masonhuang/cluster4npu
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4 Commits

Author SHA1 Message Date
Masonmason
183300472e fix: Resolve array comparison error and add inference stop functionality 
- Fix ambiguous truth value error in InferencePipeline result handling
- Add stop inference button to deployment dialog with proper UI state management
- Improve error handling for tuple vs dict result types

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-07-17 09:46:31 +08:00
Masonmason
af9adc8e82 fix: Address file path and data processing bugs, add real-time viewer 2025-07-17 09:18:27 +08:00
Masonmason
e0169cd845 Fix device detection format and pipeline deployment compatibility 
Device Detection Updates:
- Update device series detection to use product_id mapping (0x100 -> KL520, 0x720 -> KL720)
- Handle JSON dict format from kp.core.scan_devices() properly
- Extract usb_port_id correctly from device descriptors
- Support multiple device descriptor formats (dict, list, object)
- Enhanced debug output shows Product ID for verification

Pipeline Deployment Fixes:
- Remove invalid preprocessor/postprocessor parameters from MultiDongle constructor
- Add max_queue_size parameter support to MultiDongle
- Fix pipeline stage initialization to match MultiDongle constructor
- Add auto_detect parameter support for pipeline stages
- Store stage processors as instance variables for future use

Example Updates:
- Update device_detection_example.py to show Product ID in output
- Enhanced error handling and format detection

Resolves pipeline deployment error: "MultiDongle.__init__() got an unexpected keyword argument 'preprocessor'"
Now properly handles real device descriptors with correct product_id to series mapping.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-07-16 21:45:14 +08:00
Masonmason
080eb5b887 Add intelligent pipeline topology analysis and comprehensive UI framework 
Major Features:
• Advanced topological sorting algorithm with cycle detection and resolution
• Intelligent pipeline optimization with parallelization analysis
• Critical path analysis and performance metrics calculation
• Comprehensive .mflow file converter for seamless UI-to-API integration
• Complete modular UI framework with node-based pipeline editor
• Enhanced model node properties (scpu_fw_path, ncpu_fw_path)
• Professional output formatting without emoji decorations

Technical Improvements:
• Graph theory algorithms (DFS, BFS, topological sort)
• Automatic dependency resolution and conflict prevention
• Multi-criteria pipeline optimization
• Real-time stage count calculation and validation
• Comprehensive configuration validation and error handling
• Modular architecture with clean separation of concerns

New Components:
• MFlow converter with topology analysis (core/functions/mflow_converter.py)
• Complete node system with exact property matching
• Pipeline editor with visual node connections
• Performance estimation and dongle management panels
• Comprehensive test suite and demonstration scripts

🤖 Generated with Claude Code (https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-07-10 12:58:47 +08:00