Install DeepStream 6.1.1 on WSL2

DeepStream 6.1.1 Is the release with support for Ubuntu 20.04 LTS

 

Microsoft Store에서 "Ubuntu 20.04.5 LTS" 설치

WSL은 기본 설치 경로가 사용자 디렉토리다.

(C:\Users\user\AppData\Local\Packages\CanonicalGroupLimited.Ubuntu20.04LTS_79rhkp1fndgsc\LocalState\ext4.vhdx)

 

Windows 상에서 네트워크 드라이브 연결

Install DeepStream

https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Quickstart.html

Quickstart Guide — DeepStream 6.1.1 Release documentation

dGPU Setup for Ubuntu

NOTE:

This document uses the term dGPU (“discrete GPU”) to refer to NVIDIA GPU expansion card products such as NVIDIA Tesla® T4 , NVIDIA GeForce® GTX 1080, NVIDIA GeForce® RTX 2080 and NVIDIA GeForce® RTX 3080. This version of DeepStream SDK runs on specific dGPU products on x86_64 platforms supported by NVIDIA driver 515.65.01 and NVIDIA TensorRT™ 8.4.1.5 and later versions.

 

You must install the following components:

• GStreamer 1.16.2
• NVIDIA driver 515.65.01
• CUDA 11.7 update 1
• TensorRT 8.4.1.5

Remove all previous DeepStream installations

To remove DeepStream 4.0 or later installations:

1. Open the uninstall.sh file in /opt/nvidia/deepstream/deepstream/
2. Set PREV_DS_VER as 4.0
3. Run the following script as

$ sudo ./uninstall.sh

Install Dependencies

$ sudo apt -y install \
    libssl1.1 \
    libgstreamer1.0-0   \
    libgstreamer1.0-dev \
    gstreamer1.0-tools        \
    gstreamer1.0-plugins-good \
    gstreamer1.0-plugins-bad  \
    gstreamer1.0-plugins-ugly \
    gstreamer1.0-libav        \
    libgstreamer-plugins-base1.0-dev \
    libgstrtspserver-1.0-0   \
    libgstrtspserver-1.0-dev \
    libjansson4 \
    libjson-glib-dev \
    libyaml-cpp-dev \
    gcc \
    make \
    git \
    python3 python-is-python3

 

https://docs.nvidia.com/cuda/archive/11.7.1/wsl-user-guide/index.html

CUDA on WSL :: CUDA Toolkit Documentation

 

[OS/Linux] - CUDA 11.7.1 on WSL2

 

Install TensorRT 8.4.1.5

$ sudo apt-get -y install \
    libnvinfer8=8.4.1-1+cuda11.6 \
    libnvinfer-plugin8=8.4.1-1+cuda11.6 \
    libnvparsers8=8.4.1-1+cuda11.6 \
    libnvonnxparsers8=8.4.1-1+cuda11.6 \
    libnvinfer-bin=8.4.1-1+cuda11.6 \
    libnvinfer-dev=8.4.1-1+cuda11.6 \
    libnvinfer-plugin-dev=8.4.1-1+cuda11.6 \
    libnvparsers-dev=8.4.1-1+cuda11.6 \
    libnvonnxparsers-dev=8.4.1-1+cuda11.6 \
    libnvinfer-samples=8.4.1-1+cuda11.6 \
    libcudnn8=8.4.1.50-1+cuda11.6 \
    libcudnn8-dev=8.4.1.50-1+cuda11.6 \
    python3-libnvinfer=8.4.1-1+cuda11.6 \
    python3-libnvinfer-dev=8.4.1-1+cuda11.6

NOTE:

• It is assumed, “deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /” has been added, as mentioned in CUDA Toolkit 11.7.1 installation step.
• To install a particular cudnn version e.g. cudnn-8.4.1.50, follow below steps before TensorRT installation:
  • Download cuDNN 8.4.1 for Ubuntu 20.04 and CUDA 11.x local repo package from:
    https://developer.nvidia.com/compute/cudnn/secure/8.4.1/local_installers/11.6/cudnn-local-repo-ubuntu2004-8.4.1.50_1.0-1_amd64.deb
  • Run follow commands to install:

$sudo dpkg -i cudnn-local-repo-ubuntu2004-8.4.1.50_1.0-1_amd64.deb``
$sudo apt-get update
$sudo apt install libcudnn8=8.4.1.50-1+cuda11.6 libcudnn8-dev=8.4.1.50-1+cuda11.6

 

Install librdkafka (to enable Kafka protocol adaptor for message broker)

1. Clone the librdkafka repository from GitHub:

2. Configure and build the library:

$ git clone https://github.com/edenhill/librdkafka.git
 
$ cd librdkafka
$ git reset --hard 7101c2310341ab3f4675fc565f64f0967e135a6a
$ ./configure
$ make
make[1]: Entering directory '/home/ym/dev/librdkafka/src'
...
Generating linker script librdkafka.lds from rdkafka.h
/usr/bin/env: ‘python’: No such file or directory
make[1]: *** [Makefile:79: librdkafka.lds] Error 127
make[1]: Leaving directory '/home/ym/dev/librdkafka/src'
make: *** [Makefile:20: libs] Error 2
 
$ whereis python3
python3: /usr/bin/python3.8 /usr/bin/python3 /usr/lib/python3.8 /usr/lib/python3.9 /usr/lib/python3 /etc/python3.8 /etc/python3 /usr/local/lib/python3.8 /usr/share/python3 /mnt/c/Users/jylee/AppData/Local/Microsoft/WindowsApps/python3.exe /mnt/c/msys64/mingw64/bin/python3.10-config /mnt/c/msys64/mingw64/bin/python3.exe /usr/share/man/man1/python3.1.gz
 
#$ sudo ln -s /usr/bin/python3 /usr/bin/python
$ sudo apt install python-is-python3
 
$ make
make[1]: Entering directory '/home/ym/dev/librdkafka/src'
...
Updating
CONFIGURATION.md CONFIGURATION.md.tmp differ: byte 345, line 6
Checking  integrity
CONFIGURATION.md               OK
examples/rdkafka_example       OK
examples/rdkafka_performance   OK
examples/rdkafka_example_cpp   OK
make[1]: Entering directory '/home/ym/dev/librdkafka/src'
Checking librdkafka integrity
librdkafka.so.1                OK
librdkafka.a                   OK
Symbol visibility              OK
make[1]: Leaving directory '/home/ym/dev/librdkafka/src'
make[1]: Entering directory '/home/ym/dev/librdkafka/src-cpp'
Checking librdkafka++ integrity
librdkafka++.so.1              OK
librdkafka++.a                 OK
make[1]: Leaving directory '/home/ym/dev/librdkafka/src-cpp'
 
$ sudo make install

If Python3 has been installed, run these commands: whereis python3

Then we create a symlink to it: sudo ln -s /usr/bin/python3 /usr/bin/python

 

3. Copy the generated libraries to the deepstream directory:

$ sudo mkdir -p /opt/nvidia/deepstream/deepstream-6.1/lib
$ sudo cp /usr/local/lib/librdkafka* /opt/nvidia/deepstream/deepstream-6.1/lib

 

Install the DeepStream SDK

Method 1: Using the DeepStream Debian package

Download the DeepStream 6.1.1 dGPU Debian package deepstream-6.1_6.1.1-1_amd64.deb:

https://developer.nvidia.com/deepstream-6.1_6.1.1-1_amd64.deb

$ sudo apt-get install ./deepstream-6.1_6.1.1-1_amd64.deb
...
---------------------------------------------------------------------------------------
NOTE: sources and samples folders will be found in /opt/nvidia/deepstream/deepstream-6.1
---------------------------------------------------------------------------------------
Processing triggers for libc-bin (2.31-0ubuntu9.9) ...

 

Run the deepstream-app (the reference application)

Go to the samples directory and enter this command:

$ deepstream-app -c <path_to_config_file>

 

Run precompiled sample applications

1. Navigate to the chosen application directory inside sources/apps/samples_apps.

2. Follow that directory's README file to run the application.

NOTE:

If the application encounters errors and cannot create Gst elements, remove the GStreamer cache, then try again. To remove the GStreamer cache, enter this command:

$ rm ${HOME}/.cache/gstreamer-1.0/registry.x86_64.bin

When the application is run for a model which does not have an existing engine file, it may take up to a few minutes (depending on the platform and the model) for the file generation and application launch. For later runs, these generated engine files can be reused for faster loading.