CreateTrainingJobRequest (AWS SDK for Java

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()

Constructors
Constructor and Description
`CreateTrainingJobRequest()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`CreateTrainingJobRequest`	`addEnvironmentEntry(String key, String value)` Add a single Environment entry
`CreateTrainingJobRequest`	`addHyperParametersEntry(String key, String value)` Add a single HyperParameters entry
`CreateTrainingJobRequest`	`clearEnvironmentEntries()` Removes all the entries added into Environment.
`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.
`CheckpointConfig`	`getCheckpointConfig()` Contains information about the output location for managed spot training checkpoint data.
`DebugHookConfig`	`getDebugHookConfig()`
`List<DebugRuleConfiguration>`	`getDebugRuleConfigurations()` Configuration information for Debugger rules for debugging output tensors.
`Boolean`	`getEnableInterContainerTrafficEncryption()` To encrypt all communications between ML compute instances in distributed training, choose `True`.
`Boolean`	`getEnableManagedSpotTraining()` To train models using managed spot training, choose `True`.
`Boolean`	`getEnableNetworkIsolation()` Isolates the training container.
`Map<String,String>`	`getEnvironment()` The environment variables to set in the Docker container.
`ExperimentConfig`	`getExperimentConfig()`
`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 location where you want to store model artifacts.
`ProfilerConfig`	`getProfilerConfig()`
`List<ProfilerRuleConfiguration>`	`getProfilerRuleConfigurations()` Configuration information for Debugger rules for profiling system and framework metrics.
`ResourceConfig`	`getResourceConfig()` The resources, including the ML compute instances and ML storage volumes, to use for model training.
`RetryStrategy`	`getRetryStrategy()` The number of times to retry the job when the job fails due to an `InternalServerError`.
`String`	`getRoleArn()` The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
`StoppingCondition`	`getStoppingCondition()` Specifies a limit to how long a model training job can run.
`List<Tag>`	`getTags()` An array of key-value pairs.
`TensorBoardOutputConfig`	`getTensorBoardOutputConfig()`
`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`	`isEnableInterContainerTrafficEncryption()` To encrypt all communications between ML compute instances in distributed training, choose `True`.
`Boolean`	`isEnableManagedSpotTraining()` To train models using managed spot training, choose `True`.
`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`	`setCheckpointConfig(CheckpointConfig checkpointConfig)` Contains information about the output location for managed spot training checkpoint data.
`void`	`setDebugHookConfig(DebugHookConfig debugHookConfig)`
`void`	`setDebugRuleConfigurations(Collection<DebugRuleConfiguration> debugRuleConfigurations)` Configuration information for Debugger rules for debugging output tensors.
`void`	`setEnableInterContainerTrafficEncryption(Boolean enableInterContainerTrafficEncryption)` To encrypt all communications between ML compute instances in distributed training, choose `True`.
`void`	`setEnableManagedSpotTraining(Boolean enableManagedSpotTraining)` To train models using managed spot training, choose `True`.
`void`	`setEnableNetworkIsolation(Boolean enableNetworkIsolation)` Isolates the training container.
`void`	`setEnvironment(Map<String,String> environment)` The environment variables to set in the Docker container.
`void`	`setExperimentConfig(ExperimentConfig experimentConfig)`
`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 location where you want to store model artifacts.
`void`	`setProfilerConfig(ProfilerConfig profilerConfig)`
`void`	`setProfilerRuleConfigurations(Collection<ProfilerRuleConfiguration> profilerRuleConfigurations)` Configuration information for Debugger rules for profiling system and framework metrics.
`void`	`setResourceConfig(ResourceConfig resourceConfig)` The resources, including the ML compute instances and ML storage volumes, to use for model training.
`void`	`setRetryStrategy(RetryStrategy retryStrategy)` The number of times to retry the job when the job fails due to an `InternalServerError`.
`void`	`setRoleArn(String roleArn)` The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
`void`	`setStoppingCondition(StoppingCondition stoppingCondition)` Specifies a limit to how long a model training job can run.
`void`	`setTags(Collection<Tag> tags)` An array of key-value pairs.
`void`	`setTensorBoardOutputConfig(TensorBoardOutputConfig tensorBoardOutputConfig)`
`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`	`withCheckpointConfig(CheckpointConfig checkpointConfig)` Contains information about the output location for managed spot training checkpoint data.
`CreateTrainingJobRequest`	`withDebugHookConfig(DebugHookConfig debugHookConfig)`
`CreateTrainingJobRequest`	`withDebugRuleConfigurations(Collection<DebugRuleConfiguration> debugRuleConfigurations)` Configuration information for Debugger rules for debugging output tensors.
`CreateTrainingJobRequest`	`withDebugRuleConfigurations(DebugRuleConfiguration... debugRuleConfigurations)` Configuration information for Debugger rules for debugging output tensors.
`CreateTrainingJobRequest`	`withEnableInterContainerTrafficEncryption(Boolean enableInterContainerTrafficEncryption)` To encrypt all communications between ML compute instances in distributed training, choose `True`.
`CreateTrainingJobRequest`	`withEnableManagedSpotTraining(Boolean enableManagedSpotTraining)` To train models using managed spot training, choose `True`.
`CreateTrainingJobRequest`	`withEnableNetworkIsolation(Boolean enableNetworkIsolation)` Isolates the training container.
`CreateTrainingJobRequest`	`withEnvironment(Map<String,String> environment)` The environment variables to set in the Docker container.
`CreateTrainingJobRequest`	`withExperimentConfig(ExperimentConfig experimentConfig)`
`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 location where you want to store model artifacts.
`CreateTrainingJobRequest`	`withProfilerConfig(ProfilerConfig profilerConfig)`
`CreateTrainingJobRequest`	`withProfilerRuleConfigurations(Collection<ProfilerRuleConfiguration> profilerRuleConfigurations)` Configuration information for Debugger rules for profiling system and framework metrics.
`CreateTrainingJobRequest`	`withProfilerRuleConfigurations(ProfilerRuleConfiguration... profilerRuleConfigurations)` Configuration information for Debugger rules for profiling system and framework metrics.
`CreateTrainingJobRequest`	`withResourceConfig(ResourceConfig resourceConfig)` The resources, including the ML compute instances and ML storage volumes, to use for model training.
`CreateTrainingJobRequest`	`withRetryStrategy(RetryStrategy retryStrategy)` The number of times to retry the job when the job fails due to an `InternalServerError`.
`CreateTrainingJobRequest`	`withRoleArn(String roleArn)` The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
`CreateTrainingJobRequest`	`withStoppingCondition(StoppingCondition stoppingCondition)` Specifies a limit to how long a model training job can run.
`CreateTrainingJobRequest`	`withTags(Collection<Tag> tags)` An array of key-value pairs.
`CreateTrainingJobRequest`	`withTags(Tag... tags)` An array of key-value pairs.
`CreateTrainingJobRequest`	`withTensorBoardOutputConfig(TensorBoardOutputConfig tensorBoardOutputConfig)`
`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 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 Amazon Web Services Region in an Amazon Web Services account.
    
