sap-ai-core-datarobot 1.0.18

content package for DataRobot integration for SAP AI Core

content package for DataRobot integration for SAP AI Core

Objective

sap-ai-core-datarobot is a content package to deploy DataRobot workflows in AI Core. This package provides support for two distinct deployment workflows, provided that a trained model is already present in DataRobot.

• Export the model from DataRobot to an Object Store and then integrate the Object Store with AI Core for model deployment.
• Directly integrate AI Core with the model in DataRobot utilizing the DataRobot API.

User Guide

1. Workflow 1: Exporting Models from DataRobot to Object Store and Integrating with AI Core

Pre-requisites

1. Complete AI Core Onboarding
   • Initial setup
   • Create a Resource Group
2. Access to the Git repository, Docker repository and Object Store 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.

The interface for sap-ai-core-datarobot content package is part of the ai-core-sdk.ai-core-sdk provides a command-line utility as well as a python library to use AICore content packages.

Please note that this sap-ai-core-datarobot package documentation provides the instructions for using this specific content package. For a more comprehensive understanding of how to use a content package, refer to the ai-core-sdk package documentation.

1.1 CLI

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. Explore the content package

   List all content packages installed in the environment.

   aicore-content list
   

   List all available pipelines in the sap-ai-core-datarobot content package.

   aicore-content list sap_datarobot
   

   View the parameters available in the selected pipeline.

   aicore-content show sap_datarobot model-jar-serving
   

   Check all available commands by using the --help flag.

   aicore-content --help
   

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

   .contentPackage: sap_datarobot
   .dockerType: default
   .workflow: model-jar-serving
   annotations:
   executables.ai.sap.com/description: <YOUR EXECUTABLE DESCRIPTION>
   executables.ai.sap.com/name: <YOUR EXECUTABLE NAME>
   scenarios.ai.sap.com/description: <YOUR SCENARIO DESCRIPTION>
   scenarios.ai.sap.com/name: <YOUR SCENARIO NAME>
   image: <YOUR DOCKER IMAGE TAG>
   imagePullSecret: <YOUR DOCKER REGISTRY SECRET NAME IN AI CORE>
   labels:
   ai.sap.com/version: <YOUR SCENARIO VERSION>
   scenarios.ai.sap.com/id: <YOUR SCENARIO ID>
   name: <YOUR SERVING TEMPLATE NAME>
   

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

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

6. Generate a docker image.

   This step involves building a docker image with the tag specified in the model_serving_config.yaml file. The command to perform this operation is as follows:

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

7. Push the docker image to your docker repository

   The image tag should correspond to the one provided in the 'model_serving_config.yaml' file.

   docker push <YOUR DOCKER IMAGE TAG>
   

8. Generate a serving template

   Clone the git repository that was registered with your SAP AI Core tenant during Onboarding.

   aicore-content create-template -p sap_datarobot -w model-jar-serving model_serving_config.yaml -o '<TEMPLATES FOLDER PATH IN YOUR CLONED GIT REPO>/model-serving-template.yaml'
   

   You can configure SAP AI Core to use different infrastructure resources for different tasks, based on demand. Within SAP AI Core, the resource plan is selected via the ai.sap.com/resourcePlan label in the serving template. By default, sap-ai-core-datarobot workflows use starter resource plan which entails the use of 1 CPU core and 3 Memeory GBs. For more information on how to select a different resource plan, you can refer to the documentation choosing a resource plan.

