sap-ai-core-datarobot 1.0.17

content package for DataRobot integration for SAP AI Core

content package for DataRobot integration for SAP AI Core

Objective

A content package to deploy DataRobot workflows in AI Core.

User Guide

1. Deploy a DataRobot model in AI Core

Pre-requisites

1. Complete AI Core Onboarding
2. Access to the Git repository, Docker repository and S3 bucket onboarded to AI Core
3. You have a registered DataRobot account, trained a model, downloaded the model from DataRobot and stored the model in Object Store configured with AI Core.

Steps

1. Install AI Core SDK

pip install ai-core-sdk[aicore_content]

2. Install this content package

pip install sap-ai-core-datarobot

3. Create a config file with the name model_serving_config.yaml with the following content.

serving_config = {
    '.contentPackage': 'sap_datarobot',
    '.workflow': 'model-jar-serving',
    '.dockerType': 'default',
    'name': '<YOUR SERVING TEMPLATE NAME>',
    'labels': {
        'scenarios.ai.sap.com/id': "<YOUR SCENARIO ID>",
        'ai.sap.com/version': "<YOUR SCENARIO VERSION>"
    },
    "annotations": {
        "scenarios.ai.sap.com/name": "<YOUR SCENARIO NAME>",
        "executables.ai.sap.com/name": "<YOUR EXECUTABLE NAME>",
        "executables.ai.sap.com/description": "<YOUR EXECUTABLE DESCRIPTION>",
        "scenarios.ai.sap.com/description": "<YOUR SCENARIO DESCRIPTION>"
    },
    'image': '<YOUR DOCKER IMAGE TAG>',
    "imagePullSecret": "<YOUR DOCKER IMAGE PULL SECRET NAME>"
}

4. Fill in the desired values in the config file. An example config file is shown below.

serving_config = {
    '.contentPackage': 'sap_datarobot',
    '.workflow': 'model-jar-serving',
    '.dockerType': 'default',
    'name': 'datarobot-model-serving',
    'labels': {
        'scenarios.ai.sap.com/id': "00db4197-1538-4640-9ea9-44731041ed88",
        'ai.sap.com/version': "0.0.1"
    },
    "annotations": {
        "scenarios.ai.sap.com/name": "my-datarobot-scenario",
        "executables.ai.sap.com/name": "datarobot-model-serving",
        "executables.ai.sap.com/description": "datarobot model serving",
        "scenarios.ai.sap.com/description": "my datarobot scenario"
    },
    'image': 'docker.io/<YOUR_DOCKER_USERNAME>/model-serve:1.0',
    "imagePullSecret": "my-docker-secret"
}

5. Generate a docker image

aicore-content create-image -p sap_datarobot -w model-jar-serving model_serving_config.yaml

6. Generate a serving template

aicore-content create-template -p sap_datarobot -w model-jar-serving model_serving_config.yaml -o './model-serving-template.yaml'

7. Push the docker image to your docker repository

docker push docker.io/<YOUR_DOCKER_USERNAME>/model-serve:1.0

8. Push the serving template to your git repository

cd <repo_path>
git add <path_within_repo>/proxy-serving-template.yaml
git commit -m 'updated template model-serving-template.yaml'
git push

9. Obtain a client credentials token to AI Core

curl --location '<YOUR AI CORE AUTH ENDPOINT URL>/oauth/token' --header 'Authorization: Basic <YOUR AI CORE CREDENTIALS>'

10. Create an artifact to connect the DataRobot model, to make it available for use in SAP AI Core. Save the model artifact id from the response.

curl --location --request POST "<YOUR AI CORE URL>/v2/lm/artifacts" \
--header "Authorization: Bearer <CLIENT CREDENTAILS TOKEN>" \
--header "Content-Type: application/json" \
--header "AI-Resource-Group: <YOUR RESOURCE GROUP NAME>" \
--data-raw '{
 "name": "my-datarobot-model",
 "kind": "model",
 "url": "ai://<YOUR OBJECTSTORE NAME>/<YOUR MODEL PATH>",
 "description": "my datarobot model jar",
 "scenarioId": "<YOUR SCENARIO ID>"
 }'

