The following are steps to run a Batch Scoring Job on a Batch Deployed Model.
Table of Contents
Pre-requisites:
Deploy a Model as Batch
Prior to running a Batch Scoring job, you should have a Model Deployed as Batch. To do so, please refer to the section https://modelopdocs.atlassian.net/wiki/spaces/dv24/pages/1216283383/Operationalizing+Models+Batch#Operationalize-a-Model---Batch-Deployment-in-a-ModelOp-Runtime.
Pepare Runtimes
Identify the target Runtimes across the requisite Environments.
Please note, it is possible this step has already been done given the pre-requisites, but it’s worth noting that the Runtime matching also happens at Job scheduling so the engine still has to match at Job creation, not only at deployment time.
For each target Runtime, complete the following:
Add “Environment/Stage Tags”. Based on the environments/stages required (see pre-requisites), add the necessary “environment/stage tag” to the runtime.
Example: add a “DEV” tag to the Runtime in their development environment, an “SIT” tag to the Runtime in their SIT environment, a “UAT” tag to the Runtime in their UAT environment, and ultimately a “PROD” tag to the Runtime in their Prod environment
Add “Model Service Tags”. The Model “Service” tag will be used to identify that this specific runtime is designed to be a target runtime for that particular model. Add the appropriate “Model Service Tag” to the runtime.
Example: add a “cc-fraud” Model Service Tag to the runtime for a 3rd party credit card model to the “Dev”, “SIT”, “UAT”, and “Prod” runtimes.
Running batch Job with MLC
Trigger Job Creation:
Launch MLC via REST API
Make sure the RunBatchJob.bpmn MLC is deployed in the MOC environment.
More info about the MLC found on the Run Batch Job MLC.
Send a request to run a batch job with the endpoint signalResponsive as below:
http://gateway/mlc-service/rest/signalResponsiveMore info about Camunda Signal endpoint found on the Trigger Execution Camunda docs. Note the endpoint used above is called signalResponsive as opposed to the documentation linked.
More info about customizing the variables on the request found Variables in the REST API Camunda docs.
Example request:
curl --request POST 'http://gateway/mlc-service/rest/signalResponsive' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer {{token}}' \
--data-raw '{
"name": "com.modelop.mlc.definitions.Signals_DEPLOYED_BATCH_JOB",
"variables": {
"TAG": {
"value": "model-service-tag"
},
"MODEL_STAGE": {
"value": "PROD"
}
}
}'
Launch MLC via MOC CLI
Make sure to have the MOC CLI installed.
Create a json file a similar structure as the one described in the body of the request above.
Trigger signal with the following command:
moc mlc trigger --file <local file>
Additional details
Follow up:
To follow up the process triggered via MLC there are several points of validation. Please look at the following diagram to identify them as explained below.
Use the ‘processInstanceId' returned by the signalResponsive endpoint mentioned above, to call the following endpoint and retrieve the “jobId" from the JSON response.
http://gateway/model-manage/api/jobHistories/search/findAllByJobMLCS_ProcessInstanceRootProcessInstanceId?processInstanceId={processInstanceId}Use the ‘jobId’ returned by the previous call, to check the status of the job in the following endpoint.
http://gateway/model-manage/api/jobs/{jobId}
If the job finished successfully or finished in error will be (or is still running), will be visible on this state.But if the job never ran due to an error during the MLC, we can follow up on the runningInstance incidents through this endpoint.
http://gateway/mlc-service/rest/incident?processInstanceId={processInstanceId}
Running batch job with CLI
Trigger Job Creation:
Launch MLC via REST API
Make sure to have the MOC CLI installed.
Create a json file a similar structure as the one described in the body of the request above.
Retrieve the deployment id
moc deployment ls <storedModel name> --state deployed --tag <target stage>Trigger signal with the following command:
moc job create deployedbatch <deployedModel ID> <input_file> <output_file> [flags]
Additional details
Follow up:
The above command returns the jobId which can be used in the following REST API endpoint to query the status:
http://gateway/model-manage/api/jobs/{jobId}