com.amazonaws.services.sagemaker.model

Class CreateTrainingJobRequest

java.lang.Object

com.amazonaws.AmazonWebServiceRequest

com.amazonaws.services.sagemaker.model.CreateTrainingJobRequest

All Implemented Interfaces:

HandlerContextAware, ReadLimitInfo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class CreateTrainingJobRequest
extends AmazonWebServiceRequest
implements Serializable, Cloneable

See Also:

AWS API Documentation, Serialized Form

Field Summary

Fields inherited from class com.amazonaws.AmazonWebServiceRequest

NOOP

Constructor Summary

Constructors

Constructor and Description
CreateTrainingJobRequest()

Method Summary

Modifier and Type	Method and Description
CreateTrainingJobRequest	addHyperParametersEntry(String key, String value)
CreateTrainingJobRequest	clearHyperParametersEntries() Removes all the entries added into HyperParameters.
CreateTrainingJobRequest	clone() Creates a shallow clone of this object for all fields except the handler context.
boolean	equals(Object obj)
AlgorithmSpecification	getAlgorithmSpecification() The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode.
Boolean	getEnableNetworkIsolation() Isolates the training container.
Map<String,String>	getHyperParameters() Algorithm-specific parameters that influence the quality of the model.
List<Channel>	getInputDataConfig() An array of Channel objects.
OutputDataConfig	getOutputDataConfig() Specifies the path to the S3 bucket where you want to store model artifacts.
ResourceConfig	getResourceConfig() The resources, including the ML compute instances and ML storage volumes, to use for model training.
String	getRoleArn() The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform tasks on your behalf.
StoppingCondition	getStoppingCondition() Sets a duration for training.
List<Tag>	getTags() An array of key-value pairs.
String	getTrainingJobName() The name of the training job.
VpcConfig	getVpcConfig() A VpcConfig object that specifies the VPC that you want your training job to connect to.
int	hashCode()
Boolean	isEnableNetworkIsolation() Isolates the training container.
void	setAlgorithmSpecification(AlgorithmSpecification algorithmSpecification) The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode.
void	setEnableNetworkIsolation(Boolean enableNetworkIsolation) Isolates the training container.
void	setHyperParameters(Map<String,String> hyperParameters) Algorithm-specific parameters that influence the quality of the model.
void	setInputDataConfig(Collection<Channel> inputDataConfig) An array of Channel objects.
void	setOutputDataConfig(OutputDataConfig outputDataConfig) Specifies the path to the S3 bucket where you want to store model artifacts.
void	setResourceConfig(ResourceConfig resourceConfig) The resources, including the ML compute instances and ML storage volumes, to use for model training.
void	setRoleArn(String roleArn) The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform tasks on your behalf.
void	setStoppingCondition(StoppingCondition stoppingCondition) Sets a duration for training.
void	setTags(Collection<Tag> tags) An array of key-value pairs.
void	setTrainingJobName(String trainingJobName) The name of the training job.
void	setVpcConfig(VpcConfig vpcConfig) A VpcConfig object that specifies the VPC that you want your training job to connect to.
String	toString() Returns a string representation of this object.
CreateTrainingJobRequest	withAlgorithmSpecification(AlgorithmSpecification algorithmSpecification) The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode.
CreateTrainingJobRequest	withEnableNetworkIsolation(Boolean enableNetworkIsolation) Isolates the training container.
CreateTrainingJobRequest	withHyperParameters(Map<String,String> hyperParameters) Algorithm-specific parameters that influence the quality of the model.
CreateTrainingJobRequest	withInputDataConfig(Channel... inputDataConfig) An array of Channel objects.
CreateTrainingJobRequest	withInputDataConfig(Collection<Channel> inputDataConfig) An array of Channel objects.
CreateTrainingJobRequest	withOutputDataConfig(OutputDataConfig outputDataConfig) Specifies the path to the S3 bucket where you want to store model artifacts.
CreateTrainingJobRequest	withResourceConfig(ResourceConfig resourceConfig) The resources, including the ML compute instances and ML storage volumes, to use for model training.
CreateTrainingJobRequest	withRoleArn(String roleArn) The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform tasks on your behalf.
CreateTrainingJobRequest	withStoppingCondition(StoppingCondition stoppingCondition) Sets a duration for training.
CreateTrainingJobRequest	withTags(Collection<Tag> tags) An array of key-value pairs.
CreateTrainingJobRequest	withTags(Tag... tags) An array of key-value pairs.
CreateTrainingJobRequest	withTrainingJobName(String trainingJobName) The name of the training job.
CreateTrainingJobRequest	withVpcConfig(VpcConfig vpcConfig) A VpcConfig object that specifies the VPC that you want your training job to connect to.

