skills.rest
sayalinvidia
sayalinvidiaCOMMUNITY
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vss-deploy-detection-tracking-2d

Deploy RTVI-CV 2D containers with REST API and asset management.

Updated Apr 16, 2026
Search Tags:#deployment#docker#rest-api#onnx#vss#deepstream#rtvi-cv
One-click install
npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill vss-deploy-detection-tracking-2d
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: vss-deploy-detection-tracking-2d
Source: https://github.com/sayalinvidia/sayali-skills-test/tree/main/skills/vss-deploy-detection-tracking-2d
Command: npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill vss-deploy-detection-tracking-2d

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python3, pyyaml, curl, docker, tar, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill enables teams to deploy, operate, debug, and tear down the Real Time Video Intelligence CV 2D detection and tracking microservice and expose a REST API for control and monitoring.

Core Features & Use Cases

  • Deploy, run, debug, and teardown RTVI-CV 2D locally or in a container, including pipeline configuration and engine cache handling.
  • Call the RTVI-CV REST API to manage streams, check health, and collect metrics for performance monitoring across warehouse and smart city use cases.
  • Manage assets (model, videos, labels, anchors) via NGC or local paths, with automated resource resolution and caching.

Quick Start

Deploy RTVI-CV 2D with a 4-stream warehouse scenario and then interact with its REST API to verify streams and health.

Frequently Asked Questions about vss-deploy-detection-tracking-2d

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I deploy a 2D detection and tracking microservice with a REST API?▼

Deploy the 2D detection and tracking microservice using this tool to manage container lifecycle, video assets, and pipeline configuration while exposing a REST API for control and monitoring. It ensures repeatable deployments and deterministic startup.

Can I manage video streams and check health for my warehouse deployment via REST API?▼

Yes, you can call the REST API to manage dynamic and static video streams, check health, and collect metrics for performance monitoring across warehouse and smart city use cases.

How does asset resolution and caching work for ONNX models and videos in DeepStream?▼

The tool handles model and video asset resolution via NGC or local paths, with automated resource resolution and engine caching to support repeatable deployments and deterministic startup.

Do I need Docker and Python to configure the RTVI-CV pipeline locally?▼

Yes, the tool requires Python3, Docker, pyyaml, curl, and tar to deploy, run, debug, and tear down the RTVI-CV 2D microservice locally or in a container, including pipeline configuration and engine cache handling.

What's the best way to tear down and debug a 2D video intelligence container deployment?▼

Use this tool to deploy, run, debug, and tear down the Real Time Video Intelligence CV 2D microservice locally or in a container, handling pipeline configuration and engine cache management throughout the lifecycle.

Does this deployment tool support NGC authentication for model assets?▼

Yes, the tool handles NGC authentication and manages assets including models, videos, labels, and anchors via NGC or local paths with automated resource resolution and caching.

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