SageMaker / Client / create_auto_ml_job_v2

create_auto_ml_job_v2#

SageMaker.Client.create_auto_ml_job_v2(**kwargs)#

Creates an Amazon SageMaker AutoML job that uses non-tabular data such as images or text for Computer Vision or Natural Language Processing problems.

Find the resulting model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.

To create an AutoMLJob using tabular data, see CreateAutoMLJob.

Note

This API action is callable through SageMaker Canvas only. Calling it directly from the CLI or an SDK results in an error.

See also: AWS API Documentation

Request Syntax

response = client.create_auto_ml_job_v2(
    AutoMLJobName='string',
    AutoMLJobInputDataConfig=[
        {
            'ChannelType': 'training'|'validation',
            'ContentType': 'string',
            'CompressionType': 'None'|'Gzip',
            'DataSource': {
                'S3DataSource': {
                    'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                    'S3Uri': 'string'
                }
            }
        },
    ],
    OutputDataConfig={
        'KmsKeyId': 'string',
        'S3OutputPath': 'string'
    },
    AutoMLProblemTypeConfig={
        'ImageClassificationJobConfig': {
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            }
        },
        'TextClassificationJobConfig': {
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'ContentColumn': 'string',
            'TargetLabelColumn': 'string'
        }
    },
    RoleArn='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    SecurityConfig={
        'VolumeKmsKeyId': 'string',
        'EnableInterContainerTrafficEncryption': True|False,
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        }
    },
    AutoMLJobObjective={
        'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'
    },
    ModelDeployConfig={
        'AutoGenerateEndpointName': True|False,
        'EndpointName': 'string'
    },
    DataSplitConfig={
        'ValidationFraction': ...
    }
)

Parameters:

AutoMLJobName (string) –
[REQUIRED]

Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
AutoMLJobInputDataConfig (list) –
[REQUIRED]

An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to InputDataConfig supported by CreateAutoMLJob. The supported formats depend on the problem type:
- ImageClassification: S3Prefix, ManifestFile, AugmentedManifestFile
- TextClassification: S3Prefix
- (dict) –
  
  A channel is a named input source that training algorithms can consume. This channel is used for the non tabular training data of an AutoML job using the V2 API. For tabular training data, see AutoMLChannel. For more information, see Channel.
  - ChannelType (string) –
    
    The type of channel. Defines whether the data are used for training or validation. The default value is training. Channels for training and validation must share the same ContentType
  - ContentType (string) –
    
    The content type of the data from the input source. The following are the allowed content types for different problems:
    - ImageClassification: image/png, image/jpeg, image/*
    - TextClassification: text/csv;header=present
  - CompressionType (string) –
    
    The allowed compression types depend on the input format. We allow the compression type Gzip for S3Prefix inputs only. For all other inputs, the compression type should be None. If no compression type is provided, we default to None.
  - DataSource (dict) –
    
    The data source for an AutoML channel.
    - S3DataSource (dict) – [REQUIRED]
      
      The Amazon S3 location of the input data.
      - S3DataType (string) – [REQUIRED]
        
        The data type.
        
        If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. The S3Prefix should have the following format: s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE
        
        If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training. A ManifestFile should have the format shown below: [ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"}, "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1", "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2", ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]
        
        If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2). Here is a minimal, single-record example of an AugmentedManifestFile: {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg", "label-metadata": {"class-name": "cat" } For more information on AugmentedManifestFile, see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.
      - S3Uri (string) – [REQUIRED]
        
        The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.
OutputDataConfig (dict) –
[REQUIRED]

Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
- KmsKeyId (string) –
  
  The Key Management Service (KMS) encryption key ID.
- S3OutputPath (string) – [REQUIRED]
  
  The Amazon S3 output path. Must be 128 characters or less.
AutoMLProblemTypeConfig (dict) –
[REQUIRED]

Defines the configuration settings of one of the supported problem types.

Note

This is a Tagged Union structure. Only one of the following top level keys can be set: ImageClassificationJobConfig, TextClassificationJobConfig.
- ImageClassificationJobConfig (dict) –
  
  Settings used to configure an AutoML job using the V2 API for the image classification problem type.
  - CompletionCriteria (dict) –
    
    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.
      