11. Create a configuration and save the configuration id from the response.

curl --request POST "<YOUR AI CORE URL>/v2/lm/configurations" \
--header "Authorization: Bearer <CLIENT CREDENTAILS TOKEN>" \
--header "AI-Resource-Group: <YOUR RESOURCE GROUP NAME>" \
--header "Content-Type: application/json" \
--data '{
    "name": "<CONFIGURATION NAME>",
    "executableId": "<YOUR EXECUTABLE ID>",
    "scenarioId": "<YOUR SCENARIO ID>",
    "parameterBindings": [
        {
            "key": "modelName",
            "value": "<YOUR MODEL JAR FILE NAME>"
        }
    ],
    "inputArtifactBindings": [
        {
            "key": "modeljar",
            "artifactId": "<YOUR MODEL ARTIFACT ID>"
        }
    ]
}'

12. Create a deployment and note down the deployment id from the response

curl --location --globoff --request POST '<YOUR AI CORE URL>/v2/lm/configurations/<YOUR CONFIGURATION ID>/deployments' \
--header 'AI-Resource-Group: <YOUR RESOURCE GROUP NAME>' \
--header 'Authorization: Bearer <CLIENT CREDENTAILS TOKEN>'

13. Check the status of the deployment. Note down the deployment URL after the status changes to RUNNING.

curl --location --globoff '<YOUR AI CORE URL>/v2/lm/deployments/<YOUR DEPLOYMENT ID>' \
--header 'AI-Resource-Group: <YOUR RESOURCE GROUP NAME>' \
--header 'Authorization: Bearer <CLIENT CREDENTAILS TOKEN>'

14. Use your deployment. The example inference data shown below is for an employee churn prediction model

curl --location '<YOUR DEPLOYMENT URL>/v1/predict' \
--header 'AI-Resource-Group: <YOUR RESOURCE GROUP NAME>' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <CLIENT CREDENTAILS TOKEN>' \
--data '[
    {
        "EMPLOYEE_ID": 00000,
        "AGE": 33,
        "AGE_GROUP10": "(25-35]",
        "AGE_GROUP5": "(30-35]",
        "GENERATION": "Generation Y",
        "CRITICAL_JOB_ROLE": "Critical",
        "RISK_OF_LOSS": "Low",
        "IMPACT_OF_LOSS": "Medium",
        "FUTURE_LEADER": "No Future Leader",
        "GENDER": "Male",
        "MGR_EMP": "No Mgr",
        "MINORITY": "Non-Minority",
        "TENURE_MONTHS": 64,
        "TENURE_INTERVAL_YEARS": "( 5 - 10]",
        "TENURE_INTERVALL_DESC": "4 - senior",
        "SALARY": 50000,
        "EMPLOYMENT_TYPE": "Full-Time",
        "EMPLOYMENT_TYPE_2": "Regular",
        "HIGH_POTENTIAL": "No High Pot",
        "PREVIOUS_FUNCTIONAL_AREA": null,
        "PREVIOUS_JOB_LEVEL": null,
        "PREVIOUS_CAREER_PATH": null,
        "PREVIOUS_PERFORMANCE_RATING": null,
        "PREVIOUS_COUNTRY": null,
        "PREVCOUNTRYLAT": null,
        "PREVCOUNTRYLON": null,
        "PREVIOUS_REGION": null,
        "TIMEINPREVPOSITIONMONTH": 4,
        "CURRENT_FUNCTIONAL_AREA": "Service",
        "CURRENT_JOB_LEVEL": "3 - Senior",
        "CURRENT_CAREER_PATH": "Functional",
        "CURRENT_PERFORMANCE_RATING": "0 - No performance Measurement",
        "CURRENT_REGION": "EMEA",
        "CURRENT_COUNTRY": "Germany",
        "CURCOUNTRYLAT": 51.08342,
        "CURCOUNTRYLON": 10.423447,
        "PROMOTION_WITHIN_LAST_3_YEARS": "No Promotion",
        "CHANGED_POSITION_WITHIN_LAST_2_YEARS": "No Change",
        "CHANGE_IN_PERFORMANCE_RATING": "0 - Not available",
        "FUNCTIONALAREACHANGETYPE": "No change",
        "JOBLEVELCHANGETYPE": "No change",
        "HEADS": 1,
        "FLIGHT_RISK": "No",
        "ID": 00000,
        "LINKEDIN": "No",
        "HRTRAINING": "No",
        "SICKDAYS": 16
    }
]'

Security Guide

See Security in SAP AI Core for general information about how SAP AI Core handles security.