Comments (2)
Is there a reason you are using 18.11 branch. The 19.02 branch was just released, can you try with that.
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i tried the the 19.02 branch, it works, thanks!
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Related Issues (20)
- `unable to create cuda shared memory handle` when using multiprocessing to send multiple requests HOT 5
- Can we include commonly used data pre-processing library in triton server docker image? HOT 2
- Multi instance a model in GPU does not increase the throughput in Triton. HOT 1
- Interaction of timeouts, ensemble scheduler and oldest sequence scheduler causes state leakage HOT 15
- error running simple example HOT 14
- How to extract model states stored in Triton (Implicit State Management) HOT 3
- Copy GPU Tensor to CPU on Python backend without using pytorch HOT 2
- conda-pack failing: Failed to initialize Python stub for auto-complete HOT 4
- Dynamic batching that supports static batch size with padding HOT 10
- UNAVAILABLE: Internal: FileNotFoundError: [Errno2] No usable temporary directory found in ['/tp', '/var/tmp','/usr/tmp', '/tmp/python_Lsk/3'] env_6Lp HOT 1
- How does share memory speed up inference? HOT 6
- Memorystore Redis IAM AUTH
- Response caching GPU tensors HOT 1
- Abnormal system memory usage while enabling GPU metrics HOT 1
- Request for Improved Metrics and Real-Time Concurrency Reporting in Triton Inference Server
- Python AsyncIO infer does not support shared memory HOT 1
- client silent failure - E0422 05:03:24.145960 1 pb_stub.cc:402] An error occurred while trying to load GPU buffers in the Python backend stub: failed to copy data: invalid argument HOT 3
- CUDA Graph not work HOT 4
- [RFE] HandleGenerate equivalent for sagemaker_server.cc HOT 1
- The time spent on the inference request process far exceeds the model inference time. How can I determine where this additional time is being consumed?
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