MLInstances端点

上次更新: 2023-05-25
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MLInstance是现有 引擎 包含一组适当的配置,可定义任何训练参数、评分参数或硬件资源配置。

创建MLInstance

您可以创建MLInstance,方法是在提供包含有效引擎ID的请求有效负载时执行POST请求({ENGINE_ID})和一组适当的默认配置。

如果引擎ID引用PySpark或Spark引擎,则您能够配置计算资源的数量,例如核心数量或内存量。 如果引用了Python引擎,则可以选择使用CPU或GPU进行训练和评分。 请参阅附录中关于 PySpark和Spark资源配置Python CPU和GPU配置 了解更多信息。

API格式

POST /mlInstances

请求

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"
                    }
                ]
            }
        ],
    }'
属性 描述
name 所需的MLInstance名称。 与此MLInstance对应的模型将继承此值,以作为模型的名称显示在UI中。
description MLInstance的可选描述。 与此MLInstance对应的模型将继承此值,以作为模型的描述显示在UI中。 此属性是必需的。如果不希望提供描述,请将其值设置为空字符串。
engineId 现有引擎的ID。
tasks 训练、评分或功能管道的一组配置。

响应

成功响应将返回一个有效负载,其中包含新创建的MLInstance的详细信息,包括其唯一标识符(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"
                }
            ]
        }
    ]
}

检索MLInstances列表

您可以通过执行单个GET请求来检索MLInstances列表。 要帮助筛选结果,您可以在请求路径中指定查询参数。 有关可用查询的列表,请参阅 用于资源检索的查询参数.

API格式

GET /mlInstances
GET /mlInstances?{QUERY_PARAMETER}={VALUE}
GET /mlInstances?{QUERY_PARAMETER_1}={VALUE_1}&{QUERY_PARAMETER_2}={VALUE_2}
参数 描述
{QUERY_PARAMETER} 其中一项 可用的查询参数 用于筛选结果。
{VALUE} 上一个查询参数的值。

请求

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}'

响应

成功响应将返回MLInstances列表及其详细信息。

{
    "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
    }
}

检索特定MLInstance

通过执行GET请求,您可以检索特定MLInstance的详细信息,该请求路径中包含所需MLInstance的ID。

API格式

GET /mlInstances/{MLINSTANCE_ID}
参数 描述
{MLINSTANCE_ID} 所需MLInstance的ID。

请求

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}'

响应

成功响应将返回MLInstance的详细信息。

{
    "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"
                }
            ]
        }
    ]
}

更新MLInstance

您可以更新现有MLInstance,方法是通过PUT请求(请求路径中包含Target MLInstance的ID)覆盖其属性,并提供包含已更新属性的JSON有效负载。

小贴士

为了确保此PUT请求成功,建议您首先执行GET请求 按ID检索MLInstance. 然后,修改并更新返回的JSON对象,并将修改后的JSON对象的整个内容应用为PUT请求的有效负载。

以下示例API调用最初具有这些属性时,将更新MLInstance的训练和评分参数:

{
    "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格式

PUT /mlInstances/{MLINSTANCE_ID}
参数 描述
{MLINSTANCE_ID} 有效的MLInstance ID。

请求

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"
                    }
                ]
            }
        ]
    }'

响应

成功的响应会返回包含MLInstance更新详细信息的有效负载。

{
    "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"
                }
            ]
        }
    ]
}

按引擎ID删除MLInstances

通过执行包含引擎ID作为查询参数的DELETE请求,可以删除共享同一引擎的所有MLInstances。

API格式

DELETE /mlInstances?engineId={ENGINE_ID}
参数 描述
{ENGINE_ID} 有效的引擎ID。

请求

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}'

响应

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

删除MLInstance

通过执行DELETE请求(请求路径中包含MLInstance的ID),可以删除单个MLInstance。

API格式

DELETE /mlInstances/{MLINSTANCE_ID}
参数 描述
{MLINSTANCE_ID} 有效的MLInstance ID。

请求

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}'

响应

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

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