Deploy Time Series Analytics Microservice#

This guide describes how to build, start, and stop the Time Series Analytics Microservice (TSAM) as part of the ViPPET stack, and how to configure it with a sample Wind Turbine anomaly detection UDF.

Prerequisites#

  • Docker and Docker Compose installed

  • make available on the host

  • wget installed (required to download UDF packages)

Configure Environment Variables - Build, Start, and Stop#

Build#

Build all required Docker images:

make build-experimental

Start#

Start all services, including the Time Series Analytics Microservice:

make run-experimental

Stop and Clean#

Stop all running services and clean any artifacts:

make stop-experimental
make clean-experimental

Deploy the Wind Turbine anomaly detection UDF#

Once the services are running, follow the steps below to deploy the Wind Turbine anomaly detection UDF into the TSAM.

The TSAM Swagger UI is available at http://localhost:5000/docs.

Step 1. Download the UDF package#

Download the pre-built Wind Turbine UDF tar archive:

wget https://raw.githubusercontent.com/open-edge-platform/edge-ai-resources/main/timeseries-udf-deployment-packages/wind-turbine-anomaly-detection.tar

Step 2. Upload the UDF package#

  1. Open http://localhost:5000/docs in a browser.

  2. Navigate to POST /udfs/package.

  3. Click Try it out.

  4. Under Choose File, select the downloaded wind-turbine-anomaly-detection.tar file. UDF Upload Diagram

  5. Click Execute.

A successful response returns the message: UDF deployment package 'wind-turbine-anomaly-detection.tar' uploaded successfully.

Step 3. Apply the configuration#

  1. Open http://localhost:5000/docs in a browser.

  2. Navigate to POST /config.

  3. Click Try it out.

  4. In the Request Body field, paste the following configuration:

{
    "udfs": {
        "name": "windturbine_anomaly_detector",
        "models": "windturbine_anomaly_detector.pkl",
        "device": "cpu"
    }
}

UDF configuration Diagram

  1. Click Execute.

A successful response returns the message: Configuration updated successfully.

Step 4. Verify the Time Series Analytics Microservice logs#

Check that processing is running correctly:

docker logs -f ia-time-series-analytics-microservice

You should see output similar to the following:

2026-05-26 04:43:45,599 - classifier_startup - INFO - Connected to Kapacitor on port 9092
2026-05-26 04:43:45,621 - classifier_startup - INFO - Kapacitor initialized successfully
2026-05-26 04:43:46,201 - classifier_startup - INFO - HTTP service listening on [::]:9092
2026-05-26 04:43:46,201 - classifier_startup - INFO - Started task windturbine_anomaly_detector
INFO: 172.18.0.7:52784 - "POST /input HTTP/1.1" 200 OK
INFO: 172.18.0.7:52786 - "POST /input HTTP/1.1" 200 OK