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chore: Regenerate all playbooks

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3
nvidia/vlm-finetuning/assets/Dockerfile
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@ -157,9 +157,10 @@ RUN git clone https://github.com/triton-lang/triton.git && \
cd ..
# install xformers from source for blackwell support
RUN git clone --depth=1 https://github.com/facebookresearch/xformers --recursive && \
RUN git clone https://github.com/facebookresearch/xformers && \
cd xformers && \
git checkout 5146f2ab37b2163985c19fb4e8fbf6183e82f8ce && \
git submodule update --init --recursive && \
export TORCH_CUDA_ARCH_LIST="12.1" && \
python setup.py install && \
cd ..

126
nvidia/vss/README.md
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@ -6,18 +6,22 @@
- [Overview](#overview)
- [Instructions](#instructions)
- [Navigate to Event Verification directory](#navigate-to-event-verification-directory)
- [Configure NGC API Key](#configure-ngc-api-key)
- [Start VSS Event Verification services](#start-vss-event-verification-services)
- [Navigate to CV Event Detector directory](#navigate-to-cv-event-detector-directory)
- [Start DeepStream CV pipeline](#start-deepstream-cv-pipeline)
- [Wait for service initialization](#wait-for-service-initialization)
- [Validate Event Reviewer deployment](#validate-event-reviewer-deployment)
- [Navigate to remote LLM deployment directory](#navigate-to-remote-llm-deployment-directory)
- [Configure environment variables](#configure-environment-variables)
- [Review model configuration](#review-model-configuration)
- [Launch Standard VSS deployment](#launch-standard-vss-deployment)
- [Validate Standard VSS deployment](#validate-standard-vss-deployment)
- [7.1 Navigate to Event Reviewer directory](#71-navigate-to-event-reviewer-directory)
- [7.2 Configure NGC API Key](#72-configure-ngc-api-key)
- [7.3 Update the VSS Image path](#73-update-the-vss-image-path)
- [7.4 Start VSS Event Reviewer services](#74-start-vss-event-reviewer-services)
- [7.5 Navigate to CV Event Detector directory](#75-navigate-to-cv-event-detector-directory)
- [7.6 Update the NV_CV_EVENT_DETECTOR_IMAGE Image path](#76-update-the-nvcveventdetectorimage-image-path)
- [7.7 Start DeepStream CV pipeline](#77-start-deepstream-cv-pipeline)
- [7.8 Wait for service initialization](#78-wait-for-service-initialization)
- [7.9 Validate Event Reviewer deployment](#79-validate-event-reviewer-deployment)
- [8.1 Obtain Nvidia API Key](#81-obtain-nvidia-api-key)
- [8.2 Navigate to remote LLM deployment directory](#82-navigate-to-remote-llm-deployment-directory)
- [8.3 Configure environment variables](#83-configure-environment-variables)
- [8.4 Update the VSS Image path](#84-update-the-vss-image-path)
- [8.5 Review model configuration](#85-review-model-configuration)
- [8.6 Launch Standard VSS deployment](#86-launch-standard-vss-deployment)
- [8.7 Validate Standard VSS deployment](#87-validate-standard-vss-deployment)
- [For Event Reviewer deployment](#for-event-reviewer-deployment)
- [For Standard VSS deployment](#for-standard-vss-deployment)
@ -31,7 +35,7 @@ Deploy NVIDIA's Video Search and Summarization (VSS) AI Blueprint to build intel
## What you'll accomplish
You will deploy NVIDIA's VSS AI Blueprint on NVIDIA Spark hardware with Blackwell architecture, choosing between two deployment scenarios: VSS Event Reviewer (completely local with VLM pipeline) or Standard VSS (hybrid deployment with remote LLM/embedding endpoints). This includes setting up Alert Bridge, VLM Pipeline, Alert Inspector UI, Video Storage Toolkit, and optional DeepStream CV pipeline for automated video analysis and event verification.
You will deploy NVIDIA's VSS AI Blueprint on NVIDIA Spark hardware with Blackwell architecture, choosing between two deployment scenarios: VSS Event Reviewer (completely local with VLM pipeline) or Standard VSS (hybrid deployment with remote LLM/embedding endpoints). This includes setting up Alert Bridge, VLM Pipeline, Alert Inspector UI, Video Storage Toolkit, and optional DeepStream CV pipeline for automated video analysis and event review.
## What to know before starting
@ -103,8 +107,9 @@ cd video-search-and-summarization
Start the system cache cleaner to optimize memory usage during container operations.
```bash
## Start the cache cleaner script in background
sudo sh deploy/scripts/sys_cache_cleaner.sh &
## 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
```
## Step 4. Set up Docker shared network
@ -120,7 +125,7 @@ docker network create vss-shared-network
## Step 5. Authenticate with NVIDIA Container Registry
Log in to NVIDIA's container registry using your NGC API Key.
Log in to NVIDIA's container registry using your [NGC API Key](https://org.ngc.nvidia.com/setup/api-keys).
```bash
## Log in to NVIDIA Container Registry
@ -140,37 +145,46 @@ 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 - VSS Event Reviewer (Completely Local)
## Step 7. Option A - [VSS Event Reviewer](https://docs.nvidia.com/vss/latest/content/vss_event_reviewer.html) (Completely Local)
### Navigate to Event Verification directory
### 7.1 Navigate to Event Reviewer directory
Change to the directory containing the Event Verification Docker Compose configuration.
Change to the directory containing the Event Reviewer Docker Compose configuration.