9. Push the serving template to your git repository

   cd <PATH TO YOUR CLONED GIT REPO>
   git add <TEMPLATES FOLDER PATH IN YOUR CLONED GIT REPO>/model-serving-template.yaml
   git commit -m 'updated template model-serving-template.yaml'
   git push
   

10. 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>'
    

11. 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>"
    }'
    

12. 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>"
            }
        ]
    }'
    

13. 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>'
    

14. 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>'
    

15. Use your deployment.

    curl --location '<YOUR DEPLOYMENT URL>/v1/models/,<YOUR MODEL JAR FILE NAME>:predict' \
    --header 'AI-Resource-Group: <YOUR RESOURCE GROUP NAME>' \
    --header 'Content-Type: application/json' \
    --header 'Authorization: Bearer <CLIENT CREDENTAILS TOKEN>' \
    --data '[
        {
            "<FEATURE_NAME>": <VALUE>,
            ...
        }
    ]'
    

1.2 Python

Steps

1. Install AI Core SDK

   !python -m pip install "ai_core_sdk[aicore-content]"
   

2. Install this content package

   !python -m pip install sap-ai-core-datarobot
   

3. Explore the content package

   List all content packages installed in the environment.

   from ai_core_sdk.content import get_content_packages
   pkgs = get_content_packages()
   for pkg in pkgs.values():
       print(pkg)
   

   List all available pipelines in the sap-ai-core-datarobot content package.

   content_pkg = pkgs['sap_datarobot']
   for workflow in content_pkg.workflows.values():
       print(workflow) 
   

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

   !python -m pip install pyyaml
   

   serving_workflow = content_pkg.workflows["model-jar-serving"]
   
   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>",
           "scenarios.ai.sap.com/description": "<YOUR SCENARIO DESCRIPTION>",
           "executables.ai.sap.com/name": "<YOUR EXECUTABLE NAME>",
           "executables.ai.sap.com/description": "<YOUR EXECUTABLE DESCRIPTION>"
       },
       'image': '<YOUR DOCKER IMAGE TAG>',
       "imagePullSecret": "<YOUR DOCKER REGISTRY SECRET NAME IN AI CORE>"
   }
   
   import yaml
   serving_config_yaml_file = "model_serving_config.yaml"
   ff = open(serving_config_yaml_file, 'w+')
   yaml.dump(serving_config, ff , allow_unicode=True)
   

5. 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"
   }
   
   import yaml
   serving_config_yaml_file = "model_serving_config.yaml"
   ff = open(serving_config_yaml_file, 'w+')
   yaml.dump(serving_config, ff , allow_unicode=True)
   

6. Generate a docker image

   This step involves building a docker image with the tag specified in the model_serving_config.yaml file.

   # keep the docker up and running before executing this cell
   # docker login
   import os
   docker_user = "[USER NAME]"
   docker_pwd = "[PASSWORD]"
   os.system(f'docker login <YOUR_DOCKER_REGISTRY_URL> -u {docker_user} -p {docker_pwd}')
   
   with open(serving_config_yaml_file) as stream:
       workflow_config = yaml.load(stream)
   serving_workflow.create_image(workflow_config) # actually build the docker container
   
   #When an error occurs, perform a dry run to debug any error occured while running the create_image() function.
   docker_build_cmd = serving_workflow.create_image(workflow_config, return_cmd = True)
   print(' '.join(docker_build_cmd))
   

7. Push the docker image to your docker repository

   os.system(f'docker push {workflow_config["image"]}') # push the container
   

8. Generate a serving template

   Clone the git repository that was registered with your SAP AI Core tenant during Onboarding.

   import pathlib
   output_file = '<TEMPLATES FOLDER PATH IN YOUR CLONED GIT REPO>/model-serving-template.yaml'
   serving_workflow.create_template(serving_config_yaml_file, output_file)
   

   You can configure SAP AI Core to use different infrastructure resources for different tasks, based on demand. Within SAP AI Core, the resource plan is selected via the ai.sap.com/resourcePlan label in the serving template. By default, sap-ai-core-datarobot workflows use starter resource plan which entails the use of 1 CPU core and 3 Memeory GBs. For more information on how to select a different resource plan, you can refer to the documentation choosing a resource plan.

