vss-deploy-detection-tracking-2d

name	vss-deploy-detection-tracking-2d
description	Use this skill when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice. Trigger when the user says things like 'deploy rtvi-cv', 'start warehouse 2d', 'add a stream', 'check rtvi-cv health', or 'stop the perception container'. Not for VLM, embedding, or analytics — use the matching vss-* skill.
license	Apache-2.0
metadata	{"version":"3.2.1","github-url":"https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization","tags":"nvidia rtvi-cv deployment rest-api docker deepstream ngc warehouse smartcity sparse4d gdino rt-detr metropolis stream-management health-check metrics"}

Purpose

Deploy, debug, and operate the RTVI-CV detection / tracking 2D microservice and drive its REST API.

Prerequisites

• Active VSS deployment reachable on $HOST_IP (see vss-deploy-profile and references/).
• NGC credentials in $NGC_CLI_API_KEY and $NVIDIA_API_KEY for any image pulls.
• curl, jq, and Docker available on the caller.

Instructions

Follow the routing tables and step-by-step workflows below. Each section that ends in workflow, quick start, or flow is intended to be executed top-to-bottom. Detailed reference material lives in references/ and helper scripts live in scripts/ — call them via run_script when the skill points to a script by name.

Examples

Worked end-to-end examples are kept under evals/ (each *.json manifest contains a runnable scenario) and inline in the per-workflow curl blocks below. Run a Tier-3 evaluation with nv-base validate <this-skill-dir> --agent-eval to replay them.

Limitations

• Requires the matching VSS profile / microservice to be deployed and reachable from the caller.
• NGC-hosted models and NIMs may be subject to rate-limits, GPU memory requirements, and license restrictions.
• Concurrency, GPU memory, and storage limits depend on the host hardware and the profile's compose file.

Troubleshooting

• Error: REST call returns connection refused. Cause: target microservice not running. Solution: probe /docs or /health; redeploy via vss-deploy-profile or the matching vss-deploy-* skill.
• Error: HTTP 401/403 from NGC pulls. Cause: missing/expired NGC_CLI_API_KEY. Solution: docker login nvcr.io and re-export the key before retrying.
• Error: container OOM or model fails to load. Cause: insufficient GPU memory for the selected profile. Solution: switch to a smaller variant or free GPUs via docker compose down.

RTVI-CV — Detection & Tracking (Unified Skill)

Unified skill for the Real Time Video Intelligence CV (RTVI-CV) microservice. Two action surfaces in one skill:

• Deploy / operate / debug / tear down the RTVI-CV container locally → see references/deploy-vss-detection-tracking-2d.md
• Call the RTVI-CV REST API (streams, health, metrics, embeddings) on a running instance → see references/usage-vss-detection-tracking-2d.md

Service: rtvi-cv (metropolis_perception_app) Image: nvcr.io/<org>/<repo>:<tag> — user-supplied at deploy time REST port: 9000 (/api/v1 — /live, /ready, /startup, /metrics, /stream/add, /stream/remove, embeddings) Hardware: x86/aarch64 dGPU (T4, A100, L40, H100, B200, RTX), SBSA (Spark, Grace-Hopper), Jetson (Thor, Orin, Xavier)

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Action routing — pick once per invocation

User intent (sample phrasing)	Flow	Load this reference
deploy rtvi-cv warehouse 2d, run rtvicv warehouse-3d with 4 streams, start smartcity gdino, launch perception app, bring up sparse4d	DEPLOY	references/deploy-vss-detection-tracking-2d.md
stop rtvi-cv, tear down, kill the perception container, cleanup rtvicv-perception-docker	TEARDOWN (handled by deploy doc → "Mode Selection")	references/deploy-vss-detection-tracking-2d.md + references/teardown-flow.md
check rtvi-cv logs, diagnose rtvi-cv crashing, troubleshoot healthcheck failing, rtvi-cv won't start	DEBUG	references/deploy-vss-detection-tracking-2d.md + references/troubleshooting.md
add a stream, remove camera, list streams, health check, is rtvi-cv ready, get metrics, what's the FPS, check GPU usage, generate text embeddings, call rtvi-cv api	API USAGE	references/usage-vss-detection-tracking-2d.md + references/api-reference.md

Selection rule: match the user's phrasing against the table above and immediately load the corresponding reference file. Do not mix the flows — DEPLOY assumes no running container yet; API USAGE assumes the container is already running on http://<host>:9000.

