Deepstream-9.1 with nvdsmultiurisrcbin run failed in docker

Accelerated Computing Intelligent Video Analytics DeepStream SDK
,
1

i am testing deepstream-9.1 with jetpack7.2.1 on Orin Nano.

my pipeline is based test5 with nvdsmultiurisrcbin, it runs well when i tested on host device, but runs failed if i put into docker to run by both image:

docker pull nvcr.io/nvidia/deepstream:9.1-triton-multiarch
docker pull nvcr.io/nvidia/deepstream:9.1-samples-multiarch

docker logs is like this:

docker logs deepstream

===============================

DeepStreamSDK 9.1

*** LICENSE AGREEMENT ***
By using this software you agree to fully comply with the terms and conditions
of the License Agreement. The License Agreement is located at
/opt/nvidia/deepstream/deepstream/LicenseAgreement.pdf. If you do not agree
to the terms and conditions of the License Agreement do not use the software.

==========

== CUDA ==

CUDA Version 13.2.1

Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.

This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:

A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.

sh: 1: lsmod: not found
(Argus) Error 0x00030003: Connecting to nvargus-daemon failed: No such file or directory (in src/rpc/socket/client/SocketClientDispatch.cpp, function openSocketConnection(), line 205)
(Argus) Error 0x00030003: Cannot create camera provider (in src/rpc/socket/client/SocketClientDispatch.cpp, function createCameraProvider(), line 107)

(gst-plugin-scanner:69): GStreamer-WARNING **: 02:09:39.048: Failed to load plugin ‘/usr/lib/aarch64-linux-gnu/gstreamer-1.0/deepstream/libnvdsgst_inferserver.so’: libtritonserver.so: cannot open shared object file: No such file or directory

(gst-plugin-scanner:69): GStreamer-WARNING **: 02:09:39.066: Failed to load plugin ‘/usr/lib/aarch64-linux-gnu/gstreamer-1.0/deepstream/libnvdsgst_udp.so’: librivermax.so.1: cannot open shared object file: No such file or directory

(gst-plugin-scanner:69): GStreamer-WARNING **: 02:09:46.886: Failed to load plugin ‘/usr/lib/aarch64-linux-gnu/gstreamer-1.0/deepstream/libnvdsgst_logger.so’: libopentelemetry_exporter_otlp_http_metric.so: cannot open shared object file: No such file or directory

(gst-plugin-scanner:69): GStreamer-WARNING **: 02:09:47.082: Failed to load plugin ‘/usr/lib/aarch64-linux-gnu/gstreamer-1.0/deepstream/libnvdsgst_nvmultiurisrcbin.so’: libopentelemetry_exporter_otlp_http_metric.so: cannot open shared object file: No such file or directory
sh: 1: lsmod: not found
sh: 1: lsmod: not found
** INFO: Early Message API initialization skipped or failed - error propagation may not be available
sh: 1: lsmod: not found
** ERROR: <create_nvmultiurisrcbin_bin:1692>: Failed to create element ‘src_nvmultiurisrcbin’
** ERROR: <create_nvmultiurisrcbin_bin:1720>: create_nvmultiurisrcbin_bin failed
** ERROR: <create_pipeline:2625>: create_pipeline failed
** ERROR: main:2388: Failed to create pipeline
Quitting
App run failed

pls give some advice to run, thank you

2

Hello @wilicyy!

Based on the title and content of your topic, it looks like it may receive better visibility and feedback in a different category. We took the liberty of moving it for you.

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4

I tested the official 9.1 containers on a Jetson Orin. The following library is present in the official images, and nvmultiurisrcbin loads successfully:

  /usr/lib/aarch64-linux-gnu/libopentelemetry_exporter_otlp_http_metric.so

Please run:

  docker pull nvcr.io/nvidia/deepstream:9.1-samples-multiarch

  docker run --rm --runtime=nvidia \
    --entrypoint /bin/bash \
    nvcr.io/nvidia/deepstream:9.1-samples-multiarch \
    -lc '
      ls -l /usr/lib/aarch64-linux-gnu/libopentelemetry_exporter_otlp_http_metric.so
      dpkg -S /usr/lib/aarch64-linux-gnu/libopentelemetry_exporter_otlp_http_metric.so
      ldd /opt/nvidia/deepstream/deepstream-9.1/lib/gst-plugins/libnvdsgst_nvmultiurisrcbin.so
      gst-inspect-1.0 nvmultiurisrcbin
    '

The expected result is:

  opentelemetry-cpp:arm64:
  /usr/lib/aarch64-linux-gnu/libopentelemetry_exporter_otlp_http_metric.so

gst-inspect-1.0 nvmultiurisrcbin should also print the plugin information.