9. Push the serving template to your git repository

   import os
   import subprocess
   repo_path = "<PATH TO YOUR CLONED GIT REPO>" 
   current_dir = os.getcwd()
   os.chdir(repo_path)
   
   # add the file to the git repository
   subprocess.run(["git", "add", f"{output_file}"])
   
   # commit the changes
   subprocess.run(["git", "commit", "-m", f'updated template {workflow_config["image"]}'])
   
   # push the changes
   subprocess.run(["git", "push"])
   
   os.chdir(current_dir)
   

10. Obtain a client credentials token to AI Core

    import json
    from ai_api_client_sdk.ai_api_v2_client import AIAPIV2Client
    from ai_api_client_sdk.models.artifact import Artifact
    from ai_api_client_sdk.models.parameter_binding import ParameterBinding
    from ai_api_client_sdk.models.input_artifact_binding import InputArtifactBinding
    from ai_api_client_sdk.models.status import Status
    from ai_api_client_sdk.models.target_status import TargetStatus
    import time
    from IPython.display import clear_output
    import requests
    import pprint
    
    # Load AICore and Object Store credentials
    credCF, credS3 = {}, {}
    with open('aicore-creds.json') as cf:
        credCF = json.load(cf)
    with open('s3-creds.json') as s3:
        credS3 = json.load(s3)
    
    #Authentication
    RESOURCE_GROUP="<YOUR RESOURCE GROUP NAME>"
    ai_api_v2_client = AIAPIV2Client(
        base_url=credCF["serviceurls"]["ML_API_URL"] + "/v2/lm",
        auth_url=credCF["url"] + "/oauth/token",
        client_id=credCF['clientid'],
        client_secret=credCF['clientsecret'],
        resource_group=RESOURCE_GROUP
    )
    

11. 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.

    # GET scenario
    response = ai_api_v2_client.scenario.query(RESOURCE_GROUP)
    ai_scenario = next(scenario_obj for scenario_obj in response.resources if scenario_obj.id == workflow_config["labels"]["scenarios.ai.sap.com/id"] )
    print("Scenario id: ", ai_scenario.id)
    print("Scenario name: ", ai_scenario.name)
    
    # GET List of scenario executables
    response = ai_api_v2_client.executable.query(scenario_id=ai_scenario.id)
    for executable in response.resources:
        print(executable)
    
    #Register the model from Object Store as an artifact
    artifact = {
            "name": "my-datarobot-model",
            "kind": Artifact.Kind.MODEL,
            "url": "ai://<YOUR OBJECTSTORE NAME>/<YOUR MODEL PATH>",
            "description": "my datarobot model jar",
            "scenario_id": ai_scenario.id
        }
    artifact_resp = ai_api_v2_client.artifact.create(**artifact)
    assert artifact_resp.message == 'Artifact acknowledged'
    print(artifact_resp.url)
    

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

    #define deployment confgiuration
    artifact_binding = {
        "key": "modeljar",
        "artifact_id": artifact_resp.id
    }
    
    parameter_binding = {
        "key": "modelName",
        "value": "<YOUR MODEL JAR FILE NAME>" #model file name in Object Store
    }
    
    deployment_configuration = {
        "name": "<CONFIGURATION NAME>",
        "scenario_id": workflow_config["labels"]["scenarios.ai.sap.com/id"],
        "executable_id": workflow_config["name"],
        "parameter_bindings": [ParameterBinding(**parameter_binding)],
        "input_artifact_bindings": [ InputArtifactBinding(**artifact_binding) ]
    }
    
    deployment_config_resp = ai_api_v2_client.configuration.create(**deployment_configuration)
    assert deployment_config_resp.message == 'Configuration created'
    

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

    deployment_resp = ai_api_v2_client.deployment.create(deployment_config_resp.id)
    

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

    # poll deployment status
    status = None
    while status != Status.RUNNING and status != Status.DEAD:
        time.sleep(5)
        clear_output(wait=True)
        deployment = ai_api_v2_client.deployment.get(deployment_resp.id)
        status = deployment.status
        print('...... deployment status ......', flush=True)
        print(deployment.status)
        print(deployment.status_details)
    
    time.sleep(10)  # time for deployment url getting ready
    print('endpoint: ', deployment.deployment_url)
    

