Before You Start

DeepStream 6.0 is a major release that brings substantial new features from our previous version. One significant change is the support of TensorRT 8.0.1. If you plan to bring models that were developed on previous versions of DeepStream and TAO Toolkit (formerly TLT) you need to re-calibrate the INT8 files so they are compatible with TensorRT 8.0.1 before you can use them in DeepStream 6.0. Details can be found in the Readme First section of the SDK Documentation.

What is DeepStream?

NVIDIA’s DeepStream SDK delivers a complete streaming analytics toolkit for AI-based multi-sensor processing for video, image, and audio understanding. DeepStream is an integral part of NVIDIA Metropolis, the platform for building end-to-end services and solutions that transform pixels and sensor data into actionable insights. DeepStream SDK features hardware-accelerated building blocks, called plugins, that bring deep neural networks and other complex processing tasks into a processing pipeline. The DeepStream SDK allows you to focus on building optimized Vision AI applications without having to design complete solutions from scratch.

The DeepStream SDK uses AI to perceive pixels and generate metadata while offering integration from the edge-to-the-cloud. The DeepStream SDK can be used to build applications across various use cases including retail analytics, patient monitoring in healthcare facilities, parking management, optical inspection, managing logistics and operations etc. DeepStream 6.0 Features

New Graph Composer development environment. Please note the Graph Composer development environment can work only on x86 systems but the graphs can be deployed on Jetson as well.
Turnkey integration with the latest TAO Toolkit AI models. Our latest additions: action recognition, 2D body pose estimation, facial landmark estimation, emotion recognition, gaze, heart rate and gesture. Check the DeepStream documentation for a complete list of supported models.
New Preprocessor plugin: Plugin for preprocessing on the predefined ROIs.
gRPC support in nvinferserver plugin: Run AI inference in TensorFlow, TensorFlow-TensorRT, and ONNX-RT on a standalone or remote Triton Inference Server
Support for audio/video synchronization for broadcasting and web conferencing applications
NVIDIA Rivermax integration: Move data directly from Mellanox NIC to GPU memory. Optimize uncompressed video pipelines by reducing CPU workload and improving PCIe bandwidth.
Develop in Python using DeepStream Python bindings: Bindings are now available in source-code. Download them from GitHub
Edge to cloud integration using standard message brokers: DeepStream now supports REDIS in addition to Kafka, MQTT and Azure IoT
Improved IoT and manageability features: bi-directional messaging between edge and cloud, over the air model (OTA) updates, smart recording and TLS based authentication for secure messaging
Updated versions of NVIDIA Compute SDKs: Triton 21.08, TensorRT™ 8.0.1 and CUDA® 10.2
Hardware accelerated video encoding/decoding and image decoding
Over 30 reference applications in Graph Composer, C/C++, and Python to get you started. Build applications that support: Action Recognition, Pose Estimation and many more. We also include a complete reference app (deepstream-app) that can be setup with intuitive configuration files.

DeepStream container for Jetson

Container support is now available for all Jetson platforms including Jetson Nano, TX1, TX2 NX, Xavier NX and AGX Xavier. The deepstream-l4t:6.0 family of containers are GPU accelerated and based on the NVIDIA Jetson products running on ARM64 architecture.

Starting with DeepStream 4.0.1 release, different container variants are being released for Jetson (ARM64) platforms to cater to different user needs. Containers are differentiated based on image tags as described below:

