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

Interface CreateTrainingJobRequest.Builder

    • Method Detail

      • trainingJobName

        CreateTrainingJobRequest.Builder trainingJobName​(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.

      • hyperParameters

        CreateTrainingJobRequest.Builder hyperParameters​(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.

        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 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.

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

      • algorithmSpecification

        CreateTrainingJobRequest.Builder algorithmSpecification​(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.

        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 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.

      • roleArn

        CreateTrainingJobRequest.Builder roleArn​(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.

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

      • inputDataConfig

        CreateTrainingJobRequest.Builder inputDataConfig​(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, 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.

        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, 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.

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

      • inputDataConfig

        CreateTrainingJobRequest.Builder inputDataConfig​(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.

        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, 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.

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

      • inputDataConfig

        CreateTrainingJobRequest.Builder inputDataConfig​(Consumer<Channel.Builder>... 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.

        This is a convenience method that creates an instance of the Channel.Builder avoiding the need to create one manually via Channel.builder().

        When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to #inputDataConfig(List).

        Parameters:
        inputDataConfig - a consumer that will call methods on Channel.Builder
        Returns:
        Returns a reference to this object so that method calls can be chained together.
        See Also:
        #inputDataConfig(java.util.Collection)

      • outputDataConfig

        CreateTrainingJobRequest.Builder outputDataConfig​(OutputDataConfig outputDataConfig)

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

        Parameters:
        outputDataConfig - Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • resourceConfig

        CreateTrainingJobRequest.Builder resourceConfig​(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.

        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 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.

      • resourceConfig

        default CreateTrainingJobRequest.Builder resourceConfig​(Consumer<ResourceConfig.Builder> 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.

        This is a convenience method that creates an instance of the ResourceConfig.Builder avoiding the need to create one manually via ResourceConfig.builder().

        When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to resourceConfig(ResourceConfig).

        Parameters:
        resourceConfig - a consumer that will call methods on ResourceConfig.Builder
        Returns:
        Returns a reference to this object so that method calls can be chained together.
        See Also:
        resourceConfig(ResourceConfig)

      • stoppingCondition

        CreateTrainingJobRequest.Builder stoppingCondition​(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.

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

      • tags

        CreateTrainingJobRequest.Builder tags​(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.

      • tags

        CreateTrainingJobRequest.Builder tags​(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.

      • tags

        CreateTrainingJobRequest.Builder tags​(Consumer<Tag.Builder>... 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.

        This is a convenience method that creates an instance of the Tag.Builder avoiding the need to create one manually via Tag.builder().

        When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to #tags(List).

        Parameters:
        tags - a consumer that will call methods on Tag.Builder
        Returns:
        Returns a reference to this object so that method calls can be chained together.
        See Also:
        #tags(java.util.Collection)

      • enableNetworkIsolation

        CreateTrainingJobRequest.Builder enableNetworkIsolation​(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.

        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, 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.

      • enableInterContainerTrafficEncryption

        CreateTrainingJobRequest.Builder enableInterContainerTrafficEncryption​(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.

      • enableManagedSpotTraining

        CreateTrainingJobRequest.Builder enableManagedSpotTraining​(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.

      • checkpointConfig

        CreateTrainingJobRequest.Builder checkpointConfig​(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.

      • debugHookConfig

        CreateTrainingJobRequest.Builder debugHookConfig​(DebugHookConfig debugHookConfig)
        Sets the value of the DebugHookConfig property for this object.
        Parameters:
        debugHookConfig - The new value for the DebugHookConfig property for this object.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • debugRuleConfigurations

        CreateTrainingJobRequest.Builder debugRuleConfigurations​(Collection<DebugRuleConfiguration> debugRuleConfigurations)

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

        Parameters:
        debugRuleConfigurations - Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • debugRuleConfigurations

        CreateTrainingJobRequest.Builder debugRuleConfigurations​(DebugRuleConfiguration... debugRuleConfigurations)

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

        Parameters:
        debugRuleConfigurations - Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • tensorBoardOutputConfig

        CreateTrainingJobRequest.Builder tensorBoardOutputConfig​(TensorBoardOutputConfig tensorBoardOutputConfig)
        Sets the value of the TensorBoardOutputConfig property for this object.
        Parameters:
        tensorBoardOutputConfig - The new value for the TensorBoardOutputConfig property for this object.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • experimentConfig

        CreateTrainingJobRequest.Builder experimentConfig​(ExperimentConfig experimentConfig)
        Sets the value of the ExperimentConfig property for this object.
        Parameters:
        experimentConfig - The new value for the ExperimentConfig property for this object.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • profilerConfig

        CreateTrainingJobRequest.Builder profilerConfig​(ProfilerConfig profilerConfig)
        Sets the value of the ProfilerConfig property for this object.
        Parameters:
        profilerConfig - The new value for the ProfilerConfig property for this object.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • profilerRuleConfigurations

        CreateTrainingJobRequest.Builder profilerRuleConfigurations​(Collection<ProfilerRuleConfiguration> profilerRuleConfigurations)

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

        Parameters:
        profilerRuleConfigurations - Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • profilerRuleConfigurations

        CreateTrainingJobRequest.Builder profilerRuleConfigurations​(ProfilerRuleConfiguration... profilerRuleConfigurations)

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

        Parameters:
        profilerRuleConfigurations - Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • environment

        CreateTrainingJobRequest.Builder environment​(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.

      • retryStrategy

        CreateTrainingJobRequest.Builder retryStrategy​(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.

      • infraCheckConfig

        CreateTrainingJobRequest.Builder infraCheckConfig​(InfraCheckConfig infraCheckConfig)

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

        Parameters:
        infraCheckConfig - Contains information about the infrastructure health check configuration for the training job.
        Returns:
        Returns a reference to this object so that method calls can be chained together.