Struct aws_sdk_sagemaker::client::fluent_builders::CreateProcessingJob

pub struct CreateProcessingJob { /* private fields */ }

Fluent builder constructing a request to CreateProcessingJob.

Creates a processing job.

Implementations

impl CreateProcessingJob

pub async fn send(
    self
) -> Result<CreateProcessingJobOutput, SdkError<CreateProcessingJobError>>

Sends the request and returns the response.

If an error occurs, an SdkError will be returned with additional details that can be matched against.

By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.

pub fn processing_inputs(self, input: ProcessingInput) -> Self

Appends an item to ProcessingInputs.

To override the contents of this collection use set_processing_inputs.

An array of inputs configuring the data to download into the processing container.

pub fn set_processing_inputs(self, input: Option<Vec<ProcessingInput>>) -> Self

An array of inputs configuring the data to download into the processing container.

pub fn processing_output_config(self, input: ProcessingOutputConfig) -> Self

Output configuration for the processing job.

pub fn set_processing_output_config(
    self,
    input: Option<ProcessingOutputConfig>
) -> Self

Output configuration for the processing job.

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

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

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

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

pub fn processing_resources(self, input: ProcessingResources) -> Self

Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.

pub fn set_processing_resources(
    self,
    input: Option<ProcessingResources>
) -> Self

Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.

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

The time limit for how long the processing job is allowed to run.

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

The time limit for how long the processing job is allowed to run.

pub fn app_specification(self, input: AppSpecification) -> Self

Configures the processing job to run a specified Docker container image.

pub fn set_app_specification(self, input: Option<AppSpecification>) -> Self

Configures the processing job to run a specified Docker container image.

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. Up to 100 key and values entries in the map are supported.

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

The environment variables to set in the Docker container. Up to 100 key and values entries in the map are supported.

pub fn network_config(self, input: NetworkConfig) -> Self

Networking options for a processing job, such as whether to allow inbound and outbound network calls to and from processing containers, and the VPC subnets and security groups to use for VPC-enabled processing jobs.

pub fn set_network_config(self, input: Option<NetworkConfig>) -> Self

Networking options for a processing job, such as whether to allow inbound and outbound network calls to and from processing containers, and the VPC subnets and security groups to use for VPC-enabled processing jobs.

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

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

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

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

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

Appends an item to Tags.

To override the contents of this collection use set_tags.

(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

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

(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

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

Trait Implementations

impl Clone for CreateProcessingJob

fn clone(&self) -> CreateProcessingJob

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateProcessingJob

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

Formats the value using the given formatter.