configuration required.

Next Phase Readiness

ResourceMonitor now provides:

• Accurate NVIDIA GPU VRAM monitoring via pynvml when available
• Graceful fallback to gpu-tracker for other GPU vendors
• Detailed GPU metrics (total/used/free VRAM, utilization, temperature)
• Optimized performance (~50ms per call) with caching
• Cross-platform compatibility (Linux, Windows, macOS)
• Backward compatibility with existing resource monitoring interface

Ready for next phase plans that will use enhanced GPU detection for intelligent model selection and proactive scaling decisions.

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Phase: 03-resource-management Completed: 2026-01-27