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Get Started with NVIDIA DeepStream SDK



Overview

DeepStream is a GStreamer based SDK for creating vision AI applications leveraging AI for image processing and object detection.

Release Highlights


Release notes

DeepStream 6.1.1 Highlights:

  • Version 6.1.1 adds the following items to release 6.1
    • Support for NVIDIA JetPack™ 5.0.2
    • An updated compute stack

DeepStream 6.1 Highlights:

  • Graph Composer UI new look and feel (extension type icons, extension metadata search, graph auto-layout, better subgraph UI, and other ease-of-use updates)
  • (Alpha) Graph Composer Windows 10 support (with remote execution only)
  • Support for RealSense stereo cameras
  • New post-processing plug-in that makes it easier to use custom models
  • Support for NMOS (Networked Media Open Specifications), enabling AI workloads to be easily integrated with IP-based infrastructures
  • Integration with NVIDIA® Rivermax® SDK. Now supports reception and transmission for compressed and uncompressed streams
  • Support for Unified Communication - X Framework (UCX)/RDMA for data transmission across distributed DeepStream pipelines and nodes (x86/dGPU only)
  • New metadata serialization/deserialization plug-ins; allows metadata to be embedded within and extracted from encoded video streams (x86/dGPU only)
  • Gst-nvstreammux (new) plug-in: Adds audio support and better tuning control enhancements for video conferencing

Operating System

NVIDIA Jetson™: Ubuntu 20.04

NVIDIA Tesla® GPUs (x86): Ubuntu 20.04

Dependencies

Jetson: JetPack: 5.0.2, NVIDIA CUDA®: 11.4, NVIDIA cuDNN: 8.4, NVIDIA TensorRT™: 8.4.1.5 , NVIDIA Triton™ 22.07, GStreamer 1.16.3

Tesla GPUs (x86): Driver: R515+, CUDA: 11.7 Update 1, cuDNN: 8+, TensorRT: 8.4.1.5, Triton 22.07, GStreamer 1.16.3


Note: For JetPack 4.6.1, please use DeepStream 6.0.1. Previous versions of DeepStream can be found here.

Product Advisory

To ensure compatibility with supported TensorRT versions , users planning to use models developed with TAO Toolkit (formerly TLT) 3.0-21.08 or earlier MUST re-generate the INT8 calibration cache before using them with DeepStream 6.1.1


Those who are using models and the INT8 calibration cache from previous versions of TensorRT will also need to re-generate the cache.


You can find details regarding regenerating the cache in the Read Me First section of the documentation. For new DeepStream developers or those not reusing old models, this is NOT an issue.

Download DeepStream SDK 6.1.1

I Agree To the Terms of the NVIDIA DeepStream SDK 6.1.1 Software License Agreement to Enable Download Links.

DeepStream 5.x applications are fully compatible with DeepStream 6.1. Please read the migration guide for more information.

Python Bindings

The Python bindings source code and pre-built wheels are now available on GitHub .

Introduction to DeepStream SDK

Quick Start Guide

Get step-by-step instructions for building vision AI pipelines using DeepStream and NVIDIA Jetson or discrete GPUs.


Get started

Introductory DeepStream Webinar

Learn how the latest features of DeepStream are making it easier than ever to achieve real-time performance, even for complex video AI applications.


Watch webinar

Introductory Jetson and Graph Composer Webinar

Learn how NVIDIA DeepStream and Graph Composer make it easier than ever to create vision AI applications for NVIDIA Jetson.


Watch webinar

Get Started

Find everything you need to start developing your vision AI applications with DeepStream, including documentation, tutorials, and reference applications.

Getting Started with C/C++


Get started

Getting Started with Python

Learn how the latest features of DeepStream are making it easier than ever to achieve real-time performance, even for complex video AI applications.


Get started Python application GitHub repository Compile and install Python bindings Python sample applications

Getting Started with Graph Composer

Learn how NVIDIA DeepStream and Graph Composer make it easier than ever to create vision AI applications for NVIDIA Jetson.


Get started

Additional Resources

Ethical AI
NVIDIA 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. Also, 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.