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Tasks completed: 2/2 - Added pynvml>=11.0.0 dependency for NVIDIA GPU monitoring - Enhanced ResourceMonitor with pynvml GPU detection and graceful fallbacks - Optimized performance with caching and failure tracking (~50ms per call) SUMMARY: .planning/phases/03-resource-management/03-01-SUMMARY.md
117 lines
4.6 KiB
Markdown
117 lines
4.6 KiB
Markdown
---
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phase: 03-resource-management
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plan: 01
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subsystem: resource-management
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tags: [pynvml, gpu-monitoring, resource-detection, performance-optimization]
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# Dependency graph
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requires:
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- phase: 02-safety
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provides: "Security assessment and sandboxing infrastructure"
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provides:
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- Enhanced ResourceMonitor with pynvml GPU detection
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- Precise NVIDIA GPU VRAM monitoring capabilities
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- Graceful fallback for non-NVIDIA GPUs and CPU-only systems
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- Optimized resource monitoring with caching
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affects: [03-02, 03-03, 03-04]
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# Tech tracking
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tech-stack:
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added: [pynvml>=11.0.0]
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patterns: ["GPU detection with fallback", "resource monitoring caching", "performance optimization"]
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key-files:
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created: []
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modified: [pyproject.toml, src/models/resource_monitor.py]
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key-decisions:
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- "Use pynvml for precise NVIDIA GPU monitoring"
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- "Implement graceful fallback to gpu-tracker for AMD/Intel GPUs"
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- "Add caching to avoid repeated pynvml initialization overhead"
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- "Track pynvml failures to skip repeated failed attempts"
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patterns-established:
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- "Pattern 1: GPU detection with primary library (pynvml) and fallback (gpu-tracker)"
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- "Pattern 2: Resource monitoring with performance caching"
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- "Pattern 3: Graceful degradation when GPU unavailable"
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# Metrics
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duration: 8min
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completed: 2026-01-27
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---
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# Phase 3 Plan 1: Enhanced GPU Detection Summary
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**Enhanced ResourceMonitor with pynvml support for precise NVIDIA GPU VRAM tracking and graceful fallback across different hardware configurations.**
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## Performance
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- **Duration:** 8 min
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- **Started:** 2026-01-27T23:13:14Z
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- **Completed:** 2026-01-27T23:21:29Z
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- **Tasks:** 2
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- **Files modified:** 2
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## Accomplishments
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- Added pynvml>=11.0.0 dependency to pyproject.toml for NVIDIA GPU support
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- Enhanced ResourceMonitor with comprehensive GPU detection using pynvml as primary library
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- Implemented detailed GPU metrics: total/used/free VRAM, utilization, temperature
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- Added graceful fallback to gpu-tracker for AMD/Intel GPUs or when pynvml fails
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- Optimized performance with caching and failure tracking to reduce overhead from ~1000ms to ~50ms
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- Maintained backward compatibility with existing gpu_vram_gb field
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- Enhanced get_current_resources() to return 9 GPU-related metrics
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- Added proper pynvml initialization and shutdown with error handling
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## Task Commits
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1. **Task 1: Add pynvml dependency** - `e202375` (feat)
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2. **Task 2: Enhance ResourceMonitor with pynvml** - `8cf9e9a` (feat)
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3. **Task 2 optimization** - `0ad2b39` (perf)
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**Plan metadata:** (included in task commits)
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## Files Created/Modified
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- `pyproject.toml` - Added pynvml>=11.0.0 dependency for NVIDIA GPU monitoring
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- `src/models/resource_monitor.py` - Enhanced with pynvml GPU detection, caching, and performance optimizations (368 lines)
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## Decisions Made
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- **Primary library choice**: Selected pynvml as primary GPU detection library for NVIDIA GPUs due to its precision and official NVIDIA support
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- **Fallback strategy**: Implemented gpu-tracker as fallback for AMD/Intel GPUs and when pynvml initialization fails
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- **Performance optimization**: Added caching mechanism to avoid repeated pynvml initialization overhead which can be expensive
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- **Failure tracking**: Added pynvml failure flag to skip repeated initialization attempts after first failure
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- **Backward compatibility**: Maintained existing gpu_vram_gb field to ensure no breaking changes for existing code
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## Deviations from Plan
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None - plan executed exactly as written with additional performance optimizations to meet the < 1% CPU overhead requirement.
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## Issues Encountered
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- **Performance issue**: Initial implementation had ~1000ms overhead due to psutil.cpu_percent(interval=1.0) blocking for 1 second
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- **Resolution**: Reduced interval to 0.05s and added GPU info caching to achieve ~50ms average call time
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- **pynvml initialization overhead**: Repeated pynvml initialization failures caused performance degradation
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- **Resolution**: Added failure tracking flag to skip repeated pynvml attempts after first failure
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## User Setup Required
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None - no external service configuration required.
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## Next Phase Readiness
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ResourceMonitor now provides:
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- Accurate NVIDIA GPU VRAM monitoring via pynvml when available
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- Graceful fallback to gpu-tracker for other GPU vendors
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- Detailed GPU metrics (total/used/free VRAM, utilization, temperature)
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- Optimized performance (~50ms per call) with caching
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- Cross-platform compatibility (Linux, Windows, macOS)
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- Backward compatibility with existing resource monitoring interface
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Ready for next phase plans that will use enhanced GPU detection for intelligent model selection and proactive scaling decisions.
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---
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*Phase: 03-resource-management*
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*Completed: 2026-01-27* |