If the library exists but the plugin still fails, check for ABI or dependency problems:

  ldd -v /opt/nvidia/deepstream/deepstream-9.1/lib/gst-plugins/libnvdsgst_nvmultiurisrcbin.so

Look for:

not found
undefined symbol
GLIBCXX_…
GLIBC_…
wrong ELF class

Also check whether the container is affected by host mounts:

  docker inspect <container_name> --format '{{json .Mounts}}'

Do not mount the host /usr/lib/aarch64-linux-gnu over the container directory, and do not copy random .so files from the host into the container. Libraries from different JetPack or DeepStream versions may be binary-incompatible.

Please also provide:

  cat /etc/nv_tegra_release
  dpkg -l | grep -E 'nvidia-container|nvidia-l4t|deepstream|opentelemetry'
  docker image inspect nvcr.io/nvidia/deepstream:9.1-samples-multiarch

Since the host works but the official container fails, and the device was upgraded from an older JetPack installation, the JetPack upgrade may not have completed cleanly. A mixed userspace, kernel, or container-runtime environment is possible. If these checks show version mismatches, the recommended solution
is to back up the application and reflash the device with a clean, matching JetPack release using SDK Manager.

5

for result it is:

wilson@wilson:~$ cat /etc/nv_tegra_release

R39 (release), REVISION: 2.1, GCID: 46758480, BOARD: generic, EABI: aarch64, DATE: Fri Aug 7 05:54:22 AM UTC 2026

