Struct aws_sdk_sagemaker::model::ProcessingJob

#[non_exhaustive]
pub struct ProcessingJob {

    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 experiment_config: Option<ExperimentConfig>,
    pub processing_job_arn: Option<String>,
    pub processing_job_status: Option<ProcessingJobStatus>,
    pub exit_message: Option<String>,
    pub failure_reason: Option<String>,
    pub processing_end_time: Option<DateTime>,
    pub processing_start_time: Option<DateTime>,
    pub last_modified_time: Option<DateTime>,
    pub creation_time: Option<DateTime>,
    pub monitoring_schedule_arn: Option<String>,
    pub auto_ml_job_arn: Option<String>,
    pub training_job_arn: Option<String>,
    pub tags: Option<Vec<Tag>>,
}

An Amazon SageMaker processing job that is used to analyze data and evaluate models. For more information, see Process Data and Evaluate Models.

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>>

List of input configurations for the processing job.

processing_output_config: Option<ProcessingOutputConfig>

Configuration for uploading output from the processing container.

processing_job_name: Option<String>

The name of the processing job.

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>

Configures conditions under which the processing job should be stopped, such as how long the processing job has been running. After the condition is met, the processing job is stopped.

app_specification: Option<AppSpecification>

Configuration to run a processing job in a specified container image.

environment: Option<HashMap<String, String>>

Sets the environment variables in the Docker container.

network_config: Option<NetworkConfig>

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

role_arn: Option<String>

The ARN of the role used to create the processing job.

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

processing_job_arn: Option<String>

The ARN of the processing job.

processing_job_status: Option<ProcessingJobStatus>

The status of the processing job.

exit_message: Option<String>

A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.

failure_reason: Option<String>

A string, up to one KB in size, that contains the reason a processing job failed, if it failed.

processing_end_time: Option<DateTime>

The time that the processing job ended.

processing_start_time: Option<DateTime>

The time that the processing job started.

last_modified_time: Option<DateTime>

The time the processing job was last modified.

creation_time: Option<DateTime>

The time the processing job was created.

monitoring_schedule_arn: Option<String>

The ARN of a monitoring schedule for an endpoint associated with this processing job.

auto_ml_job_arn: Option<String>

The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.

training_job_arn: Option<String>

The ARN of the training job associated with this processing job.

tags: Option<Vec<Tag>>

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.

Implementations

impl ProcessingJob

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

List of input configurations for the processing job.

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

Configuration for uploading output from the processing container.

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

The name of the processing job.

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>

Configures conditions under which the processing job should be stopped, such as how long the processing job has been running. After the condition is met, the processing job is stopped.

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

Configuration to run a processing job in a specified container image.

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

Sets the environment variables in the Docker container.

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

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

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

The ARN of the role used to create the processing job.

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

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

The ARN of the processing job.

pub fn processing_job_status(&self) -> Option<&ProcessingJobStatus>

The status of the processing job.

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

A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.

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

A string, up to one KB in size, that contains the reason a processing job failed, if it failed.

pub fn processing_end_time(&self) -> Option<&DateTime>

The time that the processing job ended.

pub fn processing_start_time(&self) -> Option<&DateTime>

The time that the processing job started.

pub fn last_modified_time(&self) -> Option<&DateTime>

The time the processing job was last modified.

pub fn creation_time(&self) -> Option<&DateTime>

The time the processing job was created.

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

The ARN of a monitoring schedule for an endpoint associated with this processing job.

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

The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.

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

The ARN of the training job associated with this processing job.

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

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.

impl ProcessingJob

pub fn builder() -> Builder

Creates a new builder-style object to manufacture ProcessingJob

Trait Implementations

impl Clone for ProcessingJob

fn clone(&self) -> ProcessingJob

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for ProcessingJob

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

Formats the value using the given formatter.

impl PartialEq<ProcessingJob> for ProcessingJob

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

This method tests for self and other values to be equal, and is used by ==.

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

This method tests for !=.

impl StructuralPartialEq for ProcessingJob