vss-deploy-profile

name	vss-deploy-profile
description	Use to select, configure, deploy, verify, debug, or tear down a VSS profile (base, search, lvs, warehouse, edge). Not for standalone microservices — use the vss-deploy-* skill.
license	Apache-2.0
metadata	{"version":"3.2.0","github-url":"https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization","tags":"nvidia blueprint deployment"}

VSS Deploy

Available Scripts

Script	Purpose	Arguments
scripts/normalize_resolved_yml.py	Strip optional depends_on entries for services filtered out of resolved.yml before deploy.	Path to resolved.yml
scripts/probe_remote_models.sh	Probe an OpenAI-compatible remote LLM/VLM endpoint and verify the selected model id.	Base URL, optional expected model id

Profile Routing

Match the user's request to a profile, then load that profile's reference for sizing, services, env recipes, and debugging.

User says	Profile	Reference
"deploy vss" / "deploy base"	base	references/base.md
"deploy alerts" / "alert verification" / "real-time alerts" / "deploy for incident report"	alerts	references/alerts.md
"deploy lvs" / "video summarization"	lvs	references/lvs-profile.md
"deploy search" / "video search"	search	references/search.md
"deploy warehouse" / "warehouse blueprint" / "vss warehouse"	warehouse	references/warehouse.md
"debug warehouse" / "warehouse not working" / "warehouse FPS low" / "warehouse BEV out of sync"	warehouse (debug)	references/warehouse-debug.md

Edge hardware routing (DGX Spark, AGX/IGX Thor): see references/edge.md. DGX Spark uses the Spark Nano 9B standalone local LLM on port 30081; AGX/IGX Thor uses the Edge 4B standalone vLLM fallback.

Each profile's reference owns its sizing table. Don't pick a deployment shape from this file — open the profile reference and check minimum GPU count for the host's hardware against the (mode × platform) matrix there.

Instructions

The deployment flow is always: copy .env to generated.env, apply overrides, dry-run compose into resolved.yml, review, normalize, deploy, then wait for readiness.

# 1. cp dev-profile-<profile>/.env dev-profile-<profile>/generated.env  (clean copy)
# 2. Apply env overrides to generated.env  (source .env stays untouched)
# 3. docker compose --env-file generated.env config > resolved.yml      (dry-run)
# 4. Review resolved.yml
# 5. docker compose --env-file generated.env -f resolved.yml up -d

.env is read-only checked-in defaults; generated.env is the per-deploy working copy. Step 1c covers this in full.

Prerequisites

1. Repo path — auto-detect video-search-and-summarization/ before asking the user. Use the detected path as $REPO for all subsequent commands.
2. Credential gates — see references/credentials.md: NGC_CLI_API_KEY for local/local_shared NIM pulls, NVIDIA_API_KEY for remote NIM endpoints, and HF_TOKEN for edge recipes that use gated HF models.
3. System prerequisites (GPU driver, Docker, NVIDIA Container Toolkit, kernel sysctls, and — if ufw is active — the Docker-bridge→host firewall allow so bridge NIMs can fetch clips from host-mode VST) — full checks in references/prerequisites.md. Canonical hardware/driver matrix is the VSS prerequisites page.

The auto-detect snippet (git-root, then a common-path probe gated on deploy/docker/compose.yml + dev-profile.sh + skills/vss-deploy-profile) lives in references/prerequisites.md. Export the resolved $REPO; if detection fails, ask the user for the checkout path.

Pre-flight check

Run before every deploy. The full system checklist and remediation steps live in references/prerequisites.md. For DGX Spark / IGX Thor / AGX Thor, also run the cache-cleaner check in references/edge.md.

Detect sudo mode first. Several pre-flight remediations and the edge cache-cleaner installer call sudo. If the host requires a sudo password, those steps will silently no-op under sudo -n and leave the deploy in a half-prepared state.

if sudo -n true 2>/dev/null; then
  echo "passwordless sudo — pre-flight will auto-install missing pieces"
else
  echo "sudo requires password — pre-flight will NOT auto-install; hand commands to the user"
fi

When sudo needs a password, the skill must not run privileged installers itself. Surface the copy-pasteable command block from references/prerequisites.md to the user with a "run this once and confirm" handoff, then resume after the user replies.