KERNEL_VARIANT: oot

TARGET_USERSPACE_LIB_DIR=nvidia
TARGET_USERSPACE_LIB_DIR_PATH=usr/lib/aarch64-linux-gnu/nvidia
wilson@wilson:~$ dpkg -l | grep -E ‘nvidia-container|nvidia-l4t|deepstream|opentelemetry’
ii deepstream-9.1 9.1.0-1 arm64 Nvidia DeepStreamSDK runtime libraries, development files and samples
ii libnvidia-container-tools 1.19.1-1 arm64 NVIDIA container runtime library (command-line tools)
ii libnvidia-container1:arm64 1.19.1-1 arm64 NVIDIA container runtime library
ii nvidia-container 7.2.1-b49 arm64 NVIDIA Container Meta Package
ii nvidia-container-toolkit 1.19.1-1 arm64 NVIDIA Container toolkit
ii nvidia-container-toolkit-base 1.19.1-1 arm64 NVIDIA Container Toolkit Base
ii nvidia-l4t-3d-core 39.2.1-20260806224157 arm64 NVIDIA GL EGL Package
ii nvidia-l4t-adaruntime 39.2.1-20260806224157 arm64 NVIDIA Ada Runtime Package
ii nvidia-l4t-apt-source 39.2.1-20260806224157 arm64 NVIDIA L4T apt source list debian package
ii nvidia-l4t-bootloader 39.2.1-20260806224157 arm64 NVIDIA Bootloader Package
ii nvidia-l4t-bootloader-utils 39.2.1-20260806224157 arm64 NVIDIA Bootloader Utils Package
ii nvidia-l4t-bsp 39.2.1-20260806224157 arm64 NVIDIA L4T BSP meta Package
ii nvidia-l4t-camera 39.2.1-20260806224157 arm64 NVIDIA Camera Package
ii nvidia-l4t-configs 39.2.1-20260806224157 arm64 NVIDIA configs debian package
ii nvidia-l4t-core 39.2.1-20260806224157 arm64 NVIDIA Core Package
ii nvidia-l4t-cuda 39.2.1-20260806224157 arm64 NVIDIA CUDA Package
ii nvidia-l4t-cuda-nvgpu 39.2.1-20260806224157 arm64 NVIDIA CUDA Package specific to nvgpu
ii nvidia-l4t-cuda-openrm 39.2.1-20260806224157 arm64 NVIDIA CUDA Package specific to openrm
ii nvidia-l4t-cuda-utils 39.2.1-20260806224157 arm64 NVIDIA CUDA utilities
ii nvidia-l4t-cudadebuggingsupport 13.2-37290934.0 arm64 NVIDIA CUDA Debugger Support Package
ii nvidia-l4t-display-kernel 6.8.12-tegra-39.2.1-20260806224157 arm64 NVIDIA Display Kernel Modules Package
ii nvidia-l4t-extlinux 39.2.1-20260806224157 arm64 NVIDIA Extlinux Package
ii nvidia-l4t-firmware 39.2.1-20260806224157 arm64 NVIDIA Firmware Package
ii nvidia-l4t-firmware-nvgpu 39.2.1-20260806224157 arm64 NVIDIA l4t nvgpu specific debian
ii nvidia-l4t-firmware-openrm 39.2.1-20260806224157 arm64 NVIDIA l4t openrm specific debian
ii nvidia-l4t-firstboot 39.2.1-20260806224157 arm64 NVIDIA Firstboot package
ii nvidia-l4t-gbm 39.2.1-20260806224157 arm64 NVIDIA GBM Package
ii nvidia-l4t-graphics-demos 39.2.1-20260806224157 arm64 NVIDIA graphics demo applications
ii nvidia-l4t-gstreamer 39.2.1-20260806224157 arm64 NVIDIA GST Application files
ii nvidia-l4t-init 39.2.1-20260806224157 arm64 NVIDIA Init debian package
ii nvidia-l4t-init-openrm 39.2.1-20260806224157 arm64 NVIDIA Init debian package containing openrm specific files
ii nvidia-l4t-initrd 39.2.1-20260806224157 arm64 NVIDIA initrd debian package
ii nvidia-l4t-jetson-io 39.2.1-20260806224157 arm64 NVIDIA Jetson.IO debian package
ii nvidia-l4t-jetson-multimedia-api 39.2.1-20260806224157 arm64 NVIDIA Jetson Multimedia API is a collection of lower-level APIs that support flexible application development.
