## **Before You Start** We have introduced a new NGC [DeepStream SDK Collection](https://catalog.ngc.nvidia.com/orgs/nvidia/collections/deepstream_sdk). This collection serves as a hub for all DeepStream assets. Make sure you check it out! ## **DeepStream containers for Jetson-based devices** Please refer to the section below which describes the different container options offered for [Jetson-based devices](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/):

Container Name	Architecture	License Type	Notes
deepstream:8.0-triton-multiarch	Multi-Arch X86 + Jetson	Deployment	The DeepStream Triton container enables inference using Triton Inference Server. With Triton developers can run inference natively using , PyTorch and ONNX-RT. Inference with Triton is supported in the reference application (deepstream-app)
deepstream:8.0-samples-multiarch	Multi-Arch X86 + Jetson	Deployment	The DeepStream samples container extends the base container to also include sample applications that are included in the DeepStream SDK along with associated config files, models, and streams. This container is ideal to understand and explore the DeepStream SDK using the provided samples.

Container Name

Architecture

License Type

Notes

deepstream:8.0-triton-multiarch

Multi-Arch

X86 + Jetson

Deployment

The DeepStream Triton container enables inference using Triton Inference Server. With Triton developers can run inference natively using , PyTorch and ONNX-RT. Inference with Triton is supported in the reference application (deepstream-app)

deepstream:8.0-samples-multiarch

Multi-Arch

X86 + Jetson

Deployment

The DeepStream samples container extends the base container to also include sample applications that are included in the DeepStream SDK along with associated config files, models, and streams. This container is ideal to understand and explore the DeepStream SDK using the provided samples.

Read the [DeepStream 8.0 Release Notes](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Release_notes.html) for full list of features of the release ***Notes:*** - DeepStream dockers or dockers derived from previous releases (before DeepStream 6.1) will need to update their CUDA GPG key to perform software updates. You can find additional details [here](https://forums.developer.nvidia.com/t/notice-cuda-linux-repository-key-rotation/212772). - These containers use the [NVIDIA Container Runtime](https://github.com/NVIDIA/nvidia-container-toolkit) for Jetson to run DeepStream applications. The NVIDIA Container Toolkit seamlessly expose specific parts of the device (i.e. BSP) to the DeepStream container, giving the applications resources need to run the application. - Since Jetpack 6.0, NVIDIA Container Runtime no longer mounts user level libraries like CUDA, cuDNN and TensorRT from the host. These will instead be installed inside the containers. - For Triton samples, while running ***/opt/nvidia/deepstream/deepstream-8.0/samples/prepare\_classification\_test\_video.sh***, FFMPEG package along with additional dependent libs need to be installed using the command below. For additional information Refer section 1.2 (for codecs: DIFFERENCES SINCE DEEPSTREAM 6.1 AND ABOVE) & section 1.3 for BREAKING CHANGES in [Release notes](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Release_notes.html).

apt-get install --reinstall libflac8 libmp3lame0 libxvidcore4 ffmpeg

## ## **Getting Started** ### Prerequisites: Ensure these prerequisites are installed in your system before proceeding to the next step:

Component	Details
JetPack 7.0	A Jetson device running L4T BSP r38.2
Codecs script	DeepStream dockers no longer package libraries for certain multimedia operations such as: audio data parsing, CPU decode, and CPU encode. This translates into limited functionality with MP4 files. We provide a script to install these components. Make sure to execute the script within the container: /opt/nvidia/deepstream/deepstream/user_additional_install.sh

### Pull the container: 1. From the top-right corner of this page, select the pull-down *Get Container* and copy the URL to the default container. Alternatively, click on *View all tags* to select a different container. 2. Open a command prompt on your Linux compatible system and run the following command. Ensure the pull completes successfully before proceeding to the next step.

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

### Run the container: 1. Allow external applications to connect to the host's X display:

xhost +

2. Run the docker container (use the desired container tag in the command line below): If using docker (recommended):

docker run -it --rm --network=host --runtime nvidia -e DISPLAY=$DISPLAY -w /opt/nvidia/deepstream/deepstream-8.0 -v /tmp/.X11-unix/:/tmp/.X11-unix nvcr.io/nvidia/deepstream:8.0-triton-multiarch

**Docker command line options explained:**

Option	Description
-it	means run in interactive mode
--runtime nvidia	Use an alternative runtime (will use the NVIDIA container runtime)
--rm	will delete the container when finished
--privileged	grants access to the container to the host resources. This flag is need to run Graph Composer from the -devel container
-v	Specifies the mounting directory and used to mount host's X11 display in the container filesystem to render output videos Users can mount additional directories (using -v option) as required to easily access configuration files, models, and other resources. (i.e., use -v /home:/home) to mount the home directory into the container filesystem.
--cap-add SYSLOG	This option needs to be included to enable usage of the nvds_logger functionality inside the container
-p	To enable RTSP out, network port needs to be mapped from container to host to enable incoming connections using the “-p” option in the command line, eg:-p 8554:8554

