AI Reference Applications

This resource contains two AI applications designed to work with ST 2110 professional broadcast media streams.

AI Virtual Cameras: Person detection application that can create cropped virtual camera feeds from an ST 2110-20 video stream containing multiple people.
Riva ASR System: Automatic speech recognition system for audio transcription for an ST 2110-30 audio stream.

AI Virtual Cameras

This application creates virtual cameras by detecting and tracking persons in a video stream using a person detection model and GStreamer. It processes a high-resolution input stream and creates multiple cropped virtual camera outputs focused on detected persons.

Overview

The application consists of two main components:

cropper.py: Manages the GStreamer pipeline and video processing
detect_person.py: Handles interaction with the person detection model

Key Features

Real-time person detection
Dynamic virtual camera creation based on detected persons
Smooth tracking and coordinate updates
Support for multiple virtual camera outputs
GStreamer-based video processing pipeline

Architecture

      Input Stream (3840x2160)\n               ↓\n       GStreamer Pipeline\n        ↓             ↓\n    ┌─────────┐   ┌─────────┐\n    │ Person  │   │ Virtual │\n    │Detection│ → │ Cameras │             \n    │  Model  │   │(cropper)│\n    └─────────┘   └─────────┘             \n                      ↓\n         Multiple Output Streams (1920x1080)    \n

GStreamer Pipeline

The application uses a GStreamer pipeline with the following key components:

\n
Input Source:
\n
- ST 2110-20 video source (nvdsudpsrc)
- YCbCr-4:2:2 10-bit format (UYVP)
- Up to 3840x2160 resolution
- Progressive scan
- Integer or fractional frame rate, e.g. 29.97fps
\n
\n
Processing Pipeline:
\n
- Video conversion
- Person detection
- Dynamic cropping based on detected persons
- Scaling up to 1920x1080
- Up to 3 virtual camera output streams
\n
\n
Output Streams:
\n
- Individual ST 2110-20 video sink (nvdsudpsink) for each virtual camera
- YCbCr-4:2:2 10-bit format (UYVP)
- Up to 1920x1080 resolution
- Progressive scan
- No frame rate conversion
\n
\n
NMOS Node:
\n
- NMOS bin element (nvdsnmosbin) for IS-04 Discovery & Registration and IS-05 Connection Management of the input and output streams
\n

Configuration

The application can be configured using environment variables:

# GitHub repo from which to load via torch.hub.load\nMODEL_NAME= # Model entrypoint\n\">

FRAMERATE=30000/1001    # Input framerate, e.g. 25 or 30000/1001\nINPUT_WIDTH=3840        # Input stream width\nINPUT_HEIGHT=2160       # Input stream height\nCROP_WIDTH=1280         # YOLO crop width\nCROP_HEIGHT=720         # YOLO crop height\nOUTPUT_WIDTH=1920       # Virtual camera output width\nOUTPUT_HEIGHT=1080      # Virtual camera output height\nNUM_CAMERAS=3           # Number of virtual cameras to create\nHOSTNAME=ai-virtual-cameras.local  # Hostname for NMOS\nHTTP_PORT=5231          # HTTP port for NMOS\nMODEL_REPO=<model-repo> # GitHub repo from which to load via torch.hub.load\nMODEL_NAME=<model-name> # Model entrypoint\n

Usage

\n
Start the application:
\n
```
python3.10 cropper.py\n
```
\n
\n
The application will:
\n
- Initialize the GStreamer pipeline
- Load the person detection model
- Create the NMOS Node with a Receiver and Senders
- Start processing the input stream when the Receiver is connected
- Stream the virtual camera outputs when the Senders are enabled
\n

Person Detection and Tracking

The application has the following features:

Real-time detection of persons in the input stream
Centered cropping around detected persons
Multiple person tracking support

Running the Application in a Cluster

A Dockerfile is provided for containerizing the application. Alternatively, you can install and execute the application manually by following these steps.

