Struct CreateProcessingJobInput

#[non_exhaustive]
pub struct CreateProcessingJobInput {
    pub processing_inputs: Option<Vec<ProcessingInput>>,
    pub processing_output_config: Option<ProcessingOutputConfig>,
    pub processing_job_name: Option<String>,
    pub processing_resources: Option<ProcessingResources>,
    pub stopping_condition: Option<ProcessingStoppingCondition>,
    pub app_specification: Option<AppSpecification>,
    pub environment: Option<HashMap<String, String>>,
    pub network_config: Option<NetworkConfig>,
    pub role_arn: Option<String>,
    pub tags: Option<Vec<Tag>>,
    pub experiment_config: Option<ExperimentConfig>,
}

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

processing_inputs: Option<Vec<ProcessingInput>>

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

processing_output_config: Option<ProcessingOutputConfig>

Output configuration for the processing job.

processing_job_name: Option<String>

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

processing_resources: Option<ProcessingResources>

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.

stopping_condition: Option<ProcessingStoppingCondition>

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

app_specification: Option<AppSpecification>

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

environment: Option<HashMap<String, String>>

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

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.

network_config: Option<NetworkConfig>

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.

role_arn: Option<String>

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

tags: Option<Vec<Tag>>

(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.

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 security-sensitive information included in the request tag variable or plain text fields.

experiment_config: 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

Implementations

impl CreateProcessingJobInput

pub fn processing_inputs(&self) -> &[ProcessingInput]

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

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .processing_inputs.is_none().

pub fn processing_output_config(&self) -> Option<&ProcessingOutputConfig>

Output configuration for the processing job.

pub fn processing_job_name(&self) -> Option<&str>

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) -> Option<&ProcessingResources>

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) -> Option<&ProcessingStoppingCondition>

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

pub fn app_specification(&self) -> Option<&AppSpecification>

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

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

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

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 network_config(&self) -> Option<&NetworkConfig>

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) -> Option<&str>

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

pub fn tags(&self) -> &[Tag]

(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.

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 security-sensitive information included in the request tag variable or plain text fields.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .tags.is_none().

pub fn 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

impl CreateProcessingJobInput

pub fn builder() -> CreateProcessingJobInputBuilder

Creates a new builder-style object to manufacture CreateProcessingJobInput.

Trait Implementations

impl Clone for CreateProcessingJobInput

fn clone(&self) -> CreateProcessingJobInput

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CreateProcessingJobInput

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

Formats the value using the given formatter.

impl PartialEq for CreateProcessingJobInput

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

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

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 CreateProcessingJobInput