GLib-GObject-CRITICAL error

Please provide complete information as applicable to your setup.

• Hardware Platform (Jetson / GPU) GTX 1650
• DeepStream Version 5
• TensorRT Version 7.0.0.11
• NVIDIA GPU Driver Version (valid for GPU only) 450.51
• Issue Type( questions, new requirements, bugs) bugs

I am trying to install deepstream-5 inside a docker container. I used a container with the following specs: 10.2-cudnn7-tensorrt7-devel-ubuntu18.04 (cuda 10.2.89, nccl 2.7.8, cudnn 7.6.5.32, tensorrt 7.0.0.11)

I get this error. I don’t know how to fix it.

I tried to remove the cache as suggested in here now I have warnings beside the errors :D. I checked, and already have the two files that in the warning.

Can you use the released container?
devel docker

(Contains the entire SDK along with a development environment for building DeepStream applications)

docker pull nvcr.io/nvidia/deepstream:5.0-20.07-devel

https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html#page/DeepStream_Development_Guide/deepstream_plugin_docker.html#wwpID0EYHA

Yes I used the devel container but I can’t find this path in it /usr/src/tensorrt/bin/. That’s why I installed trt and nvidia-deepstream manually.

Is there a way to solve this bug in the manual installation approach?. What is it’s cause?

Thanks

TensorRT installed by default,
root@b371c4cf76e9:~# ll /usr/lib/x86_64-linux-gnu/libnvinfer
libnvinfer.so libnvinfer.so.7.0.0 libnvinfer_plugin.so.7 libnvinfer_plugin_static.a
libnvinfer.so.7 libnvinfer_plugin.so libnvinfer_plugin.so.7.0.0 libnvinfer_static.a

the warning you see “GStreamer-WARNING: Failed to load plugin ‘…libnvdsgst_inferserver.so’: libtrtserver.so: cannot open shared object file: No such file or directory”
This is a harmless. check this,

https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html#page/DeepStream_Faq_2019/deepstream_plugin_faq.html#
Why am I getting following waring when running deepstream app for first time?

your issue is can not find libGLESv2.so.2, i used the docker as suggested, but i can find it.
root@b371c4cf76e9:~# ll /usr/lib/x86_64-linux-gnu/libGLESv2.so.2
lrwxrwxrwx 1 root root 18 May 10 2019 /usr/lib/x86_64-linux-gnu/libGLESv2.so.2 -> libGLESv2.so.2.0.0

please check your environments. you may pull one clean docker and try again.

Yes I know that tensorRT is on the docker, but as I said I can’t find this path on the docker
/usr/src/tensorrt/bin/ That’s why I turned to manual installation.
I tried your ll line and it outputs same as you, but I still have the same GLIB critical error. Why?

Can you share output of ldd /usr/lib/x86_64-linux-gnu/gstreamer-1.0/deepstream/libnvdsgst_eglglessink.so
root@b371c4cf76e9:/opt/nvidia/deepstream/deepstream-5.0# ldd /usr/lib/x86_64-linux-gnu/gstreamer-1.0/deepstream/libnvdsgst_eglglessink.so
linux-vdso.so.1 (0x00007ffd93b0a000)
libcudart.so.10.2 => /usr/local/cuda-10.2/lib64/libcudart.so.10.2 (0x00007f0548350000)
libcuda.so.1 => /usr/lib/x86_64-linux-gnu/libcuda.so.1 (0x00007f0546eb2000)
libGLESv2.so.2 => /usr/lib/x86_64-linux-gnu/libGLESv2.so.2 (0x00007f0546ca0000)
libEGL.so.1 => /usr/lib/x86_64-linux-gnu/libEGL.so.1 (0x00007f0546a8c000)
libX11.so.6 => /usr/lib/x86_64-linux-gnu/libX11.so.6 (0x00007f0546754000)
libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007f05463b6000)
libgstreamer-1.0.so.0 => /usr/lib/x86_64-linux-gnu/libgstreamer-1.0.so.0 (0x00007f054607b000)
libgstbase-1.0.so.0 => /usr/lib/x86_64-linux-gnu/libgstbase-1.0.so.0 (0x00007f0545e06000)
libgstvideo-1.0.so.0 => /usr/lib/x86_64-linux-gnu/libgstvideo-1.0.so.0 (0x00007f0545b6d000)
libglib-2.0.so.0 => /usr/lib/x86_64-linux-gnu/libglib-2.0.so.0 (0x00007f0545856000)
libgobject-2.0.so.0 => /usr/lib/x86_64-linux-gnu/libgobject-2.0.so.0 (0x00007f0545602000)
libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007f0545211000)
/lib64/ld-linux-x86-64.so.2 (0x00007f05487ed000)
libdl.so.2 => /lib/x86_64-linux-gnu/libdl.so.2 (0x00007f054500d000)
libpthread.so.0 => /lib/x86_64-linux-gnu/libpthread.so.0 (0x00007f0544dee000)
librt.so.1 => /lib/x86_64-linux-gnu/librt.so.1 (0x00007f0544be6000)
libGLdispatch.so.0 => /usr/lib/x86_64-linux-gnu/libGLdispatch.so.0 (0x00007f0544930000)
libxcb.so.1 => /usr/lib/x86_64-linux-gnu/libxcb.so.1 (0x00007f0544708000)
libgmodule-2.0.so.0 => /usr/lib/x86_64-linux-gnu/libgmodule-2.0.so.0 (0x00007f0544504000)
liborc-0.4.so.0 => /usr/lib/x86_64-linux-gnu/liborc-0.4.so.0 (0x00007f0544288000)
libpcre.so.3 => /lib/x86_64-linux-gnu/libpcre.so.3 (0x00007f0544016000)
libffi.so.6 => /usr/lib/x86_64-linux-gnu/libffi.so.6 (0x00007f0543e0e000)
libXau.so.6 => /usr/lib/x86_64-linux-gnu/libXau.so.6 (0x00007f0543c0a000)
libXdmcp.so.6 => /usr/lib/x86_64-linux-gnu/libXdmcp.so.6 (0x00007f0543a04000)
libbsd.so.0 => /lib/x86_64-linux-gnu/libbsd.so.0 (0x00007f05437ef000)

