菜单
Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/NVIDIA/DeepStream --skill amc-setup-calibration-stack命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar. For RTSP/live streams, use amc-run-rtsp-calibration instead.
NVIDIA DeepStream SDK development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker integration.
基于 SOC 职业分类
正在显示 SKILL.md
| name | amc-setup-calibration-stack |
| description | Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key. |
| metadata | {"author":"Shubham Agrawal <shuagrawal@nvidia.com>","tags":["amc","deepstream","docker","calibration","setup","ngc"]} |
| owner | NVIDIA CORPORATION |
| service | auto-magic-calib |
| version | 1.0.0 |
| reviewed | 2026-04-28 |
| license | Apache-2.0 |
Set up the AutoMagicCalib microservice and UI from release containers: resolve an AMC checkout, authenticate to NGC, optionally download VGGT, configure Docker Compose, launch services, and verify readiness.
auto-magic-calib repo on disk. Step 0b resolves the current repo, DeepStream tools/auto-magic-calib, DEEPSTREAM_REPO_ROOT, or ~/auto-magic-calib; otherwise it asks before cloning https://github.com/NVIDIA-AI-IOT/auto-magic-calib.sudo; verify with docker ps before continuing.docker ps
docker group. Ask the user to run:
sudo usermod -aG docker $USER && newgrp docker
docker ps works before continuing.Agent note: If
docker pscannot be run from within the agent sandbox, ask the user to confirm it works (e.g. "Can you confirmdocker psruns without sudo?") before proceeding.
The skill needs AMC repo assets (compose/, sample data, and models/). Resolve an existing checkout first; ask before cloning into ~/auto-magic-calib.
REPO_URL="https://github.com/NVIDIA-AI-IOT/auto-magic-calib.git"
DEFAULT_CLONE_DIR="$HOME/auto-magic-calib"
CURRENT_GIT_ROOT="$(git rev-parse --show-toplevel 2>/dev/null || true)"
is_amc_checkout() {
[ -n "$1" ] \
&& [ -f "/README.md" ] \
&& grep -q 2>/dev/null \
&& [ -f ] \
&& grep -q 2>/dev/null \
&& grep -q 2>/dev/null
}
REPO_ROOT=
candidate \
\
\
\
\
;
is_amc_checkout ;
REPO_ROOT=
[ -z ];
[ -n ] && [ -d ];
1
REPO_ROOT
Agent note: never clone silently. Prefer initialized DeepStream
tools/auto-magic-calib; do not clone over that submodule path. If it exists but is empty, ask the user to rungit submodule update --init tools/auto-magic-calib. Honour an alternate AMC path if provided.
On a fresh system, pip and python3-venv may not be available. Install them first:
# Create a venv for HuggingFace CLI (project-local preferred)
REPO_DIR="$(git rev-parse --show-toplevel 2>/dev/null || pwd)"
HF_VENV="${REPO_DIR}/venv"
python3 -m venv "$HF_VENV" 2>/dev/null || {
echo "ERROR: python3-venv not available." >&2
echo "Install it manually: sudo apt install -y python3-venv python3-pip" >&2
exit 1
}
# Install HuggingFace hub (needed for VGGT download)
"$HF_VENV/bin/pip" install --upgrade pip huggingface_hub
Note: Skip this step if a venv with
hfalready exists (checkvenv/bin/hfin the repo root or~/venv/amc/bin/hf).
Ask the user for their NGC API key using the host's question mechanism; if none is available, ask in chat and wait. Then run:
echo "<NGC_API_KEY>" | docker login nvcr.io --username '$oauthtoken' --password-stdin
echo "✓ NGC authentication complete"
export REPO_ROOT=$(git rev-parse --show-toplevel)
cd "$REPO_ROOT"
if [ -f "models/vggt/vggt_1B_commercial.pt" ]; then
echo "✓ VGGT model already present"
else
echo "✗ VGGT model not found"
echo "Options:"
echo " 1. Continue without VGGT (AMC only - sufficient for most use cases)"
echo " 2. Download VGGT model (~4.7GB, requires HuggingFace account)"
fi
To download VGGT: ask the user to accept the license at https://huggingface.co/facebook/VGGT-1B-Commercial and provide a read token from https://huggingface.co/settings/tokens using the host's question mechanism. Pass it through HF_TOKEN so it is not exposed in ps output:
REPO_DIR="$(git rev-parse --show-toplevel 2>/dev/null || pwd)"
cd "$REPO_DIR"
# Find the HuggingFace CLI binary (named 'hf', not 'huggingface-cli')
HF_BIN="$(find "$REPO_DIR/venv" ~/venv/amc -name hf -type f 2>/dev/null | head -1)"
{ [ -z "$HF_BIN" ] || [ ! -x "$HF_BIN" ]; } && { echo "ERROR: hf binary not found or not executable; install the hf CLI (Step 0c) or set HF_BIN" >&2; exit 1; }
# Do NOT use --token on the command line (leaks via ps/argv). The HF CLI
# reads HF_TOKEN from the environment automatically.
HF_TOKEN="<HF_TOKEN>" "$HF_BIN" download facebook/VGGT-1B-Commercial \
--local-dir models/vggt/
# Verify
ls -lh models/vggt/vggt_1B_commercial.pt
# Should show ~4.7GB file
Important: Download BEFORE setting
chown 1000:1000on the models directory — the current user needs write access during download. Set permissions in Step 4 after download completes.
