MLInstances endpoint

Last update: 2024-08-05
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  • Developer
NOTE

Data Science Workspace is no longer available for purchase.

This documentation is intended for existing customers with prior entitlements to Data Science Workspace.

An MLInstance is a pairing of an existing Engine with an appropriate set of configurations that defines any training parameters, scoring parameters, or hardware resource configurations.

Create an MLInstance

You can create an MLInstance by performing a POST request while providing a request payload consisting of a valid Engine ID ({ENGINE_ID}) and an appropriate set of default configurations.

If the Engine ID references a PySpark or Spark Engine then you have the ability to configure the amount of computation resources such as the number of cores or the amount of memory. If a Python Engine is referenced then you can choose between using either a CPU or GPU for training and scoring purposes. Refer to the appendix sections on PySpark and Spark resource configurations and Python CPU and GPU configurations for more information.

API Format

POST /mlInstances

Request

curl -X POST \
    https://platform.adobe.io/data/sensei/mlInstances \
    -H 'Authorization: Bearer {ACCESS_TOKEN}' \
    -H 'x-api-key: {API_KEY}' \
    -H 'x-gw-ims-org-id: {ORG_ID}' \
    -H 'x-sandbox-name: {SANDBOX_NAME}'
    -H 'content-type: application/vnd.adobe.platform.sensei+json;profile=mlInstance.v1.json' \
    -d '{
        "name": "A name for this MLInstance",
        "description": "A description for this MLInstance",
        "engineId": "22f4166f-85ba-4130-a995-a2b8e1edde32",
        "tasks": [
            {
                "name": "train",
                "parameters": [
                    {
                        "key": "training parameter",
                        "value": "parameter value"
                    }
                ]
            },
            {
                "name": "score",
                "parameters": [
                    {
                        "key": "scoring parameter",
                        "value": "parameter value"
                    }
                ]
            },
            {
                "name": "fp",
                "parameters": [
                    {
                        "key": "feature pipeline parameter",
                        "value": "parameter value"
                    }
                ]
            }
        ],
    }'
Property Description
name The desired name for the MLInstance. The Model corresponding to this MLInstance will inherit this value to be displayed in the UI as the Model’s name.
description An optional description for the MLInstance. The Model corresponding to this MLInstance will inherit this value to be displayed in UI as the Model’s description. This property is required. If you do not want to provide a description, set its value to be an empty string.
engineId The ID of an existing Engine.
tasks A set of configurations for training, scoring, or feature pipelines.

Response

A successful response returns a payload containing the details of the newly created MLInstance including its unique identifier (id).

{
    "id": "46986c8f-7739-4376-8509-0178bdf32cda",
    "name": "A name for this MLInstance",
    "description": "A description for this MLInstance",
    "engineId": "22f4166f-85ba-4130-a995-a2b8e1edde32",
    "created": "2019-01-01T00:00:00.000Z",
    "createdBy": {
        "userId": "Jane_Doe@AdobeID"
    },
    "updated": "2019-01-01T00:00:00.000Z",
    "tasks": [
        {
            "name": "train",
            "parameters": [
                {
                    "key": "training parameter",
                    "value": "parameter value"
                }
            ]
        },
        {
            "name": "score",
            "parameters": [
                {
                    "key": "scoring parameter",
                    "value": "parameter value"
                }
            ]
        },
        {
            "name": "fp",
            "parameters": [
                {
                    "key": "feature pipeline parameter",
                    "value": "parameter value"
                }
            ]
        }
    ]
}

Retrieve a list of MLInstances

You can retrieve a list of MLInstances by performing a single GET request. To help filter results, you can specify query parameters in the request path. For a list of available queries, refer to the appendix section on query parameters for asset retrieval.

API Format

GET /mlInstances
GET /mlInstances?{QUERY_PARAMETER}={VALUE}
GET /mlInstances?{QUERY_PARAMETER_1}={VALUE_1}&{QUERY_PARAMETER_2}={VALUE_2}
Parameter Description
{QUERY_PARAMETER} One of the available query parameters used to filter results.
{VALUE} The value for the preceding query parameter.