If intent is genuinely ambiguous (e.g., the user says just "I want to use rtvi-cv"), ask one AskQuestion: deploy a new instance, or call an already-running one?

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What lives where

vss-deploy-detection-tracking-2d/
├── SKILL.md          # this file (routing + contracts)
├── assets/           # data files (deploy-defaults.yml — single source of truth for tags / refs / paths / GPU)
├── evals/            # Tier-3 eval manifests (deploy-evals.json, usage-evals.json)
├── scripts/          # 23 bash + python helpers (see `scripts/` for the full inventory)
└── references/       # workflow runbooks (deploy / api-usage / teardown / troubleshooting / …)

For the full per-file inventory and what each reference covers, see references/workflow-reference.md.

All scripts are invoked from the skill root via $SKILL_DIR/scripts/<name> — paths inside the deploy reference doc are preserved verbatim and resolve correctly when the agent runs from skill root.

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Available Scripts

Helpers live in scripts/ and are invoked from the skill root by name — call each via run_script("scripts/<name>") so the agent records a proper tool invocation.

Script	Purpose	Arguments
load_defaults.sh	Detect platform (x86 dGPU / SBSA / Jetson) and resolve YAML defaults from assets/deploy-defaults.yml.	--usecase <name>
fetch_resources.sh	Download + extract NGC resources, scan for layout.	--ngc-ref <ref> (optional)
apply_in_container.sh	Host-side wrapper for Step 4 (apply_config.sh inside the running container).	<container_name>
apply_config.sh	In-container path-substitution, batch, sink, sources, engine cache.	<usecase> <stream_count> <sink_type>
start_app_in_container.sh	Host-side wrapper for Step 5 (run_app_and_wait.sh).	<container_name>
run_app_and_wait.sh	In-container app launch + readiness + metrics + log.	<config_path>
add_streams.sh / update_stream_sources.sh	REST stream lifecycle for Step 6.	<rtsp_or_file_uri>...
collect_metrics.sh	Pull /api/v1/metrics snapshot.	none
discover_streams.sh	Enumerate active streams via /stream/get-stream-info.	none
synthesize_docker_run.sh	Print the platform-correct docker run line for the resolved env.	none
render_box.sh	Render the fixed-width step receipt.	<step_label>
calibration_manager.py	Manage calibration artefacts + per-use-case engine cache invalidation.	--usecase <name> --reset

For the full inventory of helpers (cache, GPU checks, setup) browse scripts/; each script's --help describes its arguments.

How to use this skill

1. Read this file first. It only routes — it does not contain workflows.
2. Match the user's intent against the routing table above.
3. Load exactly one reference doc (DEPLOY or API USAGE). Don't preload both — each reference is large and contains its own full contract.
4. Follow the loaded reference exactly. The reference docs are the byte-for-byte preserved contracts from the predecessor skills vss-deploy-detection-tracking-2d (deploy/teardown/debug) and rtvicv-api (REST API) — every step ordering invariant, bash-batching rule, box-rendering rule, and AskQuestion contract is retained.
5. For DEPLOY, the reference doc enforces its own startup contract: one-line acknowledgement → planning-tool call (TodoWrite array of 5 todos, OR 5 successive TaskCreate calls on newer Claude Code) → Step 1 question. Do not narrate, do not pre-flight, and never print "loading TodoWrite/TaskCreate" or any deferred-tool resolution prose — the planning tool is loaded silently.

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Output contract — DEPLOY flow

When running the DEPLOY / TEARDOWN / DEBUG flow, the agent MUST honour all four items below on every successful deploy. These are the user's only feedback channel between steps; skipping any of them is a behaviour regression.

