describe-model¶

Description¶

Provides a JSON containing the overall information about a specific ML model, including model name and ARN, dataset, training and evaluation information, status, and so on.

Synopsis¶

  describe-model
--model-name <value>
[--cli-input-json | --cli-input-yaml]
[--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¶

--model-name (string)

The name of the ML model to be described.

--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.

--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¶

ModelName -> (string)

The name of the ML model being described.

ModelArn -> (string)

The Amazon Resource Name (ARN) of the ML model being described.

DatasetName -> (string)

The name of the dataset being used by the ML being described.

DatasetArn -> (string)

The Amazon Resouce Name (ARN) of the dataset used to create the ML model being described.

Schema -> (string)

A JSON description of the data that is in each time series dataset, including names, column names, and data types.

LabelsInputConfiguration -> (structure)

Specifies configuration information about the labels input, including its S3 location.

S3InputConfiguration -> (structure)

Contains location information for the S3 location being used for label data.

Bucket -> (string)

The name of the S3 bucket holding the label data.

Prefix -> (string)

The prefix for the S3 bucket used for the label data.

LabelGroupName -> (string)

The name of the label group to be used for label data.

TrainingDataStartTime -> (timestamp)

Indicates the time reference in the dataset that was used to begin the subset of training data for the ML model.

TrainingDataEndTime -> (timestamp)

Indicates the time reference in the dataset that was used to end the subset of training data for the ML model.

EvaluationDataStartTime -> (timestamp)

Indicates the time reference in the dataset that was used to begin the subset of evaluation data for the ML model.

EvaluationDataEndTime -> (timestamp)

Indicates the time reference in the dataset that was used to end the subset of evaluation data for the ML model.

RoleArn -> (string)

The Amazon Resource Name (ARN) of a role with permission to access the data source for the ML model being described.

DataPreProcessingConfiguration -> (structure)

The configuration is the TargetSamplingRate , which is the sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute.

When providing a value for the TargetSamplingRate , you must attach the prefix “PT” to the rate you want. The value for a 1 second rate is therefore PT1S , the value for a 15 minute rate is PT15M , and the value for a 1 hour rate is PT1H

TargetSamplingRate -> (string)

The sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute.

When providing a value for the TargetSamplingRate , you must attach the prefix “PT” to the rate you want. The value for a 1 second rate is therefore PT1S , the value for a 15 minute rate is PT15M , and the value for a 1 hour rate is PT1H

Status -> (string)

Specifies the current status of the model being described. Status describes the status of the most recent action of the model.

TrainingExecutionStartTime -> (timestamp)

Indicates the time at which the training of the ML model began.

TrainingExecutionEndTime -> (timestamp)

Indicates the time at which the training of the ML model was completed.

FailedReason -> (string)

If the training of the ML model failed, this indicates the reason for that failure.

ModelMetrics -> (string)

The Model Metrics show an aggregated summary of the model’s performance within the evaluation time range. This is the JSON content of the metrics created when evaluating the model.

LastUpdatedTime -> (timestamp)

Indicates the last time the ML model was updated. The type of update is not specified.

CreatedAt -> (timestamp)

Indicates the time and date at which the ML model was created.

ServerSideKmsKeyId -> (string)

Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.

OffCondition -> (string)

Indicates that the asset associated with this sensor has been shut off. As long as this condition is met, Lookout for Equipment will not use data from this asset for training, evaluation, or inference.

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