Prerequisites

Access to a Holoscan for Media cluster or a Local Kubernetes Setup

Setup Steps

\n
Start Media Gateway Pod
\n
- Start a Media Gateway pod with the description set as nvdsnmosbin in the Helm values.\n
```
description: nvdsnmosbin\n
```
  \n
\n
\n
Set up VS Code Server
\n
- Follow the Local Developer Setup with Kubernetes: Accessing Cluster with VS Code guide to connect to the Kubernetes cluster
- Attach to the newly created pod using the Attach Visual Studio Code command. This opens a new VS Code window where the contents of the pod can be browsed. This provides a development environment in the cluster that is inside the pod to easily experiment with GStreamer, DeepStream, Rivermax, NMOS, etc.
- Copy the tarball inside the pod and extract it
\n

Install Dependencies and Run

# Install required Python packages\npip install -r requirements.txt\n\n# From entrypoint.sh\nexport NVDSNMOS_ORIGINAL_CORE_SET=$(/workspace/coreaffinity.py)\nexport LC_ALL=C\nexport NVDS_NMOS_DEFAULT_SEED=\"aivc\" # A unique string to override the default NMOS seed in the container\n\n# Export environment variables as mentioned above in the Configuration section\n\n# Start the application\npython3.10 cropper.py\n

Riva ASR System

This system provides real-time Automatic Speech Recognition (ASR) capabilities by connecting an ST 2110-30 audio source to the NVIDIA Riva ASR NIM (NVIDIA Inference Microservice), deployed locally. It uses GStreamer to capture audio from a multicast stream, processes it through the Riva ASR service, and provides real-time transcriptions via a web user interface.

Deploying the NIM

The NIM can be deployed simply by using the YAML at NIM Deployment/config.yaml. An NGC API key needs to be provided to download the models.

Project Description

The system consists of a Python client application that:

Connects to an ST 2110-30 multicast audio stream using GStreamer
Processes the audio through NVIDIA's Riva ASR service
Provides real-time transcriptions via a web interface using Server-Sent Events (SSE)

The client handles:

Audio stream capture and preprocessing
Transcript generation
Web-based transcription streaming
Timestamp synchronization

Default Audio Pipeline Configuration

The system uses a GStreamer pipeline with the following default configuration:

Input:\n
- ST 2110-30 multicast audio stream (48kHz, 16 channels, 24-bit, i.e. S24BE) with 0.125 ms packet time
- The default pipeline expects the stream at a multicast IP address configurable through the SOURCE_MC_IP environment variable and port 5004
\n
Processing:\n
- Channel conversion to mono
- Sample rate conversion to 16kHz
- Format conversion to S16LE
\n
Output: Processed audio stream for ASR

Environment Variables Configuration

The following environment variables need to be configured:

Riva Service Configuration

: with the provided config.yaml\n\">

# Riva server connection details\nRIVA_SERVER_GRPC_IP_PORT=\"192.168.23.152:32322\" # gRPC IP & port of the Riva NIM server deployed <nodeip>:<nodeport> with the provided config.yaml\n

Audio Source Configuration

# ST 2110-30 multicast audio stream configuration\nSOURCE_MC_IP=\"232.220.109.111\"   # Multicast IP for audio source that is to be consumed via the GStreamer pipeline\nLOCAL_IFACE_IP=\"192.168.20.79\"   # Local interface IP of the pod where application is running\n

Usage

Configure the environment variables
Start the client application:\n
```
python riva-static-client.py\n
```
\n
Access the transcription stream at:\n
:5000/stream\n\">
```
http://<server_ip>:5000/stream\n
```
\n

Riva Frontend

The Riva frontend is a Create React App application that provides a simple web UI to visualize the transcriptions generated by the Riva client.

Frontend Configuration

The frontend uses environment variables for configuration. To set up:

\n
Modify the .env file in the frontend directory with appropriate Riva Client IP for consuming transcripts.
\n

Build and run the frontend locally:

cd frontend\nnpm install\nnpm run build\n

Build and run using Docker:

# Build with custom backend URL\ndocker build --build-arg REACT_APP_BACKEND_URL=http://your-backend-host:5000 -t riva-frontend .\n\n# Run the container\ndocker run -p 3000:3000 riva-frontend\n

Running the Application in a Cluster

A Dockerfile is provided for containerizing the application. Alternatively, you can install and execute the application manually by following these steps.