Request

curl -X GET \
    https://platform.adobe.io/data/sensei/mlInstances \
    -H 'Authorization: Bearer {ACCESS_TOKEN}' \
    -H 'x-api-key: {API_KEY}' \
    -H 'x-gw-ims-org-id: {ORG_ID}' \
    -H 'x-sandbox-name: {SANDBOX_NAME}'

Response

A successful response returns a list of MLInstances and their details.

{
    "children": [
        {
            "id": "46986c8f-7739-4376-8509-0178bdf32cda",
            "name": "A name for this MLInstance",
            "description": "A description for this MLInstance",
            "engineId": "22f4166f-85ba-4130-a995-a2b8e1edde32",
            "created": "2019-01-01T00:00:00.000Z",
            "createdBy": {
                "displayName": "Jane Doe",
                "userId": "Jane_Doe@AdobeID"
            },
            "updated": "2019-01-01T00:00:00.000Z"
        },
        {
            "id": "56986c8f-7739-4376-8509-0178bdf32cda",
            "name": "Retail Sales Model",
            "description": "A Model created with the Retail Sales Recipe",
            "engineId": "32f4166f-85ba-4130-a995-a2b8e1edde32",
            "created": "2019-01-01T00:00:00.000Z",
            "createdBy": {
                "displayName": "Jane Doe",
                "userId": "Jane_Doe@AdobeID"
            },
            "updated": "2019-01-01T00:00:00.000Z"
        }
    ],
    "_page": {
        "property": "deleted==false",
        "totalCount": 2,
        "count": 2
    }
}

Retrieve a specific MLInstance

You can retrieve the details of a specific MLInstance by performing a GET request that includes the ID of the desired MLInstance in the request path.

API Format

GET /mlInstances/{MLINSTANCE_ID}
Parameter Description
{MLINSTANCE_ID} The ID of the desired MLInstance.

Request

curl -X GET \
    https://platform.adobe.io/data/sensei/mlInstances/46986c8f-7739-4376-8509-0178bdf32cda \
    -H 'Authorization: Bearer {ACCESS_TOKEN}' \
    -H 'x-api-key: {API_KEY}' \
    -H 'x-gw-ims-org-id: {ORG_ID}' \
    -H 'x-sandbox-name: {SANDBOX_NAME}'

Response

A successful response returns the details of the MLInstance.

{
    "id": "46986c8f-7739-4376-8509-0178bdf32cda",
    "name": "A name for this MLInstance",
    "description": "A description for this MLInstance",
    "engineId": "22f4166f-85ba-4130-a995-a2b8e1edde32",
    "created": "2019-01-01T00:00:00.000Z",
    "createdBy": {
        "displayName": "Jane Doe",
        "userId": "Jane_Doe@AdobeID"
    },
    "updated": "2019-01-01T00:00:00.000Z",
    "tasks": [
        {
            "name": "train",
            "parameters": [
                {
                    "key": "training parameter",
                    "value": "parameter value"
                }
            ]
        },
        {
            "name": "score",
            "parameters": [
                {
                    "key": "scoring parameter",
                    "value": "parameter value"
                }
            ]
        },
        {
            "name": "featurePipeline",
            "parameters": [
                {
                    "key": "feature pipeline parameter",
                    "value": "parameter value"
                }
            ]
        }
    ]
}

Update an MLInstance

You can update an existing MLInstance by overwriting its properties through a PUT request that includes the target MLInstance’s ID in the request path and providing a JSON payload containing updated properties.

TIP

In order to ensure the success of this PUT request, it is suggested that first you perform a GET request to retrieve the MLInstance by ID. Then, modify and update the returned JSON object and apply the entirety of the modified JSON object as the payload for the PUT request.

The following sample API call will update an MLInstance’s training and scoring parameters while having these properties initially:

{
    "name": "A name for this MLInstance",
    "description": "A description for this MLInstance",
    "engineId": "00000000-0000-0000-0000-000000000000",
    "created": "2019-01-01T00:00:00.000Z",
    "createdBy": {
        "displayName": "Jane Doe",
        "userId": "Jane_Doe@AdobeID"
    },
    "tasks": [
        {
            "name": "train",
            "parameters": [
                {
                    "key": "learning_rate",
                    "value": "0.3"
                }
            ]
        },
        {
            "name": "score",
            "parameters": [
                {
                    "key": "output_dataset_id",
                    "value": "output-dataset-000"
                }
            ]
        }
    ]
}

API Format

PUT /mlInstances/{MLINSTANCE_ID}
Parameter Description
{MLINSTANCE_ID} A valid MLInstance ID.

