• Hardware Platform (GPU) - NVIDIA GeForce RTX 3080
• DeepStream Version -6.2
• TensorRT Version - 8.5.2-1+cuda11.8
• NVIDIA GPU Driver Version- 525.105.17
• Deepstream is used inside deepstream:6.2-triton docker container and container is built with the command
docker run --net=host --ipc=host -it --gpus all -e DISPLAY=$DISPLAY --name deepstream_trition deepstream_triton_image
**• The issue **
- I am trying to use my custom detector model which implements Yolov5 as baseline. But I got the App Failed with **Segmentation fault(core dumped) message.
**• How to reproduce the issue ?
For using my custom detector, I refered to the Deepstream-Yolo github repo. I generated cfg file and wts file according to the repository. And my pretrained custom model is converted to TensorRT engine using “trtexec”.
Before that, I tried to run original Yolo sampe sucessfully provided by Deepstream under
/opt/nvidia/deepstream/deepstream-6.2/sources/objectDetector_Yolo/deepstream_app_config_yoloV3.txt .But I notice one thing that my custom detector is different from deepstream original sample is that I don’t have “kernel.cu” and “kernel.o”.
If not having that 2 files (kernel.cu and kernel.o) is actual issue, please help me with how I can create those files.
I’ve been working on the issue for a week.
Any kind of help is sincerely accepted~~
Thanks in advanced~