Struct CreateTrainingJobInputBuilder

#[non_exhaustive]
pub struct CreateTrainingJobInputBuilder { /* private fields */ }

A builder for CreateTrainingJobInput.

Implementations

impl CreateTrainingJobInputBuilder

pub fn training_job_name(self, input: impl Into<String>) -> Self

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

This field is required.

pub fn set_training_job_name(self, input: Option<String>) -> Self

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

pub fn get_training_job_name(&self) -> &Option<String>

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

pub fn hyper_parameters( self, k: impl Into<String>, v: impl Into<String>, ) -> Self

Adds a key-value pair to hyper_parameters.

To override the contents of this collection use set_hyper_parameters.

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 fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request hyperparameter variable or plain text fields.

pub fn set_hyper_parameters( self, input: Option<HashMap<String, String>>, ) -> Self

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 fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request hyperparameter variable or plain text fields.

pub fn get_hyper_parameters(&self) -> &Option<HashMap<String, String>>

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 fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request hyperparameter variable or plain text fields.

pub fn algorithm_specification(self, input: AlgorithmSpecification) -> Self

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.

This field is required.

pub fn set_algorithm_specification( self, input: Option<AlgorithmSpecification>, ) -> Self

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.

pub fn get_algorithm_specification(&self) -> &Option<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.

pub fn role_arn(self, input: impl Into<String>) -> Self

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.

This field is required.

pub fn set_role_arn(self, input: Option<String>) -> Self

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.

pub fn get_role_arn(&self) -> &Option<String>

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.

pub fn input_data_config(self, input: Channel) -> Self

Appends an item to input_data_config.

To override the contents of this collection use set_input_data_config.

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.

pub fn set_input_data_config(self, input: Option<Vec<Channel>>) -> Self

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.

pub fn get_input_data_config(&self) -> &Option<Vec<Channel>>

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.

pub fn output_data_config(self, input: OutputDataConfig) -> Self

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

This field is required.

pub fn set_output_data_config(self, input: Option<OutputDataConfig>) -> Self

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

pub fn get_output_data_config(&self) -> &Option<OutputDataConfig>

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

pub fn resource_config(self, input: ResourceConfig) -> Self

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 field is required.

pub fn set_resource_config(self, input: Option<ResourceConfig>) -> Self

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.

pub fn get_resource_config(&self) -> &Option<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.

pub fn vpc_config(self, input: VpcConfig) -> Self

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.

pub fn set_vpc_config(self, input: Option<VpcConfig>) -> Self

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.

pub fn get_vpc_config(&self) -> &Option<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.

pub fn stopping_condition(self, input: StoppingCondition) -> Self

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.

This field is required.

pub fn set_stopping_condition(self, input: Option<StoppingCondition>) -> Self

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.

pub fn get_stopping_condition(&self) -> &Option<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.

pub fn tags(self, input: Tag) -> Self

Appends an item to tags.

To override the contents of this collection use set_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.

Do not include any security-sensitive information including account access IDs, secrets, or tokens in any tags. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request tag variable or plain text fields.

pub fn set_tags(self, input: Option<Vec<Tag>>) -> Self

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.

Do not include any security-sensitive information including account access IDs, secrets, or tokens in any tags. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request tag variable or plain text fields.

pub fn get_tags(&self) -> &Option<Vec<Tag>>

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.

Do not include any security-sensitive information including account access IDs, secrets, or tokens in any tags. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request tag variable or plain text fields.

pub fn enable_network_isolation(self, input: bool) -> Self

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.

pub fn set_enable_network_isolation(self, input: Option<bool>) -> Self

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.

pub fn get_enable_network_isolation(&self) -> &Option<bool>

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.

pub fn enable_inter_container_traffic_encryption(self, input: bool) -> Self

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.

pub fn set_enable_inter_container_traffic_encryption( self, input: Option<bool>, ) -> Self

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.

pub fn get_enable_inter_container_traffic_encryption(&self) -> &Option<bool>

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.

pub fn enable_managed_spot_training(self, input: bool) -> Self

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.

pub fn set_enable_managed_spot_training(self, input: Option<bool>) -> Self

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.

pub fn get_enable_managed_spot_training(&self) -> &Option<bool>

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.

pub fn checkpoint_config(self, input: CheckpointConfig) -> Self

Contains information about the output location for managed spot training checkpoint data.

pub fn set_checkpoint_config(self, input: Option<CheckpointConfig>) -> Self

Contains information about the output location for managed spot training checkpoint data.

pub fn get_checkpoint_config(&self) -> &Option<CheckpointConfig>

Contains information about the output location for managed spot training checkpoint data.

pub fn debug_hook_config(self, input: DebugHookConfig) -> Self

Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

pub fn set_debug_hook_config(self, input: Option<DebugHookConfig>) -> Self

Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

pub fn get_debug_hook_config(&self) -> &Option<DebugHookConfig>

Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

pub fn debug_rule_configurations(self, input: DebugRuleConfiguration) -> Self

Appends an item to debug_rule_configurations.