15. Use your deployment.

    with open('sample_payload.json') as cf:
        sample_input = json.load(cf)
    
    # inference
    endpoint = "{deploy_url}/v1/models/{model_name}:predict".format(deploy_url=deployment.deployment_url, model_name = parameter_binding["value"])
    headers = {"Authorization": ai_api_v2_client.rest_client.get_token(), 'ai-resource-group': RESOURCE_GROUP}
    
    response = requests.post(endpoint, headers=headers, json=test_input)
    pprint.pprint(['inference result:', response.json()])
    time.sleep(10)   
    

2. Direct Integration of AI Core with DataRobot Models via DataRobot API

Pre-requisites

1. Complete AI Core Onboarding
   • Initial setup
   • Create a Resource Group
2. Access to the Git repository, Docker repository and Object Store onboarded to AI Core
3. You have a registered DataRobot account, trained a model in DataRobot.

The interface for sap-ai-core-datarobot content package is part of the ai-core-sdk.ai-core-sdk provides a command-line utility as well as a python library to use AICore content packages.

Please note that this sap-ai-core-datarobot package documentation provides the instructions for using this specific content package. For a more comprehensive understanding of how to use a content package, refer to the ai-core-sdk package documentation here.

2.1 CLI

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. Explore the content package

   List all content packages installed in the environment.

   aicore-content list
   

   List all available pipelines in the sap-ai-core-datarobot content package.

   aicore-content list sap_datarobot
   

   View the parameters available in the selected pipeline.

   aicore-content show sap_datarobot model-id-serving
   

   Check all available commands by using the --help flag.

   aicore-content --help
   

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

   .contentPackage: sap_datarobot
   .dockerType: default
   .workflow: model-id-serving
   annotations:
   executables.ai.sap.com/description: <YOUR EXECUTABLE DESCRIPTION>
   executables.ai.sap.com/name: <YOUR EXECUTABLE NAME>
   scenarios.ai.sap.com/description: <YOUR SCENARIO DESCRIPTION>
   scenarios.ai.sap.com/name: <YOUR SCENARIO NAME>
   image: <YOUR DOCKER IMAGE TAG>
   imagePullSecret: <YOUR DOCKER REGISTRY SECRET NAME IN AI CORE>
   datarobotToken: <DATAROBOT-API-TOKEN SECRET NAME IN AI CORE>
   labels:
   ai.sap.com/version: <YOUR SCENARIO VERSION>
   scenarios.ai.sap.com/id: <YOUR SCENARIO ID>
   name: <YOUR SERVING TEMPLATE NAME>
   

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

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

6. Generate a docker image

   This step involves building a docker image with the tag specified in the model_serving_config.yaml file. The command to perform this operation is as follows:

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

7. Push the docker image to your docker repository

   The image tag should correspond to the one provided in the 'model_serving_config.yaml' file.

   docker push <YOUR DOCKER IMAGE TAG>
   

8. Generate a serving template

   Clone the git repository that was registered with your SAP AI Core tenant during Onboarding.

   aicore-content create-template -p sap_datarobot -w model-id-serving model_serving_config.yaml -o '<TEMPLATES FOLDER PATH IN YOUR CLONED GIT REPO>/model-serving-template.yaml'
   

   You can configure SAP AI Core to use different infrastructure resources for different tasks, based on demand. Within SAP AI Core, the resource plan is selected via the ai.sap.com/resourcePlan label in the serving template. By default, sap-ai-core-datarobot workflows use starter resource plan which entails the use of 1 CPU core and 3 Memeory GBs. For more information on how to select a different resource plan, you can refer to the documentation choosing a resource plan.

9. Fill in the datarobot secrets name in serving template

   In the model-serving-template.yaml serving template file, substitute <DATAROBOT-ENDPOINT-TOKEN> with the name of your datarobot secrets.

