Nvv4l2decoder fails when running nvidia-cuda-mps-server

Accelerated Computing Intelligent Video Analytics DeepStream SDK
#1

I have been using Deepstream 4.0 for some time now for the purpose of utilizing the “nvv4l2decoder” gstreamer plugin. So far it has been working well. However, I am working on performance optimizations which include multi-processing and running the nvidia-cuda-mps-server to improve concurrent utilization. I have successfully implemented multiprocessing and see a 2-3x throughput improvement when nvidia-cuda-mps-server is running. This is where I ran into a problem. My gstreamer video decoding pipeline fails when cuda-mps is running. Simply disabling cuda-mps resolves the problem.

Hardware Platform
GTX 1080 Ti

DeepStream Version
4.0.2

TensorRT Version
6.0.1.5

NVIDIA GPU Driver Version
440.33.01 w/ CUDA 10.2

Issue Type( questions, new requirements, bugs)
Bug

How to reproduce the issue?

Tested with following command:
gst-launch-1.0 rtspsrc location=“rtsp://192.168.1.72/axis-media/media.amp” ! rtph264depay ! h264parse ! nvv4l2decoder ! nvvideoconvert ! autovideosink

Without enabling cuda-mps the pipeline works as expected. However after enabling cuda-mps via:

sudo nvidia-cuda-mps-control -d

The pipeline blocks forever and is unrecoverable. To get it working again all I have to do it stop the cuda-mps-server. I also tried changing the io/memory parameters for the plugin without any luck.

I have attached two logs resulting from running the above pipeline with GST_DEBUG=4 in both scenarios.

gst-failure.log (111.9 KB)
gst-success.log (221.6 KB)

If I were to guess this has something to do with GPU architecture, but it is not clear to me why it is failing. Everything else behaves as expected.

#3

Firstly, Deepstream4.0 is not supported now. Can you upgrade to latest DeepStream 5.0.1? https://developer.nvidia.com/deepstream-getting-started

The following pipeline can work with “nvidia-cuda-mps-control -d”:
gst-launch-1.0 --gst-debug=v4l2videodec:5 rtspsrc location=“rtsp://xxxxx” ! rtph264depay ! nvv4l2decoder ! nvvideoconvert ! nveglglessink

#4

Thank you for the suggestions. I have upgraded to TensorRT 7 and Deepstream 5. I am still seeing the same behavior as before. I have attached two logs resulting from the same test I initially ran, except I used your modified pipeline instead. The pipeline blocks forever when it is handling frame 0 with cuda-mps enabled.

deepstream5-failure.log (5.3 KB)
deepstream5-success.log (20.0 KB)

#5

What will happen if you use fakesink instead of autovideosink?

#6

The behavior does not change. The pipeline never reaches the sink when cuda-mps is running.

#7

We can reproduce the problem with GTX1080Ti and will investigate it. Will be back when there is any progress.

#9

Thank you for looking into this. I have some deadlines coming up that require these optimizations to increase throughput. Could you please provide information on which GPUs this configuration will currently work on?

I think the list includes a T4 and a V100. Is it possible for you to test this on a Quadro RTX6000?

#10

Formally, deepstream supports T4/P4 and Jetson, https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Quickstart.html#quickstart-guide.

#11

Also please note that similar issues occur when using the following plugins:

nvdec
nvh264enc

It appears to impact all decoding and encoding.

#12

We have pointed out that “The NVIDIA Codec SDK: https://developer.nvidia.com/nvidia-video-codec-sdk is
not supported under MPS on pre-Volta MPS clients.” in chapter 2.3.2 in Multi-Process Service (nvidia.com), https://docs.nvidia.com/deploy/mps/index.html#topic_3_3_2
You can not use nvidia-cuda-mps-server with DS in GTX 1080 Ti.

#13

Thanks for all of the information. I have a better understanding of what is happening now. It would seem that any architecture >= Volta will work and implements Volta MPS.

I am going to add a check to enable MPS when the CUDA Compute Capability is >= 7.0. Otherwise I will not enable MPS. Generally speaking, deployments will use newer GPUs, but I ran into this issue on a couple of my development platforms that do not have new GPUs.

#14

Hello,
Would I know ampere (e.g., RTX 3090) support MPS?

#15

@gpu_developer Please refer to the gpu list CUDA GPUs | NVIDIA Developer , if the “compute capability” value is higher than or equal to 7.0, it is a Volta or after-Volta MPS client. Or else, it is a “pre-Volta MPS client”.

#16

@Fiona.Chen Thank you for reply. So, RTX series compute capability is higher than 7.0, it supports hardware-based MPS. Is it right?
I was confused since the architecture of RTX series is “ampere”.

#17

“compute capability” is clearer than the name of “ampere”.