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[ aws . sagemaker ]

create-auto-ml-job

Description

Creates an AutoPilot job.

See also: AWS API Documentation

See 'aws help' for descriptions of global parameters.

Synopsis

  create-auto-ml-job
--auto-ml-job-name <value>
--input-data-config <value>
--output-data-config <value>
[--problem-type <value>]
[--auto-ml-job-objective <value>]
[--auto-ml-job-config <value>]
--role-arn <value>
[--generate-candidate-definitions-only | --no-generate-candidate-definitions-only]
[--tags <value>]
[--cli-input-json <value>]
[--generate-cli-skeleton <value>]

Options

--auto-ml-job-name (string)

Identifies an AutoPilot job. Must be unique to your account and is case-insensitive.

--input-data-config (list)

Similar to InputDataConfig supported by Tuning. Format(s) supported: CSV.

Shorthand Syntax:

DataSource={S3DataSource={S3DataType=string,S3Uri=string}},CompressionType=string,TargetAttributeName=string ...

JSON Syntax:

[
  {
    "DataSource": {
      "S3DataSource": {
        "S3DataType": "ManifestFile"|"S3Prefix",
        "S3Uri": "string"
      }
    },
    "CompressionType": "None"|"Gzip",
    "TargetAttributeName": "string"
  }
  ...
]

--output-data-config (structure)

Similar to OutputDataConfig supported by Tuning. Format(s) supported: CSV.

Shorthand Syntax:

KmsKeyId=string,S3OutputPath=string

JSON Syntax:

{
  "KmsKeyId": "string",
  "S3OutputPath": "string"
}

--problem-type (string)

Defines the kind of preprocessing and algorithms intended for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression.

Possible values:

  • BinaryClassification
  • MulticlassClassification
  • Regression

--auto-ml-job-objective (structure)

Defines the job's objective. You provide a MetricName and AutoML will infer minimize or maximize. If this is not provided, the most commonly used ObjectiveMetric for problem type will be selected.

Shorthand Syntax:

MetricName=string

JSON Syntax:

{
  "MetricName": "Accuracy"|"MSE"|"F1"|"F1macro"
}

--auto-ml-job-config (structure)

Contains CompletionCriteria and SecurityConfig.

JSON Syntax:

{
  "CompletionCriteria": {
    "MaxCandidates": integer,
    "MaxRuntimePerTrainingJobInSeconds": integer,
    "MaxAutoMLJobRuntimeInSeconds": integer
  },
  "SecurityConfig": {
    "VolumeKmsKeyId": "string",
    "EnableInterContainerTrafficEncryption": true|false,
    "VpcConfig": {
      "SecurityGroupIds": ["string", ...],
      "Subnets": ["string", ...]
    }
  }
}

--role-arn (string)

The ARN of the role that will be used to access the data.

--generate-candidate-definitions-only | --no-generate-candidate-definitions-only (boolean)

This will generate possible candidates without training a model. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

--tags (list)

Each tag consists of a key and an optional value. Tag keys must be unique per resource.

Shorthand Syntax:

Key=string,Value=string ...

JSON Syntax:

[
  {
    "Key": "string",
    "Value": "string"
  }
  ...
]

--cli-input-json (string) Performs service operation based on the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, the CLI 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.

--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. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

See 'aws help' for descriptions of global parameters.

Output

AutoMLJobArn -> (string)

When a job is created, it is assigned a unique ARN.