Before You Start

We have introduced a new NGC DeepStream SDK Collection.

This collection serves as a hub for all DeepStream assets. Make sure you check it out!

DeepStream container for Enterprise Grade GPUs

Please refer to the section below which describes the different container options offered for NVIDIA Data Center GPUs running on x86 platform.

Read the DeepStream 8.0 Release Notes for full list of features of the release

NOTE: Dockers from previous CUDA releases 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 for details.

Getting Started

Prerequisites:

Ensure these prerequisites are installed in your system before proceeding to the next step:

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 +

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

For x86 Systems:

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

For ARM SBSA Systems:

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

Docker command line options explained:

• NOTES:
  Please refer to /opt/nvidia/deepstream/deepstream-8.0/README inside the container for details on deepstream-app usage.

• Using the Triton docker as a base image:
  For creating a base image using the Triton (x86) docker as a baseline, one approach is to use an entry point with a combined script so end users can run a specific script for their application.

ENTRYPOINT ["/bin/sh", "-c" , "/opt/nvidia/deepstream/deepstream- 8.0/entrypoint.sh && \custom command\"]

• 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 command below. For additional information please refer to section 1.2 (for codecs: DIFFERENCES SINCE DEEPSTREAM 6.1 AND ABOVE) & section 1.3 for BREAKING CHANGES in Release notes.

apt-get install --reinstall libflac8 libmp3lame0 libxvidcore4 ffmpeg

Known Limitations

DeepStream 8.0 Triton containers (x86 and SBSA) have the following CVE(s):

License

The following licenses apply to the DeepStream SDK assets:

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

• hiredis

• cmake

• autoconf ( license and license exception )

• libtool

• libglvnd-dev

• libgl1-mesa-dev,libegl1-mesa-dev,libgles2-mesa-dev

In addition, the (deepstream:8.0-gc-triton-devel) container includes the Vulkan Validation Layers (v1.1.123) to support the NVIDIA Graph Composer.

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.

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.