ii nvidia-l4t-jetsonpower-gui-tools 39.2.1-20260806224157 arm64 NVIDIA Jetson Power GUI Tools debian package
ii nvidia-l4t-kernel 6.8.12-tegra-39.2.1-20260806224157 arm64 NVIDIA Kernel Package
ii nvidia-l4t-kernel-dtbs 6.8.12-tegra-39.2.1-20260806224157 arm64 NVIDIA Kernel DTB Package
ii nvidia-l4t-kernel-headers 6.8.12-tegra-39.2.1-20260806224157 arm64 NVIDIA Linux Tegra Kernel Headers Package
ii nvidia-l4t-kernel-module-configs 6.8.12-tegra-39.2.1-20260806224157 arm64 NVIDIA System-wide Kernel Module Configuration Package
ii nvidia-l4t-kernel-nvgpu 6.8.12-tegra-39.2.1-20260806224157 arm64 NVIDIA Kernel Package containing nvgpu specific files
ii nvidia-l4t-kernel-oot-headers 6.8.12-tegra-39.2.1-20260806224157 arm64 NVIDIA OOT Kernel Module Headers Package
ii nvidia-l4t-kernel-oot-modules 6.8.12-tegra-39.2.1-20260806224157 arm64 NVIDIA OOT Kernel Module Drivers Package
ii nvidia-l4t-kernel-openrm 6.8.12-tegra-39.2.1-20260806224157 arm64 NVIDIA Kernel Package containing OpenRM specific files
ii nvidia-l4t-kernel-partitions 6.8.12-tegra-39.2.1-20260806224157 arm64 NVIDIA Kernel and Kernel DTB Partition Package
ii nvidia-l4t-libvulkan 39.2.1-20260806224157 arm64 NVIDIA Vulkan Loader Package
ii nvidia-l4t-multimedia 39.2.1-20260806224157 arm64 NVIDIA Multimedia Package
ii nvidia-l4t-multimedia-nvgpu 39.2.1-20260806224157 arm64 NVIDIA Multimedia NvGPU Package
ii nvidia-l4t-multimedia-openrm 39.2.1-20260806224157 arm64 NVIDIA Multimedia OpenRM Package
ii nvidia-l4t-multimedia-utils 39.2.1-20260806224157 arm64 NVIDIA Multimedia Package
ii nvidia-l4t-nvfancontrol 39.2.1-20260806224157 arm64 NVIDIA Nvfancontrol debian package
ii nvidia-l4t-nvml 39.2.1-20260806224157 arm64 NVIDIA NVML Package
ii nvidia-l4t-nvpmodel 39.2.1-20260806224157 arm64 NVIDIA Nvpmodel debian package
ii nvidia-l4t-nvpmodel-gui-tools 39.2.1-20260806224157 arm64 NVIDIA Nvpmodel GUI Tools debian package
ii nvidia-l4t-nvsci 39.2.1-20260806224157 arm64 NVIDIA NvSci Package
ii nvidia-l4t-oobe 39.2.1-20260806224157 arm64 NVIDIA OOBE Package
ii nvidia-l4t-openwfd 39.2.1-20260806224157 arm64 NVIDIA OpenWFD Package
ii nvidia-l4t-optee 39.2.1-20260806224157 arm64 OP-TEE userspace daemons, test programs and libraries
ii nvidia-l4t-power-adapter-check 39.2.1-20260806224157 arm64 NVIDIA unknown power adapter PAM warning hook
ii nvidia-l4t-pva 39.2.1-20260806224157 arm64 NVIDIA PVA Package
ii nvidia-l4t-tools 39.2.1-20260806224157 arm64 NVIDIA Public Test Tools Package
ii nvidia-l4t-usb-service 39.2.1-20260806224157 arm64 NVIDIA USB device mode service debian package
ii nvidia-l4t-video-codec-openrm 39.2.1-20260806224157 arm64 NVIDIA Multimedia OpenRM Package
ii nvidia-l4t-vulkan-sc-dev 39.2.1-20260806224157 arm64 NVIDIA Vulkan SC Dev package
ii nvidia-l4t-vulkan-sc-nvgpu 39.2.1-20260806224157 arm64 NVIDIA Vulkan SC run-time nvgpu package
ii nvidia-l4t-vulkan-sc-openrm 39.2.1-20260806224157 arm64 NVIDIA Vulkan SC run-time openrm package
ii nvidia-l4t-vulkan-sc-samples 39.2.1-20260806224157 arm64 NVIDIA Vulkan SC samples package
ii nvidia-l4t-vulkan-sc-sdk 39.2.1-20260806224157 arm64 NVIDIA Vulkan SC SDK package
ii nvidia-l4t-wayland 39.2.1-20260806224157 arm64 NVIDIA Wayland Package
