Struct aws_sdk_sagemaker::input::CreateTransformJobInput

#[non_exhaustive]
pub struct CreateTransformJobInput {

    pub transform_job_name: Option<String>,
    pub model_name: Option<String>,
    pub max_concurrent_transforms: Option<i32>,
    pub model_client_config: Option<ModelClientConfig>,
    pub max_payload_in_mb: Option<i32>,
    pub batch_strategy: Option<BatchStrategy>,
    pub environment: Option<HashMap<String, String>>,
    pub transform_input: Option<TransformInput>,
    pub transform_output: Option<TransformOutput>,
    pub transform_resources: Option<TransformResources>,
    pub data_processing: Option<DataProcessing>,
    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.

transform_job_name: Option<String>

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

model_name: Option<String>

The name of the model that you want to use for the transform job. ModelName must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.

max_concurrent_transforms: Option<i32>

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, Amazon 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 more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms.

model_client_config: Option<ModelClientConfig>

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

max_payload_in_mb: Option<i32>

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, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.

batch_strategy: Option<BatchStrategy>

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.

To enable the batch strategy, you must set the SplitType property to Line, RecordIO, or TFRecord.

To use only one record when making an HTTP invocation request to a container, set BatchStrategy to SingleRecord and SplitType to Line.

To fit as many records in a mini-batch as can fit within the MaxPayloadInMB limit, set BatchStrategy to MultiRecord and SplitType to Line.

environment: Option<HashMap<String, String>>

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

transform_input: Option<TransformInput>

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

transform_output: Option<TransformOutput>

Describes the results of the transform job.

transform_resources: Option<TransformResources>

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

data_processing: Option<DataProcessing>

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.

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.

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 CreateTransformJobInput

pub async fn make_operation(
    &self,
    _config: &Config
) -> Result<Operation<CreateTransformJob, AwsErrorRetryPolicy>, BuildError>

Consumes the builder and constructs an Operation<CreateTransformJob>

pub fn builder() -> Builder

Creates a new builder-style object to manufacture CreateTransformJobInput

impl CreateTransformJobInput

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

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

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

The name of the model that you want to use for the transform job. ModelName must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.

pub fn max_concurrent_transforms(&self) -> Option<i32>

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, Amazon 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 more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms.

pub fn model_client_config(&self) -> Option<&ModelClientConfig>

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

pub fn max_payload_in_mb(&self) -> Option<i32>

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, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.

pub fn batch_strategy(&self) -> Option<&BatchStrategy>

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.

To enable the batch strategy, you must set the SplitType property to Line, RecordIO, or TFRecord.

To use only one record when making an HTTP invocation request to a container, set BatchStrategy to SingleRecord and SplitType to Line.

To fit as many records in a mini-batch as can fit within the MaxPayloadInMB limit, set BatchStrategy to MultiRecord and SplitType to Line.

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

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) -> Option<&TransformInput>

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

pub fn transform_output(&self) -> Option<&TransformOutput>

Describes the results of the transform job.

pub fn transform_resources(&self) -> Option<&TransformResources>

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

pub fn data_processing(&self) -> Option<&DataProcessing>

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 tags(&self) -> Option<&[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.

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

Trait Implementations

impl Clone for CreateTransformJobInput

fn clone(&self) -> CreateTransformJobInput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateTransformJobInput

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

Formats the value using the given formatter.

impl PartialEq<CreateTransformJobInput> for CreateTransformJobInput

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

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

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

This method tests for !=.

impl StructuralPartialEq for CreateTransformJobInput