Comments (1)
I have successfully performed asynchronous inference using shared memory. The key is to add await before the function for registering shared memory.
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Related Issues (20)
- Support histogram custom metric in Python backend HOT 2
- Backend support for .keras files?
- triton-inference-server cannot be started HOT 1
- Incorrect asset tritonserver2.35.0-jetpack5.1.2-update-2.tgz HOT 1
- How does Triton implement one instance to handle multiple requests simultaneously? HOT 1
- ONNX backend with TensorRT optimizer sometimes fails to start HOT 1
- Any example of triton-vllm with c++ client?
- Tritonserver for FIL backend not starting HOT 1
- Why is my model in ensemble receiving out-of-order input HOT 2
- Add TT-Metalium as a backend
- unexpected datatype TYPE_INT64 for inference input ,expecting TYPE_INT32 HOT 1
- triton malloc fail HOT 7
- Peaks in instantaneous traffic lead to high TP99 inference latency. HOT 2
- Low QPS with momentary traffic surges cause significant increases in inference TP99 latency.
- Single docker layer is too large
- Memory over 100% with decoupled dali video model
- When the request is large, the Triton server has a very high TTFT.
- Uneven QPS leads to low throughput and high latency as well as low GPU utilization
- Triton Server 24.05 can't detect CUDA drivers if host system has installed Nvidia driver 555.85
- Building and developing with libtritonserver.so
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