Package software.amazon.awssdk.services.sagemaker.model

Class CreateTrainingJobRequest

    • Method Detail

      • 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.

      • 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.

      • 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.

      • 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