The Compose environment file controls ports and paths. Update it before launching:
cd $REPO_ROOT/compose
# Find available backend port (8000-8009)
for port in {8000..8009}; do
if ! lsof -Pi :$port -sTCP:LISTEN -t >/dev/null 2>&1; then
MS_PORT=$port
echo "Using backend port: $MS_PORT"
break
fi
done
[ -z "$MS_PORT" ] && { echo "ERROR: no free backend port in 8000-8009; free one or widen the range." >&2; exit 1; }
# Find available UI port (5000-5009)
for port in {5000..5009}; do
if ! lsof -Pi :$port -sTCP:LISTEN -t >/dev/null 2>&1; then
UI_PORT=$port
echo "Using UI port: $UI_PORT"
break
fi
done
[ -z "$UI_PORT" ] && { echo "ERROR: no free UI port in 5000-5009; free one or widen the range." >&2; exit 1; }
# Get host IP
HOST_IP=$(hostname -I | awk '{print $1}')
echo "Host IP: $HOST_IP"
# Preserve existing keys and restrict permissions on the Compose environment file.
COMPOSE_ENV_BASENAME="env"
ENV_FILE=".${COMPOSE_ENV_BASENAME}"
if [ -f "$ENV_FILE" ]; then
BACKUP="${ENV_FILE}.bak.$(date +%s)"
cp "$ENV_FILE" "$BACKUP"
chmod 600 "$BACKUP"
fi
touch "$ENV_FILE"
chmod 600 "$ENV_FILE"
set_env_key() {
local k="$1" v="$2"
if grep -qE "^${k}=" "$ENV_FILE"; then
sed -i "s|^${k}=.*|${k}=${v}|" "$ENV_FILE"
else
echo "${k}=${v}" >> "$ENV_FILE"
fi
}
set_env_key AUTO_MAGIC_CALIB_MS_PORT "${MS_PORT}"
set_env_key AUTO_MAGIC_CALIB_UI_PORT "${UI_PORT}"
set_env_key PROJECT_DIR "../../projects"
set_env_key MODEL_DIR "../../models"
set_env_key HOST_IP "${HOST_IP}"
# Keep timestamped Compose environment backups out of git.
GITIGNORE="$REPO_ROOT/.gitignore"
touch "$GITIGNORE"
BACKUP_PATTERN="compose/${ENV_FILE}.bak.*"
grep -qxF "$BACKUP_PATTERN" "$GITIGNORE" || echo "$BACKUP_PATTERN" >> "$GITIGNORE"
echo "✓ Compose environment file updated"
cat "$ENV_FILE"
Important: HOST_IP must be the machine's network IP (not localhost) so the UI container can reach the backend from a browser.
Optional: set VGGT_MODEL_PATH only if the VGGT model is mounted at a non-default container path; default is /tmp/vggt_model/vggt_1B_commercial.pt inside the MS container.
Optional for RTSP calibration: use skills/amc-run-rtsp-calibration/SKILL.md after launch. That skill verifies VIOS reachability and, when needed, relaunches the microservice with a temporary compose override that exports VIOS_BASE_URL without changing checked-in compose files.
The containers run as UID/GID 1000. The projects and models directories must be owned by this UID for containers to read/write properly:
cd "$REPO_ROOT"
# Create projects directory if it doesn't exist
mkdir -p projects
# Set ownership (required for containers to write calibration outputs).
# Do this AFTER VGGT download is complete (current user needs write access during download).
# Get explicit user confirmation before running sudo chown — it recursively changes
# ownership of $REPO_ROOT/projects and $REPO_ROOT/models to UID/GID 1000.
[ -d projects ] && [ -d models ] || {
echo "ERROR: expected projects/ and models/ under $REPO_ROOT" >&2; exit 1;
}
echo "About to chown -R 1000:1000 on:"
echo " $REPO_ROOT/projects"
echo " $REPO_ROOT/models"
echo "(required because containers run as UID 1000). Confirm before proceeding."
sudo chown 1000:1000 -R projects
sudo chown 1000:1000 -R models
echo "✓ Permissions set"
Before pulling, fail fast if the NGC key authenticated in Step 1 but cannot actually access a release image — otherwise docker compose up aborts partway with a 401/403 after some work is already done.
cd $REPO_ROOT/compose
# Fail-fast image-access check: confirm the NGC key can reach every release
# image BEFORE pulling. `docker manifest inspect` checks registry access without
# downloading layers, and the image list is read from the resolved compose so it
# tracks the release tag automatically.
IMAGES=$(docker compose config --images | sort -u)
[ -z "$IMAGES" ] && { echo "ERROR: no images resolved from compose — check the Compose environment settings and chosen profile." >&2; exit 1; }
for img in $IMAGES; do
echo "Checking access: $img"
if ! docker manifest inspect "$img" >/dev/null 2>&1; then
echo "NGC login succeeded, but this key cannot access the required image:" >&2
echo " $img" >&2
echo "Provide an NGC key with access to this image's namespace, then re-run Step 1 (login) and retry." >&2
exit 1
fi
done
# Start all services (images pulled automatically on first run)
docker compose up -d
# Check containers are running
docker compose ps
The exact image tags change by release; read them from the active compose files instead of hardcoding a version.