      For V2 jobs (jobs created by calling CreateAutoMLJobV2), the supported value is 1.
    - MaxRuntimePerTrainingJobInSeconds (integer) –
      
      The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
      
      For V2 jobs (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.
    - MaxAutoMLJobRuntimeInSeconds (integer) –
      
      The maximum runtime, in seconds, an AutoML job has to complete.
      
      If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
- TextClassificationJobConfig (dict) –
  
  Settings used to configure an AutoML job using the V2 API for the text classification problem type.
  - CompletionCriteria (dict) –
    
    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.
      
      For V2 jobs (jobs created by calling CreateAutoMLJobV2), the supported value is 1.
    - MaxRuntimePerTrainingJobInSeconds (integer) –
      
      The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
      
      For V2 jobs (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.
    - MaxAutoMLJobRuntimeInSeconds (integer) –
      
      The maximum runtime, in seconds, an AutoML job has to complete.
      
      If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
  - ContentColumn (string) –
    
    The name of the column used to provide the sentences to be classified. It should not be the same as the target column.
  - TargetLabelColumn (string) –
    
    The name of the column used to provide the class labels. It should not be same as the content column.
RoleArn (string) –
[REQUIRED]

The ARN of the role that is used to access the data.
Tags (list) –
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.
- (dict) –
  
  A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
  
  You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.
  
  For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.
  - Key (string) – [REQUIRED]
    
    The tag key. Tag keys must be unique per resource.
  - Value (string) – [REQUIRED]
    
    The tag value.
SecurityConfig (dict) –
The 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 (dict) –
  
  The VPC configuration.
  - SecurityGroupIds (list) – [REQUIRED]
    
    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) – [REQUIRED]
    
    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) –
AutoMLJobObjective (dict) –
Specifies a metric to minimize or maximize as the objective of a job. For CreateAutoMLJobV2, only Accuracy is supported.
- MetricName (string) – [REQUIRED]
  
  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:
  
  Accuracy
  
  The ratio of the number of correctly classified items to the total number of (correctly and incorrectly) classified items. It is used for both binary and multiclass classification. Accuracy measures how close the predicted class values are to the actual values. Values for accuracy metrics vary between zero (0) and one (1). A value of 1 indicates perfect accuracy, and 0 indicates perfect inaccuracy.
  
  AUC
  
  The area under the curve (AUC) metric is used to compare and evaluate binary classification by algorithms that return probabilities, such as logistic regression. To map the probabilities into classifications, these are compared against a threshold value.
  
  The relevant curve is the receiver operating characteristic curve (ROC curve). The ROC curve 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 ROC curve. Therefore, AUC provides an aggregated measure of the model performance across all possible classification thresholds. AUC scores vary between 0 and 1. A score of 1 indicates perfect accuracy, and a score of one half (0.5) indicates that the prediction is not better than a random classifier.
  
  BalancedAccuracy
  
  BalancedAccuracy is a metric that measures the ratio of accurate predictions to all predictions. This ratio is calculated after normalizing true positives (TP) and true negatives (TN) by the total number of positive (P) and negative (N) values. It is used in both binary and multiclass classification and is defined as follows: 0.5*((TP/P)+(TN/N)), with values ranging from 0 to 1. BalancedAccuracy gives a better measure of accuracy when the number of positives or negatives differ greatly from each other in an imbalanced dataset. For example, when only 1% of email is spam.
  
  F1
  
  The F1 score is the harmonic mean of the precision and recall, defined as follows: F1 = 2 * (precision * recall) / (precision + 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, and false when they do not.
  
  Precision is the ratio of the true positive predictions to all positive predictions, and it includes the false positives in a dataset. Precision 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. Recall measures how completely a model predicts the actual class members in a dataset.
  
  F1 scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.
  
  F1macro
  
  The F1macro score applies F1 scoring to multiclass classification problems. It does this by calculating the precision and recall, and then taking their harmonic mean to calculate the F1 score for each class. Lastly, the F1macro averages the individual scores to obtain the F1macro score. F1macro scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.
  