Base: The DeepStream base container contains the plugins and libraries that are part of the DeepStream SDK along with dependencies such as CUDA, TensorRT, GStreamer, etc. This image is the recommended one for users that want to create docker images for their own DeepStream based applications. Please note that the base images do not contain sample apps. (deepstream-l4t:6.0-base)
Samples: 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-l4t:6.0-samples)
IoT: The DeepStream IoT container extends the base container to include the DeepStream test5 application along with associated configs and models. This container can be used to enable multi-stream DeepStream applications that can be integrated with the various messaging backends including Kafka, Azure IoT, REDIS, and MQTT thereby enabling IoT use cases. (deepstream-l4t:6.0-iot)
Deployment with Triton: The DeepStream Triton container enables running inference using Triton Inference server. With this, developers can run inference natively using TensorFlow, TensorFlow-TensorRT and ONNX-RT. Inference with Triton is supported in the reference application (deepstream-app). This image contains development libraries and packages necessary for building deepstream reference applications from within the container. Note: Applications that may depend on opencv4 will not compile as opencv4 is not packaged within this container. To learn more about how to use Triton with DeepStream, refer to the Plugin guide in DeepStream 6.0 documentation (Gst-nvinferserver). (deepstream-l4t:6.0-triton)

These containers leverage the NVIDIA Container Runtime on Jetson, which is available for install as part of NVIDIA JetPack version 4.6 . The platform specific libraries and select device nodes for a particular device are mounted by the NVIDIA Container Runtime into the DeepStream container from the underlying host, thereby providing necessary dependencies for DeepStream applications to execute within the container.

Similarly, CUDA and TensorRT are ready to use within the DeepStream container as they are made available from the host by the NVIDIA Container Runtime.

Running DeepStream

Prerequisites

Ensure these prerequisites are available on your system:

Jetson device running L4T BSP r32.6.1
JetPack 4.6

Pull the container

Before running the container, use docker pull to ensure an up-to-date image is installed. Once the pull is complete, you can run the container image.

Procedure

In the Pull column, click the icon to copy the docker pull command for the deepstream container.
Open a command prompt and paste the pull command. The pulling of the container image begins. Ensure the pull completes successfully before proceeding to the next step.

Run the container

To run the container:

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

Run the docker container using the nvidia-docker (use the desired container tag in the command line below):

sudo docker run -it --rm --net=host --runtime nvidia  -e DISPLAY=$DISPLAY -w /opt/nvidia/deepstream/deepstream-6.0 -v /tmp/.X11-unix/:/tmp/.X11-unix nvcr.io/nvidia/deepstream-l4t:6.0-[CONTAINER-TAG]

Command line options explained:

[CONTAINER-TAG] - Select the container that you want to run: devel, base, samples, iot or triton. (i.e., deepstream-l4t:6.0-samples)

-it means run in interactive mode
--rm will delete the container when finished
-v is the mounting directory, and used to mount host's X11 display in the container filesystem
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.
Additionally, --cap-add SYSLOG option needs to be included to enable usage of the nvds_logger functionality inside the container.

See /opt/nvidia/deepstream/deepstream-6.0/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

Limitations

The NVIDIA Container Runtime available in JetPack 4.6. Please see the list below for limitations in the current enablement of DeepStream for Jetson containers.

Supports deployment only: The DeepStream container for Jetson is intended to be a deployment container and is not set up for building sources except for the Triton docker. Please refer “Docker Containers” Section within the DeepStream 6.0 Plugin Guide Section for instructions on how to build custom containers based on DeepStream from either Jetson device or your workstation

AMQP support is not included inside the container. Please refer “AMQP Protocol Adapter” Section within the DeepStream 6.0 Plugin Guide Section for instructions on how to install necessary dependencies for enabling AMQP if required

There are known bugs and limitations in the SDK. To learn more about those, refer to the release notes

License

-All Jetson containers are released under NVIDIA License Agreement

A copy of the license can also be found within a specific container at the following location: /opt/nvidia/deepstream/deepstream-6.0/LicenseAgreement.pdf. By pulling and using the DeepStream SDK (deepstream) container from NGC, you accept the terms and conditions of this license.

Please note that all container images come with the following packages installed: librdkafka, hiredis.

Technical blogs

Read the technical tutorial on how PeopleNet model can be trained with custom data using by TAO Toolkit (earlier NVIDIA Transfer Learning Toolkit.)

Description

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Multinode Support

Multi-Arch Support

6.0-base (Latest) Scan Results