Vss Deploy Detection Tracking 2d
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.
- Skill ID
- nvidia/skills/vss-deploy-detection-tracking-2d
- Publisher
- nvidia
- Repository
- skills
- Installs
- 0
- Files
- 51
- License
- Apache-2.0
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
nvidia/skills/vss-deploy-detection-tracking-2dInstalls these files- BENCHMARK.md
- SKILL.md
- assets/deploy-defaults.yml
- evals/deploy-evals.json
- evals/evals.json
- evals/usage-evals.json
- references/api-reference.md
- references/apply-config.md
- references/container-reuse.md
- references/deploy-vss-detection-tracking-2d.md
- references/environment.md
- references/next-steps.md
- references/ngc-setup.md
- references/pipeline-config.md
- references/platforms.md
- references/resource-plan.md
- references/start-app.md
- references/task-list.md
- references/teardown-flow.md
- references/troubleshooting.md
- references/upgrade-rollback.md
- references/usage-vss-detection-tracking-2d.md
- references/usecases.md
- references/ux-conventions.md
- references/workflow-reference.md
- scripts/add_streams.sh
- scripts/apply_config.sh
- scripts/apply_in_container.sh
- scripts/cache_nvinfer_engine.sh
- scripts/calibration_manager.py
- scripts/check_container_gpu.sh
- scripts/clean_engine_cache.sh
- scripts/collect_metrics.sh
- scripts/common.sh
- scripts/discover_streams.sh
- scripts/fetch_resources.sh
- scripts/load_defaults.sh
- scripts/prelaunch_nvinfer_engine.sh
- scripts/render_box.sh
- scripts/run_app_and_wait.sh
- scripts/setup_gdino.sh
- scripts/setup_sparse4d.sh
- scripts/setup_tracker_reid.sh
- scripts/start_app_in_container.sh
- scripts/synthesize_docker_run.sh
- scripts/update_batch_size.sh
- scripts/update_output_sink.sh
- scripts/update_stream_sources.sh
- scripts/write_deployment_log.sh
- skill-card.md
- skill.oms.sig
What this skill tells the agent
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(seevss-deploy-profileandreferences/). - NGC credentials in
$NGC_CLI_API_KEYand$NVIDIA_API_KEYfor 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
/docsor/health; redeploy viavss-deploy-profileor the matchingvss-deploy-*skill. - Error: HTTP 401/403 from NGC pulls. Cause: missing/expired
NGC_CLI_API_KEY. Solution:docker login nvcr.ioand 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)
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?
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.
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
- Read this file first. It only routes — it does not contain workflows.
- Match the user's intent against the routing table above.
- Load exactly one reference doc (DEPLOY or API USAGE). Don't preload both — each reference is large and contains its own full contract.
- 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) andrtvicv-api(REST API) — every step ordering invariant, bash-batching rule, box-rendering rule, andAskQuestioncontract is retained.
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