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chore: Regenerate all playbooks

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nvidia/vss/README.md
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@ -74,7 +74,36 @@ nvcc --version
docker --version && docker compose version
```
## Step 2. Clone the VSS repository
## Step 2. 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:
```bash
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:
```bash
sudo usermod -aG docker $USER
newgrp docker
```
> **Warning**: After running usermod, you must log out and log back in to start a new
> session with updated group permissions.
Additionally, configure Docker so that it can use the NVIDIA Container Runtime.
```bash
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
```
## Step 3. Clone the VSS repository
Clone the Video Search and Summarization repository from NVIDIA's public GitHub.
@ -84,7 +113,7 @@ git clone https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization
cd video-search-and-summarization
```
## Step 3. Run the cache cleaner script
## Step 4. Run the cache cleaner script
Start the system cache cleaner to optimize memory usage during container operations.
@ -94,7 +123,7 @@ Start the system cache cleaner to optimize memory usage during container operati
sudo sh deploy/scripts/sys_cache_cleaner.sh
```
## Step 4. Set up Docker shared network
## Step 5. Set up Docker shared network
Create a Docker network that will be shared between VSS services and CV pipeline containers.
@ -105,7 +134,7 @@ docker network create vss-shared-network
> **Warning:** If the network already exists, you may see an error. Remove it first with `docker network rm vss-shared-network` if needed.
## Step 5. Authenticate with NVIDIA Container Registry
## Step 6. Authenticate with NVIDIA Container Registry
Log in to NVIDIA's container registry using your [NGC API Key](https://org.ngc.nvidia.com/setup/api-keys).
@ -116,7 +145,7 @@ docker login nvcr.io
## Password: <PASTE_NGC_API_KEY_HERE>
```
## Step 6. Choose deployment scenario
## Step 7. Choose deployment scenario
Choose between two deployment options based on your requirements:
@ -127,11 +156,11 @@ Choose between two deployment options based on your requirements:
Proceed with **Option A** for Event Reviewer or **Option B** for Standard VSS.
## Step 7. Option A
## Step 8. Option A
**[VSS Event Reviewer](https://docs.nvidia.com/vss/latest/content/vss_event_reviewer.html) (Completely Local)**
**7.1 Navigate to Event Reviewer directory**
**8.1 Navigate to Event Reviewer directory**
Change to the directory containing the Event Reviewer Docker Compose configuration.
@ -139,7 +168,7 @@ Change to the directory containing the Event Reviewer Docker Compose configurati
cd deploy/docker/event_reviewer/
```
**7.2 Configure NGC API Key**
**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:
@ -148,7 +177,7 @@ Update the environment file with your NGC API Key. You can do this by editing th
echo "NGC_API_KEY=<YOUR_NGC_API_KEY>" >> .env
```
**7.3 Update the VSS Image path**
**8.3 Update the VSS Image path**
Update `VSS_IMAGE` to `nvcr.io/nvidia/blueprint/vss-engine-sbsa:2.4.0` in `.env`.
@ -157,7 +186,7 @@ Update `VSS_IMAGE` to `nvcr.io/nvidia/blueprint/vss-engine-sbsa:2.4.0` in `.env`
echo "VSS_IMAGE=nvcr.io/nvidia/blueprint/vss-engine-sbsa:2.4.0" >> .env
```
**7.4 Start VSS Event Reviewer services**
**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.
@ -168,7 +197,7 @@ IS_SBSA=1 IS_AARCH64=1 ALERT_REVIEW_MEDIA_BASE_DIR=/tmp/alert-media-dir docker c
> **Note:** This step will take several minutes as containers are pulled and services initialize. The VSS backend requires additional startup time.
**7.5 Navigate to CV Event Detector directory**
**8.5 Navigate to CV Event Detector directory**
In a new terminal session, navigate to the computer vision event detector configuration.
@ -176,7 +205,7 @@ In a new terminal session, navigate to the computer vision event detector config
cd video-search-and-summarization/examples/cv-event-detector
```
**7.6 Update the NV_CV_EVENT_DETECTOR_IMAGE Image path**
**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`.
@ -185,7 +214,7 @@ Update `NV_CV_EVENT_DETECTOR_IMAGE` to `nvcr.io/nvidia/blueprint/nv-cv-event-det
echo "NV_CV_EVENT_DETECTOR_IMAGE=nvcr.io/nvidia/blueprint/nv-cv-event-detector-sbsa:2.4.0" >> .env
```
**7.7 Start DeepStream CV pipeline**
**8.7 Start DeepStream CV pipeline**
Launch the DeepStream computer vision pipeline and CV UI services.
