I was testing a project performance on a system with a Titan RTX the frame rate drops to ~3 fps when the GPU still has a lot of VRAM, CPU, and RAM available, and while the GPU isn’t at 100% utilization what is the cause for that ? the model used is a converted yolo v4 [image] [image] **• Hard…

could you refer to GitHub - NVIDIA-AI-IOT/deepstream_tao_apps: Sample apps to demonstrate how to deploy models trained with TAO on DeepStream to use trtexec to profile the inference perf of the TRT engine which has only 3.8 fps? Thanks!

And, you can extract the trtexec from the TensorRT tar package that can be downloaded from https://developer.nvidia.com/nvidia-tensorrt-7x-download .

I used an explicit batch since it’s a dynamic model and here’s the result of running it is this what’s needed, and what does it indicate? root@e06e7092f0cf:/workspace/pytorch-YOLOv4# trtexec --explicitBatch --workspace=15120 --fp16 --optShapes=input:3x3x608x608 --maxShapes=input:30x3x608x608 --…

[image] mai.algendy: [10/18/2021-09:17:09] [I] mean: 143.377 ms from the log - “[10/18/2021-09:17:09] [I] mean: 143.377 ms”, its inference fps could be “(1000 / 143) * batch_size” = (1000 / 143) * 30 = 209 fps.

GPU frame rate maxes when the GPU util isn't at max

Accelerated Computing Intelligent Video Analytics DeepStream SDK

mchi October 21, 2021, 3:59am 7

how do you check the fps? what’s the pipeline? could you refer to DeepStream SDK FAQ - #10 by mchi to dump the pipeline graph?

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GPU frame rate maxes when the GPU util isn't at max

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