DeepStream | NVIDIA NGC

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Features

Description

DeepStream SDK delivers a complete streaming analytics toolkit for AI based video and image understanding and multi-sensor processing. This container is for NVIDIA Enterprise GPUs.

Publisher

NVIDIA

Latest Tag

6.4-samples-multiarch

Modified

April 5, 2024

Compressed Size

6.63 GB

Multinode Support

Multi-Arch Support

Yes

6.4-samples-multiarch (Latest) Security Scan Results

Linux / amd64

Linux / arm64

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.

Container Name	Architecture	License Type	Notes
deepstream:6.4-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 TensorFlow, TensorFlow-TensorRT, PyTorch and ONNX-RT. Inference with Triton is supported in the reference application (deepstream-app)
deepstream:6.4-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.
deepstream:6.4-gc-triton-devel	x86	Development	The DeepStream development container is the recommended container to get you started as it includes Graph Composer, the build toolchains, development libraries and packages necessary for building DeepStream reference applications within the container. This container is slightly larger in size by virtue of including the build dependencies.

deepstream:6.4-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 TensorFlow, TensorFlow-TensorRT, PyTorch and ONNX-RT. Inference with Triton is supported in the reference application (deepstream-app)

deepstream:6.4-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.

deepstream:6.4-gc-triton-devel

x86

Development

The DeepStream development container is the recommended container to get you started as it includes Graph Composer, the build toolchains, development libraries and packages necessary for building DeepStream reference applications within the container. This container is slightly larger in size by virtue of including the build dependencies.

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:

Component	Details
nvidia-docker	We recommend using Docker 20.10.13 along with the latest nvidia-container-toolkit as described in the installation steps. Usage of nvidia-docker2 packages in conjunction with prior docker versions are now deprecated.
NVIDIA GPU Driver	Use version: 535 TRD4 for production deployments Please Note that for GeForce and RTX cards GPU driver must be 535 or higher.
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`
Fix for RTSP EOS issue	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

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:6.4-gc-triton-devel

Run the container:

Allow external applications to connect to the host's X display:

xhost +

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

docker run --gpus all -it --rm --net=host --privileged -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -w /opt/nvidia/deepstream/deepstream-6.4 nvcr.io/nvidia/deepstream:6.4-gc-triton-devel

Docker command line options explained:

Option	Description
-it	means run in interactive mode
--gpus	This option makes GPUs accessible inside the container. It is also possible to specify a device (i.e. '"'device=0'")
--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 it can be 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

NOTES
Please refer to /opt/nvidia/deepstream/deepstream-6.4/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-6.4/entrypoint.sh && \<custom command\>"]

Known Limitations

DeepStream 6.4 Triton containers (x86 and Jetson) have the following CVE(s):

CVE	Description
CVE-2022-29501	This impacts both libslurm37 and libpmi2-0-dev packages. SchedMD Slurm 21.08.x through 20.11.x has Incorrect Access Control that leads to Escalation of Privileges and code execution. There is no public fix available for Ubuntu 22.04. There is a patch for those who have an Ubuntu PRO subscription. Also, there is a fix available upstream. This is not a CVE specific to DeepStream.
CVE-2022-29500	This impacts both libslurm37 and libpmi2-0 packages. SchedMD Slurm 21.08.x through 20.11.x has Incorrect Access Control that leads to Information Disclosure. There is no public fix available for Ubuntu 22.04. There is a patch for those who have an Ubuntu PRO subscription. Also, there is a fix available upstream. This is not specific to DeepStream.
CVE-2023-4563	This is a duplicate of CVE-2023-4244. This impacts the Linux Kernel. CVE-2023-4244 A use-after-free vulnerability in the Linux kernel’s netfilter: nf_tables component can be exploited to achieve local privilege escalation. This was discovered late in the development cycle. There is a patch available that users can apply. This impacts systems that DeepStream containers run on top of a Linux Kernel.
CVE-2023-6176	This impacts the Linux Kernel. A null pointer dereference flaw was found in the Linux kernel API for the cryptographic algorithm scatterwalk functionality. This issue occurs when a user constructs a malicious packet with specific socket configuration, which could allow a local user to crash the system or escalate their privileges on the system. This was discovered during the release cycle. There is a patch for those who have an Ubuntu PRO subscription. Also, a fix is available upstream. This impacts systems that run DeepStream containers on top of a Linux Kernel.

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-6.4/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.

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:6.4-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), contact oss-requests@nvidia.com. 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.