10. Push the serving template to your git repository

    cd <PATH TO YOUR CLONED GIT REPO>
    git add <TEMPLATES FOLDER PATH IN YOUR CLONED GIT REPO>/model-serving-template.yaml
    git commit -m 'updated template model-serving-template.yaml'
    git push
    

11. 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>'
    

12. Create Generic Secrets in ResourceGroup

    To authenticate with DataRobot's API, your code needs to have access to an endpoint and token. In AI Core, create a generic secret for the Endpoint and the token; these secrets are used to access the model from DataRobot. Refer AI Core documentation to create a generic secret.

    Note that the AI Core AI API expects sensitive data to be Base64-encoded. You can easily encode your data in Base64 format using the following command on Linux or MacOS:

    echo -n 'my-sensitive-data' | base64
    

    curl --location --request POST "<YOUR AI CORE URL>/v2/admin/secrets" \
    --header "Authorization: Bearer <CLIENT CREDENTAILS TOKEN>" \
    --header 'Content-Type: application/json' \
    --header 'AI-Resource-Group: <YOUR RESOURCE GROUP NAME>' \
    --data-raw '{
        "name": "<DATAROBOT-API-TOKEN SECRET NAME IN AI CORE>",
        "data": {
            "endpoint": "<BASE64-ENCODED DATAROBOT API ENDPOINT>",
            "token": "<BASE64-ENCODED DATAROBOT API TOKEN>"
        }
    }'				
    

13. 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": "projectID",
                "value": "<PROJECT ID OF YOUR MODEL IN DATAROBOT>"
            },
            {
                "key": "modelID",
                "value": "<YOUR MODEL ID FROM DATAROBOT>"
            }
        ]
    }'
    

14. 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>'
    

15. 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>'
    

16. Use your deployment.

    curl --location '<YOUR DEPLOYMENT URL>/v1/models/model:predict' \
    --header 'AI-Resource-Group: <YOUR RESOURCE GROUP NAME>' \
    --header 'Content-Type: application/json' \
    --header 'Authorization: Bearer <CLIENT CREDENTAILS TOKEN>' \
    --data '[
        {
            "<FEATURE_NAME>": <FEATURE_VALUE>,
            ...
        }
    ]'
    

2.2 Python

Steps

1. Install AI Core SDK

   !python -m pip install "ai_core_sdk[aicore-content]"
   

2. Install this content package

   !python -m pip install sap-ai-core-datarobot
   

3. Explore the content package

   List all content packages installed in the environment.

   from ai_core_sdk.content import get_content_packages
   pkgs = get_content_packages()
   for pkg in pkgs.values():
       print(pkg)
   

   List all available pipelines in the sap-ai-core-datarobot content package.

   content_pkg = pkgs['sap_datarobot']
   for workflow in content_pkg.workflows.values():
       print(workflow) 
   

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

   !python -m pip install pyyaml
   

   serving_workflow = content_pkg.workflows["model-id-serving"]
   
   serving_config = {
       '.contentPackage': 'sap_datarobot',
       '.workflow': 'model-id-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>",
           "scenarios.ai.sap.com/description": "<YOUR SCENARIO DESCRIPTION>",
           "executables.ai.sap.com/name": "<YOUR EXECUTABLE NAME>",
           "executables.ai.sap.com/description": "<YOUR EXECUTABLE DESCRIPTION>"
       },
       'image': '<YOUR DOCKER IMAGE TAG>',
       "imagePullSecret": "<YOUR DOCKER REGISTRY SECRET NAME IN AI CORE>",
       "datarobotToken": "<DATAROBOT-API-TOKEN SECRET NAME IN AI CORE>"
   }
   
   import yaml
   serving_config_yaml_file = "model_serving_config.yaml"
   ff = open(serving_config_yaml_file, 'w+')
   yaml.dump(serving_config, ff , allow_unicode=True)
   

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

   serving_config = {
       '.contentPackage': 'sap_datarobot',
       '.workflow': 'model-id-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",
       "datarobotToken": "my-datarobot-secret"
   }
   
   import yaml
   serving_config_yaml_file = "model_serving_config.yaml"
   ff = open(serving_config_yaml_file, 'w+')
   yaml.dump(serving_config, ff , allow_unicode=True)
   

6. Generate a docker image

   This step involves building a docker image with the tag specified in the model_serving_config.yaml file.