Prerequisites

Access to a Holoscan for Media cluster or a Local Kubernetes Setup

Setup Steps

Deploying the NIM

The NIM can be deployed with just oc apply -f config.yaml or kubectl apply -f config.yaml

Deploying the Client

\n
Start Media Gateway Pod
\n
- Start a Media Gateway pod with the description set as nvdsnmosbin in the Helm values.\n
```
description: nvdsnmosbin\n
```
  \n
\n
\n
Set up VS Code Server
\n
- Follow the Local Developer Setup with Kubernetes: Accessing Cluster with VS Code guide to connect to the Kubernetes cluster
- Attach to the newly created pod using the Attach Visual Studio Code command. This opens a new VS Code window where the contents of the pod can be browsed. This provides a development environment in the cluster that is inside the pod to easily experiment with GStreamer, DeepStream, Rivermax, NMOS, etc.
- Copy the tarball inside the pod and extract it
\n

Install Dependencies and Run

# Install required Python packages\npip install -r requirements.txt --ignore-installed\n\n# export environment variables as mentioned above in the Environment Variables Configuration section\n\n# Start the Riva client application\npython3.10 riva-static-client.py\n

Deploying the React Frontend

The React app can be containerised with the steps above and then deployed on the cluster without any additional configuration.

License

By downloading and using this software, you accept the terms and conditions of the NVIDIA AI Product Agreement.

The source code for the AI Reference Applications themselves is licensed under the Apache License, Version 2.0.\nThe notices, attribution, licenses and source code are included in the tarball.

