DeepStream Getting Started
Developer Preview: DeepStream SDK 5.0
-
Highlights:
- Integration with Triton Inference Server (previously TensorRT Inference Server) enables developers to deploy a model natively from TensorFlow, TensorFlow-TensorRT, PyTorch, or ONNX in the DeepStream pipeline
- Python development support with sample apps
- IoT Capabilities
- DeepStream app control from edge or cloud with bi-directional IoT messaging
- Dynamic AI model update on the go to reduce app downtime
- Interoperability with Transfer Learning Toolkit 2.0 (developer preview)
- Secure communication between edge and cloud using Kafka message broker and SSL communication
- Jetson Xavier NX support
| Jetson | T4 (x86) | |
|---|---|---|
| Operating System | Ubuntu 18.04 | Ubuntu 18.04 |
| Dependencies |
CUDA: 10.2 cuDNN: 8.0.0 TensorRT: 7.1.0 JetPack: 4.4 |
CUDA: 10.2 cuDNN: 7.6.5+ TensorRT: 7.0.0 Driver: R440+ |
Getting Started Resources
Downloads
Python Sample Apps & Bindings
FAQ
- Check out the frequently asked questions on DeepStream SDK in the technical FAQ
Documentation & Forums
- Release Notes
- Getting Started Guide
- DeepStream Python API
- Sample Applications
- Plug-ins manual
- Post your questions or feedback in the DeepStream SDK developer forums
Sample Apps
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
GitHub Repository
- DeepStream Python Apps
- Using DeepStream SDK for Redaction
- 360d smart parking application with DeepStream
- Add/delete source at Runtime
- Anomaly detection
- Back-to-back detectors
- DeepStream apps with TLT models
Blogs & Tutorials
- Building IVA Apps using NVIDIA DeepStream 5.0 (developer preview edition)
- Training with Purpose-built Pre-trained Models Using the NVIDIA Transfer Learning Toolkit
- How to integrate NVIDIA DeepStream on Jetson Modules with AWS IoT Core and AWS IoT Greengrass
- Corporate blog: AI Team at MiR Helps Factory Robots Find Their Way
- Corporate blog: Netherlands-Based Rolloos Taps AI to Make Oil Rigs Safer for Workers
- Building a Real-time Redaction App using NVIDIA DeepStream Part 1 | Part 2
- Using custom YOLO models in DeepStream 4.0.1
- Build and Deploy Accurate Deep Learning Models for Intelligent Image and Video Analytics
- Calibration of a 360 degree camera to yield geo-coordinates
- Jetson Nano Brings AI Computing to Everyone
- Multi-Camera Large-Scale Intelligent Video
Webinars
- 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.