    Parameters:
    
    trainingJobName - The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
  - getTrainingJobName
```
public String getTrainingJobName()
```
    The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
    
    Returns:
    
    The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
  - withTrainingJobName
```
public CreateTrainingJobRequest withTrainingJobName(String trainingJobName)
```
    The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
    
    Parameters:
    
    trainingJobName - The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services 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)
```
    Add a single HyperParameters entry
    
    See Also:
    
    withHyperParameters(java.util.Map<java.lang.String, java.lang.String>)
  - 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 can assume 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 can assume 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 can assume 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 can assume 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 can assume 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 can assume 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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
    
    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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
  - 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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
    
    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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
  - 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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
    
    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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
    
    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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
    
    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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
    
    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 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
    
    Parameters:
    
    outputDataConfig - Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
  - getOutputDataConfig
```
public OutputDataConfig getOutputDataConfig()
```
    Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
    
    Returns:
    
    Specifies the path to the S3 location 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 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
    
    Parameters:
    
    outputDataConfig - Specifies the path to the S3 location 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)
```
    Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API 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 can use this 120-second window to save the model artifacts, so the results of training are not lost.
    
    Parameters:
    
    stoppingCondition - Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API 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 can use this 120-second window to save the model artifacts, so the results of training are not lost.
  - getStoppingCondition
```
public StoppingCondition getStoppingCondition()
```
    Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API 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 can use this 120-second window to save the model artifacts, so the results of training are not lost.
    