Methods inherited from class com.amazonaws.AmazonWebServiceRequest

addHandlerContext, getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getHandlerContext, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestCredentialsProvider, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeout

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

CreateTrainingJobRequest

public CreateTrainingJobRequest()

Method Detail

setTrainingJobName

public void setTrainingJobName(String trainingJobName)

The name of the training job. The name must be unique within an AWS Region in an AWS account.

Parameters:

trainingJobName - The name of the training job. The name must be unique within an AWS Region in an AWS account.

getTrainingJobName

public String getTrainingJobName()

The name of the training job. The name must be unique within an AWS Region in an AWS account.

Returns:

The name of the training job. The name must be unique within an AWS Region in an AWS account.

withTrainingJobName

public CreateTrainingJobRequest withTrainingJobName(String trainingJobName)

The name of the training job. The name must be unique within an AWS Region in an AWS account.

Parameters:

trainingJobName - The name of the training job. The name must be unique within an AWS Region in an AWS account.

Returns:

Returns a reference to this object so that method calls can be chained together.

getHyperParameters

public Map<String,String> getHyperParameters()

Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.

You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint.

Returns:

Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.

You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint.

setHyperParameters

public void setHyperParameters(Map<String,String> hyperParameters)

Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.

You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint.

Parameters:

hyperParameters - Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.

You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint.

withHyperParameters

public CreateTrainingJobRequest withHyperParameters(Map<String,String> hyperParameters)

Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.

You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint.

Parameters:

hyperParameters - Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.

You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint.

Returns:

Returns a reference to this object so that method calls can be chained together.

addHyperParametersEntry

public CreateTrainingJobRequest addHyperParametersEntry(String key,
                                                        String value)

clearHyperParametersEntries

public CreateTrainingJobRequest clearHyperParametersEntries()

Removes all the entries added into HyperParameters.

Returns:

Returns a reference to this object so that method calls can be chained together.

setAlgorithmSpecification

public void setAlgorithmSpecification(AlgorithmSpecification algorithmSpecification)

The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

Parameters:

algorithmSpecification - The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

getAlgorithmSpecification

public AlgorithmSpecification getAlgorithmSpecification()

The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

Returns:

The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

withAlgorithmSpecification

public CreateTrainingJobRequest withAlgorithmSpecification(AlgorithmSpecification algorithmSpecification)

The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

Parameters:

algorithmSpecification - The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

Returns:

Returns a reference to this object so that method calls can be chained together.

setRoleArn

public void setRoleArn(String roleArn)

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform tasks on your behalf.

During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

Parameters:

roleArn - The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform tasks on your behalf.

During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

getRoleArn

public String getRoleArn()

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform tasks on your behalf.

During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

Returns:

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform tasks on your behalf.

During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

withRoleArn

public CreateTrainingJobRequest withRoleArn(String roleArn)

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform tasks on your behalf.

During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

Parameters:

roleArn - The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform tasks on your behalf.

During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

Returns:

Returns a reference to this object so that method calls can be chained together.

getInputDataConfig

public List<Channel> getInputDataConfig()

An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.

Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3 location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams.

Returns:

An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.

Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3 location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams.

setInputDataConfig

public void setInputDataConfig(Collection<Channel> inputDataConfig)

An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.

Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3 location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams.

Parameters:

inputDataConfig - An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.

Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3 location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams.

withInputDataConfig

public CreateTrainingJobRequest withInputDataConfig(Channel... inputDataConfig)

An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.

Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3 location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams.

NOTE: This method appends the values to the existing list (if any). Use setInputDataConfig(java.util.Collection) or withInputDataConfig(java.util.Collection) if you want to override the existing values.

Parameters:

inputDataConfig - An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.

Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3 location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams.

Returns:

Returns a reference to this object so that method calls can be chained together.

withInputDataConfig

public CreateTrainingJobRequest withInputDataConfig(Collection<Channel> inputDataConfig)

An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.

Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3 location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams.

Parameters:

inputDataConfig - An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.

Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3 location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams.

