Video Search and Summarization

Verify environment requirements

Check that your system meets the hardware and software prerequisites.

# Verify driver version
nvidia-smi | grep "Driver Version"
# Expected output: Driver Version: 580.82.09 or higher

# Verify CUDA version
nvcc --version
# Expected output: release 13.0

# Verify Docker is running
docker --version && docker compose version

Configure Docker

To easily manage containers without sudo, you must be in the docker group. If you choose to skip this step, you will need to run Docker commands with sudo. Open a new terminal and test Docker access. In the terminal, run:

docker ps

If you see a permission denied error (something like permission denied while trying to connect to the Docker daemon socket), add your user to the docker group so that you don't need to run the command with sudo .

sudo usermod -aG docker $USER
newgrp docker

Additionally, configure Docker so that it can use the NVIDIA Container Runtime.

sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

#Run a sample workload to verify the setup
sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi

Clone the VSS repository

Clone the Video Search and Summarization repository from NVIDIA's public GitHub.

# Clone the VSS AI Blueprint repository
git clone https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization.git
cd video-search-and-summarization

Run the cache cleaner script

Start the system cache cleaner to optimize memory usage during container operations.

# In another terminal, start the cache cleaner script.
# Alternatively, append " &" to the end of the command to run it in the background.
sudo sh deploy/scripts/sys_cache_cleaner.sh

Set up Docker shared network

Create a Docker network that will be shared between VSS services and CV pipeline containers.

# Create shared network (may require sudo depending on Docker configuration)
docker network create vss-shared-network

Authenticate with NVIDIA Container Registry

Log in to NVIDIA's container registry using your NGC API Key.

# Log in to NVIDIA Container Registry
docker login nvcr.io
# Username: $oauthtoken
# Password: <PASTE_NGC_API_KEY_HERE>

Choose deployment scenario

Choose between two deployment options based on your requirements:

Proceed with Option A for Event Reviewer or Option B for Standard VSS.

Option A

VSS Event Reviewer (Completely Local)

8.1 Navigate to Event Reviewer directory

Change to the directory containing the Event Reviewer Docker Compose configuration.

cd deploy/docker/event_reviewer/

8.2 Configure NGC API Key

Update the environment file with your NGC API Key. You can do this by editing the .env file directly, or by running the following command:

# Edit the .env file and update NGC_API_KEY
echo "NGC_API_KEY=<YOUR_NGC_API_KEY>" >> .env

8.3 Update the VSS Image path

Update VSS_IMAGE to nvcr.io/nvidia/blueprint/vss-engine-sbsa:2.4.0 in .env.

# Edit the .env file and update VSS_IMAGE
echo "VSS_IMAGE=nvcr.io/nvidia/blueprint/vss-engine-sbsa:2.4.0" >> .env

8.4 Start VSS Event Reviewer services

Launch the complete VSS Event Reviewer stack including Alert Bridge, VLM Pipeline, Alert Inspector UI, and Video Storage Toolkit.

# Start VSS Event Reviewer with ARM64 and SBSA optimizations
IS_SBSA=1 IS_AARCH64=1 ALERT_REVIEW_MEDIA_BASE_DIR=/tmp/alert-media-dir docker compose up

8.5 Navigate to CV Event Detector directory

In a new terminal session, navigate to the computer vision event detector configuration.

cd video-search-and-summarization/examples/cv-event-detector

8.6 Update the NV_CV_EVENT_DETECTOR_IMAGE Image path

Update NV_CV_EVENT_DETECTOR_IMAGE to nvcr.io/nvidia/blueprint/nv-cv-event-detector-sbsa:2.4.0 in .env.

# Edit the .env file and update NV_CV_EVENT_DETECTOR_IMAGE
echo "NV_CV_EVENT_DETECTOR_IMAGE=nvcr.io/nvidia/blueprint/nv-cv-event-detector-sbsa:2.4.0" >> .env

8.7 Start DeepStream CV pipeline

Launch the DeepStream computer vision pipeline and CV UI services.

# Start CV pipeline with ARM64 and SBSA optimizations
IS_SBSA=1 IS_AARCH64=1 ALERT_REVIEW_MEDIA_BASE_DIR=/tmp/alert-media-dir docker compose up

8.8 Wait for service initialization

Allow time for all containers to fully initialize before accessing the user interfaces.

# Monitor container status
docker ps
# Verify all containers show "Up" status and VSS backend logs (vss-engine-sbsa:2.4.0) show ready state "Uvicorn running on http://0.0.0.0:7860"
# In total, there should be 8 containers:
# nvcr.io/nvidia/blueprint/nv-cv-event-detector-ui:2.4.0
# nvcr.io/nvidia/blueprint/nv-cv-event-detector-sbsa:2.4.0
# nginx:alpine
# nvcr.io/nvidia/blueprint/vss-alert-inspector-ui:2.4.0
# nvcr.io/nvidia/blueprint/alert-bridge:0.19.0-multiarch
# nvcr.io/nvidia/blueprint/vss-engine-sbsa:2.4.0
# nvcr.io/nvidia/blueprint/vst-storage:2.1.0-25.07.1
# redis/redis-stack-server:7.2.0-v9

8.9 Validate Event Reviewer deployment

Access the web interfaces to confirm successful deployment and functionality.

