AWS CLI Command Reference

[ aws . machinelearning ]

describe-ml-models

Description

Returns a list of MLModel that match the search criteria in the request.

See also: AWS API Documentation

describe-ml-models is a paginated operation. Multiple API calls may be issued in order to retrieve the entire data set of results. You can disable pagination by providing the --no-paginate argument. When using --output text and the --query argument on a paginated response, the --query argument must extract data from the results of the following query expressions: Results

Synopsis

  describe-ml-models
[--filter-variable <value>]
[--eq <value>]
[--gt <value>]
[--lt <value>]
[--ge <value>]
[--le <value>]
[--ne <value>]
[--prefix <value>]
[--sort-order <value>]
[--cli-input-json | --cli-input-yaml]
[--starting-token <value>]
[--page-size <value>]
[--max-items <value>]
[--generate-cli-skeleton <value>]
[--debug]
[--endpoint-url <value>]
[--no-verify-ssl]
[--no-paginate]
[--output <value>]
[--query <value>]
[--profile <value>]
[--region <value>]
[--version <value>]
[--color <value>]
[--no-sign-request]
[--ca-bundle <value>]
[--cli-read-timeout <value>]
[--cli-connect-timeout <value>]
[--cli-binary-format <value>]
[--no-cli-pager]
[--cli-auto-prompt]
[--no-cli-auto-prompt]

Options

--filter-variable (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.

Possible values:

  • CreatedAt

  • LastUpdatedAt

  • Status

  • Name

  • IAMUser

  • TrainingDataSourceId

  • RealtimeEndpointStatus

  • MLModelType

  • Algorithm

  • TrainingDataURI

--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

--sort-order (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 .

Possible values:

  • asc

  • dsc

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--starting-token (string)

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

For usage examples, see Pagination in the AWS Command Line Interface User Guide .

--page-size (integer)

The size of each page to get in the AWS service call. This does not affect the number of items returned in the command’s output. Setting a smaller page size results in more calls to the AWS service, retrieving fewer items in each call. This can help prevent the AWS service calls from timing out.

For usage examples, see Pagination in the AWS Command Line Interface User Guide .

--max-items (integer)

The total number of items to return in the command’s output. If the total number of items available is more than the value specified, a NextToken is provided in the command’s output. To resume pagination, provide the NextToken value in the starting-token argument of a subsequent command. Do not use the NextToken response element directly outside of the AWS CLI.

For usage examples, see Pagination in the AWS Command Line Interface User Guide .

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command. The generated JSON skeleton is not stable between versions of the AWS CLI and there are no backwards compatibility guarantees in the JSON skeleton generated.

Global Options

--debug (boolean)

Turn on debug logging.

--endpoint-url (string)

Override command’s default URL with the given URL.

--no-verify-ssl (boolean)

By default, the AWS CLI uses SSL when communicating with AWS services. For each SSL connection, the AWS CLI will verify SSL certificates. This option overrides the default behavior of verifying SSL certificates.

--no-paginate (boolean)

Disable automatic pagination.

--output (string)

The formatting style for command output.

  • json

  • text

  • table

  • yaml

  • yaml-stream

--query (string)

A JMESPath query to use in filtering the response data.

--profile (string)

Use a specific profile from your credential file.

--region (string)

The region to use. Overrides config/env settings.

--version (string)

Display the version of this tool.

--color (string)

Turn on/off color output.

  • on

  • off

  • auto

--no-sign-request (boolean)

Do not sign requests. Credentials will not be loaded if this argument is provided.

--ca-bundle (string)

The CA certificate bundle to use when verifying SSL certificates. Overrides config/env settings.

--cli-read-timeout (int)

The maximum socket read time in seconds. If the value is set to 0, the socket read will be blocking and not timeout. The default value is 60 seconds.

--cli-connect-timeout (int)

The maximum socket connect time in seconds. If the value is set to 0, the socket connect will be blocking and not timeout. The default value is 60 seconds.

--cli-binary-format (string)

The formatting style to be used for binary blobs. The default format is base64. The base64 format expects binary blobs to be provided as a base64 encoded string. The raw-in-base64-out format preserves compatibility with AWS CLI V1 behavior and binary values must be passed literally. When providing contents from a file that map to a binary blob fileb:// will always be treated as binary and use the file contents directly regardless of the cli-binary-format setting. When using file:// the file contents will need to properly formatted for the configured cli-binary-format.

  • base64

  • raw-in-base64-out

--no-cli-pager (boolean)

Disable cli pager for output.

--cli-auto-prompt (boolean)

Automatically prompt for CLI input parameters.

--no-cli-auto-prompt (boolean)

Disable automatically prompt for CLI input parameters.

Output

Results -> (list)

A list of MLModel that meet the search criteria.

(structure)

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 -> (timestamp)

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

LastUpdatedAt -> (timestamp)

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 -> (long)

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

EndpointInfo -> (structure)

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 -> (timestamp)

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 -> (map)

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.

key -> (string)

String type.

value -> (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 -> (timestamp)

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 -> (long)

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

FinishedAt -> (timestamp)

A timestamp represented in epoch time.

StartedAt -> (timestamp)

A timestamp represented in epoch time.

NextToken -> (string)

The ID of the next page in the paginated results that indicates at least one more page follows.

© Copyright 2018, Amazon Web Services. Created using Sphinx.