This project contanis a UI that helps in setting up your bpmn.ai preprocessing pipeline easily.
The easiest way to run bpmn.ai-ui is by using the provided Docker image.
In order to run bpmn.ai-ui you can use the following command:
docker run -it --rm \
--network bpmnai-ui-demo \
-v <local_folder_for_bpmn.ai_files>:/data \
-p 7000:7000 \
viadee/bpmn.ai-ui:latest
This run the latest version of bpmn.ai-ui and maps your local folder as /data inside the container (has to be /data) and makes the UI available on port 7000 on the docker host. It is configured to use all cpu cores available to the docker container.
By using "--network bpmnai-ui-demo" the docker container is joined into the docker network bpmnai-ui-demo. This should match the docker network of your kafka server if it is also running in a docker container.
You can run bpmn.ai-ui by building a standalone jar and run it on your machine.
In the project root folder run the following command to build a standalone runnable jar:
mvn clean package -PstandaloneJar
After a succesful build the standalone jar can be found in the folder backend/target/bpmnai-ui-<version>-jar-with-dependencies.jar
In order to run the strandalone jar you can use the following command:
java -Dspark.master="local[*]" -jar <path_to_bpmnai-ui-\<version\>-jar-with-dependencies.jar
-Dspark.master="local[*]" will tell Apache Spark to use all available cpu cores. You can replace the asterisk with the number of cpu cores Spark should be using.
The UI will be available on port 7000.
If you want to add you own pipeline steps while using the UI you simply change the bpmn.ai-core dependency of the bpmn.ai-ui to your maven project based on bpmn.ai-core containing your pipeline steps. Just make sure that your project is using bpmn.ai-core as a dependency with at least version 1.2.1.
For details on building your own pipeline step see the bpmn.ai wiki.