Getting Started

This guide walks you through deploying your first QScan instance and verifying it is operational.

Prerequisites

Before deploying QScan, ensure you have:

  • A registration token from app.qpoint.ioarrow-up-right (Settings -> API Tokens)

  • S3-compatible object storage configured and accessible (AWS S3, GCS, MinIO, or Qpoint's managed storage)

  • Network access to the Pulse API (api-pulse.qpoint.io on TCP 443) and your S3 endpoint

  • A container runtime (Docker, Kubernetes, or Cloud Run)

Quick Start with Docker

Pull and run the QScan container with your registration token:

docker run -d \
  --name qscan \
  -e REGISTRATION_TOKEN=your-registration-token \
  -e METRICS_PORT=8080 \
  -e LOG_LEVEL=info \
  -p 8080:8080 \
  --memory=12g \
  --cpus=2 \
  us-docker.pkg.dev/qpoint-edge/public/qscan:latest
circle-info

When using Qpoint's managed S3 storage (the default), you do not need to configure S3 credentials separately. The registration token is used as the S3 access key automatically.

If you are using your own S3-compatible storage, add the S3 configuration:

Verify Operation

Check the metrics endpoint:

A successful response indicates the QScan process is running and the metrics server is healthy.

Check the logs:

You should see log messages indicating that QScan has loaded its ML models and begun polling Pulse for scan jobs. Initial model loading takes 1-2 minutes depending on your hardware.

Next Steps

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