Returns:

Returns a reference to this object so that method calls can be chained together.

setOutputDataConfig

public void setOutputDataConfig(OutputDataConfig outputDataConfig)

Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

Parameters:

outputDataConfig - Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

getOutputDataConfig

public OutputDataConfig getOutputDataConfig()

Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

Returns:

Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

withOutputDataConfig

public CreateTrainingJobRequest withOutputDataConfig(OutputDataConfig outputDataConfig)

Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

Parameters:

outputDataConfig - Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

Returns:

Returns a reference to this object so that method calls can be chained together.

setResourceConfig

public void setResourceConfig(ResourceConfig resourceConfig)

The resources, including the ML compute instances and ML storage volumes, to use for model training.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

Parameters:

resourceConfig - The resources, including the ML compute instances and ML storage volumes, to use for model training.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

getResourceConfig

public ResourceConfig getResourceConfig()

The resources, including the ML compute instances and ML storage volumes, to use for model training.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

Returns:

The resources, including the ML compute instances and ML storage volumes, to use for model training.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

withResourceConfig

public CreateTrainingJobRequest withResourceConfig(ResourceConfig resourceConfig)

The resources, including the ML compute instances and ML storage volumes, to use for model training.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

Parameters:

resourceConfig - The resources, including the ML compute instances and ML storage volumes, to use for model training.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

Returns:

Returns a reference to this object so that method calls can be chained together.

setVpcConfig

public void setVpcConfig(VpcConfig vpcConfig)

A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

Parameters:

vpcConfig - A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

getVpcConfig

public VpcConfig getVpcConfig()

A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

Returns:

A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

withVpcConfig

public CreateTrainingJobRequest withVpcConfig(VpcConfig vpcConfig)

A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

Parameters:

vpcConfig - A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

Returns:

Returns a reference to this object so that method calls can be chained together.

setStoppingCondition

public void setStoppingCondition(StoppingCondition stoppingCondition)

Sets a duration for training. Use this parameter to cap model training costs. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job. This intermediate data is a valid model artifact. You can use it to create a model using the CreateModel API.

Parameters:

stoppingCondition - Sets a duration for training. Use this parameter to cap model training costs. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job. This intermediate data is a valid model artifact. You can use it to create a model using the CreateModel API.

getStoppingCondition

public StoppingCondition getStoppingCondition()

Sets a duration for training. Use this parameter to cap model training costs. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job. This intermediate data is a valid model artifact. You can use it to create a model using the CreateModel API.

Returns:

Sets a duration for training. Use this parameter to cap model training costs. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job. This intermediate data is a valid model artifact. You can use it to create a model using the CreateModel API.

withStoppingCondition

public CreateTrainingJobRequest withStoppingCondition(StoppingCondition stoppingCondition)

Sets a duration for training. Use this parameter to cap model training costs. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job. This intermediate data is a valid model artifact. You can use it to create a model using the CreateModel API.

Parameters:

stoppingCondition - Sets a duration for training. Use this parameter to cap model training costs. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job. This intermediate data is a valid model artifact. You can use it to create a model using the CreateModel API.

Returns:

Returns a reference to this object so that method calls can be chained together.

getTags

public List<Tag> getTags()

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Returns:

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

setTags

public void setTags(Collection<Tag> tags)

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Parameters:

tags - An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

withTags

public CreateTrainingJobRequest withTags(Tag... tags)

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

NOTE: This method appends the values to the existing list (if any). Use setTags(java.util.Collection) or withTags(java.util.Collection) if you want to override the existing values.

Parameters:

tags - An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Returns:

Returns a reference to this object so that method calls can be chained together.

withTags

public CreateTrainingJobRequest withTags(Collection<Tag> tags)

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Parameters:

tags - An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Returns:

Returns a reference to this object so that method calls can be chained together.

setEnableNetworkIsolation

public void setEnableNetworkIsolation(Boolean enableNetworkIsolation)

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specifed VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

Parameters:

enableNetworkIsolation - Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specifed VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

getEnableNetworkIsolation

public Boolean getEnableNetworkIsolation()

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specifed VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

Returns:

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specifed VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

withEnableNetworkIsolation

public CreateTrainingJobRequest withEnableNetworkIsolation(Boolean enableNetworkIsolation)

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specifed VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

Parameters:

enableNetworkIsolation - Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specifed VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

Returns:

Returns a reference to this object so that method calls can be chained together.

isEnableNetworkIsolation

public Boolean isEnableNetworkIsolation()

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specifed VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

Returns:

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specifed VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

toString

public String toString()

Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.

Overrides:

toString in class Object

Returns:

A string representation of this object.

See Also:

Object.toString()

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object

clone

public CreateTrainingJobRequest clone()

Description copied from class: AmazonWebServiceRequest

Creates a shallow clone of this object for all fields except the handler context. Explicitly does not clone the deep structure of the other fields in the message.

Overrides:

clone in class AmazonWebServiceRequest

See Also:

Object.clone()