TrainingJobDefinition (AWS SDK for Java

java.lang.Object
- com.amazonaws.services.sagemaker.model.TrainingJobDefinition

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class TrainingJobDefinition
extends Object
implements Serializable, Cloneable, StructuredPojo
```
Defines the input needed to run a training job using the algorithm.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors
Constructor and Description

TrainingJobDefinition()

Constructors
Constructor and Description
`TrainingJobDefinition()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`TrainingJobDefinition`	`addHyperParametersEntry(String key, String value)` Add a single HyperParameters entry
`TrainingJobDefinition`	`clearHyperParametersEntries()` Removes all the entries added into HyperParameters.
`TrainingJobDefinition`	`clone()`
`boolean`	`equals(Object obj)`
`Map<String,String>`	`getHyperParameters()` The hyperparameters used for the training job.
`List<Channel>`	`getInputDataConfig()` An array of `Channel` objects, each of which specifies an input source.
`OutputDataConfig`	`getOutputDataConfig()` 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.
`StoppingCondition`	`getStoppingCondition()` Specifies a limit to how long a model training job can run.
`String`	`getTrainingInputMode()` The input mode used by the algorithm for the training job.
`int`	`hashCode()`
`void`	`marshall(ProtocolMarshaller protocolMarshaller)` Marshalls this structured data using the given `ProtocolMarshaller`.
`void`	`setHyperParameters(Map<String,String> hyperParameters)` The hyperparameters used for the training job.
`void`	`setInputDataConfig(Collection<Channel> inputDataConfig)` An array of `Channel` objects, each of which specifies an input source.
`void`	`setOutputDataConfig(OutputDataConfig outputDataConfig)` 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`	`setStoppingCondition(StoppingCondition stoppingCondition)` Specifies a limit to how long a model training job can run.
`void`	`setTrainingInputMode(String trainingInputMode)` The input mode used by the algorithm for the training job.
`String`	`toString()` Returns a string representation of this object.
`TrainingJobDefinition`	`withHyperParameters(Map<String,String> hyperParameters)` The hyperparameters used for the training job.
`TrainingJobDefinition`	`withInputDataConfig(Channel... inputDataConfig)` An array of `Channel` objects, each of which specifies an input source.
`TrainingJobDefinition`	`withInputDataConfig(Collection<Channel> inputDataConfig)` An array of `Channel` objects, each of which specifies an input source.
`TrainingJobDefinition`	`withOutputDataConfig(OutputDataConfig outputDataConfig)` the path to the S3 bucket where you want to store model artifacts.
`TrainingJobDefinition`	`withResourceConfig(ResourceConfig resourceConfig)` The resources, including the ML compute instances and ML storage volumes, to use for model training.
`TrainingJobDefinition`	`withStoppingCondition(StoppingCondition stoppingCondition)` Specifies a limit to how long a model training job can run.
`TrainingJobDefinition`	`withTrainingInputMode(String trainingInputMode)` The input mode used by the algorithm for the training job.
`TrainingJobDefinition`	`withTrainingInputMode(TrainingInputMode trainingInputMode)` The input mode used by the algorithm for the training job.

Methods inherited from class java.lang.Object
getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - TrainingJobDefinition
```
public TrainingJobDefinition()
```
- Method Detail
  - setTrainingInputMode
```
public void setTrainingInputMode(String trainingInputMode)
```
    The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms.
    
    If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
    
    Parameters:
    
    trainingInputMode - The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms.
    
    If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
    
    See Also:
    
    TrainingInputMode
  - getTrainingInputMode
```
public String getTrainingInputMode()
```
    The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms.
    
    If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
    
    Returns:
    
    The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms.
    
    If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
    
    See Also:
    
    TrainingInputMode
  - withTrainingInputMode
```
public TrainingJobDefinition withTrainingInputMode(String trainingInputMode)
```
    The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms.
    
    If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
    
    Parameters:
    
    trainingInputMode - The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms.
    
    If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    TrainingInputMode
  - withTrainingInputMode
```
public TrainingJobDefinition withTrainingInputMode(TrainingInputMode trainingInputMode)
```
    The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms.
    
    If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
    
    Parameters:
    
    trainingInputMode - The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms.
    
    If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    TrainingInputMode
  - getHyperParameters
```
public Map<String,String> getHyperParameters()
```
    The hyperparameters used for the training job.
    
    Returns:
    
    The hyperparameters used for the training job.
  - setHyperParameters
```
public void setHyperParameters(Map<String,String> hyperParameters)
```
    The hyperparameters used for the training job.
    
    Parameters:
    
    hyperParameters - The hyperparameters used for the training job.
  - withHyperParameters
```
