DeepStream IVA Deployment Demo

DeepStream IVA Deployment Demo

Logo for DeepStream IVA Deployment Demo
How to deploy pre-trained models from NGC into an intelligent video analytics (IVA) pipeline in DeepStream
Latest Tag
May 1, 2024
Compressed Size
1.73 GB
Multinode Support
Multi-Arch Support
20.06 (Latest) Security Scan Results

Linux / amd64

Sorry, your browser does not support inline SVG.

IVA Deployment with DeepStream

This container provides a demonstration of how to deploy pre-trained models from NGC into an intelligent video analytics (IVA) pipeline in DeepStream. These models could be fine-tuned on additional data using Transfer Learning Toolkit.

Why DeepStream?

NVIDIA DeepStream lets you deploy deep learning models in intelligent video analytics (IVA) pipelines. This enables real time object detection, tracking, and classification. This is possible through a combination of tools such as TensorRT, Triton, Transfer Learning Toolkit the NVIDIA Codec SDK and the NVIDIA Optical Flow SDK.

To learn more

Please review the following resources:

Installation and Getting Started

Getting started with the application is pretty straightforward with nvidia-docker.

Running from NGC container

This image contains the complete DeepStream Jupyter Lab environment and tutorial.

1. Download the container from NGC

docker pull

2. Run the notebook server

docker run --gpus all --rm -it -p 8888:8888 --env NVIDIA_VISIBLE_DEVICES=0

Note: Depending on your docker version you may have to use ‘docker run --runtime=nvidia’ or remove ‘--gpus all’

3. Connect to notebook server

Jupyter Lab will be available on port 8888!

e.g. if running on a local machine

(or first available port after that, 8889, 8890 etc if 8888 is occupied - see command output)

4. Run the notebooks

  • deepstream_deployment: Introduction to DeepStream and step-by-step deployment of 4 different pre-trained models from NGC.

  • TLT_Sample_Model: This notebook will not run inside this container, it is merely intended to demonstrate the process is for training models in Transfer Learning Toolkit.

Getting Help & Support

If you have any questions or need help, please email