#[non_exhaustive]pub struct CreateTransformJobInput {Show 14 fields
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 data_capture_config: Option<BatchDataCaptureConfig>,
pub transform_resources: Option<TransformResources>,
pub data_processing: Option<DataProcessing>,
pub tags: Option<Vec<Tag>>,
pub experiment_config: Option<ExperimentConfig>,
}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. 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.
The value of MaxPayloadInMB cannot be greater than 100 MB. If you specify the MaxConcurrentTransforms parameter, the value of (MaxConcurrentTransforms * MaxPayloadInMB) also cannot exceed 100 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. Don't include any sensitive data in your environment variables. 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.
data_capture_config: Option<BatchDataCaptureConfig>Configuration to control how SageMaker captures inference data.
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.
(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:
Implementations§
Source§impl CreateTransformJobInput
impl CreateTransformJobInput
Sourcepub fn transform_job_name(&self) -> Option<&str>
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.
Sourcepub fn model_name(&self) -> Option<&str>
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.
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, 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.
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.
The value of MaxPayloadInMB cannot be greater than 100 MB. If you specify the MaxConcurrentTransforms parameter, the value of (MaxConcurrentTransforms * MaxPayloadInMB) also cannot exceed 100 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.
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.
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.
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. Don't include any sensitive data in your environment variables. 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 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 the 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.
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 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.
(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.
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().
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:
Source§impl CreateTransformJobInput
impl CreateTransformJobInput
Sourcepub fn builder() -> CreateTransformJobInputBuilder
pub fn builder() -> CreateTransformJobInputBuilder
Creates a new builder-style object to manufacture CreateTransformJobInput.
Trait Implementations§
Source§impl Clone for CreateTransformJobInput
impl Clone for CreateTransformJobInput
Source§fn clone(&self) -> CreateTransformJobInput
fn clone(&self) -> CreateTransformJobInput
1.0.0 · Source§const fn clone_from(&mut self, source: &Self)
const fn clone_from(&mut self, source: &Self)
source. Read moreSource§impl Debug for CreateTransformJobInput
impl Debug for CreateTransformJobInput
Source§impl PartialEq for CreateTransformJobInput
impl PartialEq for CreateTransformJobInput
Source§fn eq(&self, other: &CreateTransformJobInput) -> bool
fn eq(&self, other: &CreateTransformJobInput) -> bool
self and other values to be equal, and is used by ==.impl StructuralPartialEq for CreateTransformJobInput
Auto Trait Implementations§
impl Freeze for CreateTransformJobInput
impl RefUnwindSafe for CreateTransformJobInput
impl Send for CreateTransformJobInput
impl Sync for CreateTransformJobInput
impl Unpin for CreateTransformJobInput
impl UnwindSafe for CreateTransformJobInput
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