sarman/dgx-spark-playbooks
1
0
Fork 0
mirror of https://github.com/NVIDIA/dgx-spark-playbooks.git synced 2026-04-22 10:03:54 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

chore: Regenerate all playbooks

Browse Source
This commit is contained in:
GitLab CI 2025-10-08 19:26:24 +00:00
parent 54920e66a0
commit ff8a75b6f6
1 changed files with 53 additions and 20 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

73
nvidia/vss/README.md
View File

@ -24,17 +24,16 @@ You will deploy NVIDIA's VSS AI Blueprint on NVIDIA Spark hardware with Blackwel
- Working with NVIDIA Docker containers and container registries
- Setting up Docker Compose environments with shared networks
- Managing environment variables and authentication tokens
- Working with NVIDIA DeepStream and computer vision pipelines
- Basic understanding of video processing and analysis workflows
## Prerequisites
- NVIDIA Spark device with ARM64 architecture and Blackwell GPU
- FastOS 1.81.38 or compatible ARM64 system
- Driver version 580.82.09 installed: `nvidia-smi | grep "Driver Version"`
- Driver version 580.82.09 or higher installed: `nvidia-smi | grep "Driver Version"`
- CUDA version 13.0 installed: `nvcc --version`
- Docker installed and running: `docker --version && docker compose version`
- Access to NVIDIA Container Registry with NGC API Key
- Access to NVIDIA Container Registry with [NGC API Key](https://org.ngc.nvidia.com/setup/api-keys)
- [Optional] NVIDIA API Key for remote model endpoints (hybrid deployment only)
- Sufficient storage space for video processing (>10GB recommended in `/tmp/`)
@ -64,7 +63,7 @@ Check that your system meets the hardware and software prerequisites.
```bash
## Verify driver version
nvidia-smi | grep "Driver Version"
## Expected output: Driver Version: 580.82.09
## Expected output: Driver Version: 580.82.09 or higher
## Verify CUDA version
nvcc --version
@ -91,7 +90,9 @@ newgrp docker
```
> **Warning**: After running usermod, you must log out and log back in to start a new
> session with updated group permissions.
> session with updated group permissions, or in rare cases restart their spark for the
> changes to take effect.
Additionally, configure Docker so that it can use the NVIDIA Container Runtime.
@ -138,6 +139,8 @@ docker network create vss-shared-network
Log in to NVIDIA's container registry using your [NGC API Key](https://org.ngc.nvidia.com/setup/api-keys).
> **Note:** If you don’t have an NVIDIA account already, you’ll have to create one and register for the [developer program](https://developer.nvidia.com/nvidia-developer-program).
```bash
## Log in to NVIDIA Container Registry
docker login nvcr.io
@ -195,7 +198,7 @@ Launch the complete VSS Event Reviewer stack including Alert Bridge, VLM Pipelin
IS_SBSA=1 IS_AARCH64=1 ALERT_REVIEW_MEDIA_BASE_DIR=/tmp/alert-media-dir docker compose up
```
> **Note:** This step will take several minutes as containers are pulled and services initialize. The VSS backend requires additional startup time.
> **Note:** This step will take several minutes as containers are pulled and services initialize. The VSS backend requires additional startup time. Proceed to the next step in a new terminal in the meantime.
**8.5 Navigate to CV Event Detector directory**
@ -230,7 +233,16 @@ Allow time for all containers to fully initialize before accessing the user inte
```bash
## Monitor container status
docker ps
## Verify all containers show "Up" status and VSS backend logs show ready state
## 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**
@ -238,18 +250,28 @@ docker ps
Access the web interfaces to confirm successful deployment and functionality.
```bash
## Test CV UI accessibility (replace <NODE_IP> with your system's IP)
curl -I http://<NODE_IP>:7862
## Test CV UI accessibility (default: localhost)
curl -I http://localhost:7862
## Expected: HTTP 200 response
## Test Alert Inspector UI accessibility
curl -I http://<NODE_IP>:7860
## 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://<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
- `http://localhost:7862` - CV UI to launch and monitor CV pipeline
- `http://localhost:7860` - Alert Inspector UI to view clips and review VLM results
> **Note:** You may now proceed to step 10.
## Step 9. Option B
@ -305,32 +327,43 @@ cat config.yaml | grep -A 10 "model"
docker compose up
```
> **Note:** This step will take several minutes as containers are pulled and services initialize. The VSS backend requires additional startup time.
**9.7 Validate Standard VSS deployment**
Access the VSS UI to confirm successful deployment.
```bash
## Test VSS UI accessibility (replace <NODE_IP> with your system's IP)
curl -I http://<NODE_IP>:9100
## 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://<NODE_IP>:9100` in your browser to access the VSS interface.
Open `http://localhost:9100` in your browser to access the VSS interface.
## Step 10. Test video processing workflow
Run a basic test to verify the video analysis pipeline is functioning based on your deployment.
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](https://docs.nvidia.com/vss/latest/content/vss_event_reviewer.html#vss-alert-inspector-ui) to access and use the Event Reviewer workflow.
- Access CV UI at `http://<NODE_IP>:7862` to upload and process videos
- Monitor results in Alert Inspector UI at `http://<NODE_IP>:7860`
- 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](https://docs.nvidia.com/vss/latest/content/ui_app.html) to navigate VSS UI - File Summarization, Q&A, and Alerts.
- Access VSS interface at `http://<NODE_IP>:9100`
- Access VSS interface at `http://localhost:9100`
- Upload videos and test summarization features
## Step 11. Troubleshooting