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Add a Python-based concurrent runner that executes multiple Qwen CLI tasks
across different models in parallel using isolated git worktrees.
Features:
- Execute N tasks × M models concurrently with configurable concurrency
- Create isolated git worktrees for each run under ~/.qwen/worktrees
- Real-time progress display with Rich library
- Capture stdout, stderr, and OpenAI API logs per run
- Atomic JSON tracking of all runs with status and metadata
- Automatic cleanup of worktrees after each run
Structure:
- runner.py: Main implementation with asyncio
- requirements.txt: Python dependencies
- README.md: Documentation and usage
- config.example.json: Configuration template
- examples/: Toy example with sample config and project
Usage:
python runner.py config.json
Each run creates an isolated output directory:
outputs/{run_id}/
├── stdout.txt
├── stderr.txt
└── logs/openai-*.json
Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
11 lines
214 B
JSON
11 lines
214 B
JSON
{
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"name": "toy-project",
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"version": "1.0.0",
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"description": "Minimal toy project for testing",
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"scripts": {
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"build": "echo 'Build complete!'"
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},
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"keywords": [],
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"author": "",
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"license": "MIT"
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}
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