Performance Tuning Tips
Solutions for resource-constrained environments:
- Component Streamlining: load only the necessary tools (e.g. time/file/math tools on demand in demo.py)
- asynchronous optimization: set uv loop strategy wisely (suggest epoll for linux, proactor for windows)
- caching strategy: Enable response caching for HttpLLM (add cache_ttl parameter)
- hardware adaptation: disable visual debugging (set debug=False at startup) to save memory
Edge computing scenario example: when deploying in Raspberry Pi, prioritize the use of lightweight models (e.g. Phi-3), turn off topology visualization, and control semaphore to less than 2, and still run the smart home control scenario stably.
This answer comes from the articleOxyGent: Python open source framework for rapidly building intelligent systemsThe
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