    Returns:
    
    Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API 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 can use this 120-second window to save the model artifacts, so the results of training are not lost.
  - withStoppingCondition
```
public CreateTrainingJobRequest withStoppingCondition(StoppingCondition stoppingCondition)
```
    Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API 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 can use this 120-second window to save the model artifacts, so the results of training are not lost.
    
    Parameters:
    
    stoppingCondition - Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API 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 can use this 120-second window to save the model artifacts, so the results of training are not lost.
    
    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. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
    
    Returns:
    
    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
  - setTags
```
public void setTags(Collection<Tag> tags)
```
    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
    
    Parameters:
    
    tags - An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
  - withTags
```
public CreateTrainingJobRequest withTags(Tag... tags)
```
    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
    
    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. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
    
    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. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
    
    Parameters:
    
    tags - An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
    
    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 you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
    
    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 you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
  - 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 you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
    
    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 you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
  - 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 you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
    
    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 you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
    
    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 you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
    
    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 you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
  - setEnableInterContainerTrafficEncryption
```
public void setEnableInterContainerTrafficEncryption(Boolean enableInterContainerTrafficEncryption)
```
    To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.
    
    Parameters:
    
    enableInterContainerTrafficEncryption - To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.
  - getEnableInterContainerTrafficEncryption
```
public Boolean getEnableInterContainerTrafficEncryption()
```
    To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.
    
    Returns:
    
    To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.
  - withEnableInterContainerTrafficEncryption
```
public CreateTrainingJobRequest withEnableInterContainerTrafficEncryption(Boolean enableInterContainerTrafficEncryption)
```
    To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.
    
    Parameters:
    
    enableInterContainerTrafficEncryption - To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - isEnableInterContainerTrafficEncryption
```
public Boolean isEnableInterContainerTrafficEncryption()
```
    To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.
    
    Returns:
    
    To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.
  - setEnableManagedSpotTraining
```
public void setEnableManagedSpotTraining(Boolean enableManagedSpotTraining)
```
    To train models using managed spot training, choose True. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.
    
    The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
    
    Parameters:
    
    enableManagedSpotTraining - To train models using managed spot training, choose True. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.
    
    The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
  - getEnableManagedSpotTraining
```
public Boolean getEnableManagedSpotTraining()
```
    To train models using managed spot training, choose True. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.
    
    The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
    
    Returns:
    
    To train models using managed spot training, choose True. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.
    
    The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
  - withEnableManagedSpotTraining
```
public CreateTrainingJobRequest withEnableManagedSpotTraining(Boolean enableManagedSpotTraining)
```
    To train models using managed spot training, choose True. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.
    
    The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
    
    Parameters:
    
    enableManagedSpotTraining - To train models using managed spot training, choose True. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.
    
    The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - isEnableManagedSpotTraining
```
public Boolean isEnableManagedSpotTraining()
```
    To train models using managed spot training, choose True. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.
    
    The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
    
    Returns:
    
    To train models using managed spot training, choose True. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.
    
    The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
  - setCheckpointConfig
```
public void setCheckpointConfig(CheckpointConfig checkpointConfig)
```
    Contains information about the output location for managed spot training checkpoint data.
    
    Parameters:
    
    checkpointConfig - Contains information about the output location for managed spot training checkpoint data.
  - getCheckpointConfig
```
public CheckpointConfig getCheckpointConfig()
```
    Contains information about the output location for managed spot training checkpoint data.
    
    Returns:
    
    Contains information about the output location for managed spot training checkpoint data.
  - withCheckpointConfig
```
public CreateTrainingJobRequest withCheckpointConfig(CheckpointConfig checkpointConfig)
```
    Contains information about the output location for managed spot training checkpoint data.
    
    Parameters:
    
    checkpointConfig - Contains information about the output location for managed spot training checkpoint data.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setDebugHookConfig
```
public void setDebugHookConfig(DebugHookConfig debugHookConfig)
```
    Parameters:
    
    debugHookConfig -
  - getDebugHookConfig
```
public DebugHookConfig getDebugHookConfig()
```
    Returns:
  - withDebugHookConfig
```
public CreateTrainingJobRequest withDebugHookConfig(DebugHookConfig debugHookConfig)
```
    Parameters:
    
    debugHookConfig -
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - getDebugRuleConfigurations
```
public List<DebugRuleConfiguration> getDebugRuleConfigurations()
```
    Configuration information for Debugger rules for debugging output tensors.
    
