DeepStream
Linux / arm64
Linux / amd64
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:7.1-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:7.1-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:7.1-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:7.1-triton-arm-sbsa |
SBSA |
Developer Preview |
SBSA container for ARM based servers and systems. |
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 |
|---|---|
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. |
|
Use version: 535.183.06 for production deployments for
Data Center GPUs |
|
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: |
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:7.1-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):
For x86 Systems:
docker run --gpus all -it --rm --network=host --privileged -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -w /opt/nvidia/deepstream/deepstream-7.1 nvcr.io/nvidia/deepstream:7.1-gc-triton-devel
For ARM SBSA Systems:
docker run -it --rm --runtime=nvidia --network=host -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics --gpus all --privileged -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v /etc/X11:/etc/X11 nvcr.io/nvidia/deepstream:7.1-triton-arm-sbsa
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-7.1/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- 7.1/entrypoint.sh && \<custom command\>"]
· For Triton samples, while running /opt/nvidia/deepstream/deepstream-7.1/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 1.4 (for codecs: DIFFERENCES WITH DEEPSTREAM 6.1 AND ABOVE) & section 1.5 for BREAKING CHANGES in Release notes.
apt-get install --reinstall libflac8 libmp3lame0 libxvidcore4 ffmpeg
Known Limitations
DeepStream 7.1 Triton containers (x86 and SBSA) have the following CVE(s):
CVE |
Description |
|---|---|
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. |
|
|
|
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 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 a CVE specific to DeepStream.
|
License
The following licenses apply to the DeepStream SDK assets:
Asset |
Applicable EULA |
Notes |
|---|---|---|
SDK |
A copy of the license is
available on the following folder of the SDK: |
|
Containers |
License grants redistribution rights allowing developers to build applications on top of the DeepStream containers |
|
Development Containers |
A development-only license. Does not allow redistribution of the container |
|
TAO Models |
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:7.1-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.