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Phase 3: Resource Management - 4 plan(s) in 2 wave(s) - 2 parallel, 2 sequential - Ready for execution
113 lines
4.3 KiB
Markdown
113 lines
4.3 KiB
Markdown
---
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phase: 03-resource-management
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plan: 01
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type: execute
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wave: 1
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depends_on: []
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files_modified: [pyproject.toml, src/models/resource_monitor.py]
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autonomous: true
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user_setup: []
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must_haves:
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truths:
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- "Enhanced resource monitor can detect NVIDIA GPU VRAM using pynvml"
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- "GPU detection falls back gracefully when GPU unavailable"
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- "Resource monitoring remains cross-platform compatible"
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artifacts:
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- path: "src/models/resource_monitor.py"
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provides: "Enhanced GPU detection with pynvml support"
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contains: "pynvml"
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min_lines: 250
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- path: "pyproject.toml"
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provides: "pynvml dependency for GPU monitoring"
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contains: "pynvml"
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key_links:
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- from: "src/models/resource_monitor.py"
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to: "pynvml library"
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via: "import pynvml"
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pattern: "import pynvml"
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---
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<objective>
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Enhance GPU detection and monitoring capabilities by integrating pynvml for precise NVIDIA GPU VRAM tracking while maintaining cross-platform compatibility and graceful fallbacks.
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Purpose: Provide accurate GPU resource detection for intelligent model selection and proactive scaling decisions.
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Output: Enhanced ResourceMonitor with reliable GPU VRAM monitoring across different hardware configurations.
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</objective>
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<execution_context>
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@~/.opencode/get-shit-done/workflows/execute-plan.md
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@~/.opencode/get-shit-done/templates/summary.md
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</execution_context>
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<context>
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@.planning/PROJECT.md
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@.planning/ROADMAP.md
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@.planning/STATE.md
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# Current implementation
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@src/models/resource_monitor.py
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@pyproject.toml
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</context>
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<tasks>
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<task type="auto">
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<name>Add pynvml dependency to project</name>
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<files>pyproject.toml</files>
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<action>Add pynvml>=11.0.0 to the main dependencies array in pyproject.toml. This ensures NVIDIA GPU monitoring capabilities are available by default rather than being optional.</action>
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<verify>grep -n "pynvml" pyproject.toml shows the dependency added correctly</verify>
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<done>pynvml dependency is available for GPU monitoring</done>
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</task>
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<task type="auto">
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<name>Enhance ResourceMonitor with pynvml GPU detection</name>
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<files>src/models/resource_monitor.py</files>
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<action>
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Enhance the _get_gpu_memory() method to use pynvml for precise NVIDIA GPU VRAM detection:
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1. Add pynvml import at the top of the file
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2. Replace the current _get_gpu_memory() implementation with pynvml-based detection:
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- Initialize pynvml with proper error handling
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- Get GPU handle and memory info using pynvml APIs
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- Return total, used, and free VRAM in GB
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- Handle NVMLError gracefully and fallback to existing gpu-tracker logic
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- Ensure pynvmlShutdown() is always called in finally block
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3. Update get_current_resources() to include detailed GPU info:
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- gpu_total_vram_gb: Total VRAM capacity
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- gpu_used_vram_gb: Currently used VRAM
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- gpu_free_vram_gb: Available VRAM
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- gpu_utilization_percent: GPU utilization (if available)
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4. Add GPU temperature monitoring if available via pynvml
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5. Maintain backward compatibility with existing return format
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The enhanced GPU detection should:
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- Try pynvml first for NVIDIA GPUs
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- Fall back to gpu-tracker for other vendors
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- Return 0 values if no GPU detected
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- Handle all exceptions gracefully
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- Log GPU detection results at debug level
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</action>
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<verify>python -c "from src.models.resource_monitor import ResourceMonitor; rm = ResourceMonitor(); resources = rm.get_current_resources(); print('GPU detection:', {k: v for k, v in resources.items() if 'gpu' in k})" returns GPU metrics without errors</verify>
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<done>ResourceMonitor provides accurate GPU VRAM monitoring using pynvml with proper fallbacks</done>
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</task>
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</tasks>
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<verification>
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Test enhanced resource monitoring across different configurations:
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- Systems with NVIDIA GPUs (pynvml should work)
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- Systems with AMD/Intel GPUs (fallback to gpu-tracker)
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- Systems without GPUs (graceful zero values)
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- Cross-platform compatibility (Linux, Windows, macOS)
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Verify monitoring overhead remains < 1% CPU usage.
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</verification>
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<success_criteria>
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ResourceMonitor successfully detects and reports GPU VRAM using pynvml when available, falls back gracefully to other methods, maintains cross-platform compatibility, and provides detailed GPU metrics for intelligent model selection.
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</success_criteria>
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<output>
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After completion, create `.planning/phases/03-resource-management/03-01-SUMMARY.md`
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</output> |