    Returns:
    
    Configuration information for Debugger rules for debugging output tensors.
  - setDebugRuleConfigurations
```
public void setDebugRuleConfigurations(Collection<DebugRuleConfiguration> debugRuleConfigurations)
```
    Configuration information for Debugger rules for debugging output tensors.
    
    Parameters:
    
    debugRuleConfigurations - Configuration information for Debugger rules for debugging output tensors.
  - withDebugRuleConfigurations
```
public CreateTrainingJobRequest withDebugRuleConfigurations(DebugRuleConfiguration... debugRuleConfigurations)
```
    Configuration information for Debugger rules for debugging output tensors.
    
    NOTE: This method appends the values to the existing list (if any). Use setDebugRuleConfigurations(java.util.Collection) or withDebugRuleConfigurations(java.util.Collection) if you want to override the existing values.
    
    Parameters:
    
    debugRuleConfigurations - Configuration information for Debugger rules for debugging output tensors.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - withDebugRuleConfigurations
```
public CreateTrainingJobRequest withDebugRuleConfigurations(Collection<DebugRuleConfiguration> debugRuleConfigurations)
```
    Configuration information for Debugger rules for debugging output tensors.
    
    Parameters:
    
    debugRuleConfigurations - Configuration information for Debugger rules for debugging output tensors.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setTensorBoardOutputConfig
```
public void setTensorBoardOutputConfig(TensorBoardOutputConfig tensorBoardOutputConfig)
```
    Parameters:
    
    tensorBoardOutputConfig -
  - getTensorBoardOutputConfig
```
public TensorBoardOutputConfig getTensorBoardOutputConfig()
```
    Returns:
  - withTensorBoardOutputConfig
```
public CreateTrainingJobRequest withTensorBoardOutputConfig(TensorBoardOutputConfig tensorBoardOutputConfig)
```
    Parameters:
    
    tensorBoardOutputConfig -
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setExperimentConfig
```
public void setExperimentConfig(ExperimentConfig experimentConfig)
```
    Parameters:
    
    experimentConfig -
  - getExperimentConfig
```
public ExperimentConfig getExperimentConfig()
```
    Returns:
  - withExperimentConfig
```
public CreateTrainingJobRequest withExperimentConfig(ExperimentConfig experimentConfig)
```
    Parameters:
    
    experimentConfig -
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setProfilerConfig
```
public void setProfilerConfig(ProfilerConfig profilerConfig)
```
    Parameters:
    
    profilerConfig -
  - getProfilerConfig
```
public ProfilerConfig getProfilerConfig()
```
    Returns:
  - withProfilerConfig
```
public CreateTrainingJobRequest withProfilerConfig(ProfilerConfig profilerConfig)
```
    Parameters:
    
    profilerConfig -
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - getProfilerRuleConfigurations
```
public List<ProfilerRuleConfiguration> getProfilerRuleConfigurations()
```
    Configuration information for Debugger rules for profiling system and framework metrics.
    
    Returns:
    
    Configuration information for Debugger rules for profiling system and framework metrics.
  - setProfilerRuleConfigurations
```
public void setProfilerRuleConfigurations(Collection<ProfilerRuleConfiguration> profilerRuleConfigurations)
```
    Configuration information for Debugger rules for profiling system and framework metrics.
    
    Parameters:
    
    profilerRuleConfigurations - Configuration information for Debugger rules for profiling system and framework metrics.
  - withProfilerRuleConfigurations
```
public CreateTrainingJobRequest withProfilerRuleConfigurations(ProfilerRuleConfiguration... profilerRuleConfigurations)
```
    Configuration information for Debugger rules for profiling system and framework metrics.
    
    NOTE: This method appends the values to the existing list (if any). Use setProfilerRuleConfigurations(java.util.Collection) or withProfilerRuleConfigurations(java.util.Collection) if you want to override the existing values.
    