   # keep the docker up and running before executing this cell
   # docker login
   import os
   docker_user = "[USER NAME]"
   docker_pwd = "[PASSWORD]"
   os.system(f'docker login <YOUR_DOCKER_REGISTRY_URL> -u {docker_user} -p {docker_pwd}')
   
   with open(serving_config_yaml_file) as stream:
       workflow_config = yaml.load(stream)
   serving_workflow.create_image(workflow_config) # actually build the docker container
   
   #When an error occurs, perform a dry run to debug any error occured while running the create_image() function.
   docker_build_cmd = serving_workflow.create_image(workflow_config, return_cmd = True)
   print(' '.join(docker_build_cmd))
   

7. Push the docker image to your docker repository

   os.system(f'docker push {workflow_config["image"]}') # push the container
   

8. Generate a serving template

   Clone the git repository that was registered with your SAP AI Core tenant during Onboarding.

   import pathlib
   output_file = '<TEMPLATES FOLDER PATH IN YOUR CLONED GIT REPO>/model-serving-template.yaml'
   serving_workflow.create_template(serving_config_yaml_file, output_file)
   

   You can configure SAP AI Core to use different infrastructure resources for different tasks, based on demand. Within SAP AI Core, the resource plan is selected via the ai.sap.com/resourcePlan label in the serving template. By default, sap-ai-core-datarobot workflows use starter resource plan which entails the use of 1 CPU core and 3 Memeory GBs. For more information on how to select a different resource plan, you can refer to the documentation choosing a resource plan.

9. Fill in the datarobot secrets name in serving template

   In the model-serving-template.yaml serving template file, substitute <DATAROBOT-ENDPOINT-TOKEN> with the name of your datarobot secrets.

   def modify_serving_template(workflow_config, template_file_path):
       import yaml
       import sys
       from yaml.resolver import BaseResolver
       with open(template_file_path, 'r') as f_read:
           content = yaml.load(f_read, yaml.FullLoader)   
       predictor_spec = content["spec"]["template"]["spec"]
       predictor_spec = predictor_spec.replace('<DATAROBOT-ENDPOINT-TOKEN>', serving_config['datarobotToken'] )
       content["spec"]["template"]["spec"] = predictor_spec
       yaml.SafeDumper.org_represent_str = yaml.SafeDumper.represent_str
       def repr_str(dumper, data):
           if '\n' in data:
               return dumper.represent_scalar(u'tag:yaml.org,2002:str', data, style='|')
           return dumper.org_represent_str(data)
       yaml.add_representer(str, repr_str, Dumper=yaml.SafeDumper)
       with open(template_file_path, 'w') as f_write:
           f_write.write(yaml.safe_dump(content))
   
   
   modify_serving_template(workflow_config, output_file)
   

10. Push the serving template to your git repository

    import os
    import subprocess
    repo_path = "<PATH TO YOUR CLONED GIT REPO>" 
    current_dir = os.getcwd()
    os.chdir(repo_path)
    
    # add the file to the git repository
    subprocess.run(["git", "add", f"{output_file}"])
    
    # commit the changes
    subprocess.run(["git", "commit", "-m", f'updated template {workflow_config["image"]}'])
    
    # push the changes
    subprocess.run(["git", "push"])
    
    os.chdir(current_dir)
    

11. Obtain a client credentials token to AI Core

    import json
    from ai_api_client_sdk.ai_api_v2_client import AIAPIV2Client
    from ai_api_client_sdk.models.artifact import Artifact
    from ai_api_client_sdk.models.parameter_binding import ParameterBinding
    from ai_api_client_sdk.models.input_artifact_binding import InputArtifactBinding
    from ai_api_client_sdk.models.status import Status
    from ai_api_client_sdk.models.target_status import TargetStatus
    import time
    from IPython.display import clear_output
    import requests
    import pprint
    
