Table Of Contents

Feedback

User Guide

First time using the AWS CLI? See the User Guide for help getting started.

Note: You are viewing the documentation for an older major version of the AWS CLI (version 1).

AWS CLI version 2, the latest major version of AWS CLI, is now stable and recommended for general use. To view this page for the AWS CLI version 2, click here. For more information see the AWS CLI version 2 installation instructions and migration guide.

[ aws . sagemaker ]

describe-auto-ml-job

Description

Returns information about an Amazon SageMaker job.

See also: AWS API Documentation

See 'aws help' for descriptions of global parameters.

Synopsis

  describe-auto-ml-job
--auto-ml-job-name <value>
[--cli-input-json <value>]
[--generate-cli-skeleton <value>]

Options

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

Request information about a job using that job's unique name.

--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

AutoMLJobName -> (string)

Returns the name of a job.

AutoMLJobArn -> (string)

Returns the job's ARN.

InputDataConfig -> (list)

Returns the job's input data config.

(structure)

Similar to Channel. A channel is a named input source that training algorithms can consume. Refer to Channel for detailed descriptions.

DataSource -> (structure)

The data source.

S3DataSource -> (structure)

The Amazon S3 location of the input data.

Note

The input data must be in CSV format and contain at least 500 rows.

S3DataType -> (string)

The data type.

S3Uri -> (string)

The URL to the Amazon S3 data source.

CompressionType -> (string)

You can use Gzip or None. The default value is None.

TargetAttributeName -> (string)

The name of the target variable in supervised learning, a.k.a. 'y'.

OutputDataConfig -> (structure)

Returns the job's output data config.

KmsKeyId -> (string)

The AWS KMS encryption key ID.

S3OutputPath -> (string)

The Amazon S3 output path. Must be 128 characters or less.

RoleArn -> (string)

The Amazon Resource Name (ARN) of the AWS Identity and Access Management (IAM) role that has read permission to the input data location and write permission to the output data location in Amazon S3.

AutoMLJobObjective -> (structure)

Returns the job's objective.

MetricName -> (string)

The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.

Here are the options:

  • MSE : The mean squared error (MSE) is the average of the squared differences between the predicted and actual values. It is used for regression. MSE values are always positive, the better a model is at predicting the actual values the smaller the MSE value. When the data contains outliers, they tend to dominate the MSE which might cause subpar prediction performance.
  • Accuracy : The ratio of the number correctly classified items to the total number (correctly and incorrectly) classified. It is used for binary and multiclass classification. Measures how close the predicted class values are to the actual values. Accuracy values vary between zero and one, one being perfect accuracy and zero perfect inaccuracy.
  • F1 : The F1 score is the harmonic mean of the precision and recall. It is used for binary classification into classes traditionally referred to as positive and negative. Predictions are said to be true when they match their actual (correct) class; false when they do not. Precision is the ratio of the true positive predictions to all positive predictions (including the false positives) in a data set and measures the quality of the prediction when it predicts the positive class. Recall (or sensitivity) is the ratio of the true positive predictions to all actual positive instances and measures how completely a model predicts the actual class members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount typically depends on specific aspects of a problem. F1 scores vary between zero and one, one being the best possible performance and zero the worst.
  • AUC : The area under the curve (AUC) metric is used to compare and evaluate binary classification by algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities into classifications. The relevant curve is the receiver operating characteristic curve that plots the true positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false positives but more false negatives. AUC is the area under this receiver operating characteristic curve and so provides an aggregated measure of the model performance across all possible classification thresholds. The AUC score can also be interpreted as the probability that a randomly selected positive data point is more likely to be predicted positive than a randomly selected negative example. AUC scores vary between zero and one, one being perfect accuracy and one half not better than a random classifier. Values less that one half predict worse than a random predictor and such consistently bad predictors can be inverted to obtain better than random predictors.
  • F1macro : The F1macro score applies F1 scoring to multiclass classification. In this context, you have multiple classes to predict. You just calculate the precision and recall for each class as you did for the positive class in binary classification. Then used these values to calculate the F1 score for each class and average them to obtain the F1macro score. F1macro scores vary between zero and one, one being the best possible performance and zero the worst.

If you do not specify a metric explicitly, the default behavior is to automatically use:

  • MSE : for regression.
  • F1 : for binary classification
  • Accuracy : for multiclass classification.

ProblemType -> (string)

Returns the job's problem type.

AutoMLJobConfig -> (structure)

Returns the job's config.

CompletionCriteria -> (structure)

How long a job is allowed to run, or how many candidates a job is allowed to generate.

MaxCandidates -> (integer)

The maximum number of times a training job is allowed to run.

MaxRuntimePerTrainingJobInSeconds -> (integer)

The maximum time, in seconds, a job is allowed to run.

MaxAutoMLJobRuntimeInSeconds -> (integer)

The maximum time, in seconds, an AutoML job is allowed to wait for a trial to complete. It must be equal to or greater than MaxRuntimePerTrainingJobInSeconds.

SecurityConfig -> (structure)

Security configuration for traffic encryption or Amazon VPC settings.

VolumeKmsKeyId -> (string)

The key used to encrypt stored data.

EnableInterContainerTrafficEncryption -> (boolean)

Whether to use traffic encryption between the container layers.

VpcConfig -> (structure)

VPC configuration.

SecurityGroupIds -> (list)

The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

(string)

Subnets -> (list)

The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .

(string)

CreationTime -> (timestamp)

Returns the job's creation time.

