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Get Started

The Visual Pipeline and Platform Evaluation Tool helps hardware decision-makers and software developers select the optimal Intel® platform by adjusting workload parameters and analyzing performance metrics. Through an intuitive web-based interface, the Smart NVR pipeline can be executed and key metrics such as throughput and CPU/GPU utilization can be evaluated to assess platform performance and determine appropriate system sizing.

By following this guide, the following tasks can be completed:

  • Set up the sample application: Use the Docker Compose tool to quickly deploy the application in a target environment.
  • Run a predefined pipeline: Execute the Smart NVR pipeline and observe metrics.

Prerequisites

Before starting, ensure the following:

  • System requirements: The system meets the minimum requirements.
  • Docker platform: Docker is installed. For details, see the Docker installation guide.
  • Dependencies installed:
    • Git: Install Git.
    • Make: Standard build tool, typically provided by the build-essential (or equivalent) package on Linux.
    • curl: Command-line tool for transferring data with URLs, typically provided by the curl package on Linux.

For GPU and/or NPU usage, appropriate drivers must be installed. The recommended method is to use the DLS installation script, which detects available devices and installs the required drivers. Follow the Prerequisites section in Install Guide Ubuntu – Prerequisites

This guide assumes basic familiarity with Git commands and terminal usage. For more information, see Git Documentation.

Setup

Follow the steps below to quickly set up the environment and start the Visual Pipeline and Platform Evaluation Tool. For alternative ways to set up the sample application, refer to How to Build from Source

  1. Set up the working directory:

    mkdir -p visual-pipeline-and-platform-evaluation-tool/models
mkdir -p visual-pipeline-and-platform-evaluation-tool/onvif_discovery
mkdir -p visual-pipeline-and-platform-evaluation-tool/shared/models
mkdir -p visual-pipeline-and-platform-evaluation-tool/shared/videos
mkdir -p visual-pipeline-and-platform-evaluation-tool/shared/onvif
cd visual-pipeline-and-platform-evaluation-tool

  2. Download all required files:

    curl -LO "https://github.com/open-edge-platform/edge-ai-libraries/raw/refs/heads/release-2026.0.0/tools/visual-pipeline-and-platform-evaluation-tool/setup_env.sh"
curl -LO "https://github.com/open-edge-platform/edge-ai-libraries/raw/refs/heads/release-2026.0.0/tools/visual-pipeline-and-platform-evaluation-tool/compose.yml"
curl -LO "https://github.com/open-edge-platform/edge-ai-libraries/raw/refs/heads/release-2026.0.0/tools/visual-pipeline-and-platform-evaluation-tool/compose.cpu.yml"
curl -LO "https://github.com/open-edge-platform/edge-ai-libraries/raw/refs/heads/release-2026.0.0/tools/visual-pipeline-and-platform-evaluation-tool/compose.gpu.yml"
curl -LO "https://github.com/open-edge-platform/edge-ai-libraries/raw/refs/heads/release-2026.0.0/tools/visual-pipeline-and-platform-evaluation-tool/compose.npu.yml"
curl -LO "https://github.com/open-edge-platform/edge-ai-libraries/raw/refs/heads/release-2026.0.0/tools/visual-pipeline-and-platform-evaluation-tool/Makefile"
curl -Lo models/Dockerfile "https://github.com/open-edge-platform/edge-ai-libraries/raw/refs/heads/release-2026.0.0/tools/visual-pipeline-and-platform-evaluation-tool/models/Dockerfile"
curl -Lo models/model_manager.sh "https://github.com/open-edge-platform/edge-ai-libraries/raw/refs/heads/release-2026.0.0/tools/visual-pipeline-and-platform-evaluation-tool/models/model_manager.sh"
curl -Lo onvif_discovery/Dockerfile "https://github.com/open-edge-platform/edge-ai-libraries/raw/refs/heads/release-2026.0.0/tools/visual-pipeline-and-platform-evaluation-tool/onvif_discovery/Dockerfile"
curl -Lo onvif_discovery/onvif_discovery_agent.py "https://github.com/open-edge-platform/edge-ai-libraries/raw/refs/heads/release-2026.0.0/tools/visual-pipeline-and-platform-evaluation-tool/onvif_discovery/onvif_discovery_agent.py"
curl -Lo shared/videos/default_recordings.yaml "https://github.com/open-edge-platform/edge-ai-libraries/raw/refs/heads/release-2026.0.0/tools/visual-pipeline-and-platform-evaluation-tool/shared/videos/default_recordings.yaml"
curl -Lo shared/models/supported_models.yaml "https://github.com/open-edge-platform/edge-ai-libraries/raw/refs/heads/release-2026.0.0/tools/visual-pipeline-and-platform-evaluation-tool/shared/models/supported_models.yaml"
chmod +x models/model_manager.sh
chmod +x setup_env.sh

  3. Start the application:

    make build-onvif-discovery build-models run

  4. Verify that the application is running:

    docker compose ps

  5. Access the application:

    • Open a browser and navigate to http://localhost (or http://<HOST-IP>) to access the Visual Pipeline and Platform Evaluation Tool.

  6. Access the application API documentation:

    • Open a browser and navigate to http://localhost/api/v1/docs (or http://<HOST-IP>/api/v1/docs) to access the Swagger UI.

Validation

Verify build success: Check the logs and look for confirmation messages indicating that the microservice has started successfully.

Model Installation and Management

When the Visual Pipeline and Platform Evaluation Tool is launched for the first time, a prompt is displayed to select and install the models to be used. This step allows installation of only the models relevant to the intended pipelines.

To manage the installed models again, run the following command:

make install-models-force

Video Generation

The Visual Pipeline and Platform Evaluation Tool enables you to create composite videos from multiple images stored in subdirectories. For more details, refer to the guide.

Supporting Resources