Hi Nvidia,
We ran a deepstream application on xavier nx 16g with jetpack 4.6.2( R32 (release), REVISION: 7.2,), but got the following errors:
nvidia@smartcow-malta:~/Desktop$ deepstream-app -c smartcam_sample_app.txt
DeepStream: Launched RTSP Streaming at rtsp://localhost:8554/ds-test
Opening in BLOCKING MODE
ERROR: Deserialize engine failed because file path: /opt/nvidia/deepstream/deepstream/samples/models/Primary_Detector/resnet10.caffemodel_b30_gpu0_int8.engine open error
0:00:03.629718368 8844 0x22d5f640 WARN nvinfer gstnvinfer.cpp:635:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1889> [UID = 1]: deserialize engine from file :/opt/nvidia/deepstream/deepstream/samples/models/Primary_Detector/resnet10.caffemodel_b30_gpu0_int8.engine failed
0:00:03.651418048 8844 0x22d5f640 WARN nvinfer gstnvinfer.cpp:635:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1996> [UID = 1]: deserialize backend context from engine from file :/opt/nvidia/deepstream/deepstream/samples/models/Primary_Detector/resnet10.caffemodel_b30_gpu0_int8.engine failed, try rebuild
0:00:03.651529376 8844 0x22d5f640 INFO nvinfer gstnvinfer.cpp:638:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1914> [UID = 1]: Trying to create engine from model files
ERROR: [TRT]: 2: [utils.cpp::checkMemLimit::380] Error Code 2: Internal Error (Assertion upperBound != 0 failed. Unknown embedded device detected. Please update the table with the entry: {{1794, 6, 16}, 12653},)
ERROR: Build engine failed from config file
ERROR: failed to build trt engine.
0:00:07.499672226 8844 0x22d5f640 ERROR nvinfer gstnvinfer.cpp:632:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1934> [UID = 1]: build engine file failed
0:00:07.521425537 8844 0x22d5f640 ERROR nvinfer gstnvinfer.cpp:632:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:2020> [UID = 1]: build backend context failed
0:00:07.521525091 8844 0x22d5f640 ERROR nvinfer gstnvinfer.cpp:632:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::initialize() <nvdsinfer_context_impl.cpp:1257> [UID = 1]: generate backend failed, check config file settings
0:00:07.521624613 8844 0x22d5f640 WARN nvinfer gstnvinfer.cpp:841:gst_nvinfer_start:<primary_gie> error: Failed to create NvDsInferContext instance
0:00:07.521658182 8844 0x22d5f640 WARN nvinfer gstnvinfer.cpp:841:gst_nvinfer_start:<primary_gie> error: Config file path: /opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_infer_primary.txt, NvDsInfer Error: NVDSINFER_CONFIG_FAILED
** ERROR: main:707: Failed to set pipeline to PAUSED
Quitting
ERROR from primary_gie: Failed to create NvDsInferContext instance
Debug info: /dvs/git/dirty/git-master_linux/deepstream/sdk/src/gst-plugins/gst-nvinfer/gstnvinfer.cpp(841): gst_nvinfer_start (): /GstPipeline:pipeline/GstBin:primary_gie_bin/GstNvInfer:primary_gie:
Config file path: /opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_infer_primary.txt, NvDsInfer Error: NVDSINFER_CONFIG_FAILED
App run failed
This is my application info:
nvidia@smartcow-malta:~/Desktop$ deepstream-app --version-all
deepstream-app version 6.0.1
DeepStreamSDK 6.0.1
CUDA Driver Version: 10.2
CUDA Runtime Version: 10.2
TensorRT Version: 8.2
cuDNN Version: 8.2
libNVWarp360 Version: 2.0.1d3
nvidia@smartcow-malta:~$ dpkg -l | grep TensorRT
ii graphsurgeon-tf 8.2.1-1+cuda10.2 arm64 GraphSurgeon for TensorRT package
ii libnvinfer-bin 8.2.1-1+cuda10.2 arm64 TensorRT binaries
ii libnvinfer-dev 8.2.1-1+cuda10.2 arm64 TensorRT development libraries and headers
ii libnvinfer-doc 8.2.1-1+cuda10.2 all TensorRT documentation
ii libnvinfer-plugin-dev 8.2.1-1+cuda10.2 arm64 TensorRT plugin libraries
ii libnvinfer-plugin8 8.2.1-1+cuda10.2 arm64 TensorRT plugin libraries
ii libnvinfer-samples 8.2.1-1+cuda10.2 all TensorRT samples
ii libnvinfer8 8.2.1-1+cuda10.2 arm64 TensorRT runtime libraries
ii libnvonnxparsers-dev 8.2.1-1+cuda10.2 arm64 TensorRT ONNX libraries
ii libnvonnxparsers8 8.2.1-1+cuda10.2 arm64 TensorRT ONNX libraries
ii libnvparsers-dev 8.2.1-1+cuda10.2 arm64 TensorRT parsers libraries
ii libnvparsers8 8.2.1-1+cuda10.2 arm64 TensorRT parsers libraries
ii nvidia-tensorrt 4.6.2-b5 arm64 NVIDIA TensorRT Meta Package
ii python3-libnvinfer 8.2.1-1+cuda10.2 arm64 Python 3 bindings for TensorRT
ii python3-libnvinfer-dev 8.2.1-1+cuda10.2 arm64 Python 3 development package for TensorRT
ii tensorrt 8.2.1.8-1+cuda10.2 arm64 Meta package of TensorRT
ii uff-converter-tf 8.2.1-1+cuda10.2 arm64 UFF converter for TensorRT package
I also found the workaround on the following link:
Could you help to share the modified libraries again?