    # Load AICore and Object Store credentials
    credCF, credS3 = {}, {}
    with open('aicore-creds.json') as cf:
        credCF = json.load(cf)
    with open('s3-creds.json') as s3:
        credS3 = json.load(s3)
    
    #Authentication
    RESOURCE_GROUP="<YOUR RESOURCE GROUP NAME>"
    ai_api_v2_client = AIAPIV2Client(
        base_url=credCF["serviceurls"]["ML_API_URL"] + "/v2/lm",
        auth_url=credCF["url"] + "/oauth/token",
        client_id=credCF['clientid'],
        client_secret=credCF['clientsecret'],
        resource_group=RESOURCE_GROUP
    )
    

12. Create Generic Secrets in ResourceGroup

    To authenticate with DataRobot's API, your code needs to have access to an endpoint and token. In AI Core, create a generic secret for the Endpoint and the token; these secrets are used to access the model from DataRobot. Refer AI Core documentation to create a generic secret.

    Note that the AI Core AI API expects sensitive data to be Base64-encoded. You can easily encode your data in Base64 format using the following command on Linux or MacOS:

    echo -n 'my-sensitive-data' | base64
    

    import requests
    
    ai_api_url = credCF["serviceurls"]["ML_API_URL"] + "/v2/admin/secrets"
    token = ai_api_v2_client.rest_client.get_token()
    
    headers = {
        "Authorization": token,
        "Content-Type": "application/json",
        "AI-Resource-Group": RESOURCE_GROUP
    }
    
    data = {
        "name": "<DATAROBOT-API-TOKEN SECRET NAME IN AI CORE>",
        "data": {
            "endpoint": "<BASE64-ENCODED DATAROBOT API ENDPOINT>",
            "token": "<BASE64-ENCODED DATAROBOT API TOKEN>"
        }
    }
    
    response = requests.post(ai_api_url, headers=headers, json=data)
    
    if response.status_code == 201:
        print("Secret created successfully!")
    else:
        print("Request failed with status code:", response.status_code)
        print("Response text:", response.text)
    
    

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

    #define deployment confgiuration
    project_id = {
        "key": "projectID",
        "value": "<PROJECT ID OF YOUR MODEL IN DATAROBOT>" 
    }
    model_id = {
        "key": "modelID",
        "value": "<YOUR MODEL ID FROM DATAROBOT>" 
    }
    
    deployment_configuration = {
        "name": "<CONFIGURATION NAME>",
        "scenario_id": workflow_config["labels"]["scenarios.ai.sap.com/id"],
        "executable_id": workflow_config["name"],
        "parameter_bindings": [ParameterBinding(**project_id), ParameterBinding(**model_id)]
    }
    
    deployment_config_resp = ai_api_v2_client.configuration.create(**deployment_configuration)
    assert deployment_config_resp.message == 'Configuration created'
    

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

    deployment_resp = ai_api_v2_client.deployment.create(deployment_config_resp.id)
    

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

    # poll deployment status
    status = None
    while status != Status.RUNNING and status != Status.DEAD:
        time.sleep(5)
        clear_output(wait=True)
        deployment = ai_api_v2_client.deployment.get(deployment_resp.id)
        status = deployment.status
        print('...... deployment status ......', flush=True)
        print(deployment.status)
        print(deployment.status_details)
    
    time.sleep(10)  # time for deployment url getting ready
    print('endpoint: ', deployment.deployment_url)
    

16. Use your deployment.

    with open('sample_payload.json') as cf:
        sample_input = json.load(cf)
    
    # inference
    endpoint = "{deploy_url}/v1/models/model:predict".format(deploy_url=deployment.deployment_url)
    headers = {"Authorization": ai_api_v2_client.rest_client.get_token(), 'ai-resource-group': RESOURCE_GROUP}
    
    response = requests.post(endpoint, headers=headers, json=test_input)
    pprint.pprint(['inference result:', response.json()])
    time.sleep(10)   
    

Security Guide

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