DataCROP Maize Workflow Management Editor Deployment

This is a demo deployment instance for the Maize DataCROP version. It deploys the DataCROP Workflow Management Engine web application for creating and managing workflows. The deployment consists of a single container.

Use this page when following the manual per-repository setup. If you use Maize MVP, the editor is deployed by the MVP script; follow the initialization steps below once it is up. See Maize Setup for the two setup options.

Prerequisites

Before proceeding, ensure you have completed the setup instructions for the Maize Model Repository.

After completing the setup, follow these steps to configure your environment variables:

  1. In the Workflow Editor repository, edit ui/.env.

  2. Update the file with the correct values for your infrastructure. Current values from maze-workflow-management-editor/ui/.env are:

     VITE_API_URL=http://<WME_API_HOST>:9090
 VITE_BACKEND_IP=<WME_API_HOST>
 VITE_AIRFLOW_IP=<AIRFLOW_HOST>
 VITE_KEYCLOAK_URL=https://<KEYCLOAK_HOST>/
 VITE_KEYCLOAK_REALM=<KEYCLOAK_REALM>
 VITE_KEYCLOAK_CLIENT_ID=<KEYCLOAK_CLIENT_ID>
 VITE_PROJECT_NAME=<PROJECT_NAME>
 VITE_DEFAULT_PRIMARY_COLOR=#DA3333

    Adjust the above values only if your deployment differs (e.g., different host IP or Keycloak realm). No secrets are stored in this file.

Once these parameters are correctly set, you can proceed with the deployment.

REQUIREMENTS

Start The Application.

  1. Clone the repository and navigate to the source directory containing the docker-compose.yml file.

  2. Run the following command:

     docker compose up -d

Verify that everything is up and running

Wait for the services to start, then run the following commands:

  1. Check if the container is running:

     docker ps --filter name=wme-ui --format "table {{.Image}}\t{{.Names}}"

    You should see the following output:

     IMAGE                                        NAMES
 node:lts-alpine                              wme-ui

  2. Check if the network is set up correctly:

     docker network ls --filter name="maze-workflow-management-editor_wme-network" --format "table {{.Name}}\t{{.Driver}}\t{{.Scope}}"

    Ensure the output matches the following:

     NAME                                          DRIVER    SCOPE
 maze-workflow-management-editor_wme-network   bridge    local

Post-deployment initialization (required)

Perform this step after the UI is reachable (applies to both Maize MVP and manual setups):

  1. Log in through Keycloak.
  2. In the Workflow Editor UI, open Settings.
  3. Click Initialize resources.

If you want instance-specific predefined processors, configure config/extra-processors.json in the Model Repository before clicking Initialize resources; use config/extra-processors.example.json as a template.

What this does:

  • Creates the base data models, workers, asset categories, data interface types, and processor definitions needed for the application to function.
  • Imports any custom processors defined in config/extra-processors.json of the Model Repository (see Model Repository Setup).

Run this once per environment after deployments. Skipping it leaves the system uninitialized.

Make Sure Everything Works

  1. Open a browser and go to http://<WME_UI_HOST>:5173/MainPage/Warehouse.
  2. You will be redirected to the Keycloak authentication page. Enter the credentials provided by your organization.
  3. After successful authentication, you will be redirected to the main page of the workflow management engine, where you can begin creating and managing workflows.

Stop everything.

Navigate to the source directory and run the following command.

docker-compose down