Thank you so much for your help :-)

root@0bda866f2d9c:/home/fadwa/workspace/model_conversion# ldd /usr/lib/x86_64-linux-gnu/gstreamer-1.0/deepstream/libnvdsgst_eglglessink.so
	linux-vdso.so.1 (0x00007fff800ef000)
	libcudart.so.10.2 => /usr/local/cuda-10.2/lib64/libcudart.so.10.2 (0x00007f7366001000)
	libcuda.so.1 => /usr/lib/x86_64-linux-gnu/libcuda.so.1 (0x00007f7364b4e000)
	libGLESv2.so.2 => /usr/lib/x86_64-linux-gnu/libGLESv2.so.2 (0x00007f736493c000)
	libEGL.so.1 => /usr/lib/x86_64-linux-gnu/libEGL.so.1 (0x00007f7364728000)
	libX11.so.6 => /usr/lib/x86_64-linux-gnu/libX11.so.6 (0x00007f73643f0000)
	libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007f7364052000)
	libgstreamer-1.0.so.0 => /usr/lib/x86_64-linux-gnu/libgstreamer-1.0.so.0 (0x00007f7363d17000)
	libgstbase-1.0.so.0 => /usr/lib/x86_64-linux-gnu/libgstbase-1.0.so.0 (0x00007f7363aa2000)
	libgstvideo-1.0.so.0 => /usr/lib/x86_64-linux-gnu/libgstvideo-1.0.so.0 (0x00007f7363809000)
	libglib-2.0.so.0 => /usr/lib/x86_64-linux-gnu/libglib-2.0.so.0 (0x00007f73634f2000)
	libgobject-2.0.so.0 => /usr/lib/x86_64-linux-gnu/libgobject-2.0.so.0 (0x00007f736329e000)
	libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007f7362ead000)
	/lib64/ld-linux-x86-64.so.2 (0x00007f736649e000)
	libdl.so.2 => /lib/x86_64-linux-gnu/libdl.so.2 (0x00007f7362ca9000)
	libpthread.so.0 => /lib/x86_64-linux-gnu/libpthread.so.0 (0x00007f7362a8a000)
	librt.so.1 => /lib/x86_64-linux-gnu/librt.so.1 (0x00007f7362882000)
	libGLdispatch.so.0 => /usr/lib/x86_64-linux-gnu/libGLdispatch.so.0 (0x00007f73625cc000)
	libxcb.so.1 => /usr/lib/x86_64-linux-gnu/libxcb.so.1 (0x00007f73623a4000)
	libgmodule-2.0.so.0 => /usr/lib/x86_64-linux-gnu/libgmodule-2.0.so.0 (0x00007f73621a0000)
	liborc-0.4.so.0 => /usr/lib/x86_64-linux-gnu/liborc-0.4.so.0 (0x00007f7361f24000)
	libpcre.so.3 => /lib/x86_64-linux-gnu/libpcre.so.3 (0x00007f7361cb2000)
	libffi.so.6 => /usr/lib/x86_64-linux-gnu/libffi.so.6 (0x00007f7361aaa000)
	libXau.so.6 => /usr/lib/x86_64-linux-gnu/libXau.so.6 (0x00007f73618a6000)
	libXdmcp.so.6 => /usr/lib/x86_64-linux-gnu/libXdmcp.so.6 (0x00007f73616a0000)
	libbsd.so.0 => /lib/x86_64-linux-gnu/libbsd.so.0 (0x00007f736148b000)

Did you still get the element create fail error?

Yes

Can you get one clean docker and try again? i do not have these issues using docker .

Yes I used the devel container but I can’t find this path in it /usr/src/tensorrt/bin/ . That’s why I installed trt and nvidia-deepstream manually.

Is there a way to solve this bug in the manual installation approach?. What is it’s cause?

You can not find the path /usr/src/tensorrt/bin/ in devel docker?
which docker you are using?

devel docker.

If you need tensorrt source samples, you need to install it by yourself.
but your error is not related to tensorrt samples.

My error is when I install deep-stream manually

if you use devel docker, you do not need to install deepstream manually. it’s installed by default.

I am feeling that we are running in circles here. Please take a look at the thread from the beginning to get a better understanding about my issue.

As stated before, please try clean docker, you will not meet the error “can not create render bin”, but you also raised another issue as you said you can not find the path /usr/src/tensorrt/bin, please see comment 17

Yes I used a clean docker and it was working fine. Now I have this error when installing manually. Please help.

download the same version as used in the docker from nvidia TensorRT download center, remove the trt package from the docker, reinstall using the package you downloaded.