This repository contains configuration files to run an end-to-end video analytics application on DeepStream v5.1. The application locates 4 different objects on the road (car, pedestrian, roadsign and bicycle) and then classifies the cars into 6 different classes - sedan, minivan, truck etc.
We are using TrafficCamNet for object detection and VehicleTypeNet for classification, both of which are pre-trained models available on NVIDIA GPU Cloud.
The deployment is first done on a single 2g.10gb MIG instance of a NVIDIA A100 GPU then scaled all the way up to 8 x A100's, all configured with the same MIG profile.
- A server with 1 or more (preferably 8) NVIDIA A100's, either on cloud (AWS p4dn.24xlarge) or on-prem.
- GPUs sliced with 2g.10gb MIG profile.
- NVIDIA Driver 460+
- Docker image - nvcr.io/nvidia/deepstream:5.1-21.02-triton
- Kubernetes setup on master and worker node (for scaling across MIG instances)
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Launch the docker container
docker run -it --rm --gpus device=<MIG-instance-UUID> nvcr.io/nvidia/deepstream:5.1-21.02-triton
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Clone the git repository
git clone https://github.com/AshishSardana/ds_triton.git
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Execute the automate script
cd ds_triton && bash automate_script.sh
On a 2g.10gb MIG instance, this application would run at 30 frames per second for 35 full HD video streams.