To override the contents of this collection use set_debug_rule_configurations.

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

pub fn set_debug_rule_configurations( self, input: Option<Vec<DebugRuleConfiguration>>, ) -> Self

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

pub fn get_debug_rule_configurations( &self, ) -> &Option<Vec<DebugRuleConfiguration>>

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

pub fn tensor_board_output_config(self, input: TensorBoardOutputConfig) -> Self

Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.

pub fn set_tensor_board_output_config( self, input: Option<TensorBoardOutputConfig>, ) -> Self

Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.

pub fn get_tensor_board_output_config(&self) -> &Option<TensorBoardOutputConfig>

Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.

pub fn experiment_config(self, input: ExperimentConfig) -> Self

Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

• CreateProcessingJob

• CreateTrainingJob

• CreateTransformJob

pub fn set_experiment_config(self, input: Option<ExperimentConfig>) -> Self

Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

• CreateProcessingJob

• CreateTrainingJob

• CreateTransformJob

pub fn get_experiment_config(&self) -> &Option<ExperimentConfig>

Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

• CreateProcessingJob

• CreateTrainingJob

• CreateTransformJob

pub fn profiler_config(self, input: ProfilerConfig) -> Self

Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.

pub fn set_profiler_config(self, input: Option<ProfilerConfig>) -> Self

Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.

pub fn get_profiler_config(&self) -> &Option<ProfilerConfig>

Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.

pub fn profiler_rule_configurations( self, input: ProfilerRuleConfiguration, ) -> Self

Appends an item to profiler_rule_configurations.

To override the contents of this collection use set_profiler_rule_configurations.

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

pub fn set_profiler_rule_configurations( self, input: Option<Vec<ProfilerRuleConfiguration>>, ) -> Self

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

pub fn get_profiler_rule_configurations( &self, ) -> &Option<Vec<ProfilerRuleConfiguration>>

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

pub fn environment(self, k: impl Into<String>, v: impl Into<String>) -> Self

Adds a key-value pair to environment.

To override the contents of this collection use set_environment.

The environment variables to set in the Docker container.

Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.

pub fn set_environment(self, input: Option<HashMap<String, String>>) -> Self

The environment variables to set in the Docker container.

Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.

pub fn get_environment(&self) -> &Option<HashMap<String, String>>

The environment variables to set in the Docker container.

Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.

pub fn retry_strategy(self, input: RetryStrategy) -> Self

The number of times to retry the job when the job fails due to an InternalServerError.

pub fn set_retry_strategy(self, input: Option<RetryStrategy>) -> Self

The number of times to retry the job when the job fails due to an InternalServerError.

pub fn get_retry_strategy(&self) -> &Option<RetryStrategy>

The number of times to retry the job when the job fails due to an InternalServerError.

pub fn remote_debug_config(self, input: RemoteDebugConfig) -> Self

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.

pub fn set_remote_debug_config(self, input: Option<RemoteDebugConfig>) -> Self

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.

pub fn get_remote_debug_config(&self) -> &Option<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.

pub fn infra_check_config(self, input: InfraCheckConfig) -> Self

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

pub fn set_infra_check_config(self, input: Option<InfraCheckConfig>) -> Self

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

pub fn get_infra_check_config(&self) -> &Option<InfraCheckConfig>

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

pub fn session_chaining_config(self, input: SessionChainingConfig) -> Self

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

pub fn set_session_chaining_config( self, input: Option<SessionChainingConfig>, ) -> Self

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

pub fn get_session_chaining_config(&self) -> &Option<SessionChainingConfig>

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

pub fn build(self) -> Result<CreateTrainingJobInput, BuildError>

Consumes the builder and constructs a CreateTrainingJobInput.

impl CreateTrainingJobInputBuilder

pub async fn send_with( self, client: &Client, ) -> Result<CreateTrainingJobOutput, SdkError<CreateTrainingJobError, HttpResponse>>

Sends a request with this input using the given client.

Trait Implementations

impl Clone for CreateTrainingJobInputBuilder

fn clone(&self) -> CreateTrainingJobInputBuilder

Returns a duplicate of the value.

const fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CreateTrainingJobInputBuilder

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for CreateTrainingJobInputBuilder

fn default() -> CreateTrainingJobInputBuilder

Returns the “default value” for a type.

impl PartialEq for CreateTrainingJobInputBuilder

fn eq(&self, other: &CreateTrainingJobInputBuilder) -> bool

Tests for self and other values to be equal, and is used by ==.

const fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for CreateTrainingJobInputBuilder