Get Started#

This guide walks you through cloning the repository, downloading AI models, and running the NICU Warmer application.

Prerequisites#

Ensure your system meets the System Requirements before proceeding.

1. Clone the Repository#

Use sparse checkout to download only the NICU Warmer component. If you want to clone a specific release branch, replace main with the desired tag. To learn more on partial cloning, check the Repository Cloning guide.

git clone --filter=blob:none --sparse --branch main \
  https://github.com/open-edge-platform/edge-ai-suites.git
cd edge-ai-suites
git sparse-checkout set health-and-life-sciences-ai-suite/NICU-Warmer
cd health-and-life-sciences-ai-suite/NICU-Warmer

2. Download Models and Video#

Run the model downloader to fetch all required AI models and the test video:

make setup

This downloads:

  • 3 detection models (person, patient, latch) from GitHub Release assets

  • Action recognition encoder/decoder from Open Model Zoo

  • MTTS-CAN rPPG model (converted to OpenVINO IR)

  • Test video file (Warmer_Testbed_YTHD.mp4)

All files are cached locally — subsequent runs skip existing files.

Important: make setup must complete before make run. If docker compose up runs first, Docker creates empty directories for missing bind-mount sources, causing pipeline failures.

3. Run the Application#

Start all services (default mixed-optimized device profile):

make run

This builds and starts 5 containers:

Service

Port

Purpose

nicu-backend

5001

Flask API + SSE stream + MQTT subscriber

nicu-ui

3001

React dashboard (nginx reverse proxy)

nicu-dlsps

8080

DL Streamer Pipeline Server (GStreamer)

nicu-mqtt

1883

Eclipse Mosquitto MQTT broker

nicu-metrics-collector

9100

Hardware telemetry (CPU/GPU/NPU/Memory)

Device Profiles#

Select a specific device profile at launch:

make run           # Mixed-optimized (GPU detect, CPU rPPG, NPU action)
make run-cpu       # All workloads on CPU
make run-gpu       # All workloads on GPU
make run-npu       # All workloads on NPU

4. Open the Dashboard#

Navigate to http://localhost:3001 in a browser.

Click Prepare & Run to start the AI pipeline. The system will:

  1. Start the GStreamer pipeline with all 5 models

  2. Process video at ~15 FPS

  3. Stream detections and vitals via MQTT

  4. Display results in real-time on the dashboard

5. Stop the Application#

Click Stop in the dashboard, or from the terminal:

make down

Troubleshooting#

Empty directories instead of model files#

If make run was executed before make setup, Docker may have created empty directories for bind-mount paths. Fix:

make down
sudo rm -rf Warmer_Testbed_YTHD.mp4 model_artifacts models_rppg
make setup
make run

Proxy configuration#

If behind a corporate proxy, set environment variables before running:

export HTTP_PROXY=http://proxy.example.com:port
export HTTPS_PROXY=http://proxy.example.com:port
export http_proxy=$HTTP_PROXY
export https_proxy=$HTTPS_PROXY

The compose file forwards these to all containers automatically.