","resourceVersions","resourceVersionsTotal","hasMoreVersions","versionMetadata","versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/holoscan-for-media/resources/ai-reference-apps/-","version","-","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/holoscan-for-media/ai-reference-apps:0.1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/holoscan-for-media/resources/ai-reference-apps/-/version-history","accessCheckResult",{"_173":174,"_175":26},"hasRelatedCollections",true,"context",{"_85":86,"_87":88},1787166461758,[],[],[206,207,208,209,210,211,212],{"_40":55,"_42":66,"_44":57,"_46":58,"_48":67,"_50":51},{"_40":55,"_42":63,"_44":45,"_46":64,"_48":65,"_50":51},{"_40":55,"_42":62,"_44":45,"_46":64,"_48":214,"_50":51},{"_40":55,"_42":60,"_44":57,"_46":58,"_48":213,"_50":51},{"_40":55,"_42":56,"_44":57,"_46":58,"_48":59,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":54,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51},75,56,"nvidia/holoscan-for-media/ai-reference-apps","nvidia/holoscan-for-media/resources/ai-reference-apps","ai-reference-apps","This contains source and dependencies tarballs of AI Reference Applications built for Holoscan For Media.","# AI Reference Applications\n\nThis resource contains two AI applications designed to work with ST 2110 professional broadcast media streams.\n\n1. **AI Virtual Cameras**: Person detection application that can create cropped virtual camera feeds from an ST 2110-20 video stream containing multiple people.\n2. **Riva ASR System**: Automatic speech recognition system for audio transcription for an ST 2110-30 audio stream.\n\n# AI Virtual Cameras\n\nThis application creates virtual cameras by detecting and tracking persons in a video stream using a person detection model and GStreamer. It processes a high-resolution input stream and creates multiple cropped virtual camera outputs focused on detected persons.\n\n## Overview\n\nThe application consists of two main components:\n1. _cropper.py_: Manages the GStreamer pipeline and video processing\n2. _detect_person.py_: Handles interaction with the person detection model\n\n### Key Features\n\n- Real-time person detection\n- Dynamic virtual camera creation based on detected persons\n- Smooth tracking and coordinate updates\n- Support for multiple virtual camera outputs\n- GStreamer-based video processing pipeline\n\n## Architecture\n\n```\n Input Stream (3840x2160)\n ↓\n GStreamer Pipeline\n ↓ ↓\n ┌─────────┐ ┌─────────┐\n │ Person │ │ Virtual │\n │Detection│ → │ Cameras │ \n │ Model │ │(cropper)│\n └─────────┘ └─────────┘ \n ↓\n Multiple Output Streams (1920x1080) \n```\n\n## GStreamer Pipeline\n\nThe application uses a GStreamer pipeline with the following key components:\n\n1. **Input Source**:\n - ST 2110-20 video source (nvdsudpsrc)\n - YCbCr-4:2:2 10-bit format (UYVP)\n - Up to 3840x2160 resolution\n - Progressive scan\n - Integer or fractional frame rate, e.g. 29.97fps\n\n2. **Processing Pipeline**:\n - Video conversion\n - Person detection\n - Dynamic cropping based on detected persons\n - Scaling up to 1920x1080\n - Up to 3 virtual camera output streams\n\n3. **Output Streams**:\n - Individual ST 2110-20 video sink (nvdsudpsink) for each virtual camera\n - YCbCr-4:2:2 10-bit format (UYVP)\n - Up to 1920x1080 resolution\n - Progressive scan\n - No frame rate conversion\n\n4. **NMOS Node**:\n - NMOS bin element (nvdsnmosbin) for IS-04 Discovery & Registration and IS-05 Connection Management of the input and output streams\n\n## Configuration\n\nThe application can be configured using environment variables:\n\n```bash\nFRAMERATE=30000/1001 # Input framerate, e.g. 25 or 30000/1001\nINPUT_WIDTH=3840 # Input stream width\nINPUT_HEIGHT=2160 # Input stream height\nCROP_WIDTH=1280 # YOLO crop width\nCROP_HEIGHT=720 # YOLO crop height\nOUTPUT_WIDTH=1920 # Virtual camera output width\nOUTPUT_HEIGHT=1080 # Virtual camera output height\nNUM_CAMERAS=3 # Number of virtual cameras to create\nHOSTNAME=ai-virtual-cameras.local # Hostname for NMOS\nHTTP_PORT=5231 # HTTP port for NMOS\nMODEL_REPO= # GitHub repo from which to load via torch.hub.load\nMODEL_NAME= # Model entrypoint\n```\n\n## Usage\n\n1. Start the application:\n ```bash\n python3.10 cropper.py\n ```\n\n2. The application will:\n - Initialize the GStreamer pipeline\n - Load the person detection model\n - Create the NMOS Node with a Receiver and Senders\n - Start processing the input stream when the Receiver is connected\n - Stream the virtual camera outputs when the Senders are enabled\n\n## Person Detection and Tracking\n\nThe application has the following features:\n- Real-time detection of persons in the input stream\n- Centered cropping around detected persons\n- Multiple person tracking support\n\n## Running the Application in a Cluster\n\nA Dockerfile is provided for containerizing the application. Alternatively, you can install and execute the application manually by following these steps.