    Parameters:
    
    profilerRuleConfigurations - Configuration information for Debugger rules for profiling system and framework metrics.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - withProfilerRuleConfigurations
```
public CreateTrainingJobRequest withProfilerRuleConfigurations(Collection<ProfilerRuleConfiguration> profilerRuleConfigurations)
```
    Configuration information for Debugger rules for profiling system and framework metrics.
    
    Parameters:
    
    profilerRuleConfigurations - Configuration information for Debugger rules for profiling system and framework metrics.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - getEnvironment
```
public Map<String,String> getEnvironment()
```
    The environment variables to set in the Docker container.
    
    Returns:
    
    The environment variables to set in the Docker container.
  - setEnvironment
```
public void setEnvironment(Map<String,String> environment)
```
    The environment variables to set in the Docker container.
    
    Parameters:
    
    environment - The environment variables to set in the Docker container.
  - withEnvironment
```
public CreateTrainingJobRequest withEnvironment(Map<String,String> environment)
```
    The environment variables to set in the Docker container.
    
    Parameters:
    
    environment - The environment variables to set in the Docker container.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - addEnvironmentEntry
```
public CreateTrainingJobRequest addEnvironmentEntry(String key,
                                                    String value)
```
    Add a single Environment entry
    
    See Also:
    
    withEnvironment(java.util.Map<java.lang.String, java.lang.String>)
  - clearEnvironmentEntries
```
public CreateTrainingJobRequest clearEnvironmentEntries()
```
    Removes all the entries added into Environment.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setRetryStrategy
```
public void setRetryStrategy(RetryStrategy retryStrategy)
```
    The number of times to retry the job when the job fails due to an InternalServerError.
    
    Parameters:
    
    retryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.
  - getRetryStrategy
```
public RetryStrategy getRetryStrategy()
```
    The number of times to retry the job when the job fails due to an InternalServerError.
    
    Returns:
    
    The number of times to retry the job when the job fails due to an InternalServerError.
  - withRetryStrategy
```
public CreateTrainingJobRequest withRetryStrategy(RetryStrategy retryStrategy)
```
    The number of times to retry the job when the job fails due to an InternalServerError.
    
    Parameters:
    
    retryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - 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()

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Class CreateTrainingJobRequest

Field Summary

Fields inherited from class com.amazonaws.AmazonWebServiceRequest

Constructor Summary

Method Summary

Methods inherited from class com.amazonaws.AmazonWebServiceRequest

Methods inherited from class java.lang.Object

Constructor Detail

CreateTrainingJobRequest

Method Detail

setTrainingJobName

getTrainingJobName

withTrainingJobName

getHyperParameters

setHyperParameters

withHyperParameters

addHyperParametersEntry

clearHyperParametersEntries

setAlgorithmSpecification

getAlgorithmSpecification

withAlgorithmSpecification

setRoleArn

getRoleArn

withRoleArn

getInputDataConfig

setInputDataConfig

withInputDataConfig

withInputDataConfig

setOutputDataConfig

getOutputDataConfig

withOutputDataConfig

setResourceConfig

getResourceConfig

withResourceConfig

setVpcConfig

getVpcConfig

withVpcConfig

setStoppingCondition

getStoppingCondition

withStoppingCondition

getTags

setTags

withTags

withTags

setEnableNetworkIsolation

getEnableNetworkIsolation

withEnableNetworkIsolation

isEnableNetworkIsolation

setEnableInterContainerTrafficEncryption

getEnableInterContainerTrafficEncryption

withEnableInterContainerTrafficEncryption

isEnableInterContainerTrafficEncryption

setEnableManagedSpotTraining

getEnableManagedSpotTraining

withEnableManagedSpotTraining

isEnableManagedSpotTraining

setCheckpointConfig

getCheckpointConfig

withCheckpointConfig

setDebugHookConfig

getDebugHookConfig

withDebugHookConfig

getDebugRuleConfigurations

setDebugRuleConfigurations

withDebugRuleConfigurations

withDebugRuleConfigurations

setTensorBoardOutputConfig

getTensorBoardOutputConfig

withTensorBoardOutputConfig

setExperimentConfig

getExperimentConfig

withExperimentConfig

setProfilerConfig

getProfilerConfig

withProfilerConfig

getProfilerRuleConfigurations

setProfilerRuleConfigurations

withProfilerRuleConfigurations

withProfilerRuleConfigurations