#[non_exhaustive]pub struct TransformJob {Show 22 fields
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 data_capture_config: Option<BatchDataCaptureConfig>,
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>>,
}Expand description
A batch transform job. For information about SageMaker batch transform, see Use Batch Transform.
Fields (Non-exhaustive)§
This struct is marked as non-exhaustive
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 theFailureReasonfield in the response to aDescribeTransformJobcall. -
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.
data_capture_config: Option<BatchDataCaptureConfig>Configuration to control how SageMaker captures inference data for batch transform jobs.
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:
A list of tags associated with the transform job.
Implementations§
Source§impl TransformJob
impl TransformJob
Sourcepub fn transform_job_name(&self) -> Option<&str>
pub fn transform_job_name(&self) -> Option<&str>
The name of the transform job.
Sourcepub fn transform_job_arn(&self) -> Option<&str>
pub fn transform_job_arn(&self) -> Option<&str>
The Amazon Resource Name (ARN) of the transform job.
Sourcepub fn transform_job_status(&self) -> Option<&TransformJobStatus>
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 theFailureReasonfield in the response to aDescribeTransformJobcall. -
Stopping- The transform job is stopping. -
Stopped- The transform job has stopped.
Sourcepub fn failure_reason(&self) -> Option<&str>
pub fn failure_reason(&self) -> Option<&str>
If the transform job failed, the reason it failed.
Sourcepub fn model_name(&self) -> Option<&str>
pub fn model_name(&self) -> Option<&str>
The name of the model associated with the transform job.
Sourcepub fn max_concurrent_transforms(&self) -> Option<i32>
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.
Sourcepub fn model_client_config(&self) -> Option<&ModelClientConfig>
pub fn model_client_config(&self) -> Option<&ModelClientConfig>
Configures the timeout and maximum number of retries for processing a transform job invocation.
Sourcepub fn max_payload_in_mb(&self) -> Option<i32>
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.
Sourcepub fn batch_strategy(&self) -> Option<&BatchStrategy>
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.
Sourcepub fn environment(&self) -> Option<&HashMap<String, String>>
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.
Sourcepub fn transform_input(&self) -> Option<&TransformInput>
pub fn transform_input(&self) -> Option<&TransformInput>
Describes the input source of a transform job and the way the transform job consumes it.
Sourcepub fn transform_output(&self) -> Option<&TransformOutput>
pub fn transform_output(&self) -> Option<&TransformOutput>
Describes the results of a transform job.
Sourcepub fn data_capture_config(&self) -> Option<&BatchDataCaptureConfig>
pub fn data_capture_config(&self) -> Option<&BatchDataCaptureConfig>
Configuration to control how SageMaker captures inference data for batch transform jobs.
Sourcepub fn transform_resources(&self) -> Option<&TransformResources>
pub fn transform_resources(&self) -> Option<&TransformResources>
Describes the resources, including ML instance types and ML instance count, to use for transform job.
Sourcepub fn creation_time(&self) -> Option<&DateTime>
pub fn creation_time(&self) -> Option<&DateTime>
A timestamp that shows when the transform Job was created.
Sourcepub fn transform_start_time(&self) -> Option<&DateTime>
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.
Sourcepub fn transform_end_time(&self) -> Option<&DateTime>
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.
Sourcepub fn labeling_job_arn(&self) -> Option<&str>
pub fn labeling_job_arn(&self) -> Option<&str>
The Amazon Resource Name (ARN) of the labeling job that created the transform job.
Sourcepub fn auto_ml_job_arn(&self) -> Option<&str>
pub fn auto_ml_job_arn(&self) -> Option<&str>
The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
Sourcepub fn data_processing(&self) -> Option<&DataProcessing>
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.
Sourcepub fn experiment_config(&self) -> Option<&ExperimentConfig>
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:
A list of tags associated with the transform job.
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().
Source§impl TransformJob
impl TransformJob
Sourcepub fn builder() -> TransformJobBuilder
pub fn builder() -> TransformJobBuilder
Creates a new builder-style object to manufacture TransformJob.
Trait Implementations§
Source§impl Clone for TransformJob
impl Clone for TransformJob
Source§fn clone(&self) -> TransformJob
fn clone(&self) -> TransformJob
1.0.0 · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source. Read moreSource§impl Debug for TransformJob
impl Debug for TransformJob
Source§impl PartialEq for TransformJob
impl PartialEq for TransformJob
impl StructuralPartialEq for TransformJob
Auto Trait Implementations§
impl Freeze for TransformJob
impl RefUnwindSafe for TransformJob
impl Send for TransformJob
impl Sync for TransformJob
impl Unpin for TransformJob
impl UnwindSafe for TransformJob
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