  MAE
  
  The mean absolute error (MAE) is a measure of how different the predicted and actual values are, when they’re averaged over all values. MAE is commonly used in regression analysis to understand model prediction error. If there is linear regression, MAE represents the average distance from a predicted line to the actual value. MAE is defined as the sum of absolute errors divided by the number of observations. Values range from 0 to infinity, with smaller numbers indicating a better model fit to the data.
  
  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 is
  
  Precision
  
  Precision measures how well an algorithm predicts the true positives (TP) out of all of the positives that it identifies. It is defined as follows: Precision = TP/(TP+FP), with values ranging from zero (0) to one (1), and is used in binary classification. Precision is an important metric when the cost of a false positive is high. For example, the cost of a false positive is very high if an airplane safety system is falsely deemed safe to fly. A false positive (FP) reflects a positive prediction that is actually negative in the data.
  
  PrecisionMacro
  
  The precision macro computes precision for multiclass classification problems. It does this by calculating precision for each class and averaging scores to obtain precision for several classes. PrecisionMacro scores range from zero (0) to one (1). Higher scores reflect the model’s ability to predict true positives (TP) out of all of the positives that it identifies, averaged across multiple classes.
  
  R2
  
  R2, also known as the coefficient of determination, is used in regression to quantify how much a model can explain the variance of a dependent variable. Values range from one (1) to negative one (-1). Higher numbers indicate a higher fraction of explained variability. R2 values close to zero (0) indicate that very little of the dependent variable can be explained by the model. Negative values indicate a poor fit and that the model is outperformed by a constant function. For linear regression, this is a horizontal line.
  
  Recall
  
  Recall measures how well an algorithm correctly predicts all of the true positives (TP) in a dataset. A true positive is a positive prediction that is also an actual positive value in the data. Recall is defined as follows: Recall = TP/(TP+FN), with values ranging from 0 to 1. Higher scores reflect a better ability of the model to predict true positives (TP) in the data, and is used in binary classification.
  
  Recall is important when testing for cancer because it’s used to find all of the true positives. A false positive (FP) reflects a positive prediction that is actually negative in the data. It is often insufficient to measure only recall, because predicting every output as a true positive yield a perfect recall score.
  
  RecallMacro
  
  The RecallMacro computes recall for multiclass classification problems by calculating recall for each class and averaging scores to obtain recall for several classes. RecallMacro scores range from 0 to 1. Higher scores reflect the model’s ability to predict true positives (TP) in a dataset. Whereas, a true positive reflects a positive prediction that is also an actual positive value in the data. It is often insufficient to measure only recall, because predicting every output as a true positive yields a perfect recall score.
  
  RMSE
  
  Root mean squared error (RMSE) measures the square root of the squared difference between predicted and actual values, and it’s averaged over all values. It is used in regression analysis to understand model prediction error. It’s an important metric to indicate the presence of large model errors and outliers. Values range from zero (0) to infinity, with smaller numbers indicating a better model fit to the data. RMSE is dependent on scale, and should not be used to compare datasets of different sizes.
  
  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.
ModelDeployConfig (dict) –
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
- AutoGenerateEndpointName (boolean) –
  
  Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False.
  
  Note
  
  If you set AutoGenerateEndpointName to True, do not specify the EndpointName; otherwise a 400 error is thrown.
- EndpointName (string) –
  
  Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
  
  Note
  
  Specify the EndpointName if and only if you set AutoGenerateEndpointName to False; otherwise a 400 error is thrown.
DataSplitConfig (dict) –
This structure specifies how to split the data into train and validation datasets.

If you are using the V1 API (for example CreateAutoMLJob) or the V2 API for Natural Language Processing problems (for example CreateAutoMLJobV2 with a TextClassificationJobConfig problem type), the validation and training datasets must contain the same headers. Also, for V1 API jobs, the validation dataset must be less than 2 GB in size.
- ValidationFraction (float) –
  
  The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.

Return type:

dict

Returns:

Response Syntax

{
    'AutoMLJobArn': 'string'
}

Response Structure

(dict) –
- AutoMLJobArn (string) –
  
  The unique ARN assigned to the AutoMLJob when it is created.

Exceptions

SageMaker.Client.exceptions.ResourceInUse
SageMaker.Client.exceptions.ResourceLimitExceeded