Deepstream not getting installed

Environment

TRT 8.2.0 :
x86 :
Driver470 :
CUDA 11 :
Ubuntu 20.04 :
Python 3.6 :

Hello,
I have installed TensorRT via python3.6 -m pip install nvidia-pyindex , python3.6 -m pip install --upgrade nvidia-tensorrt

But I am getting the following error when I am trying to install Deepstream 5.1

Note, selecting ‘deepstream-5.1’ instead of ‘./deepstream-5.1_5.1.0-1_amd64.deb’
Some packages could not be installed. This may mean that you have
requested an impossible situation or if you are using the unstable
distribution that some required packages have not yet been created
or been moved out of Incoming.
The following information may help to resolve the situation:

The following packages have unmet dependencies:
deepstream-5.1 : Depends: libnvinfer7 (>= 7.2.1) but it is not installable
Depends: libnvparsers7 (>= 7.2.1) but it is not installable
Depends: libnvonnxparsers7 (>= 7.2.1) but it is not installable
Depends: libnvinfer-plugin7 (>= 7.2.1) but it is not installable
E: Unable to correct problems, you have held broken packages.

Then if I try to install libnvparsers8 via sudo I get :
libnvparsers8 is already the newest version (8.2.0-1+cuda11.4).

dpkg -l | grep TensorRT :

hi libnvinfer-dev 8.2.0-1+cuda11.4 amd64 TensorRT development libraries and headers
hi libnvinfer-plugin-dev 8.2.0-1+cuda11.4 amd64 TensorRT plugin libraries
hi libnvinfer-plugin8 8.2.0-1+cuda11.4 amd64 TensorRT plugin libraries
hi libnvinfer8 8.2.0-1+cuda11.4 amd64 TensorRT runtime libraries
hi libnvonnxparsers-dev 8.2.0-1+cuda11.4 amd64 TensorRT ONNX libraries
hi libnvonnxparsers8 8.2.0-1+cuda11.4 amd64 TensorRT ONNX libraries
hi libnvparsers-dev 8.2.0-1+cuda11.4 amd64 TensorRT parsers libraries
hi libnvparsers8 8.2.0-1+cuda11.4 amd64 TensorRT parsers libraries
hi python3-libnvinfer 8.2.0-1+cuda11.4 amd64 Python 3 bindings for TensorRT
ii python3-libnvinfer-dev 8.2.0-1+cuda11.4 amd64 Python 3 development package for TensorRT

Hi,
Please refer to the installation steps from the below link if in case you are missing on anything

Also, we suggest you to use TRT NGC containers to avoid any system dependency related issues.

Thanks!