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* Use re-usable inference request to reduce CPU usage * Share tensor * Don't count performance * Create openvino runner class * Break apart onnx runner * Add specific note about inability to use CUDA graphs for some models * Adjust rknn to use RKNNRunner * Use optimized runner * Add support for non-complex models for CudaExecutionProvider * Use core mask for rknn * Correctly handle cuda input * Cleanup * Sort imports |
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.. | ||
onnx | ||
__init__.py | ||
embeddings.py | ||
maintainer.py | ||
util.py |