Class CreateTrainingJobRequest

java.lang.Object
software.amazon.awssdk.core.SdkRequest
software.amazon.awssdk.awscore.AwsRequest
software.amazon.awssdk.services.sagemaker.model.SageMakerRequest
software.amazon.awssdk.services.sagemaker.model.CreateTrainingJobRequest
All Implemented Interfaces:
SdkPojo, ToCopyableBuilder<CreateTrainingJobRequest.Builder,CreateTrainingJobRequest>
@Generated("software.amazon.awssdk:codegen") public final class CreateTrainingJobRequest extends SageMakerRequest implements ToCopyableBuilder<CreateTrainingJobRequest.Builder,CreateTrainingJobRequest>

  • Method Details

    • trainingJobName

      public final 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.

      Returns:
      The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

    • hasHyperParameters

      public final boolean hasHyperParameters()
      For responses, this returns true if the service returned a value for the HyperParameters property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

    • hyperParameters

      public final 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 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.

      Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.

      Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

      This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasHyperParameters() method.

      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 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.

      Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.

    • algorithmSpecification

      public final 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 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 SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

    • roleArn

      public final String roleArn()

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

      During model training, 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 SageMaker Roles.

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

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

      During model training, 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 SageMaker Roles.

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

    • hasInputDataConfig

      public final boolean hasInputDataConfig()
      For responses, this returns true if the service returned a value for the InputDataConfig property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

    • inputDataConfig

      public final List<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, 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 are available as input streams. They do not need to be downloaded.

      Your input must be in the same Amazon Web Services region as your training job.

      Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

      This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasInputDataConfig() method.

      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, 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 are available as input streams. They do not need to be downloaded.

      Your input must be in the same Amazon Web Services region as your training job.

    • outputDataConfig

      public final OutputDataConfig outputDataConfig()

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

      Returns:
      Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.

    • resourceConfig

      public final 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 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 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.

    • vpcConfig

      public final 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.

      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.

    • stoppingCondition

      public final 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, SageMaker ends the training job. Use this API to cap model training costs.

      To stop a job, 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, SageMaker ends the training job. Use this API to cap model training costs.

      To stop a job, 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.

    • hasTags

      public final boolean hasTags()
      For responses, this returns true if the service returned a value for the Tags property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

    • tags

      public final List<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.

      Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

      This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasTags() method.

      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.

    • enableNetworkIsolation

      public final 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, 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, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

    • enableInterContainerTrafficEncryption

      public final 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.

      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.

    • enableManagedSpotTraining

      public final 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.

      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.

    • checkpointConfig

      public final CheckpointConfig checkpointConfig()

      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.

    • debugHookConfig

      public final DebugHookConfig debugHookConfig()
      Returns the value of the DebugHookConfig property for this object.
      Returns:
      The value of the DebugHookConfig property for this object.

    • hasDebugRuleConfigurations

      public final boolean hasDebugRuleConfigurations()
      For responses, this returns true if the service returned a value for the DebugRuleConfigurations property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

    • debugRuleConfigurations

      public final List<DebugRuleConfiguration> debugRuleConfigurations()

      Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.

      Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

      This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasDebugRuleConfigurations() method.

      Returns:
      Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.

    • tensorBoardOutputConfig

      public final TensorBoardOutputConfig tensorBoardOutputConfig()
      Returns the value of the TensorBoardOutputConfig property for this object.
      Returns:
      The value of the TensorBoardOutputConfig property for this object.

    • experimentConfig

      public final ExperimentConfig experimentConfig()
      Returns the value of the ExperimentConfig property for this object.
      Returns:
      The value of the ExperimentConfig property for this object.

    • profilerConfig

      public final ProfilerConfig profilerConfig()
      Returns the value of the ProfilerConfig property for this object.
      Returns:
      The value of the ProfilerConfig property for this object.

    • hasProfilerRuleConfigurations

      public final boolean hasProfilerRuleConfigurations()
      For responses, this returns true if the service returned a value for the ProfilerRuleConfigurations property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

    • profilerRuleConfigurations

      public final List<ProfilerRuleConfiguration> profilerRuleConfigurations()

      Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.

      Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

      This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasProfilerRuleConfigurations() method.

      Returns:
      Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.

    • hasEnvironment

      public final boolean hasEnvironment()
      For responses, this returns true if the service returned a value for the Environment property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

    • environment

      public final Map<String,String> environment()

      The environment variables to set in the Docker container.

      Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

      This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasEnvironment() method.

      Returns:
      The environment variables to set in the Docker container.

    • retryStrategy

      public final RetryStrategy retryStrategy()

      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.

    • remoteDebugConfig

      public final RemoteDebugConfig remoteDebugConfig()

      Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.

      Returns:
      Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.

    • infraCheckConfig

      public final InfraCheckConfig infraCheckConfig()

      Contains information about the infrastructure health check configuration for the training job.

      Returns:
      Contains information about the infrastructure health check configuration for the training job.

    • sessionChainingConfig

      public final SessionChainingConfig sessionChainingConfig()

      Contains information about attribute-based access control (ABAC) for the training job.

      Returns:
      Contains information about attribute-based access control (ABAC) for the training job.

    • toBuilder

      public CreateTrainingJobRequest.Builder toBuilder()
      Description copied from interface: ToCopyableBuilder
      Take this object and create a builder that contains all of the current property values of this object.
      Specified by:
      toBuilder in interface ToCopyableBuilder<CreateTrainingJobRequest.Builder,CreateTrainingJobRequest>
      Specified by:
      toBuilder in class SageMakerRequest
      Returns:
      a builder for type T

    • builder

      public static CreateTrainingJobRequest.Builder builder()

    • serializableBuilderClass

      public static Class<? extends CreateTrainingJobRequest.Builder> serializableBuilderClass()

    • hashCode

      public final int hashCode()
      Overrides:
      hashCode in class AwsRequest

    • equals

      public final boolean equals(Object obj)
      Overrides:
      equals in class AwsRequest

    • equalsBySdkFields

      public final boolean equalsBySdkFields(Object obj)
      Description copied from interface: SdkPojo
      Indicates whether some other object is "equal to" this one by SDK fields. An SDK field is a modeled, non-inherited field in an SdkPojo class, and is generated based on a service model.

      If an SdkPojo class does not have any inherited fields, equalsBySdkFields and equals are essentially the same.

      Specified by:
      equalsBySdkFields in interface SdkPojo
      Parameters:
      obj - the object to be compared with
      Returns:
      true if the other object equals to this object by sdk fields, false otherwise.

    • toString

      public final 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

    • getValueForField

      public final <T> Optional<T> getValueForField(String fieldName, Class<T> clazz)
      Description copied from class: SdkRequest
      Used to retrieve the value of a field from any class that extends SdkRequest. The field name specified should match the member name from the corresponding service-2.json model specified in the codegen-resources folder for a given service. The class specifies what class to cast the returned value to. If the returned value is also a modeled class, the SdkRequest.getValueForField(String, Class) method will again be available.
      Overrides:
      getValueForField in class SdkRequest
      Parameters:
      fieldName - The name of the member to be retrieved.
      clazz - The class to cast the returned object to.
      Returns:
      Optional containing the casted return value

    • sdkFields

      public final List<SdkField<?>> sdkFields()
      Specified by:
      sdkFields in interface SdkPojo
      Returns:
      List of SdkField in this POJO. May be empty list but should never be null.