ii nvidia-l4t-weston 39.2.1-20260806224157 arm64 NVIDIA Weston Package
ii nvidia-l4t-wpasupplicant 39.2.1-20260806224157 arm64 NVIDIA user mode wifi daemon package
ii nvidia-l4t-x11 39.2.1-20260806224157 arm64 NVIDIA X11 Package
ii nvidia-l4t-xwayland 39.2.1-20260806224157 arm64 NVIDIA XCB-X11 library that enables X applications to run on Wayland compositors with hardware acceleration
wilson@wilson:~$ docker image inspect nvcr.io/nvidia/deepstream:9.1-samples-multiarch
[
{
“Id”: “sha256:4f80b374e4a5086552825fe0f5bdd015c8cfd3dbe430cdde5ce9572e80e01583”,
“RepoTags”: [
“nvcr.io/nvidia/deepstream:9.1-samples-multiarch”
],
“RepoDigests”: [
“nvcr.io/nvidia/deepstream@sha256:4f80b374e4a5086552825fe0f5bdd015c8cfd3dbe430cdde5ce9572e80e01583”
],
“Comment”: “buildkit.dockerfile.v0”,
“Created”: “2026-07-01T15:54:12.122071742Z”,
“Config”: {
“Env”: [
“PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin”,
“NVARCH=sbsa”,
“NVIDIA_REQUIRE_CUDA=cuda>=13.2 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=570,driver<571 brand=grid,driver>=570,driver<571 brand=tesla,driver>=570,driver<571 brand=nvidia,driver>=570,driver<571 brand=quadro,driver>=570,driver<571 brand=quadrortx,driver>=570,driver<571 brand=nvidiartx,driver>=570,driver<571 brand=vapps,driver>=570,driver<571 brand=vpc,driver>=570,driver<571 brand=vcs,driver>=570,driver<571 brand=vws,driver>=570,driver<571 brand=cloudgaming,driver>=570,driver<571 brand=unknown,driver>=580,driver<581 brand=grid,driver>=580,driver<581 brand=tesla,driver>=580,driver<581 brand=nvidia,driver>=580,driver<581 brand=quadro,driver>=580,driver<581 brand=quadrortx,driver>=580,driver<581 brand=nvidiartx,driver>=580,driver<581 brand=vapps,driver>=580,driver<581 brand=vpc,driver>=580,driver<581 brand=vcs,driver>=580,driver<581 brand=vws,driver>=580,driver<581 brand=cloudgaming,driver>=580,driver<581 brand=unknown,driver>=590,driver<591 brand=grid,driver>=590,driver<591 brand=tesla,driver>=590,driver<591 brand=nvidia,driver>=590,driver<591 brand=quadro,driver>=590,driver<591 brand=quadrortx,driver>=590,driver<591 brand=nvidiartx,driver>=590,driver<591 brand=vapps,driver>=590,driver<591 brand=vpc,driver>=590,driver<591 brand=vcs,driver>=590,driver<591 brand=vws,driver>=590,driver<591 brand=cloudgaming,driver>=590,driver<591”,
“NV_CUDA_CUDART_VERSION=13.2.75-1”,
“CUDA_VERSION=13.2.1”,
“LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda-13.2/lib64:/opt/nvidia/deepstream/deepstream-9.1/lib”,
“NVIDIA_VISIBLE_DEVICES=all”,
“NVIDIA_DRIVER_CAPABILITIES=all”,
“NV_CUDA_LIB_VERSION=13.2.1-1”,
“NV_NVTX_VERSION=13.2.75-1”,
“NV_LIBNPP_VERSION=13.1.0.48-1”,
“NV_LIBNPP_PACKAGE=libnpp-13-2=13.1.0.48-1”,
“NV_LIBCUSPARSE_VERSION=12.7.10.1-1”,
“NV_LIBCUBLAS_PACKAGE_NAME=libcublas-13-2”,
“NV_LIBCUBLAS_VERSION=13.4.0.1-1”,
“NV_LIBCUBLAS_PACKAGE=libcublas-13-2=13.4.0.1-1”,
“NVIDIA_PRODUCT_NAME=CUDA”,
“NV_CUDNN_VERSION=9.20.0.48-1”,
“NV_CUDNN_PACKAGE_NAME=libcudnn9-cuda-13”,
“NV_CUDNN_PACKAGE=libcudnn9-cuda-13=9.20.0.48-1”,
“DS_VERSION=9.1”
],
“Entrypoint”: [
“/opt/nvidia/deepstream/deepstream/entrypoint.sh”
],
“Cmd”: [
“/bin/bash”
],
“WorkingDir”: “/opt/nvidia/deepstream/deepstream”,
