Struct aws_sdk_sagemaker::model::TransformJob

#[non_exhaustive]
pub struct TransformJob {

    pub transform_job_name: Option<String>,
    pub transform_job_arn: Option<String>,
    pub transform_job_status: Option<TransformJobStatus>,
    pub failure_reason: 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 creation_time: Option<DateTime>,
    pub transform_start_time: Option<DateTime>,
    pub transform_end_time: Option<DateTime>,
    pub labeling_job_arn: Option<String>,
    pub auto_ml_job_arn: Option<String>,
    pub data_processing: Option<DataProcessing>,
    pub experiment_config: Option<ExperimentConfig>,
    pub tags: Option<Vec<Tag>>,
}

A batch transform job. For information about SageMaker batch transform, see Use Batch Transform.

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.

transform_job_arn: Option<String>

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

transform_job_status: Option<TransformJobStatus>

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.

failure_reason: Option<String>

If the transform job failed, the reason it failed.

model_name: Option<String>

The name of the model associated with the transform job.

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, 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.

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, 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.

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 of a transform job and the way the transform job consumes it.

transform_output: Option<TransformOutput>

Describes the results of a transform job.

transform_resources: Option<TransformResources>

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

creation_time: Option<DateTime>

A timestamp that shows when the transform Job was created.

transform_start_time: Option<DateTime>

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

transform_end_time: Option<DateTime>

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.

labeling_job_arn: Option<String>

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

auto_ml_job_arn: Option<String>

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

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

tags: Option<Vec<Tag>>

A list of tags associated with the transform job.

Implementations

impl TransformJob

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

The name of the transform job.

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

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

pub fn transform_job_status(&self) -> Option<&TransformJobStatus>

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) -> Option<&str>

If the transform job failed, the reason it failed.

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

The name of the model associated with the transform job.

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, 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) -> 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, 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.

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 of a transform job and the way the transform job consumes it.

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

Describes the results of a transform job.

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

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

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

A timestamp that shows when the transform Job was created.

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

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) -> Option<&DateTime>

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) -> Option<&str>

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

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

The Amazon Resource Name (ARN) of the AutoML job that created 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 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 tags(&self) -> Option<&[Tag]>

A list of tags associated with the transform job.

impl TransformJob

pub fn builder() -> Builder

Creates a new builder-style object to manufacture TransformJob

Trait Implementations

impl Clone for TransformJob

fn clone(&self) -> TransformJob

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for TransformJob

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

Formats the value using the given formatter.

impl PartialEq<TransformJob> for TransformJob

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

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

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

This method tests for !=.

impl StructuralPartialEq for TransformJob