```bash
cd deploy/docker/event_reviewer/
```
### Configure NGC API Key
### 7.2 Configure NGC API Key
Update the environment file with your 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:
```bash
## Edit the .env file and update NGC_API_KEY
echo "NGC_API_KEY=<YOUR_NGC_API_KEY>" >> .env
```
### Start VSS Event Verification services
### 7.3 Update the VSS Image path
Launch the complete VSS Event Verification stack including Alert Bridge, VLM Pipeline, Alert Inspector UI, and Video Storage Toolkit.
Update `VSS_IMAGE` to `nvcr.io/nvidia/blueprint/vss-engine-sbsa:2.4.0` in `.env`.
```bash
## Start VSS Event Verification with ARM64 and SBSA optimizations
## Edit the .env file and update VSS_IMAGE
echo "VSS_IMAGE=nvcr.io/nvidia/blueprint/vss-engine-sbsa:2.4.0" >> .env
```
### 7.4 Start VSS Event Reviewer services
Launch the complete VSS Event Reviewer stack including Alert Bridge, VLM Pipeline, Alert Inspector UI, and Video Storage Toolkit.
```bash
## 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
```
> **Note:** This step will take several minutes as containers are pulled and services initialize. The VSS backend requires additional startup time.
### Navigate to CV Event Detector directory
### 7.5 Navigate to CV Event Detector directory
In a new terminal session, navigate to the computer vision event detector configuration.
@ -178,16 +192,25 @@ In a new terminal session, navigate to the computer vision event detector config
cd video-search-and-summarization/examples/cv-event-detector
```
### Start DeepStream CV pipeline
### 7.6 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`.
```bash
## 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
```
### 7.7 Start DeepStream CV pipeline
Launch the DeepStream computer vision pipeline and CV UI services.
```bash
## Start CV pipeline with ARM64 and SBSA optimizations
IS_SBSA=1 IS_AARCH64=1 ALERT_VERIFICATION_MEDIA_BASE_DIR=/tmp/alert-media-dir docker compose up
IS_SBSA=1 IS_AARCH64=1 ALERT_REVIEW_MEDIA_BASE_DIR=/tmp/alert-media-dir docker compose up
```
### Wait for service initialization
### 7.8 Wait for service initialization
Allow time for all containers to fully initialize before accessing the user interfaces.
@ -197,7 +220,7 @@ docker ps
## Verify all containers show "Up" status and VSS backend logs show ready state
```
### Validate Event Reviewer deployment
### 7.9 Validate Event Reviewer deployment
Access the web interfaces to confirm successful deployment and functionality.
@ -213,19 +236,27 @@ curl -I http://<NODE_IP>:7860
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 verification results
- `http://<NODE_IP>:7860` - Alert Inspector UI to view clips and review VLM results
## Step 8. Option B - Standard VSS (Hybrid Deployment)
## Step 8. Option B - [Standard VSS](https://docs.nvidia.com/vss/latest/content/architecture.html) (Hybrid Deployment)
### Navigate to remote LLM deployment directory
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 Obtain Nvidia API Key
- Log in to https://build.nvidia.com/explore/discover.
- Navigate to any NIM for example, https://build.nvidia.com/meta/llama3-70b.
- Search for **Get API Key** on the page and click on it.
### 8.2 Navigate to remote LLM deployment directory
```bash
cd deploy/docker/remote_llm_deployment/
```
### Configure environment variables
### 8.3 Configure environment variables
Update the environment file with your API keys and deployment preferences.
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:
```bash
## Edit .env file with required keys
@ -235,23 +266,32 @@ echo "DISABLE_CV_PIPELINE=true" >> .env # Set to false to enable CV
echo "INSTALL_PROPRIETARY_CODECS=false" >> .env # Set to true to enable CV
```
### Review model configuration
### 8.4 Update the VSS Image path
Verify that the config.yaml file contains the correct remote endpoints.
Update `VIA_IMAGE` to `nvcr.io/nvidia/blueprint/vss-engine-sbsa:2.4.0` in `.env`.
```bash
## Edit the .env file and update VIA_IMAGE
echo "VIA_IMAGE=nvcr.io/nvidia/blueprint/vss-engine-sbsa:2.4.0" >> .env
```
### 8.5 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 `.
```bash
## Check model server endpoints in config.yaml
cat config.yaml | grep -A 10 "model_server"
cat config.yaml | grep -A 10 "model"
```
### Launch Standard VSS deployment
### 8.6 Launch Standard VSS deployment
```bash
## Start Standard VSS with hybrid deployment
docker compose up
```
### Validate Standard VSS deployment
### 8.7 Validate Standard VSS deployment
Access the VSS UI to confirm successful deployment.
@ -268,10 +308,12 @@ Open `http://<NODE_IP>:9100` in your browser to access the VSS interface.
Run a basic test to verify the video analysis pipeline is functioning based on your deployment.
### 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`
### 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`
- Upload videos and test summarization features
@ -293,9 +335,9 @@ To completely remove the VSS deployment and free up system resources.
```bash
## For Event Reviewer deployment
cd deploy/docker/event_reviewer/
docker compose down
IS_SBSA=1 IS_AARCH64=1 ALERT_REVIEW_MEDIA_BASE_DIR=/tmp/alert-media-dir docker compose down
cd ../../examples/cv-event-detector/
docker compose down
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/
@ -315,7 +357,7 @@ With VSS deployed, you can now:
**Event Reviewer deployment:**
- Upload video files through the CV UI at port 7862
- Monitor automated event detection and verification
- Monitor automated event detection and reviewing
- Review analysis results in the Alert Inspector UI at port 7860
- Configure custom event detection rules and thresholds