Minimum smoke test (must succeed):

nvidia-smi --query-gpu=index,name --format=csv,noheader
docker info 2>/dev/null | grep -qi runtimes \
  && docker run --rm --gpus all ubuntu:22.04 nvidia-smi >/dev/null 2>&1 \
  && echo "nvidia runtime OK"

If the smoke test fails, do not proceed; open references/prerequisites.md for the remediation tree.

Model Selection

• $LLM_REMOTE_URL / $VLM_REMOTE_URL if the user asks for remote
• $NGC_CLI_API_KEY (local NIMs) or $NVIDIA_API_KEY (remote)

Endpoint intent gate. Don't infer remote placement from stray env vars (LLM_ENDPOINT_URL, VLM_ENDPOINT_URL, LLM_BASE_URL, VLM_BASE_URL may be leftovers). Use remote LLM/VLM only when (1) the user asked for / supplied a remote endpoint, (2) local sizing can't fit the selected models and the user agrees, or (3) an edge recipe needs a standalone local service VSS treats as remote (e.g. DGX Spark Nano 9B on localhost:30081). If an endpoint var is set but the user didn't ask for remote, surface it in Step 1 and ask — never silently deploy remote because a var happened to exist.

If no combination on this host satisfies the profile's sizing requirements, stop and report the blocker — don't silently pick another shape.

Edge shared mode is platform-specific. Full recipes are in references/edge.md.

Deployment Flow

Always follow this sequence. Never skip the dry-run.

Step 0 — Tear down any existing deployment + clear data volumes

If a deployment already exists, tear it down AND clear stale data volumes before redeploying.

Full procedure lives in references/teardown.md.

Step 0a — Credentials gate (run before any env mutation)

Validate every credential and selected remote endpoint the chosen profile needs before Step 1c copies .env to generated.env. A 401 here is a 30-second failure; the same 401 inside a NIM cold-start is a 10–20 min failure. Run the discovery and probe flow in references/credentials.md, including scripts/probe_remote_models.sh for any LLM/VLM endpoint you plan to write into generated.env. Map the result against the chosen mode: missing or invalid required credentials/endpoints are blockers, optional credentials are not.

Step 1 — Gather context

Before building env overrides, confirm:

Value	How to determine
Profile	Match user intent to the routing table above. Default: base
Repo path	Use the $REPO value auto-detected in prerequisites. If auto-detect failed, ask the user for the checkout path before continuing.
Hardware	nvidia-smi --query-gpu=name,memory.total --format=csv,noheader
LLM/VLM placement	Explicitly decide local / local_shared / remote. Cross-reference available GPUs against the chosen profile's Minimum GPU count table. If endpoint env vars are present but the user did not request remote, ask whether to use or ignore them.
API keys	NGC_CLI_API_KEY for local NIMs, NVIDIA_API_KEY for remote
HOST_IP	In-cluster dial address: ip route get 1.1.1.1 src (like dev-profile.sh; correct on LAN + cloud). If that interface is a VPN/tunnel, fall back to the LAN IP and prompt the user — Network addressing.
EXTERNAL_IP	Browser-facing address; defaults to ${HOST_IP}. Override when the browser path differs — cloud public IP, Brev secure-link (Step 1d), or tunnel; ask the user where they browse from if unsure. Network addressing.
HAPROXY_PORT	Browser-facing ingress port. Default 7777; ensure it is free.

Before docker compose up, verify EXTERNAL_IP, HAPROXY_PORT, VSS_PUBLIC_HOST, and VSS_PUBLIC_PORT are populated with browser-reachable values. Otherwise the stack may appear healthy while UI/API/VST links 404 or loop through Cloudflare Access.

Step 1b — Prepare the data directory

Layout (asset paths, ownership, mount points, profile-specific subdirs) is documented in references/data-directory.md. Read that file before deploying for the first time on a host or when changing profiles.

Step 1c — Initialize generated.env

The skill's per-deploy working copy. Always start from a fresh copy of the source .env , never mutate the source.

PROFILE=base
ENV_SRC=$REPO/deploy/docker/developer-profiles/dev-profile-$PROFILE/.env
ENV_GEN=$REPO/deploy/docker/developer-profiles/dev-profile-$PROFILE/generated.env

cp "$ENV_SRC" "$ENV_GEN"

All subsequent writes (Brev EXTERNAL_IP, the env_overrides dict from Step 2) go to $ENV_GEN. $ENV_SRC is read-only from here on.

Step 1d — Brev only: detect first, then set EXTERNAL_IP to the secure-link domain

Detect Brev before anything else — a Brev-provisioned instance sets BREV_ENV_ID in /etc/environment; nothing else does:

grep -qE '^BREV_ENV_ID=' /etc/environment && echo "on Brev" || echo "not Brev"

• not Brev → skip the rest of this step and do not read references/brev.md; keep the normal ${HOST_IP}-based EXTERNAL_IP.
• on Brev → apply the Brev secure-link overrides from references/brev.md § Setup flow to generated.env (NOT .env). Those set EXTERNAL_IP / VSS_PUBLIC_HOST to the secure-link domain and VSS_PUBLIC_HTTP_PROTOCOL=https / VSS_PUBLIC_WS_PROTOCOL=wss / VSS_PUBLIC_PORT=443 — setting EXTERNAL_IP alone leaves http://…:7777 UI/API/WS links that the browser blocks as mixed content.

Step 2 — Build env_overrides

Produce an env_overrides dict from the user request and the gathered context: explicitly choose remote/local LLM/VLM, set credentials, point at endpoints, set platform-specific flags. Do not let existing shell env vars silently pick placement; write the selected LLM_MODE / VLM_MODE and matching endpoint/model fields into generated.env. The full mapping (every override key, when it applies, defaults, profile-specific differences) lives in references/env-overrides.md. Each profile reference has worked examples for that profile's common scenarios.

Step 3 — Apply overrides + dry-run

Working env file: <repo>/deploy/docker/developer-profiles/dev-profile-<profile>/generated.env (created in Step 1c).

Reminder (see Step 1c): apply all overrides (Step 2 dict + Brev EXTERNAL_IP) to generated.env; --env-file always points at it, and post-deploy verifiers read it for the actually-deployed values.

# (Step 1c already ran: cp $ENV_SRC $ENV_GEN)

# Apply the env_overrides dict from Step 2 to generated.env
# (read lines, update matching keys, append new keys, write)
# Example:
#   sed -i "s|^LLM_MODE=.*|LLM_MODE=remote|" "$ENV_GEN"
#   sed -i "s|^LLM_BASE_URL=.*|LLM_BASE_URL=http://localhost:30081|" "$ENV_GEN"

# Resolve compose
cd $REPO/deploy/docker
docker compose --env-file $ENV_GEN config > resolved.yml

The resolved YAML is saved to <repo>/deploy/docker/resolved.yml.

Step 3b — Verify resolved.yml has no unexpanded ${...} tokens

Unexpanded ${VAR} tokens in resolved.yml mean compose did not see those env values. Diagnostic procedure and common culprits live in references/troubleshooting.md.

Step 3c — Verify access to selected NGC artifacts

Do this after resolved.yml exists and before docker compose up. The NGC token probe in Step 0a proves only that the key authenticates; it does not prove the key's org/team can access the selected image or model repositories.

Build the artifact list from the actual selected deployment:

• resolved.yml: every image: under nvcr.io/... that Compose will pull.
• $ENV_GEN: NGC-backed model/resource paths such as RTVI_VLM_MODEL_PATH=ngc:nim/nvidia/cosmos-reason2-8b:hf-1208. Skip none, git:..., local paths, and remote endpoint URLs.
• Profile staging steps: any NGC model/resource downloads documented in the profile reference, such as alerts/search perception model staging.

Probe each selected artifact with the normalized NGC key before continuing:

• Container images: docker manifest inspect <nvcr.io/...> after docker login nvcr.io — for gated nvcr.io repos a 401/403 here is a definitive no-entitlement signal (manifest read requires the same org/team grant as the layer pull); or the matching ngc registry image info ... when the artifact maps cleanly to an NGC image path.
• NGC model/resource paths (e.g. the Cosmos checkpoint RT-VLM downloads at runtime): run the matching ngc registry model info ... or ngc registry resource info ... for the exact repo/tag the profile will load or download; these use NGC's scoped auth. Do NOT probe a model with docker manifest inspect (returns "no such manifest" because a model is not an OCI image) or a raw Authorization: Bearer <key> REST call (returns 403 because that is not NGC's auth flow); both are expected false negatives, not entitlement failures. If the ngc CLI is unavailable, treat the container-image probe above as the entitlement signal, since NGC grants org/team access across images and models together.
• Profile-staged TAO/perception models: run the corresponding ngc registry model info ... / resource info ... for each repo/tag before the staging block downloads files.