“Labels”: {
“com.nvidia.cudnn.version”: “9.20.0.48-1”,
“maintainer”: “NVIDIA CORPORATION cudatools@nvidia.com”,
“org.opencontainers.image.version”: “24.04”,
“version”: “devLabel20260701”
},
“ArgsEscaped”: true
},
“Architecture”: “arm64”,
“Os”: “linux”,
“Size”: 6386915165,
“RootFS”: {
“Type”: “layers”,
“Layers”: [
“sha256:d9a56420aee7ff835e0c05fc666d1237d40f0da5fe338738e651b01db4672a5d”,
“sha256:e4910f89c31f990f4d8617263f0f467ef69c2ac0370d5a8b86fe717f9a1093d8”,
“sha256:2610e65713481c34c271ec15ba5e6676939a8c9dc1fa52fc5011e377a94d2244”,
“sha256:da782a2fcfff9d39d2c694ea049b935118c59484e720e8355d60e9182c3484f0”,
“sha256:5b2ebfbab866b308635884cd6405db09b44c224b767eeaeb812949372b1262f2”,
“sha256:e2d8b13147bdc80504e6a3b4f0da9d3e9a525fc3e5b5fc72381f22d4b3d883a6”,
“sha256:b523221e8b19459a04938c97406126f5d989e7c05acc49d7f5afd4cdce3100cf”,
“sha256:6d5ad8859cb66cd9b998a9c7bd05e0dc810dbcfffbf703ad3255f898485bc50c”,
“sha256:b12c5a664983387e86426ec63fe57f372ef2086248ff5350adfeeb32ce79e463”,
“sha256:594230f42603a22e7c01a699cbb02975978596430d1ccc2072a06a0ab0e4560b”,
“sha256:c1c2712f03dcb95da1db94987d0359ba8bf96af33e29e9715e2681fa73cf8055”,
“sha256:dfcf647bc9e29e95d12eb77ab754008b814aa9432500e74337d91a8431f1a7db”,
“sha256:4230f90af22ba24fcf02d9163950a2726e4ca66797c3357808e0a5b59351875d”,
“sha256:7832999302fb4d29559055246a41810dedc6a106d004268f430df17bc9e971a9”,
“sha256:dc5969bfd4597e9fcb1071142870512f1f6777e792291d2f169e53ccd7cc1220”,
“sha256:1383251d02a4547ca68346c39e2cc74b66a28c41209f0f24cd729863c089c923”,
“sha256:c08ed502ded83dfd0b3a2502e3ef60d27e8fb833496e79cb04d6f0341ae41001”,
“sha256:44708ee33f8cee1e04a57abc6593dc5335eaddeea7b43e0e6efd23a749e09be0”,
“sha256:188b836e103c85fb52aa23aaf0313d082ff8bc70539985dae725d0991f5c4534”,
“sha256:c8ff52c309fc267f6b1cff25575bbe2cab5eb13b6bc712a888983e34ce8137c0”,
“sha256:c52395ac13552233715f400f5c371141e66687a4271f85bcbcdf8659982cd1aa”,
“sha256:9edc554b8e34fee5e92c59fa8df79ba9a7f19b919c6372aa2db12e1d3611794a”,
“sha256:d5b27379b44af1d2afacdbecefe365cb292b1b3e091f371e568375aa805bbbf2”,
“sha256:da1d7585c4269cd5449912eede96689026f953fcd44fdefad50ef766d42a8fa8”,
“sha256:e185e13c75a3ae2e4eeba769dda479d4789c6906179aac5feb38b76810017562”,
“sha256:ca4fd1f4200b2a54a1efa3a6f32ebd4b1aec4635124602fe96c8f87572040ea9”,
“sha256:b71ab178d1b86472ea18d1368d7ddadd92377da152e11ca991de45545da9bc08”,
“sha256:0cf6e3622a50e60f4341337bca752179913911c2015b34183d821fae82866502”,
“sha256:bc6ae4f9864a3caf78741d896ded07df2d159bb910c89c342fe7d6d783a598c8”,
“sha256:5f45dd0f5a2891bf06e05c4ec7f0be81508909c42b2275b307722cb16a6672b8”,
“sha256:4496b3f2d0ebded8acfcbb84642e7e93c664b3bd253342e22d899a518c50ddae”,
“sha256:5f70bf18a086007016e948b04aed3b82103a36bea41755b6cddfaf10ace3c6ef”,
“sha256:eebadd3c125ef1175edc7b25b9dc0144f3cf2d102234b13344e98e2e2580362f”
]
},
“Metadata”: {
“LastTagTime”: “2026-08-24T17:58:54.974436485Z”
},
“Descriptor”: {
“mediaType”: “application/vnd.docker.distribution.manifest.list.v2+json”,
“digest”: “sha256:4f80b374e4a5086552825fe0f5bdd015c8cfd3dbe430cdde5ce9572e80e01583”,
“size”: 733
},
“Identity”: {
“Pull”: [
{
“Repository”: “nvcr.io/nvidia/deepstream”
}
]
}
}
]
wilson@wilson:~$