\n\n### Prerequisites\n\n1. Access to a [Holoscan for Media cluster](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan-for-media/resources/automation) or a [Local Kubernetes Setup](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan-for-media/resources/local-developer-setup)\n\n### Setup Steps\n\n1. **Start Media Gateway Pod**\n - Start a [Media Gateway](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan-for-media/helm-charts/nvds-nmos-bin) pod with the description set as nvdsnmosbin in the Helm values.\n ```yaml\n description: nvdsnmosbin\n ```\n\n2. **Set up VS Code Server**\n - Follow the [Local Developer Setup with Kubernetes: Accessing Cluster with VS Code](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan-for-media/resources/local-developer-setup) guide to connect to the Kubernetes cluster\n - Attach to the newly created pod using the Attach Visual Studio Code command. This opens a new VS Code window where the contents of the pod can be browsed. This provides a development environment in the cluster that is inside the pod to easily experiment with GStreamer, DeepStream, Rivermax, NMOS, etc.\n - Copy the tarball inside the pod and extract it\n\n3. **Install Dependencies and Run**\n\n ```bash\n # Install required Python packages\n pip install -r requirements.txt\n\n # From entrypoint.sh\n export NVDSNMOS_ORIGINAL_CORE_SET=$(/workspace/coreaffinity.py)\n export LC_ALL=C\n export NVDS_NMOS_DEFAULT_SEED=\"aivc\" # A unique string to override the default NMOS seed in the container\n\n # Export environment variables as mentioned above in the Configuration section\n\n # Start the application\n python3.10 cropper.py\n ```\n\n---\n\n# Riva ASR System\n\nThis system provides real-time Automatic Speech Recognition (ASR) capabilities by connecting an ST 2110-30 audio source to the [NVIDIA Riva ASR NIM](https://docs.nvidia.com/nim/riva/asr/latest/index.html) (NVIDIA Inference Microservice), deployed locally. It uses GStreamer to capture audio from a multicast stream, processes it through the Riva ASR service, and provides real-time transcriptions via a web user interface.\n\n## Deploying the NIM\n\nThe NIM can be deployed simply by using the YAML at _NIM Deployment/config.yaml_. An NGC API key needs to be provided to download the models.\n\n## Project Description\n\nThe system consists of a Python client application that:\n1. Connects to an ST 2110-30 multicast audio stream using GStreamer\n2. Processes the audio through NVIDIA's Riva ASR service\n3. Provides real-time transcriptions via a web interface using Server-Sent Events (SSE)\n\nThe client handles:\n- Audio stream capture and preprocessing\n- Transcript generation\n- Web-based transcription streaming\n- Timestamp synchronization\n\n## Default Audio Pipeline Configuration\n\nThe system uses a GStreamer pipeline with the following default configuration:\n- Input: \n - ST 2110-30 multicast audio stream (48kHz, 16 channels, 24-bit, i.e. S24BE) with 0.125 ms packet time\n - The default pipeline expects the stream at a multicast IP address configurable through the `SOURCE_MC_IP` environment variable and port 5004\n- Processing: \n - Channel conversion to mono\n - Sample rate conversion to 16kHz\n - Format conversion to S16LE\n- Output: Processed audio stream for ASR\n\n## Environment Variables Configuration\n\nThe following environment variables need to be configured:\n\n### Riva Service Configuration\n\n```bash\n# Riva server connection details\nRIVA_SERVER_GRPC_IP_PORT=\"192.168.23.152:32322\" # gRPC IP & port of the Riva NIM server deployed : with the provided config.yaml\n```\n\n### Audio Source Configuration\n\n```bash\n# ST 2110-30 multicast audio stream configuration\nSOURCE_MC_IP=\"232.220.109.111\" # Multicast IP for audio source that is to be consumed via the GStreamer pipeline\nLOCAL_IFACE_IP=\"192.168.20.79\" # Local interface IP of the pod where application is running\n```\n\n## Usage\n\n1. Configure the environment variables\n2. Start the client application:\n ```bash\n python riva-static-client.py\n ```\n3. Access the transcription stream at:\n ```\n http://:5000/stream\n ```\n\n## Riva Frontend\n\nThe Riva frontend is a Create React App application that provides a simple web UI to visualize the transcriptions generated by the Riva client.