@ -194,7 +223,7 @@ Launch the DeepStream computer vision pipeline and CV UI services.
IS_SBSA=1 IS_AARCH64=1 ALERT_REVIEW_MEDIA_BASE_DIR=/tmp/alert-media-dir docker compose up
```
**7.8 Wait for service initialization**
**8.8 Wait for service initialization**
Allow time for all containers to fully initialize before accessing the user interfaces.
@ -204,7 +233,7 @@ docker ps
## Verify all containers show "Up" status and VSS backend logs show ready state
```
**7.9 Validate Event Reviewer deployment**
**8.9 Validate Event Reviewer deployment**
Access the web interfaces to confirm successful deployment and functionality.
@ -222,24 +251,24 @@ Open these URLs in your browser:
- `http://<NODE_IP>:7862` - CV UI to launch and monitor CV pipeline
- `http://<NODE_IP>:7860` - Alert Inspector UI to view clips and review VLM results
## Step 8. Option B
## Step 9. Option B
**[Standard VSS](https://docs.nvidia.com/vss/latest/content/architecture.html) (Hybrid Deployment)**
In this hybrid deployment, we would use NIMs from [build.nvidia.com](https://build.nvidia.com/). Alternatively, you can configure your own hosted endpoints by following the instructions in the [VSS remote deployment guide](https://docs.nvidia.com/vss/latest/content/installation-remote-docker-compose.html).
**8.1 Get NVIDIA API Key**
**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.
**8.2 Navigate to remote LLM deployment directory**
**9.2 Navigate to remote LLM deployment directory**
```bash
cd deploy/docker/remote_llm_deployment/
```
**8.3 Configure environment variables**
**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:
@ -251,7 +280,7 @@ echo "DISABLE_CV_PIPELINE=true" >> .env # Set to false to enable CV
echo "INSTALL_PROPRIETARY_CODECS=false" >> .env # Set to true to enable CV
```
**8.4 Update the VSS Image path**
**9.4 Update the VSS Image path**
Update `VIA_IMAGE` to `nvcr.io/nvidia/blueprint/vss-engine-sbsa:2.4.0` in `.env`.
@ -260,7 +289,7 @@ Update `VIA_IMAGE` to `nvcr.io/nvidia/blueprint/vss-engine-sbsa:2.4.0` in `.env`
echo "VIA_IMAGE=nvcr.io/nvidia/blueprint/vss-engine-sbsa:2.4.0" >> .env
```
**8.5 Review model configuration**
**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 `.
@ -269,14 +298,14 @@ Verify that the config.yaml file contains the correct remote endpoints. For NIMs
cat config.yaml | grep -A 10 "model"
```
**8.6 Launch Standard VSS deployment**
**9.6 Launch Standard VSS deployment**
```bash
## Start Standard VSS with hybrid deployment
docker compose up
```
**8.7 Validate Standard VSS deployment**
**9.7 Validate Standard VSS deployment**
Access the VSS UI to confirm successful deployment.
@ -288,7 +317,7 @@ curl -I http://<NODE_IP>:9100
Open `http://<NODE_IP>:9100` in your browser to access the VSS interface.
## Step 9. Test video processing workflow
## Step 10. Test video processing workflow
Run a basic test to verify the video analysis pipeline is functioning based on your deployment.
@ -304,7 +333,7 @@ Follow the steps [here](https://docs.nvidia.com/vss/latest/content/ui_app.html)
- Access VSS interface at `http://<NODE_IP>:9100`
- Upload videos and test summarization features
## Step 10. Troubleshooting
## Step 11. Troubleshooting
| Symptom | Cause | Fix |
|---------|--------|-----|
@ -313,7 +342,7 @@ Follow the steps [here](https://docs.nvidia.com/vss/latest/content/ui_app.html)
| Services fail to communicate | Incorrect environment variables | Verify `IS_SBSA=1 IS_AARCH64=1` are set correctly |
| Web interfaces not accessible | Services still starting or port conflicts | Wait 2-3 minutes, check `docker ps` for container status |
## Step 11. Cleanup and rollback
## Step 12. Cleanup and rollback
To completely remove the VSS deployment and free up system resources:
@ -338,7 +367,7 @@ rm -rf /tmp/alert-media-dir
sudo pkill -f sys_cache_cleaner.sh
```
## Step 12. Next steps
## Step 13. Next steps
With VSS deployed, you can now: