MachineLearning / Paginator / DescribeMLModels

DescribeMLModels

class MachineLearning.Paginator.DescribeMLModels

paginator = client.get_paginator('describe_ml_models')

paginate(**kwargs)

Creates an iterator that will paginate through responses from MachineLearning.Client.describe_ml_models().

See also: AWS API Documentation

Request Syntax

response_iterator = paginator.paginate(
    FilterVariable='CreatedAt'|'LastUpdatedAt'|'Status'|'Name'|'IAMUser'|'TrainingDataSourceId'|'RealtimeEndpointStatus'|'MLModelType'|'Algorithm'|'TrainingDataURI',
    EQ='string',
    GT='string',
    LT='string',
    GE='string',
    LE='string',
    NE='string',
    Prefix='string',
    SortOrder='asc'|'dsc',
    PaginationConfig={
        'MaxItems': 123,
        'PageSize': 123,
        'StartingToken': 'string'
    }
)

Parameters:

• FilterVariable (string) –

  Use one of the following variables to filter a list of MLModel:

  • CreatedAt - Sets the search criteria to MLModel creation date.

  • Status - Sets the search criteria to MLModel status.

  • Name - Sets the search criteria to the contents of MLModel Name.

  • IAMUser - Sets the search criteria to the user account that invoked the MLModel creation.

  • TrainingDataSourceId - Sets the search criteria to the DataSource used to train one or more MLModel.

  • RealtimeEndpointStatus - Sets the search criteria to the MLModel real-time endpoint status.

  • MLModelType - Sets the search criteria to MLModel type: binary, regression, or multi-class.

  • Algorithm - Sets the search criteria to the algorithm that the MLModel uses.

  • TrainingDataURI - Sets the search criteria to the data file(s) used in training a MLModel. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.

• EQ (string) – The equal to operator. The MLModel results will have FilterVariable values that exactly match the value specified with EQ.

• GT (string) – The greater than operator. The MLModel results will have FilterVariable values that are greater than the value specified with GT.

• LT (string) – The less than operator. The MLModel results will have FilterVariable values that are less than the value specified with LT.

• GE (string) – The greater than or equal to operator. The MLModel results will have FilterVariable values that are greater than or equal to the value specified with GE.

• LE (string) – The less than or equal to operator. The MLModel results will have FilterVariable values that are less than or equal to the value specified with LE.

• NE (string) – The not equal to operator. The MLModel results will have FilterVariable values not equal to the value specified with NE.

• Prefix (string) –

  A string that is found at the beginning of a variable, such as Name or Id.

  For example, an MLModel could have the Name 2014-09-09-HolidayGiftMailer. To search for this MLModel, select Name for the FilterVariable and any of the following strings for the Prefix:

  • 2014-09

  • 2014-09-09

  • 2014-09-09-Holiday

• SortOrder (string) –

  A two-value parameter that determines the sequence of the resulting list of MLModel.

  • asc - Arranges the list in ascending order (A-Z, 0-9).

  • dsc - Arranges the list in descending order (Z-A, 9-0).

  Results are sorted by FilterVariable.

• PaginationConfig (dict) –

  A dictionary that provides parameters to control pagination.

  • MaxItems (integer) –

    The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.

  • PageSize (integer) –

    The size of each page.

  • StartingToken (string) –

    A token to specify where to start paginating. This is the NextToken from a previous response.

Return type:

dict

Returns:

Response Syntax

{
    'Results': [
        {
            'MLModelId': 'string',
            'TrainingDataSourceId': 'string',
            'CreatedByIamUser': 'string',
            'CreatedAt': datetime(2015, 1, 1),
            'LastUpdatedAt': datetime(2015, 1, 1),
            'Name': 'string',
            'Status': 'PENDING'|'INPROGRESS'|'FAILED'|'COMPLETED'|'DELETED',
            'SizeInBytes': 123,
            'EndpointInfo': {
                'PeakRequestsPerSecond': 123,
                'CreatedAt': datetime(2015, 1, 1),
                'EndpointUrl': 'string',
                'EndpointStatus': 'NONE'|'READY'|'UPDATING'|'FAILED'
            },
            'TrainingParameters': {
                'string': 'string'
            },
            'InputDataLocationS3': 'string',
            'Algorithm': 'sgd',
            'MLModelType': 'REGRESSION'|'BINARY'|'MULTICLASS',
            'ScoreThreshold': ...,
            'ScoreThresholdLastUpdatedAt': datetime(2015, 1, 1),
            'Message': 'string',
            'ComputeTime': 123,
            'FinishedAt': datetime(2015, 1, 1),
            'StartedAt': datetime(2015, 1, 1)
        },
    ],

}

Response Structure

• (dict) –

  Represents the output of a DescribeMLModels operation. The content is essentially a list of MLModel.

  • Results (list) –

    A list of MLModel that meet the search criteria.

    • (dict) –

      Represents the output of a GetMLModel operation.

      The content consists of the detailed metadata and the current status of the MLModel.

      • MLModelId (string) –

        The ID assigned to the MLModel at creation.

      • TrainingDataSourceId (string) –

        The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

      • CreatedByIamUser (string) –

        The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

      • CreatedAt (datetime) –

        The time that the MLModel was created. The time is expressed in epoch time.

      • LastUpdatedAt (datetime) –

        The time of the most recent edit to the MLModel. The time is expressed in epoch time.

      • Name (string) –

        A user-supplied name or description of the MLModel.

      • Status (string) –

        The current status of an MLModel. This element can have one of the following values:

        • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.

        • INPROGRESS - The creation process is underway.

        • FAILED - The request to create an MLModel didn’t run to completion. The model isn’t usable.

        • COMPLETED - The creation process completed successfully.

        • DELETED - The MLModel is marked as deleted. It isn’t usable.

      • SizeInBytes (integer) –

        Long integer type that is a 64-bit signed number.

      • EndpointInfo (dict) –

        The current endpoint of the MLModel.

        • PeakRequestsPerSecond (integer) –

          The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second.

        • CreatedAt (datetime) –

          The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.

        • EndpointUrl (string) –

          The URI that specifies where to send real-time prediction requests for the MLModel.

          Note: The application must wait until the real-time endpoint is ready before using this URI.

        • EndpointStatus (string) –

          The current status of the real-time endpoint for the MLModel. This element can have one of the following values:

          • NONE - Endpoint does not exist or was previously deleted.

          • READY - Endpoint is ready to be used for real-time predictions.

          • UPDATING - Updating/creating the endpoint.

      • TrainingParameters (dict) –

        A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

        The following is the current set of training parameters:

        • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

        • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

        • sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model’s ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.

        • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can’t be used when L2 is specified. Use this parameter sparingly.

        • sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can’t be used when L1 is specified. Use this parameter sparingly.

        • (string) –

          String type.

          • (string) –

            String type.

      • InputDataLocationS3 (string) –

        The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

      • Algorithm (string) –

        The algorithm used to train the MLModel. The following algorithm is supported:

        • SGD – Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

      • MLModelType (string) –

        Identifies the MLModel category. The following are the available types:

        • REGRESSION - Produces a numeric result. For example, “What price should a house be listed at?”

        • BINARY - Produces one of two possible results. For example, “Is this a child-friendly web site?”.

        • MULTICLASS - Produces one of several possible results. For example, “Is this a HIGH-, LOW-, or MEDIUM-risk trade?”.

      • ScoreThreshold (float) –

      • ScoreThresholdLastUpdatedAt (datetime) –

        The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

      • Message (string) –

        A description of the most recent details about accessing the MLModel.

      • ComputeTime (integer) –

        Long integer type that is a 64-bit signed number.

      • FinishedAt (datetime) –

        A timestamp represented in epoch time.

      • StartedAt (datetime) –

        A timestamp represented in epoch time.