Struct aws_sdk_sagemaker::model::transform_job::Builder

#[non_exhaustive]
pub struct Builder { /* fields omitted */ }

A builder for TransformJob

Implementations

impl Builder

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

The name of the transform job.

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

The name of the transform job.

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

The Amazon Resource Name (ARN) of the transform job.

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

The Amazon Resource Name (ARN) of the transform job.

pub fn transform_job_status(self, input: TransformJobStatus) -> Self

The status of the transform job.

Transform job statuses are:

• InProgress - The job is in progress.

• Completed - The job has completed.

• Failed - The transform job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTransformJob call.

• Stopping - The transform job is stopping.

• Stopped - The transform job has stopped.

pub fn set_transform_job_status(self, input: Option<TransformJobStatus>) -> Self

The status of the transform job.

Transform job statuses are:

• InProgress - The job is in progress.

• Completed - The job has completed.

• Failed - The transform job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTransformJob call.

• Stopping - The transform job is stopping.

• Stopped - The transform job has stopped.

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

If the transform job failed, the reason it failed.

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

If the transform job failed, the reason it failed.

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

The name of the model associated with the transform job.

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

The name of the model associated with the transform job.

pub fn max_concurrent_transforms(self, input: i32) -> Self

The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms.

pub fn set_max_concurrent_transforms(self, input: Option<i32>) -> Self

The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms.

pub fn model_client_config(self, input: ModelClientConfig) -> Self

Configures the timeout and maximum number of retries for processing a transform job invocation.

pub fn set_model_client_config(self, input: Option<ModelClientConfig>) -> Self

Configures the timeout and maximum number of retries for processing a transform job invocation.

pub fn max_payload_in_mb(self, input: i32) -> Self

The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in algorithms do not support HTTP chunked encoding.

pub fn set_max_payload_in_mb(self, input: Option<i32>) -> Self

The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in algorithms do not support HTTP chunked encoding.

pub fn batch_strategy(self, input: BatchStrategy) -> Self

Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.

pub fn set_batch_strategy(self, input: Option<BatchStrategy>) -> Self

Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.

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. We support up to 16 key and values entries in the map.

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

The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

pub fn transform_input(self, input: TransformInput) -> Self

Describes the input source of a transform job and the way the transform job consumes it.

pub fn set_transform_input(self, input: Option<TransformInput>) -> Self

Describes the input source of a transform job and the way the transform job consumes it.

pub fn transform_output(self, input: TransformOutput) -> Self

Describes the results of a transform job.

pub fn set_transform_output(self, input: Option<TransformOutput>) -> Self

Describes the results of a transform job.

pub fn transform_resources(self, input: TransformResources) -> Self

Describes the resources, including ML instance types and ML instance count, to use for transform job.

pub fn set_transform_resources(self, input: Option<TransformResources>) -> Self

Describes the resources, including ML instance types and ML instance count, to use for transform job.

pub fn creation_time(self, input: DateTime) -> Self

A timestamp that shows when the transform Job was created.

pub fn set_creation_time(self, input: Option<DateTime>) -> Self

A timestamp that shows when the transform Job was created.

pub fn transform_start_time(self, input: DateTime) -> Self

Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.

pub fn set_transform_start_time(self, input: Option<DateTime>) -> Self

Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.

pub fn transform_end_time(self, input: DateTime) -> Self

Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime.

pub fn set_transform_end_time(self, input: Option<DateTime>) -> Self

Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime.

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

The Amazon Resource Name (ARN) of the labeling job that created the transform job.

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

The Amazon Resource Name (ARN) of the labeling job that created the transform job.

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

The Amazon Resource Name (ARN) of the AutoML job that created the transform job.

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

The Amazon Resource Name (ARN) of the AutoML job that created the transform job.

pub fn data_processing(self, input: DataProcessing) -> Self

The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.

pub fn set_data_processing(self, input: Option<DataProcessing>) -> Self

The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.

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 tags(self, input: impl Into<Tag>) -> Self

Appends an item to tags.

To override the contents of this collection use set_tags.

A list of tags associated with the transform job.

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

A list of tags associated with the transform job.

pub fn build(self) -> TransformJob

Consumes the builder and constructs a TransformJob

Trait Implementations

impl Clone for Builder

fn clone(&self) -> Builder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Builder

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

Formats the value using the given formatter.

impl Default for Builder

fn default() -> Builder

Returns the “default value” for a type.

impl PartialEq<Builder> for Builder

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

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

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

This method tests for !=.

impl StructuralPartialEq for Builder