If any probe returns 401, 403, permission, not being a member of the organization that owns the repo, missing org/repo, or a similar access error, stop and prompt the user for an NGC key from an org/team entitled to those artifacts. Do not start Compose and discover the failure during NIM cold start.

Step 3d — Strip dangling optional depends_on from resolved.yml

MUST run after Step 3, before Step 5. Skipping this aborts the deploy:

Normalize - drop optional dependencies for services filtered out from resolved.yml

# From the repo root
uv run skills/vss-deploy-profile/scripts/normalize_resolved_yml.py "$REPO/deploy/docker/resolved.yml"

If uv isn't on the host, install it once with curl -LsSf https://astral.sh/uv/install.sh | sh (no root needed). Re-validate before up -d:

docker compose -f "$REPO/deploy/docker/resolved.yml" config --quiet && echo "resolved.yml OK"

If validation still fails after the normalizer runs, capture the error and inspect — that's a different bug (a dependency that's not optional, or another schema violation), not the dangling-depends_on case.

Step 4 — Review

Show the user a summary of what will be deployed:

• Profile name and hardware
• LLM/VLM models and mode (local/remote/local_shared)
• Services that will start
• GPU device assignment
• Key endpoints (UI port, agent port)

Ask: "Looks good — deploy now?" and wait for confirmation before Step 5.

Exception — autonomous mode. If the user's request already asks you to run autonomously (e.g. "deploy X autonomously", "run without confirmation", "non-interactive"), skip the confirmation prompt and proceed straight to Step 5. This path exists so automated eval / CI invocations don't hang waiting for a human reply they'll never get. In all other cases, a human must approve.

Step 5 — Deploy

cd $REPO/deploy/docker
docker compose --env-file $ENV_GEN -f resolved.yml up -d

--env-file is mandatory. Without the same generated.env used in Step 3, COMPOSE_PROFILES may be unset and up -d can exit 0 with zero selected services.

Avoid broad --force-recreate on ordinary retries — it destroys warm NIM containers (another 3–5 min torch.compile + CUDA-graph capture each). Fix the root cause (usually perms or an env typo) and just re-run up -d; use targeted --force-recreate --no-deps <service...> only when a profile reference documents it as the recovery path.

docker compose up -d only creates containers; it does not wait for internal services to finish warming. Never declare deploy success until the readiness gates pass.

Step 5b — Wait until the stack is actually healthy

Gate 0 — container count must be > 0. Refuse to proceed past up -d until the started count (docker compose -f resolved.yml ps -q | wc -l) is non-zero and ≥ the expected count (config --services | wc -l); a zero/short count almost always means a missing --env-file in Step 5. The exact gate plus the full readiness procedure live in references/readiness.md.

Cold deploys can take 10–20 min, and each profile reference lists the required endpoints. Never declare deploy done after up -d; only after every documented endpoint succeeds.

Tear Down

To tear down a deployment — full host reclaim or cache-preserving redeploy / profile switch — follow references/teardown.md. Always tear down by the mdx project with -v --remove-orphans; a plain docker compose down leaves volumes and networks behind.

Debugging a Deployment

Use this workflow when the user asks to "debug the deploy", "verify it's working", "why is the agent not responding", or similar. The goal is to confirm the full video-ingestion-to-agent-answer path, not just that containers are "Up".

Each profile reference has a Debugging section listing the exact commands and failure-mode table for that profile.

Quick checks (all profiles)

# 1. All expected containers Up
docker ps --format 'table {{.Names}}\t{{.Status}}'

# 2. Agent API + UI responding
curl -sf http://localhost:8000/health >/dev/null && echo "agent OK"
curl -sf http://localhost:3000/ >/dev/null && echo "ui OK"

The LLM/VLM NIM probes — including the *_MODE=remote handling that skips localhost:3008x (where a connection refused is expected) and probes the selected *_BASE_URL/v1/models via scripts/probe_remote_models.sh — are in references/troubleshooting.md.

Limitations

• This skill deploys compose-based VSS profiles only; standalone microservice deployment belongs to the matching vss-deploy-* skill.
• Hardware sizing, model placement, and profile-specific readiness are owned by profile references; do not infer them from memory.
• Privileged host remediation requires user approval when passwordless sudo is unavailable.

Troubleshooting

The common-error quick reference, the full symptom → cause → fix table, the unexpanded-${...} diagnostic, and the NIM endpoint probes are consolidated in references/troubleshooting.md — start there for any deploy, runtime, or probe failure, then continue in the matching per-profile reference's Debugging section.