public TrainingJobDefinition withHyperParameters(Map<String,String> hyperParameters)
```
    The hyperparameters used for the training job.
    
    Parameters:
    
    hyperParameters - The hyperparameters used for the training job.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - addHyperParametersEntry
```
public TrainingJobDefinition addHyperParametersEntry(String key,
                                                     String value)
```
    Add a single HyperParameters entry
    
    See Also:
    
    withHyperParameters(java.util.Map<java.lang.String, java.lang.String>)
  - clearHyperParametersEntries
```
public TrainingJobDefinition clearHyperParametersEntries()
```
    Removes all the entries added into HyperParameters.
    
    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 of which specifies an input source.
    
    Returns:
    
    An array of Channel objects, each of which specifies an input source.
  - setInputDataConfig
```
public void setInputDataConfig(Collection<Channel> inputDataConfig)
```
    An array of Channel objects, each of which specifies an input source.
    
    Parameters:
    
    inputDataConfig - An array of Channel objects, each of which specifies an input source.
  - withInputDataConfig
```
public TrainingJobDefinition withInputDataConfig(Channel... inputDataConfig)
```
    An array of Channel objects, each of which specifies an input source.
    
    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 of which specifies an input source.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - withInputDataConfig
```
public TrainingJobDefinition withInputDataConfig(Collection<Channel> inputDataConfig)
```
    An array of Channel objects, each of which specifies an input source.
    
    Parameters:
    
    inputDataConfig - An array of Channel objects, each of which specifies an input source.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setOutputDataConfig
```
public void setOutputDataConfig(OutputDataConfig outputDataConfig)
```
    the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
    
    Parameters:
    
    outputDataConfig - the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
  - getOutputDataConfig
```
public OutputDataConfig getOutputDataConfig()
```
    the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
    
    Returns:
    
    the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
  - withOutputDataConfig
```
public TrainingJobDefinition withOutputDataConfig(OutputDataConfig outputDataConfig)
```
    the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
    
    Parameters:
    
    outputDataConfig - 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.
    
    Parameters:
    
    resourceConfig - The resources, including the ML compute instances and ML storage volumes, to use for model training.
  - getResourceConfig
```
public ResourceConfig getResourceConfig()
```
    The resources, including the ML compute instances and ML storage volumes, to use for model training.
    
    Returns:
    
    The resources, including the ML compute instances and ML storage volumes, to use for model training.
  - withResourceConfig
```
public TrainingJobDefinition withResourceConfig(ResourceConfig resourceConfig)
```
    The resources, including the ML compute instances and ML storage volumes, to use for model training.
    
    Parameters:
    
    resourceConfig - The resources, including the ML compute instances and ML storage volumes, to use for model training.
    
    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. 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.
    
    Parameters:
    
    stoppingCondition - Specifies a limit to how long a model training job can run. 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.
  - getStoppingCondition
```
public StoppingCondition getStoppingCondition()
```
    Specifies a limit to how long a model training job can run. 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.
    
    Returns:
    
    Specifies a limit to how long a model training job can run. 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.
  - withStoppingCondition
```
public TrainingJobDefinition withStoppingCondition(StoppingCondition stoppingCondition)
```
    Specifies a limit to how long a model training job can run. 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.
    
    Parameters:
    
    stoppingCondition - Specifies a limit to how long a model training job can run. 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.
    
    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 TrainingJobDefinition clone()
```
    Overrides:
    
    clone in class Object
  - marshall
```
public void marshall(ProtocolMarshaller protocolMarshaller)
```
    Description copied from interface: StructuredPojo
    
    Marshalls this structured data using the given ProtocolMarshaller.
    
    Specified by:
    
    marshall in interface StructuredPojo
    
    Parameters:
    
    protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.

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Class TrainingJobDefinition

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

TrainingJobDefinition

Method Detail

setTrainingInputMode

getTrainingInputMode

withTrainingInputMode

withTrainingInputMode

getHyperParameters

setHyperParameters

withHyperParameters

addHyperParametersEntry

clearHyperParametersEntries

getInputDataConfig

setInputDataConfig

withInputDataConfig

withInputDataConfig

setOutputDataConfig

getOutputDataConfig

withOutputDataConfig

setResourceConfig

getResourceConfig

withResourceConfig

setStoppingCondition

getStoppingCondition

withStoppingCondition

toString

equals

hashCode

clone

marshall