# Read ports from the Compose environment file.
COMPOSE_ENV_BASENAME="env"
COMPOSE_ENV_FILE="$REPO_ROOT/compose/.${COMPOSE_ENV_BASENAME}"
MS_PORT=$(grep AUTO_MAGIC_CALIB_MS_PORT "$COMPOSE_ENV_FILE" | cut -d= -f2)
UI_PORT=$(grep AUTO_MAGIC_CALIB_UI_PORT "$COMPOSE_ENV_FILE" | cut -d= -f2)
HOST_IP=$(grep HOST_IP "$COMPOSE_ENV_FILE" | cut -d= -f2)
# Wait for microservice readiness. Cold image pulls or first startup can need
# extra time after `docker compose up -d` returns.
READY_URL="http://localhost:${MS_PORT}/v1/ready"
echo "Waiting for microservice readiness at ${READY_URL} ..."
ready_response=""
for attempt in $(seq 1 24); do
if ready_response=$(curl -fsS --max-time 5 "${READY_URL}" 2>/dev/null) && \
echo "${ready_response}" | grep -q '"code"[[:space:]]*:[[:space:]]*0'; then
echo "Microservice ready: ${ready_response}"
break
fi
if [ "${attempt}" -lt 24 ]; then
printf " [%02d/24] Microservice not ready yet; retrying in 5s...\n" "${attempt}"
sleep 5
fi
done
if ! echo "${ready_response}" | grep -q '"code"[[:space:]]*:[[:space:]]*0'; then
echo "ERROR: microservice did not report ready within 120 seconds: ${READY_URL}" >&2
echo "Check status and logs:" >&2
echo " cd ${REPO_ROOT}/compose && docker compose ps" >&2
echo " cd ${REPO_ROOT}/compose && docker compose logs auto-magic-calib-ms" >&2
exit 1
fi
# Check UI is serving
UI_STATUS=$(curl -s -o /dev/null -w "%{http_code}" --max-time 5 "http://localhost:${UI_PORT}")
if [ "${UI_STATUS}" != "200" ]; then
echo "ERROR: Web UI returned HTTP ${UI_STATUS}; check docker compose ps and UI logs." >&2
exit 1
fi
echo "Web UI ready: HTTP ${UI_STATUS}"
echo "Microservice: http://${HOST_IP}:${MS_PORT}"
echo "Web UI: http://${HOST_IP}:${UI_PORT}"
docker compose ps shows MS and UI containers Up; MS should be healthy./v1/ready returns code:0 and Step 6 prints the microservice and UI URLs.http://<HOST_IP>:<AUTO_MAGIC_CALIB_UI_PORT> works.$REPO_ROOT/projects/.| Issue | Fix |
|---|---|
| Docker permission denied | Ask the user to run sudo usermod -aG docker $USER && newgrp docker, then retry docker ps. |
docker login rejected | Ask for a current NGC key and log in again. |
| Required image inaccessible | The key lacks image namespace access; ask for a key with access, then retry Step 1 and Step 5. |
python3 -m venv, pip, or hf missing | Install python3-venv/python3-pip; the HF binary is named hf. |
| VGGT permission error | Download VGGT before chown 1000:1000; to recover, restore user ownership of models/ and re-download. |
| Port in use | Pick a free MS port in 8000-8009 and UI port in 5000-5009, then update the Compose environment file. |
| Readiness timeout or exited container | Run cd $REPO_ROOT/compose && docker compose ps and inspect docker compose logs auto-magic-calib-ms. |
| Project/model permission denied | Re-run Step 4 for projects/ or models/ only. |
| UI cannot reach backend | Verify HOST_IP in the Compose environment file is the machine network IP, not localhost. |
| GPU unavailable | Verify NVIDIA runtime with docker run --rm --runtime=nvidia --gpus all ubuntu:20.04 nvidia-smi. |
Common Fixes:
cd $REPO_ROOT/compose
# View logs
docker compose logs -f
# View logs for specific service
docker compose logs -f auto-magic-calib-ms
# Restart all services
docker compose restart
# Stop and remove containers
docker compose down
# Update Compose environment settings and relaunch
docker compose up -d
cd $REPO_ROOT/compose
# Stop all services (containers removed, data persisted)
docker compose down
# Stop and remove volumes
docker compose down -v
skills/amc-run-sample-calibration/SKILL.md - Sanity-check the running stack with the bundled sample datasetskills/amc-run-video-calibration/SKILL.md - Calibrate from your own pre-recorded MP4s via REST APIskills/amc-run-rtsp-calibration/SKILL.md - Calibrate from live RTSP streams through VIOS capture