Request

curl -X PUT \
    https://platform.adobe.io/data/sensei/mlInstances/46986c8f-7739-4376-8509-0178bdf32cda \
    -H 'Authorization: Bearer {ACCESS_TOKEN}' \
    -H 'x-api-key: {API_KEY}' \
    -H 'x-gw-ims-org-id: {ORG_ID}' \
    -H 'x-sandbox-name: {SANDBOX_NAME}' \
    -H 'content-type: application/vnd.adobe.platform.sensei+json;profile=mlInstance.v1.json' \
    -d '{
        "name": "A name for this MLInstance",
        "description": "A description for this MLInstance",
        "engineId": "00000000-0000-0000-0000-000000000000",
        "created": "2019-01-01T00:00:00.000Z",
        "createdBy": {
            "displayName": "Jane Doe",
            "userId": "Jane_Doe@AdobeID"
        },
        "tasks": [
            {
                "name": "train",
                "parameters": [
                    {
                        "key": "learning_rate",
                        "value": "0.5"
                    }
                ]
            },
            {
                "name": "score",
                "parameters": [
                    {
                        "key": "output_dataset_id",
                        "value": "output-dataset-001"
                    }
                ]
            }
        ]
    }'

Response

A successful response returns a payload containing the MLInstance’s updated details.

{
    "id": "46986c8f-7739-4376-8509-0178bdf32cda",
    "name": "A name for this MLInstance",
    "description": "A description for this MLInstance",
    "engineId": "00000000-0000-0000-0000-000000000000",
    "created": "2019-01-01T00:00:00.000Z",
    "createdBy": {
        "displayName": "Jane Doe",
        "userId": "Jane_Doe@AdobeID"
    },
    "updated": "2019-01-02T00:00:00.000Z",
    "tasks": [
        {
            "name": "train",
            "parameters": [
                {
                    "key": "learning_rate",
                    "value": "0.5"
                }
            ]
        },
        {
            "name": "score",
            "parameters": [
                {
                    "key": "output_dataset_id",
                    "value": "output-data-set-001"
                }
            ]
        }
    ]
}

Delete MLInstances by Engine ID

You can delete all MLInstances sharing the same Engine by performing a DELETE request that includes the Engine ID as a query parameter.

API Format

DELETE /mlInstances?engineId={ENGINE_ID}
Parameter Description
{ENGINE_ID} A valid Engine ID.

Request

curl -X DELETE \
    https://platform.adobe.io/data/sensei/mlInstances?engineId=22f4166f-85ba-4130-a995-a2b8e1edde32 \
    -H 'Authorization: Bearer {ACCESS_TOKEN}' \
    -H 'x-api-key: {API_KEY}' \
    -H 'x-gw-ims-org-id: {ORG_ID}' \
    -H 'x-sandbox-name: {SANDBOX_NAME}'

Response

{
    "title": "Success",
    "status": 200,
    "detail": "MLInstances successfully deleted"
}

Delete an MLInstance

You can delete a single MLInstance by performing a DELETE request that includes the target MLInstance’s ID in the request path.

API Format

DELETE /mlInstances/{MLINSTANCE_ID}
Parameter Description
{MLINSTANCE_ID} A valid MLInstance ID.

Request

curl -X DELETE \
    https://platform.adobe.io/data/sensei/mlInstances/46986c8f-7739-4376-8509-0178bdf32cda \
    -H 'Authorization: Bearer {ACCESS_TOKEN}' \
    -H 'x-api-key: {API_KEY}' \
    -H 'x-gw-ims-org-id: {ORG_ID}' \
    -H 'x-sandbox-name: {SANDBOX_NAME}'

Response

{
    "title": "Success",
    "status": 200,
    "detail": "MLInstance deletion was successful"
}
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