# Test CV UI accessibility (default: localhost)
curl -I http://localhost:7862
# Expected: HTTP 200 response

# Test Alert Inspector UI accessibility (default: localhost)
curl -I http://localhost:7860
# Expected: HTTP 200 response

# If you are running your Spark in Remote or Accessory mode, replace 'localhost' with the IP address or hostname of your Spark device.
# To find your Spark's IP address, run the following command on the Spark system:
hostname -I
# Or to get the hostname:
hostname
# Then use the IP/hostname in place of 'localhost', for example:
# curl -I http://<SPARK_IP_OR_HOSTNAME>:7862

Open these URLs in your browser:

• http://localhost:7862 - CV UI to launch and monitor CV pipeline
• http://localhost:7860 - Alert Inspector UI to view clips and review VLM results

Option B

Standard VSS (Hybrid Deployment)

In this hybrid deployment, we would use NIMs from build.nvidia.com. Alternatively, you can configure your own hosted endpoints by following the instructions in the VSS remote deployment guide.

9.1 Get NVIDIA API Key

• Log in to https://build.nvidia.com/explore/discover.
• Search for Get API Key on the page and click on it.

9.2 Navigate to remote LLM deployment directory

cd deploy/docker/remote_llm_deployment/

9.3 Configure environment variables

Update the environment file with your API keys and deployment preferences. You can do this by editing the .env file directly, or by running the following commands:

# Edit .env file with required keys
echo "NVIDIA_API_KEY=<YOUR_NVIDIA_API_KEY>" >> .env
echo "NGC_API_KEY=<YOUR_NGC_API_KEY>" >> .env
echo "DISABLE_CV_PIPELINE=true" >> .env  # Set to false to enable CV
echo "INSTALL_PROPRIETARY_CODECS=false" >> .env  # Set to true to enable CV

9.4 Update the VSS Image path

Update VIA_IMAGE to nvcr.io/nvidia/blueprint/vss-engine-sbsa:2.4.0 in .env.

# Edit the .env file and update VIA_IMAGE
echo "VIA_IMAGE=nvcr.io/nvidia/blueprint/vss-engine-sbsa:2.4.0" >> .env

9.5 Review model configuration

Verify that the config.yaml file contains the correct remote endpoints. For NIMs, it should be set to https://integrate.api.nvidia.com/v1 .

# Check model server endpoints in config.yaml
cat config.yaml | grep -A 10 "model"

9.6 Launch Standard VSS deployment

# Start Standard VSS with hybrid deployment
docker compose up

9.7 Validate Standard VSS deployment

Access the VSS UI to confirm successful deployment.

# Test VSS UI accessibility
# If running locally on your Spark device, use localhost:
curl -I http://localhost:9100
# Expected: HTTP 200 response

# If your Spark is running in Remote/Accessory mode, replace 'localhost' with the IP address or hostname of your Spark device.
# To find your Spark's IP address, run the following command on the Spark terminal:
hostname -I
# Or to get the hostname:
hostname
# Then test accessibility (replace <SPARK_IP_OR_HOSTNAME> with the actual value):
curl -I http://<SPARK_IP_OR_HOSTNAME>:9100

Open http://localhost:9100 in your browser to access the VSS interface.

Test video processing workflow

Run a basic test to verify the video analysis pipeline is functioning based on your deployment. The UI comes with a few example videos pre-populated for uploading and testing

For Event Reviewer deployment

Follow the steps here to access and use the Event Reviewer workflow.

• Access CV UI at http://localhost:7862 to upload and process videos
• Monitor results in Alert Inspector UI at http://localhost:7860

For Standard VSS deployment

Follow the steps here to navigate VSS UI - File Summarization, Q&A, and Alerts.

• Access VSS interface at http://localhost:9100
• Upload videos and test summarization features

Cleanup and rollback

To completely remove the VSS deployment and free up system resources:

# For Event Reviewer deployment
cd deploy/docker/event_reviewer/
IS_SBSA=1 IS_AARCH64=1 ALERT_REVIEW_MEDIA_BASE_DIR=/tmp/alert-media-dir docker compose down
cd ../../examples/cv-event-detector/
IS_SBSA=1 IS_AARCH64=1 ALERT_REVIEW_MEDIA_BASE_DIR=/tmp/alert-media-dir docker compose down

# For Standard VSS deployment
cd deploy/docker/remote_llm_deployment/
docker compose down

# Remove shared network (if using Event Reviewer)
docker network rm vss-shared-network

# Clean up temporary media files and stop cache cleaner
rm -rf /tmp/alert-media-dir
sudo pkill -f sys_cache_cleaner.sh

Next steps

With VSS deployed, you can now:

Event Reviewer deployment:

• Upload video files through the CV UI at port 7862
• Monitor automated event detection and reviewing
• Review analysis results in the Alert Inspector UI at port 7860
• Configure custom event detection rules and thresholds

Standard VSS deployment:

• Access full VSS capabilities at port 9100
• Test video summarization and Q&A features
• Configure knowledge graphs and graph databases
• Integrate with existing video processing workflows