EndTime -> (timestamp)

Returns the job's end time.

LastModifiedTime -> (timestamp)

Returns the job's last modified time.

FailureReason -> (string)

Returns the job's FailureReason.

BestCandidate -> (structure)

Returns the job's BestCandidate.

CandidateName -> (string)

The candidate name.

FinalAutoMLJobObjectiveMetric -> (structure)

The best candidate result from an AutoML training job.

Type -> (string)

The type of metric with the best result.

MetricName -> (string)

The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName .

Value -> (float)

The value of the metric with the best result.

ObjectiveStatus -> (string)

The objective status.

CandidateSteps -> (list)

The candidate's steps.

(structure)

Information about the steps for a Candidate, and what step it is working on.

CandidateStepType -> (string)

Whether the Candidate is at the transform, training, or processing step.

CandidateStepArn -> (string)

The ARN for the Candidate's step.

CandidateStepName -> (string)

The name for the Candidate's step.

CandidateStatus -> (string)

The candidate's status.

InferenceContainers -> (list)

The inference containers.

(structure)

A list of container definitions that describe the different containers that make up one AutoML candidate. Refer to ContainerDefinition for more details.

Image -> (string)

The ECR path of the container. Refer to ContainerDefinition for more details.

ModelDataUrl -> (string)

The location of the model artifacts. Refer to ContainerDefinition for more details.

Environment -> (map)

Environment variables to set in the container. Refer to ContainerDefinition for more details.

key -> (string)

value -> (string)

CreationTime -> (timestamp)

The creation time.

EndTime -> (timestamp)

The end time.

LastModifiedTime -> (timestamp)

The last modified time.

FailureReason -> (string)

The failure reason.

AutoMLJobStatus -> (string)

Returns the job's AutoMLJobStatus.

AutoMLJobSecondaryStatus -> (string)

Returns the job's AutoMLJobSecondaryStatus.

GenerateCandidateDefinitionsOnly -> (boolean)

Returns the job's output from GenerateCandidateDefinitionsOnly.

AutoMLJobArtifacts -> (structure)

Returns information on the job's artifacts found in AutoMLJobArtifacts.

CandidateDefinitionNotebookLocation -> (string)

The URL to the notebook location.

DataExplorationNotebookLocation -> (string)

The URL to the notebook location.

ResolvedAttributes -> (structure)

This contains ProblemType, AutoMLJobObjective and CompletionCriteria. They're auto-inferred values, if not provided by you. If you do provide them, then they'll be the same as provided.

AutoMLJobObjective -> (structure)

Specifies a metric to minimize or maximize as the objective of a job.

MetricName -> (string)

The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.

Here are the options:

  • MSE : The mean squared error (MSE) is the average of the squared differences between the predicted and actual values. It is used for regression. MSE values are always positive, the better a model is at predicting the actual values the smaller the MSE value. When the data contains outliers, they tend to dominate the MSE which might cause subpar prediction performance.
  • Accuracy : The ratio of the number correctly classified items to the total number (correctly and incorrectly) classified. It is used for binary and multiclass classification. Measures how close the predicted class values are to the actual values. Accuracy values vary between zero and one, one being perfect accuracy and zero perfect inaccuracy.
  • F1 : The F1 score is the harmonic mean of the precision and recall. It is used for binary classification into classes traditionally referred to as positive and negative. Predictions are said to be true when they match their actual (correct) class; false when they do not. Precision is the ratio of the true positive predictions to all positive predictions (including the false positives) in a data set and measures the quality of the prediction when it predicts the positive class. Recall (or sensitivity) is the ratio of the true positive predictions to all actual positive instances and measures how completely a model predicts the actual class members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount typically depends on specific aspects of a problem. F1 scores vary between zero and one, one being the best possible performance and zero the worst.
  • AUC : The area under the curve (AUC) metric is used to compare and evaluate binary classification by algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities into classifications. The relevant curve is the receiver operating characteristic curve that plots the true positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false positives but more false negatives. AUC is the area under this receiver operating characteristic curve and so provides an aggregated measure of the model performance across all possible classification thresholds. The AUC score can also be interpreted as the probability that a randomly selected positive data point is more likely to be predicted positive than a randomly selected negative example. AUC scores vary between zero and one, one being perfect accuracy and one half not better than a random classifier. Values less that one half predict worse than a random predictor and such consistently bad predictors can be inverted to obtain better than random predictors.
  • F1macro : The F1macro score applies F1 scoring to multiclass classification. In this context, you have multiple classes to predict. You just calculate the precision and recall for each class as you did for the positive class in binary classification. Then used these values to calculate the F1 score for each class and average them to obtain the F1macro score. F1macro scores vary between zero and one, one being the best possible performance and zero the worst.

If you do not specify a metric explicitly, the default behavior is to automatically use:

  • MSE : for regression.
  • F1 : for binary classification
  • Accuracy : for multiclass classification.

ProblemType -> (string)

The problem type.

CompletionCriteria -> (structure)

How long a job is allowed to run, or how many candidates a job is allowed to generate.

MaxCandidates -> (integer)

The maximum number of times a training job is allowed to run.

MaxRuntimePerTrainingJobInSeconds -> (integer)

The maximum time, in seconds, a job is allowed to run.

MaxAutoMLJobRuntimeInSeconds -> (integer)

The maximum time, in seconds, an AutoML job is allowed to wait for a trial to complete. It must be equal to or greater than MaxRuntimePerTrainingJobInSeconds.