Comments (2)
deadeyegoodwin
commented on May 18, 2024
You need to be able to pull containers from registry on NGC (NVIDIA GPU Cloud). You can register on the site to get an API key and then "docker login" with that key to be able to pull (I think there is a guest access now too that doesn't require registration). The docs give instructions for this: https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-master-branch-guide/docs/build.html#building
If those instruction aren't clear let us know so we can improve them.
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SashaOv
commented on May 18, 2024
Yep I missed this sentence in the doc 😳. Thank you, I can pull now.
As a suggestion, you may want to put prerequisites in a separate section in the beginning so it would be harder to miss.
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Related Issues (20)
- 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
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- On server/deploy/oci -> running "helm install example ." to deploy the Inference Server and pod doesn't get to running due to Liveness probe failed & Readiness probe failed HOT 1
- trt_profile_max_shapes not supported for ONNX-TRT backend HOT 1
- Failed to initialize Python stub + ModuleNotFoundError: No module named 'nvtabular', 'merlin' HOT 1
- does triton support different model-repository assemble into a batch? HOT 1
- Question: Which backends automatically warm up models? HOT 1
- [Question] Is it possible to shutdown Triton if we detect certain cuda errors ? HOT 1
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- Python Backend: one model instance over multiple GPUs HOT 2
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