I am trying to combine test4 and nvdsanalytics and also trying to display using RTSP. Do you have any samples. I keep getting pipeline could not be made.
*** DeepStream: Launched RTSP Streaming at rtsp://localhost:8554/ds-test ***
Failed to load config file: Key file contains line ?infer_config {? which is not a key-value pair, group, or comment
** ERROR: <gst_nvinfer_parse_config_file:1158>: failed
0:00:00.434796374 9027 0x55a2d5dd2a00 WARN nvinfer gstnvinfer.cpp:747:gst_nvinfer_start:<primary_gie> error: Configuration file parsing failed
0:00:00.434816632 9027 0x55a2d5dd2a00 WARN nvinfer gstnvinfer.cpp:747:gst_nvinfer_start:<primary_gie> error: Config file path: /opt/nvidia/deepstream/deepstream-5.0/samples/configs/deepstream-app-trtis/config_infer_primary_detector_ssd_inception_v2_coco_2018_01_28.txt
** ERROR: main:651: Failed to set pipeline to PAUSED
Quitting
ERROR from primary_gie: Configuration file parsing failed
Debug info: gstnvinfer.cpp(747): gst_nvinfer_start (): /GstPipeline:pipeline/GstBin:primary_gie_bin/GstNvInfer:primary_gie:
Config file path: /opt/nvidia/deepstream/deepstream-5.0/samples/configs/deepstream-app-trtis/config_infer_primary_detector_ssd_inception_v2_coco_2018_01_28.txt
App run failed
This is due to the mismatch in the file format that is expected.
This is strange. I was able to run these apps on the NVIDIA Docker images. But, when I built these on the local machine, the above error occurs. Any input will be appreciated.
The problem seems to be due to using “deepstream-app-trtis” config file for ‘standard’ (non Trtiton Inference Server) environment. Using a ‘standard’ “deepstream-app” config file solved the problem.
This is a very basic question:
The performance on a Ubuntu 18.04, with dGPU (RTX2080) is poor. %CPU is very high 50-70% where as %GPU is low 5%-7% as compared to running under the docker container on the same machine.
I couldn’t get any reference to do the setup (environmental variables, enabling CUDA etc on the NVIDIA Quick Start pages (of 5.0). I re-used some of the items from the docker container (5.0-x-3-devel) but that didn’t help.
Any suggestions or pointers on how to utilize the GPU on a Ubuntu system?
nvidia-smi provides this output for running it outside the container: