Installation

NVIDIA^® DeepStream Software Development Kit (SDK) is an accelerated AI framework to build intelligent video analytics (IVA) pipelines. DeepStream runs on NVIDIA^® T4, NVIDIA® Hopper, NVIDIA^® Blackwell, NVIDIA^® Ampere, NVIDIA^® ADA and platform such as NVIDIA^® Jetson AGX Thor. For dGPU platforms Enterprise GPUs are highly recommended for deployments that are expected to run 24x7. Gaming GPUs are not designed to perform in such type of environments.

Jetson Setup

This section explains how to prepare a Jetson device before installing the DeepStream SDK.

Note

Steps to install DeepStream SDK locally, assume that ~/.local/bin/ has been added to the ~/.bashrc or ~/.profile

Install Jetson SDK components

Download NVIDIA SDK Manager from https://developer.nvidia.com/embedded/jetpack. You will use this to install JetPack 7.0 GA (corresponding to L4T 38.2 release)

• NVIDIA SDK Manager is a graphical application which flashes and installs the JetPack packages.

• The flashing procedure takes approximately 10-30 minutes, depending on the host system.

Install Dependencies

Install prerequisite packages

Enter the following commands to install the prerequisite packages:

$ sudo apt install \
libssl3 \
libssl-dev \
libgstreamer1.0-0 \
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 \
libjansson4 \
libyaml-cpp-dev \
libmosquitto1

Note

Sometimes with RTSP streams the application gets stuck on reaching EOS. This is because of an issue in rtpjitterbuffer component. To fix this issue, a script “update_rtpmanager.sh” at /opt/nvidia/deepstream/deepstream/ has been provided with required details to update gstrtpmanager library. The script should be executed once above mentioned packages are installed as prerequisite.

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

1. Install confluent-platform:

   sudo mkdir -p /etc/apt/keyrings
   
   wget -qO - https://packages.confluent.io/deb/8.0/archive.key | gpg \
   --dearmor | sudo tee /etc/apt/keyrings/confluent.gpg > /dev/null
   
   CP_DIST=$(lsb_release -cs)
   echo "Types: deb
   URIs: https://packages.confluent.io/deb/8.0
   Suites: stable
   Components: main
   Architectures: $(dpkg --print-architecture)
   Signed-by: /etc/apt/keyrings/confluent.gpg
   
   Types: deb
   URIs: https://packages.confluent.io/clients/deb/
   Suites: ${CP_DIST}
   Components: main
   Architectures: $(dpkg --print-architecture)
   Signed-By: /etc/apt/keyrings/confluent.gpg" | sudo tee /etc/apt/sources.list.d/confluent-platform.sources > /dev/null
   
   sudo apt-get update && sudo apt-get install confluent-platform
   

2. Install librdkafka:

   sudo apt-get install librdkafka-dev
   

Install latest NVIDIA BSP packages

Installation of JetPack 7.0 GA will ensure that latest NVIDIA BSP packages are installed.

Install the DeepStream SDK

• Method 1: Using SDK Manager

  Select DeepStreamSDK from the Additional SDKs section along with JP 7.0 GA software components for installation.

• Method 2: Using the DeepStream tar package: https://catalog.ngc.nvidia.com/orgs/nvidia/resources/deepstream

  1. Download the DeepStream 8.0 Jetson tar package deepstream_sdk_v8.0.0_jetson.tbz2 to the Jetson device.

  2. Enter the following commands to extract and install the DeepStream SDK:

     $  sudo tar -xvf deepstream_sdk_v8.0.0_jetson.tbz2 -C /
     $ cd /opt/nvidia/deepstream/deepstream-8.0
     $ sudo ./install.sh
     $ sudo ldconfig
     

• Method 3: Using the DeepStream Debian package: https://catalog.ngc.nvidia.com/orgs/nvidia/resources/deepstream

  Download the DeepStream 8.0 Jetson Debian package deepstream-8.0_8.0.0-1_arm64.deb to the Jetson device. Enter the following command:

  $ sudo apt-get install ./deepstream-8.0_8.0.0-1_arm64.deb
  

• Method 4: Use Docker containers

  DeepStream docker containers are available on NGC. See the Docker Containers section to learn about developing and deploying DeepStream using docker containers.

Note

Verification: Once DeepStream SDK installation is successful, refer to Expected output (deepstream-app) for the expected output.

dGPU Setup for Ubuntu

This section explains how to prepare an Ubuntu x86_64 system with NVIDIA dGPU devices before installing the DeepStream SDK.

Note

Steps to install DeepStream SDK locally, assume that ~/.local/bin/ has been added to the ~/.bashrc or ~/.profile

Note

This document uses the term dGPU (“discrete GPU”) to refer to NVIDIA GPU expansion card products such as NVIDIA Tesla^® T4, NVIDIA® Hopper, NVIDIA^® Blackwell, NVIDIA^® Ampere, NVIDIA^® ADA, NVIDIA GeForce^® RTX 2080, NVIDIA GeForce^® RTX 3080, NVIDIA GeForce^® RTX 4080 and GeForce^®/NVIDIA RTX/QUADRO. This version of DeepStream SDK runs on GPUs supported by NVIDIA driver 570.133.20 and NVIDIA TensorRT™ 10.9.0.34 and later versions.

Prerequisites

You must install the following components:

• Ubuntu 24.04

• GStreamer 1.24.2

• NVIDIA driver 570.133.20

• CUDA 12.8

• TensorRT 10.9.0.34

Remove all previous DeepStream installations

Enter the following commands to remove all previous DeepStream 3.0 or prior installations:

$ sudo rm -rf /usr/local/deepstream /usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstnv* /usr/bin/deepstream* /usr/lib/x86_64-linux-gnu/gstreamer-1.0/libnvdsgst*
/usr/lib/x86_64-linux-gnu/gstreamer-1.0/deepstream*
/opt/nvidia/deepstream/deepstream*
$ sudo rm -rf /usr/lib/x86_64-linux-gnu/libv41/plugins/libcuvidv4l2_plugin.so

To remove DeepStream 4.0 or later installations:

1. Open the uninstall.sh file in /opt/nvidia/deepstream/deepstream/

2. Run the following script as sudo ./uninstall.sh

Install Dependencies

Install prerequisite packages

Enter the following commands to install the necessary packages before installing the DeepStream SDK:

$ sudo apt install \
libssl3 \
libssl-dev \
libgles2-mesa-dev \
libgstreamer1.0-0 \
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 \
libjansson4 \
libyaml-cpp-dev \
libjsoncpp-dev \
protobuf-compiler \
libmosquitto1 \
gcc \
make \
git \
python3

Note

Sometimes with RTSP streams the application gets stuck on reaching EOS. This is because of an issue in rtpjitterbuffer component. To fix this issue,a script “update_rtpmanager.sh” at /opt/nvidia/deepstream/deepstream/ has been provided with required details to update gstrtpmanager library. The script should be executed once above mentioned packages are installed as prerequisite.

Install CUDA Toolkit 12.8

Run the following commands (reference, https://developer.nvidia.com/cuda-downloads):

$ sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/3bf863cc.pub
$ sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/ /"
$ sudo apt-get update
$ sudo apt-get install cuda-toolkit-12-8

Note

If you observe following errors while CUDA installation, refer to https://developer.nvidia.com/blog/updating-the-cuda-linux-gpg-repository-key/.

W: GPG error: https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64 InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC
E: The repository 'https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64 InRelease' is no longer signed.

Install NVIDIA driver 570.133.20

• Download and install using NVIDIA driver 570.133.20 from NVIDIA Unix drivers page at: https://www.nvidia.com/en-us/drivers/details/242548/

• Run the following commands:

  $ chmod 755 NVIDIA-Linux-x86_64-570.133.20.run
  $ sudo ./NVIDIA-Linux-x86_64-570.133.20.run --no-cc-version-check
  

Note

Ensure gdm, lightdm or Xorg service is stopped while installing nvidia driver

Use command : sudo service gdm stop sudo service lightdm stop sudo pkill -9 Xorg

Install TensorRT 10.9.0.34

Run the following command to install TensorRT 10.9.0.34:

version="10.9.0.34-1+cuda12.8"
sudo apt-get install libnvinfer-dev=${version} libnvinfer-dispatch-dev=${version} \
libnvinfer-dispatch10=${version} libnvinfer-headers-dev=${version} libnvinfer-headers-plugin-dev=${version} \
libnvinfer-lean-dev=${version} libnvinfer-lean10=${version} libnvinfer-plugin-dev=${version} \
libnvinfer-plugin10=${version} libnvinfer-vc-plugin-dev=${version} libnvinfer-vc-plugin10=${version} \
libnvinfer10=${version} libnvonnxparsers-dev=${version} libnvonnxparsers10=${version} tensorrt-dev=${version}

Note

• It is assumed, “deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/” has been added, as mentioned in CUDA Toolkit 12.8 installation step.

• libnvparsers are deprecated since TRT 9.0.

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

1. Clone the librdkafka repository from GitHub:

   $ git clone https://github.com/confluentinc/librdkafka.git
   

2. Configure and build the library:

   $ cd librdkafka
   $ git checkout tags/v2.2.0
   $ ./configure --enable-ssl
   $ make
   $ sudo make install
   

3. Copy the generated libraries to the deepstream directory:

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

Install the DeepStream SDK

• Method 1: Using the DeepStream Debian package

  Download the DeepStream 8.0 dGPU Debian package deepstream-8.0_8.0.0-1_amd64.deb : https://catalog.ngc.nvidia.com/orgs/nvidia/resources/deepstream

  Enter the command:

  $ sudo apt-get install ./deepstream-8.0_8.0.0-1_amd64.deb
  

• Method 2: Download the DeepStream tar package: https://catalog.ngc.nvidia.com/orgs/nvidia/resources/deepstream

  Navigate to the location of the downloaded DeepStream package to extract and install the DeepStream SDK:

  $ sudo tar -xvf deepstream_sdk_v8.0.0_x86_64.tbz2 -C /
  $ cd /opt/nvidia/deepstream/deepstream-8.0/
  $ sudo ./install.sh
  $ sudo ldconfig
  

• Method 3: Use Docker containers

  DeepStream docker containers are available on NGC. See the Docker Containers section to learn about developing and deploying DeepStream using docker containers.

Note

Verification: Once DeepStream SDK installation is successful, refer to Expected output (deepstream-app) for the expected output.

IGX/dGPU on ARM Setup for Ubuntu

Note

DeepStream for IGX is not supported in this release.

This section explains how to prepare a NVIDIA IGX system with NVIDIA dGPU devices before installing the DeepStream SDK.

Note

This document uses the term dGPU (“discrete GPU”) to refer to NVIDIA GPU expansion card products such as NVIDIA RTX A6000 and NVIDIA RTX 6000 Ada. This version of DeepStream SDK has been validated with NVIDIA driver 570.133.20 and NVIDIA TensorRT™ 10.9.0.34.

You must install the following components:

• Please refer IGX-Software Install Guide for installing Base-OS: IGX-Software Guide

• Docker

• Nvidia Container Toolkit

Run ARM SBSA docker on IGX/dGPU

1. Pull the DeepStream Triton Inference Server docker

   docker pull nvcr.io/nvidia/deepstream:8.0-triton-arm-sbsa
   

2. Start the docker

   sudo docker run -it --rm --runtime=nvidia --network=host -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics --gpus all --privileged -e DISPLAY=:0 -v /tmp/.X11-unix:/tmp/.X11-unix -v /etc/X11:/etc/X11 nvcr.io/nvidia/deepstream:8.0-triton-arm-sbsa
   

dGPU on ARM Setup for Ubuntu

This section explains how to prepare an Ubuntu aarch64 system with NVIDIA dGPU devices before installing the DeepStream SDK.

You must install the following components:

• NVIDIA driver 570.133.20

• Docker

• Nvidia Container Toolkit

Install NVIDIA driver 570.133.20

• Download and install using NVIDIA driver 570.133.20 from NVIDIA Unix drivers page at: https://www.nvidia.com/en-us/drivers/details/242547/

• Run the following commands:

  $ chmod 755 NVIDIA-Linux-aarch64-570.133.20.run
  $ sudo ./NVIDIA-Linux-aarch64-570.133.20.run --no-cc-version-check
  

Note

Ensure gdm, lightdm or Xorg service is stopped while installing Nvidia driver

Use command : sudo service gdm stop sudo service lightdm stop sudo pkill -9 Xorg

Run dGPU on ARM Docker (SBSA)

1. Pull the DeepStream Triton Inference Server docker

   docker pull nvcr.io/nvidia/deepstream:8.0-triton-arm-sbsa
   

2. Start the docker

   sudo docker run -it --rm --runtime=nvidia --network=host -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics --gpus all --privileged -e DISPLAY=:0 -v /tmp/.X11-unix:/tmp/.X11-unix -v /etc/X11:/etc/X11 nvcr.io/nvidia/deepstream:8.0-triton-arm-sbsa
   

dGPU Setup for RedHat Enterprise Linux (RHEL)

DeepStream for RHEL is not supported in this release.

Platform and OS Compatibility

The following table provides information about platform and operating system compatibility in the current and earlier versions of DeepStream.

Jetson model Platform and OS Compatibility

DS release	DS 6.1.1	DS 6.2	DS 6.3	DS 6.4	DS 7.0	DS 7.1	DS 8.0
Jetson platforms	AGX Xavier, Jetson NX, Jetson Orin	Xavier AGX , Xavier NX, AGX Orin, Orin NX	Xavier AGX , Xavier NX, AGX Orin, Orin NX, Orin nano	AGX Orin, Orin NX, Orin nano	AGX Orin, Orin NX, Orin nano	AGX Orin, Orin NX, Orin nano	AGX Thor
OS	L4T Ubuntu 20.04	L4T Ubuntu 20.04	L4T Ubuntu 20.04	L4T Ubuntu 22.04	L4T Ubuntu 22.04	L4T Ubuntu 22.04	L4T Ubuntu 24.04
JetPack release	5.0.2 GA Revision 1	5.1 GA	5.1.2 GA	6.0 DP	6.0 GA	6.1 GA	7.0 GA
L4T release	35.1	35.2.1	35.4	36.2	36.3	36.4	38.2
CUDA release	CUDA 11.4	CUDA 11.4	CUDA 11.4	CUDA 12.2	CUDA 12.2	CUDA 12.6	CUDA 13.0
cuDNN release	cuDNN 8.4.1.50+	cuDNN 8.6.0.166+	cuDNN 8.6.0.166+	cuDNN 8.9.4.25+	cuDNN 8.9.4.25+	cuDNN 9.3.0	CuDNN 9.12.0
TensorRT release	TRT 8.4.1.5	TRT 8.5.2.2	TRT 8.5.2.2	TRT 8.6.2.3	TRT 8.6.2.3	TRT 10.3.0.31	TRT 10.13.2.6
OpenCV release	OpenCV 4.2.0	OpenCV 4.2.0	OpenCV 4.5.4	OpenCV 4.8.0	OpenCV 4.8.0	OpenCV 4.8.0	OpenCV 4.8.0
Vision­Works	NA	NA	NA	NA	NA	NA	NA
GStreamer	GStreamer 1.16.2	GStreamer 1.16.3	GStreamer 1.16.3	GStreamer 1.20.3	GStreamer 1.20.3	GStreamer 1.20.3	GStreamer 1.24.2
Docker image	deepstream-l4t:6.1.1	deepstream-l4t:6.2	deepstream-l4t:6.3	deepstream:6.4	deepstream:7.0	deepstream:7.1	deepstream:8.0

dGPU model Platform and OS Compatibility

DS release	DS 6.1.1	DS 6.2	DS 6.3	DS 6.4	DS 7.0	DS 7.1	DS 8.0
GPU platforms	T4, V100, A2, A10, A30, A100, RTX Ampere (Ax000/RTX30x0)	T4, V100, A2, A10, A30, A100, RTX Ampere (Ax000/RTX30x0), Hopper, ADA	T4, V100, A2, A10, A30, A100, RTX Ampere (Ax000/RTX30x0), Hopper, ADA	T4, V100, A2, A10, A30, A100, RTX Ampere (Ax000/RTX30x0), Hopper, ADA	T4, V100, A2, A10, A30, A100, RTX Ampere (Ax000/RTX30x0), Hopper, ADA	T4, A2, A10, A30, A100, RTX Ampere (Ax000/RTX30x0), Hopper, ADA	Turing, Ampere, Hopper, ADA, Blackwell
OS	Ubuntu 20.04	Ubuntu 20.04	Ubuntu 20.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 24.04
GCC	GCC 9.4.0	GCC 9.4.0	GCC 9.4.0	GCC 11.4.0	GCC 11.4.0	GCC 11.4.0	GCC 11.4.0
CUDA release	CUDA 11.7.1	CUDA 11.8	CUDA 12.1	CUDA 12.2	CUDA 12.2	CUDA 12.6	CUDA 12.8
cuDNN release	cuDNN 8.4.1.50+	cuDNN 8.7.0.84-1+	cuDNN 8.8.1.3-1+	cuDNN 8.9.4.25-1+	cuDNN 8.9.6.50-1+	cuDNN 9.3.0	CuDNN 9.7.1
TRT release	TRT 8.4.1.5	TRT 8.5.2.2	TRT 8.5.3.1	TRT 8.6.1.6	TRT 8.6.1.6	TRT 10.3.0.26	TRT 10.9.0.34
Display Driver	R515.65.01	R525.85.12	R525.125.06	R535.104.12	R535.161.08	R535.183.06(Data Center GPUs), R560.35.03(RTX GPUs)	R570.133.20
VideoSDK release	SDK 9.1	SDK 9.1	SDK 9.1	SDK 9.1	SDK 9.1	SDK 9.1	SDK 9.1
OFSDK release	2.0.23	2.0.23	2.0.23	2.0.23	2.0.23	2.0.23	2.0.23
GStreamer release	GStreamer 1.16.2	GStreamer 1.16.3	GStreamer 1.16.3	GStreamer 1.20.3	GStreamer 1.20.3	GStreamer 1.20.3	GStreamer 1.24.2
OpenCV release	OpenCV 4.2.0	OpenCV 4.2.0	OpenCV 4.2.0	OpenCV 4.5.4	OpenCV 4.5.4	OpenCV 4.5.4	OpenCV 4.6
Docker image	deepstream:6.1.1	deepstream:6.2	deepstream:6.3	deepstream:6.4	deepstream:7.0	deepstream:7.1	deepstream:8.0
NVAIE release	NA	NVAIE-3.x	NVAIE-3.x	NVAIE-4.x	NA	NA	NA

Note

By default, OpenCV has been deprecated. However, OpenCV can be enabled in plugins such as nvinfer (nvdsinfer) and dsexample (gst-dsexample) by setting WITH_OPENCV=1 in the Makefile of these components. Please refer component README for more instructions.

NA: “Not Applicable”

DeepStream 8.0 support is currently enabled by SBSA triton docker. SBSA category currently supports GH200 and GB200.