See /opt/nvidia/deepstream/deepstream-8.0/README inside the container for deepstream-app usage information. Additional argument to add to above docker command for accessing CSI Camera from Docker: -v /tmp/argus\_socket:/tmp/argus\_socket For USB Camera additional argument --device /dev/video ***NOTE*** Please refer to /opt/nvidia/deepstream/deepstream-8.0/README inside the container for details on deepstream-app usage. The DeepStream containers for Jetson are intended to be a deployment vehicle. Please refer [Docker Containers](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_docker_containers.html#creating-custom-deepstream-dockers-for-dgpu-or-jetson-using-deepstreamsdk-package) Section within the DeepStream 8.0 Dev Guide Section for instructions on how to build custom containers based on DeepStream from either Jetson device or your workstation. ## Known Limitations **DeepStream 8.0 Triton container (Jetson) has the known following CVE(s):**

CVE	Description
CVE-2025-3887	GStreamer H265 Codec Parsing Stack-based Buffer Overflow Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of GStreamer. This impacts Gstreamer H265 parser on Ubuntu 24.04. There is no publicly available fix for Ubuntu 24.04. Users can opt for Ubuntu Pro which has this fix included from Canonical.

CVE

Description

CVE-2025-3887

GStreamer H265 Codec Parsing Stack-based Buffer Overflow Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of GStreamer. This impacts Gstreamer H265 parser on Ubuntu 24.04.

There is no publicly available fix for Ubuntu 24.04. Users can opt for Ubuntu Pro which has this fix included from Canonical.

## **License** The following licenses apply to the DeepStream SDK assets:

Asset	Applicable EULA	Notes
SDK	DeepStream SDK EULA	A copy of the license is available on the following folder of the SDK: /opt/nvidia/deepstream/deepstream-8.0/LicenseAgreement.pdf
Containers	DeepStream NGC License	License grants redistribution rights allowing developers to build applications on top of the DeepStream containers
Development Containers	DeepStream NGC Development License	A development-only license. Does not allow redistribution of the container
TAO Models	NVIDIA AI Product License	All TAO pre-trained models included in the DeepStream SDK are covered by the NVIDIA AI Product License.

**NOTE: By pulling, downloading, or using the DeepStream SDK, you accept the terms and conditions of the EULA licenses listed above.** This project will download and install additional third-party open-source software projects. Review the license terms of these open source projects before use. Please note that all container images come with the following packages installed: - [librdkafka](https://github.com/edenhill/librdkafka/blob/7101c2310341ab3f4675fc565f64f0967e135a6a/LICENSE) - [hiredis](https://github.com/redis/hiredis/blob/d5b4c69b7113213c1da3a0ccbfd1ee1b40443c7a/COPYING) - [cmake](https://gitlab.kitware.com/cmake/cmake/-/blob/v3.16.3/Copyright.txt) - [autoconf](https://www.gnu.org/software/autoconf/) ( [license](https://www.gnu.org/licenses/gpl-3.0.txt) and [license exception](https://www.gnu.org/licenses/autoconf-exception-3.0.en.html) ) - [libtool](https://git.savannah.gnu.org/cgit/libtool.git/tree/AUTHORS) - [libglvnd-dev](http://changelogs.ubuntu.com/changelogs/pool/main/libg/libglvnd/libglvnd_1.3.2-1~ubuntu0.20.04.2/copyright) - [libgl1-mesa-dev,libegl1-mesa-dev,libgles2-mesa-dev](http://changelogs.ubuntu.com/changelogs/pool/main/m/mesa/mesa_21.2.6-0ubuntu0.1~20.04.2/copyright) The software listed below is provided under the terms of GPLv3. To obtain source code for software provided under licenses that require redistribution of source code, including the GNU General Public License (GPL) and GNU Lesser General Public License (LGPL), available under DeepStream Resources 8.0\_3rd\_party\_oss. This offer is valid for a period of three (3) years from the date of the distribution of this product by NVIDIA CORPORATION.

Component	License
autoconf	GPL 3.0
libtool	GPL 3.0
libglvnd-dev	GPL 3.0
libgl1-mesa-dev	GPL 3.0
libegl1-mesa-dev	GPL 3.0
libgles2-mesa-dev	GPL 3.0

## **Ethical AI** *NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.*