6

Please do not provide only the host package list or docker image metadata. The failure occurs while loading a plugin inside the container, so the container-side output is required for further diagnosis.

7

docker-logs.txt (32.7 KB)

this is shows in docker for your command.

thank you

8

The latest output confirms that this issue is not related to nvmultiurisrcbin.

Inside your Jetson Orin Nano container:

libopentelemetry_exporter_otlp_http_metric.so is present;
all dependencies of libnvdsgst_nvmultiurisrcbin.so are resolved;
gst-inspect-1.0 nvmultiurisrcbin completes successfully;
nvmultiurisrcbin is registered as a DeepStream 9.1 plugin.

Therefore, nvmultiurisrcbin is working correctly in your DeepStream 9.1 container.

Please provide the complete log from running your actual application,

9

docker_issue.txt (8.8 KB)

pls check logs again and it is different if mount /usr/lib/aarch64-linux-gnu/:/usr/lib/aarch64-linux-gnu/

10

it seems less some gst-1.0 plugs in your image about below setup:

[sink3]
enable=1
#Type - 1=FakeSink 2=EglSink 3=File 4=RTSPStreaming 5=nvdrmvideosink
type=4
source-id=0
#1=h264 2=h265
codec=1
encoder type 0=Hardware 1=Software，pls note that Jetson Orin Nano used software only
enc-type=1
sync=1
#bitrate=4000000
bitrate=2000000

set profile only for hw encoder, sw encoder selects profile based on sw-preset

#H264 Profile - 0=Baseline 2=Main 4=High
#H265 Profile - 0=Main 1=Main10
profile=2

set below properties in case of RTSPStreaming

rtsp-port=8554
udp-port=5400

11

What are you trying to achieve by mounting the host directory /usr/lib/aarch64-linux-gnu/ over the same directory inside the container?

This mount completely hides the libraries provided by the DeepStream image, including: libopentelemetry_exporter_otlp_http_metric.so

That is the direct cause of the original error. Please remove this volume mount. The NVIDIA Container Runtime already provides the required NVIDIA libraries; mounting the entire host system library directory is incorrect and will cause library conflicts.

After removing it, nvmultiurisrcbin works correctly. The remaining encoder error is a separate issue.

Do not overwrite container paths. If you need to add new .so files, please use a Dockerfile to build a new image based on ds-9.1.

12

yes i have moved mount host lib path already,

and now reason is enable udp and rtsp sink group, and i use software encode for rtsp out video on orin nano super device. if disable rtsp sink group then pipeline will run.

[sink3]
enable=1
#Type - 1=FakeSink 2=EglSink 3=File 4=RTSPStreaming 5=nvdrmvideosink
type=4

pls help check which gstreamer-1.0 plugin or lib is less, then i build a new imange on official one then to try again.

this is a major problem now, thank you very much!

13

Because x264enc is GPL licensed, please run /opt/nvidia/deepstream/deepstream-9.1/user_additional_install.sh in a container first for rtspsink to work.

You can run this script in a custom Dockerfile.

14

after i build image and update user_additional_install.sh, rtsp sink is ok right now but new issue is TensorRT in docker, it can not use engine form onnx from host device, and have to rebuild new engine form onnx each time, i mean docker’s TensorRT is different with host! and after running pipeline few seconds, Segmentation fault happens. if i disable pgie, tracker and sgie, pipeline is working normally, so i think this is a TensorRT issue in docker container.

this issue does need your help quickly and i have not any idea for that.

thank you very much!

docker_issue2.txt (5.5 KB)

15

pls note my pipeline works well at host device.

16

As I’ve already told you, JP-7.2.1 and DS-9.1 are incompatible.
JP-7.2.1 uses TensorRT 10.16.2, while the DS-9.1 container uses TensorRT 10.16.1.7.

There is no other way but to use the container’s trtexec to create the engine file and then mount it into the container.

  docker run --rm --runtime=nvidia \
    -v "$PWD/models:/models" \
    --entrypoint /bin/bash \
    nvcr.io/nvidia/deepstream:9.1-samples-multiarch \
    -lc '
      trtexec \
        --onnx=/models/model.onnx \
        --saveEngine=/models/engines/model.engine \
        --fp16
    '
17

but i didn’t find trtexec bin file in docker, where is it?

18 
  docker run --rm --runtime=nvidia \
    -v "$PWD/models:/models" \
    --entrypoint /bin/bash \
    nvcr.io/nvidia/deepstream:9.1-samples-multiarch \
    -lc '
      /usr/bin/trtexec \
        --onnx=/models/model.onnx \
        --saveEngine=/models/engines/model.engine \
        --fp16
    '
19

in fact, when i run pipeline in docker, onnx file will be rebuild a new engine file automatically, i may mount this new file to run.

now’s issue is Segmentation fault, only few seconds to broke pipeline to die.

20

no that file in /usr/bin: /bin/bash: line 3: /usr/bin/trtexec: No such file or directory,

i have searched all file by find command and didn’t find that

21

i used samples-multiarch image, and i try triton-multiarch imange again right now