Failed to build CUDA engine

[osamaabdelatif]

Hi, I am trying to run object detection after building the project in my Jetson Nano,

detectnet --camera=csi://0

But i am getting below errors:
[TRT] 1: [convolutionRunner.cpp::executeConv::511] Error Code 1: Cudnn (CUDNN_STATUS_EXECUTION_FAILED)
[TRT] device GPU, failed to build CUDA engine
[TRT] device GPU, failed to load networks/SSD-Mobilenet-v2/ssd_mobilenet_v2_coco.uff
[TRT] detectNet -- failed to initialize.
detectnet: failed to load detectNet model

My environment as below:
Python 3.6.9

OS:

Linux ubuntu 4.9.337-tegra #1 SMP PREEMPT Tue Jun 11 16:07:05 PDT 2024 aarch64 aarch64 aarch64 GNU/Linux

#nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Sun_Feb_28_22:34:44_PST_2021
Cuda compilation tools, release 10.2, V10.2.300
Build cuda_10.2_r440.TC440_70.29663091_0

tensorrt version

Name: tensorrt
Version: 8.2.1.9

nvidia-l4t-core

nvidia-l4t-core 32.7.5-20240611161210

highly appreciating any advice to fix or overcome this issue !
Thank you,

[dusty-nv]

Hi @osamaabdelatif, have you been able to run any of the other examples, like imagenet.py images/jellyfish.jpg ? I am wondering if you cuDNN installation is working ok.

[osamaabdelatif]

Hi @dusty-nv
yea i got the same issue using imagenet.py images/jellyfish.jpg... How to check if cuDNN installed correctly, i used SDK Manager to flash the unit.

[TRT] 1: [convolutionRunner.cpp::executeConv::511] Error Code 1: Cudnn (CUDNN_STATUS_EXECUTION_FAILED)
[TRT] device GPU, failed to build CUDA engine
[TRT] device GPU, failed to load networks/Googlenet/bvlc_googlenet.caffemodel
[TRT] failed to load networks/Googlenet/bvlc_googlenet.caffemodel
[TRT] imageNet -- failed to initialize.

[osamaabdelatif]

Hi @dusty-nv
I have below packages installed

ii libcudnn8 8.2.1.32-1+cuda10.2 arm64 cuDNN runtime libraries
ii libcudnn8-dev 8.2.1.32-1+cuda10.2 arm64 cuDNN development libraries and headers
ii libcudnn8-samples 8.2.1.32-1+cuda10.2 arm64 cuDNN documents and samples

and running cat /usr/include/cudnn_version.h | grep CUDNN_MAJOR -A 2 i get below output to check the cuDNN version

#define CUDNN_MAJOR 8
#define CUDNN_MINOR 2
#define CUDNN_PATCHLEVEL 1

#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)

#endif /* CUDNN_VERSION_H */