---
phase: 03-resource-management
plan: 01
subsystem: resource-management
tags: [pynvml, gpu-monitoring, resource-detection, performance-optimization]

# Dependency graph
requires:
  - phase: 02-safety
    provides: "Security assessment and sandboxing infrastructure"
provides:
  - 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
affects: [03-02, 03-03, 03-04]

# Tech tracking
tech-stack:
  added: [pynvml>=11.0.0]
  patterns: ["GPU detection with fallback", "resource monitoring caching", "performance optimization"]

key-files:
  created: []
  modified: [pyproject.toml, src/models/resource_monitor.py]

key-decisions:
  - "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"

patterns-established:
  - "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"

# Metrics
duration: 8min
completed: 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.

---

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