\n\n### Frontend Configuration\n\nThe frontend uses environment variables for configuration. To set up:\n\n1. Modify the `.env` file in the `frontend` directory with appropriate Riva Client IP for consuming transcripts.\n\n2. Build and run the frontend locally:\n ```bash\n cd frontend\n npm install\n npm run build\n ```\n\n3. Build and run using Docker:\n ```bash\n # Build with custom backend URL\n docker build --build-arg REACT_APP_BACKEND_URL=http://your-backend-host:5000 -t riva-frontend .\n\n # Run the container\n docker run -p 3000:3000 riva-frontend\n\n## Running the Application in a Cluster\n\nA Dockerfile is provided for containerizing the application. Alternatively, you can install and execute the application manually by following these steps.\n\n### Prerequisites\n\n1. Access to a [Holoscan for Media cluster](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan-for-media/resources/automation) or a [Local Kubernetes Setup](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan-for-media/resources/local-developer-setup)\n\n### Setup Steps\n\n#### Deploying the NIM\n\n1. The NIM can be deployed with just `oc apply -f config.yaml` or `kubectl apply -f config.yaml`\n\n#### Deploying the Client\n\n1. **Start Media Gateway Pod**\n - Start a [Media Gateway](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan-for-media/helm-charts/nvds-nmos-bin) pod with the description set as nvdsnmosbin in the Helm values.\n ```yaml\n description: nvdsnmosbin\n ```\n\n2. **Set up VS Code Server**\n - Follow the [Local Developer Setup with Kubernetes: Accessing Cluster with VS Code](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan-for-media/resources/local-developer-setup) guide to connect to the Kubernetes cluster\n - Attach to the newly created pod using the Attach Visual Studio Code command. This opens a new VS Code window where the contents of the pod can be browsed. This provides a development environment in the cluster that is inside the pod to easily experiment with GStreamer, DeepStream, Rivermax, NMOS, etc.\n - Copy the tarball inside the pod and extract it\n\n3. **Install Dependencies and Run**\n\n ```bash\n # Install required Python packages\n pip install -r requirements.txt --ignore-installed\n\n # export environment variables as mentioned above in the Environment Variables Configuration section\n\n # Start the Riva client application\n python3.10 riva-static-client.py\n ```\n\n#### Deploying the React Frontend\n\nThe React app can be containerised with the steps above and then deployed on the cluster without any additional configuration.\n\n## License\n\nBy downloading and using this software, you accept the terms and conditions of the [NVIDIA AI Product Agreement](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).\n\nThe source code for the AI Reference Applications themselves is licensed under the Apache License, Version 2.0.\nThe notices, attribution, licenses and source code are included in the tarball.","Holoscan for Media AI Reference Applications",["D",1748446276451],"0.1.0","/api/image/nvidia/holoscan-for-media/resources/ai-reference-apps",3965512524,"bias","explainability","privacy","safetyAndSecurity",[],[234,235,236,237],[],[],[],{"_92":246,"_124":246,"_125":247,"_127":128},{"_92":242,"_124":243,"_125":244,"_127":128},{"_92":123,"_124":123,"_125":241,"_127":128},{"_92":238,"_124":238,"_125":239,"_127":128},"Script",{"_129":240,"_124":238},"type",{"_129":128,"_124":123},"Media & Entertainment","indus_media_entertainment",{"_129":245,"_124":243},"industry","English",{"_129":248,"_124":246},"language",{"_156":1224,"_158":1225},"ngcApps",[1177],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1158":1159,"_1160":1161,"_1162":1163,"_1164":1165,"_1166":260,"_188":286,"_1167":1168,"_1169":51,"_1170":199,"_1171":199,"_1172":1173,"_1174":199,"_1175":1176},"amplitude",{"_1155":1156,"_800":-7,"_1157":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1149":1150,"_1151":1152,"_1153":1154},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787166461643,"1.12.0","defaultSearchSuggestions",{"_1040":368,"_1041":1042,"_1043":1044,"_1045":1046},"announcements",[],"consentState",{"_324":1030,"_1031":199,"_1032":26,"_1033":26,"_1034":1035},"consentConfig",{"_306":307,"_308":309,"_310":311,"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325},"showContactAdmin",1787166461644,[],[],[300,301,302,303,304,305],{"_40":55,"_42":66,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":63,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":54,"_50":51},{"_40":41,"_42":43,"_44":61,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486],[349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365],{"_366":460,"_92":460,"_91":368,"_339":461},{"_366":456,"_92":456,"_91":368,"_339":457},{"_366":451,"_92":451,"_91":368,"_339":452},{"_366":447,"_92":447,"_91":368,"_339":448},{"_366":442,"_92":442,"_91":368,"_339":443},{"_366":437,"_92":437,"_91":368,"_339":438},{"_366":429,"_92":429,"_91":368,"_339":430},{"_366":421,"_92":421,"_91":368,"_339":422},{"_366":416,"_92":416,"_91":368,"_339":417},{"_366":411,"_92":411,"_91":368,"_339":412},{"_366":406,"_92":406,"_91":368,"_339":407},{"_366":401,"_92":401,"_91":368,"_339":402},{"_366":396,"_92":396,"_91":368,"_339":397},{"_366":389,"_92":389,"_91":368,"_339":390},{"_366":384,"_92":384,"_91":368,"_339":385},{"_366":377,"_92":377,"_91":368,"_339":378},{"_366":367,"_92":367,"_91":368,"_339":369},"hostName","amazon-adsystem.com","",[370,371],{"_333":376,"_373":367,"_374":26,"_91":375},{"_333":372,"_373":367,"_374":26,"_91":375},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[379,380],{"_333":383,"_373":377,"_374":26,"_91":382},{"_333":381,"_373":377,"_374":26,"_91":382},"IDE","This domain is owned by Doubleclick (Google). 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581],[528,529,530,531,532],{"_366":442,"_92":442,"_91":368,"_339":552},{"_366":416,"_92":416,"_91":368,"_339":547},{"_366":542,"_92":542,"_91":368,"_339":543},{"_366":537,"_92":537,"_91":368,"_339":538},{"_366":533,"_92":533,"_91":368,"_339":534},"nvidia.partners",[535],{"_333":536,"_373":533,"_374":26,"_91":410},"edge","app.hushly.com",[539],{"_333":540,"_373":537,"_374":26,"_91":541},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[544],{"_333":545,"_373":542,"_374":26,"_91":546},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668],[620,621,622,623],{"_366":635,"_92":635,"_91":368,"_339":636},{"_366":630,"_92":630,"_91":368,"_339":631},{"_366":429,"_92":429,"_91":368,"_339":627},{"_366":542,"_92":542,"_91":368,"_339":624},[625],{"_333":626,"_373":542,"_374":199,"_91":546},"_gh_sess",[628],{"_333":629,"_373":429,"_374":199,"_91":436},"YSC","bizible.com",[632],{"_333":633,"_373":630,"_374":26,"_91":634},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[637],{"_333":638,"_373":635,"_374":26,"_91":639},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_333":710,"_373":488,"_374":26,"_91":711},{"_333":695,"_373":709,"_374":199,"_91":696},{"_333":678,"_373":491,"_374":26,"_91":603},{"_333":678,"_373":591,"_374":26,"_91":603},{"_333":707,"_373":488,"_374":26,"_91":708},{"_333":695,"_373":494,"_374":199,"_91":696},{"_333":705,"_373":488,"_374":26,"_91":706},{"_333":695,"_373":491,"_374":199,"_91":696},{"_333":703,"_373":590,"_374":26,"_91":704},{"_333":695,"_373":612,"_374":199,"_91":696},{"_333":675,"_373":491,"_374":26,"_91":677},{"_333":701,"_373":488,"_374":199,"_91":702},{"_333":699,"_373":488,"_374":26,"_91":700},{"_333":697,"_373":488,"_374":199,"_91":698},{"_333":695,"_373":586,"_374":199,"_91":696},{"_333":693,"_373":488,"_374":26,"_91":694},{"_333":691,"_373":488,"_374":26,"_91":692},{"_333":689,"_373":488,"_374":199,"_91":690},{"_333":687,"_373":595,"_374":26,"_91":688},{"_333":675,"_373":586,"_374":26,"_91":677},{"_333":685,"_373":488,"_374":26,"_91":686},{"_333":683,"_373":488,"_374":199,"_91":684},{"_333":681,"_373":488,"_374":26,"_91":682},{"_333":679,"_373":488,"_374":26,"_91":680},{"_333":678,"_373":488,"_374":26,"_91":603},{"_333":675,"_373":676,"_374":26,"_91":677},{"_333":673,"_373":488,"_374":26,"_91":674},{"_333":671,"_373":488,"_374":199,"_91":672},{"_333":669,"_373":488,"_374":26,"_91":670},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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