1. Render every step's exit in a fixed-width box — Step 1 Deploy targets, Step 2 Pipeline configuration, Step 3 Container, Step 4 Apply configuration, Step 5 Plan + Results. Not just the final summary. The box is the user's step receipt. Geometry is fixed (see § "Universal box format" below). Per-step content rules (what rows go inside each box) live in references/deploy-vss-detection-tracking-2d.md under "Step N box content rule".
2. After the Step 5 Results box, issue the Step 6 AskUserQuestion from references/next-steps.md § "11.c" — never replace it with a free-form Next steps bullet list. The menu is the deploy's exit handle: it lets the user run metrics, manage streams, tail logs, or tear down with one click instead of having to remember curl URLs.
3. After the user picks a Step 6 bucket, issue the follow-up AskUserQuestion from references/next-steps.md § "11.d" — never substitute prose + ready-to-copy curl examples + a free-text "want me to run X?" question. Each bucket has its own menu of concrete actions; the user picks the action, then the skill emits the API box and runs the curl. Per-bucket follow-ups:
   • Manage streams → Add / Remove / List. Remove builds its options dynamically from /stream/get-stream-info — one option per active stream labelled <camera_id> · <camera_url> plus "Remove ALL" when ACTIVE > 1 (full spec: § "remove_streams sub-flow").
   • Stop the deployment → Stop app / Stop container / Full teardown.
   • Check metrics & FPS → no follow-up; run collect_metrics.sh directly after printing the /api/v1/metrics API box.
   • Check liveness / readiness → no follow-up; probe all three health endpoints after printing their API boxes.
4. Render the FULL per-step content, not an overview row — rendering the box is necessary but not sufficient. Each step has a row composition spec in references/deploy-vss-detection-tracking-2d.md under "Step N box content rule". Step 4 (Apply configuration) is where the agent collapses most often — its canonical per-use-case key list lives in references/apply-config.md § "Per-use-case complete edit list", and the agent MUST emit one ✔ [section] key=value — annotation row per key in that table for the active use case + settings. A section with 5 keys → 5 rows; a section with 6 keys → 6 rows. Never one overview row per section.

Forbidden (these are the shortcuts the agent falls back to under pressure, and they break the user's UX):

• ❌ Internal tool-loading narration. Never print "I need to load TodoWrite (a deferred tool the skill calls for the task widget)", "Loading TaskCreate…", "Calling ToolSearch for the planning tool…", or any other text about resolving / loading / fetching deferred tools. The agent loads tools silently. The user only ever sees the ✔ <pinned-values> summary line followed by the widget — never any scaffolding around tool resolution.
• ❌ Collapsing all 5 deploy steps into a single TaskCreate's description field. When TaskCreate is the available planning tool, issue 5 separate TaskCreate calls back-to-back (one per step). See references/task-list.md § "Initial TaskCreate calls" for the verbatim template. Same rule for TodoWrite — one call with all 5 todos in the todos:[…] array; never one todo whose content is a multi-line list.
• ❌ Silently choosing dynamic stream-mode. The skill default is stream_mode=static — the agent bakes auto-discovered file:// URLs into the DS main config's [source-list] block before app start. Switch to dynamic only when the user explicitly asks ("add streams later via REST", "use dynamic stream mode") OR when they pick dynamic in the Step 2 AskQuestion. Picking dynamic for a generic "deploy rtvi-cv with N streams" query breaks the deploy rubric and the user's /metrics expectations. See references/pipeline-config.md § "Defaults — the skill is static-mode by default" for the full rationale.
• ❌ A one-line ✔ App ready in Ns, N streams, fps total Y in place of the Step 5 Results box.
• ❌ ASCII box-drawing chars (+, -, =, *) instead of light box-drawing chars (┌ ─ ┐ │ └ ┘).
• ❌ Skipping Step 6 on the assumption "the user knows what to do next".
• ❌ After Step 6, dumping a markdown wall of prose + multiple curl blocks + a closing "want me to run any of these?" — that's the shape the agent falls back to and it bypasses both the 11.d menu and the per-API-call box. The user picks from a menu; the skill shows the resolved API box; the skill runs it. No free-text Q.
• ❌ Step 4 overview collapses — these are explicitly banned by the deploy doc's Step 4 content rule:
  • ✔ Batch size 3 (tile grid: 1×3) → required: 5 separate rows ([streammux] batch-size=3, [primary-gie] batch-size=3, [source-list] max-batch-size=3, [tiled-display] rows=1, [tiled-display] columns=3).
  • ✔ Output sink eglsink → required: one row per sink key (4 keys for eglsink, e.g. [sink0] enable=1, type=2, sync=0, qos=0 — read apply-config.md for the exact list).
  • ✔ Sources static (3 streams, http-port=9000) → required: six annotated [source-list] rows.
  • ✔ Tile grid 1 row × 3 cols (single row) → required: two rows, [tiled-display] rows=1 and [tiled-display] columns=3.

Universal box format

The geometry contract for every step-exit box (Step 1 through Step 5 Results). The same shape across every box; only the title and the body rows change per step.

• Width: 128 chars corner-to-corner — ┌ at column 1, ┐ at