13 Commits

Author SHA1 Message Date
b316c7c68a ignore timeout to prevent error 2025-07-24 12:13:57 +08:00
bab06b9fa4 Merge branch 'main' of github.com:HuangMason320/cluster4npu 2025-07-24 11:56:32 +08:00
bc92761a83 fix: Optimize multi-dongle inference for proper parallel processing
- Enable USB timeout (5000ms) for stable communication
- Fix send thread timeout from 0.01s to 1.0s for better blocking
- Update WebcamInferenceRunner to use async pattern (non-blocking)
- Add non-blocking put_input option to prevent frame drops
- Improve thread stopping mechanism with better cleanup

These changes follow Kneron official example pattern and should
enable proper parallel processing across multiple dongles.

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

Co-Authored-By: Claude <noreply@anthropic.com>
2025-07-24 10:39:20 +08:00
cb9dff10a9 fix: Correct device scanning to access device_descriptor_list properly
Fixed DeviceDescriptorList object attribute error by properly accessing
the device_descriptor_list attribute instead of treating the result as
a direct list of devices.

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

Co-Authored-By: Claude <noreply@anthropic.com>
2025-07-24 10:13:17 +08:00
183b5659b7 feat: Integrate dongle model detection and refactor scan_devices
This commit integrates the dongle model detection logic into .
It refactors the  method to:
- Handle  in list or object format.
- Extract  and  for each device.
- Use  to identify dongle models.
- Return a more detailed device information structure.

The previously deleted files were moved to the  directory.
2025-07-24 10:01:56 +08:00
e97fd7a025 fix: Resolve remaining numpy array comparison errors in MultiDongle
- Fix ambiguous truth value error in get_latest_inference_result method
- Fix ambiguous truth value error in postprocess function
- Replace direct array evaluation with explicit length checks
- Use proper None checks instead of truthy evaluation on numpy arrays

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

Co-Authored-By: Claude <noreply@anthropic.com>
2025-07-17 10:11:38 +08:00
c94eb5ee30 fix import path problem in deployment.py 2025-07-17 09:25:07 +08:00
049dedf2f7 Fix firmware path initialization and upload logic in MultiDongle
- Always store firmware paths (scpu_fw_path, ncpu_fw_path) when provided, not just when upload_fw=True
- Restore firmware upload condition to only run when upload_fw=True
- Fix 'MultiDongle' object has no attribute 'scpu_fw_path' error during pipeline initialization
- Ensure firmware paths are available for both upload and non-upload scenarios

This resolves the pipeline deployment error where firmware paths were missing
even when provided to the constructor, causing initialization failures.

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

Co-Authored-By: Claude <noreply@anthropic.com>
2025-07-16 22:11:42 +08:00
e34cdfb856 Add TODO comment and device log 2025-07-16 21:53:31 +08:00
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
9020be5e7a Add Kneron device auto-detection and connection features
- Add scan_devices() method using kp.core.scan_devices() for device discovery
- Add connect_auto_detected_devices() for automatic device connection
- Add device series detection (KL520, KL720, KL630, KL730, KL540, etc.)
- Add auto_detect parameter to MultiDongle constructor
- Add get_device_info() and print_device_info() methods to display port IDs and series
- Update connection logic to use kp.core.connect_devices() per official docs
- Add device_detection_example.py with usage examples
- Maintain backward compatibility with manual port specification

Features display dongle series and port ID as requested for better device management.

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

Co-Authored-By: Claude <noreply@anthropic.com>
2025-07-16 21:13:33 +08:00
f5e017b099 Fix DataProcessor missing class error in pipeline deployment
- Add DataProcessor abstract base class with process method
- Add PostProcessor class for handling inference output data
- Fix PreProcessor inheritance from DataProcessor
- Resolves "name 'DataProcessor' is not defined" error during pipeline deployment

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

Co-Authored-By: Claude <noreply@anthropic.com>
2025-07-16 20:53:41 +08:00
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