• Hardware Platform : GPU
• DeepStream Version : 5.0
• NVIDIA GPU Driver Version : 440.64
I’m trying python deepstream-ssd example (it uses triton for infer), on deepstream-5 docker image, container is spawed with recommend config(shm, ulimit etc)
I added multi-rtsp support to it, but the gpu core usage(not memory utilization) doesn’t go above 40%, eventually the app will crash cause of memory when channels are too many.
Sometimes util even goes down a couple percent on avg when channels are increased.
Tried all the recommendation settings in troubleshooting section : increasing buffer-surface, set sink to sync=0, gave them all gpu-id=0
I also removed all the plugins after pgie to get clarity and tried changing memory allocation of model from triton config file.
I also read triton’s optimization guide and added dynamic-batching and tensorrt acceleration, however I wasn’t able to change no of instance from 1->2 as the app would stop running,
I have multi-gpu config, other gpus also automatically occupy ~600MB when app starts although there’s no utilization in them
I’m attaching the required two tars, one is code and second is triton model with config.
Command : python3 deepstream_ssd_parser.py <no_of_copies_to_make_from_the_url>
Edit : clarified meaning of utilization