DeepStream Getting Started
DeepStream SDK 5.1
- Highlights:
- Support for NVIDIA Ampere GPUs with third generation tensor core additions and various performance optimizations
- Support for audio with a sample application
- New audio/video template plugin for implementing custom algorithms
- New sample apps:
- Standalone smart record application
- Optical flow and segmentation in python
- Analytics using region of interest (ROI) and line crossing in Python
- Audio application to show audio classifier usage
| Jetson | T4 and A100 (x86) | |
|---|---|---|
| Operating System | Ubuntu 18.04 | Ubuntu 18.04 RHEL 8 |
| Dependencies | CUDA: 10.2.89 cuDNN: 8.0.0+ TensorRT: 7.1.3 JetPack: 4.5.1 | CUDA: 11.1 cuDNN: 8.0.0+ TensorRT: 7.2.2 Driver: R460.32+ |
Getting Started Resources
Downloads
DeepStream 4.0 applications are fully compatible with DeepStream 5.0. Please read the migration guide for more information.
Python Sample Apps & Bindings
Python bindings is now integrated in the DeepStream SDK
Visit the DeepStream Python Apps Github page for documentation and sample apps.
Check out the DeepStream SDK technical FAQ for questions commonly asked.
FAQ
- Check out the frequently asked questions on DeepStream SDK in the technical FAQ
Documentation & Forums
- Release Notes
- Getting Started Guide
- DeepStream Python API
- DeepStream C/C++ API
- Post your questions or feedback in the DeepStream SDK developer forums
Reference Implementations
- DeepStream Python Apps: repository contains Python bindings and sample applications
- DeepStream C/C++ Apps: repository contains C/C++ bindings and sample applications
- DeepStream Pose Estimation: Learn about deploying pose estimation model on DeepStream
- License Plate Recognition using DeepStream: Use license plate detection and recognition pre-trained model for smart city solution
- Add/delete source at RuntimeRuntime source addition/deletion application to show the capability of Deepstream SDK
- DeepStream apps with TLT models : This repository provides a DeepStream sample application to run six Transfer Learning Toolkit models (DetectNet_v2 / Faster-RCNN / YoloV3 / SSD / DSSD / RetinaNet).
- Using custom YOLO models in DeepStream : The objectDetector_Yolo sample app provides a working example of the open source YOLO models such as YOLOv2, YOLOv3, tiny YOLOv2, and tiny YOLOv3
Blogs & Tutorials
- Building Intelligent Video Analytics Apps Using NVIDIA DeepStream 5.0
- Deploying Models from TensorFlow Model Zoo Using NVIDIA DeepStream and NVIDIA Triton Inference Server
- Video tutorial DeepStream best practices for unlocking greater performance
- Building a Real-time Redaction App using NVIDIA DeepStream Part 1 | Part 2
- Bringing Cloud-Native Agility to Edge AI Devices with the NVIDIA Jetson Xavier NX Developer Kit
- Preparing State-of-the-Art Models for Classification and Object Detection with the NVIDIA Transfer Learning Toolkit
- Creating a Real-Time License Plate Detection and Recognition App
- Training with Purpose-built Pre-trained Models Using the NVIDIA Transfer Learning Toolkit
- Build and Deploy Accurate Deep Learning Models for Intelligent Image and Video Analytics
DeepStream Turnkey Integration with Cloud Services
AI Training with Transfer Learning Toolkit
Perception & Analytics
Beginner Friendly Free Self-Paced DLI Online Courses
- Learn how to build end-to-end intelligent video analytics pipelines using DeepStream and Jetson Nano >> Enroll now
- Learn how to get started with AI using Jetson Nano >> Enroll now
Webinars
- GTC 2020: Deepstream with Open-Horizon (Presented by IBM)
- GTC 2020: Implementing Real-time Vision AI Apps Using NVIDIA DeepStream SDK
- Create intelligent places using NVIDIA pre-trained vision models and DeepStream SDK
- Build with DeepStream, deploy and manage with AWS IoT services
- Intelligent Video Analytics using DeepStream 5.0
- Build and deploy AI-powered intelligent streaming analytic apps with DeepStream SDK
- GTC 2020: NVIDIA Tools to Train, Build, and Deploy Intelligent Vision Applications at the Edge
- GTC 2020: How to Build a Multi-Camera Media Server for AI Processing on Jetson
- GTC 2020: Google Cloud AutoML Video and Edge Deployment
- GTC 2020: Visual Anomaly Detection using NVIDIA Deepstream IoT Workshop
- GTC 2020: Productionizing GPU Accelerated IoT Workloads at the Edge
- DeepStream edge-to-cloud with Azure IoT
- DeepStream SDK- Accelerating Real-Time AI Based Video And Image Analytics
- Real-time Object Detection for Disaster Response
Additional Resources
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.