pub struct CreateTransformJobRequest {Show 13 fields
pub batch_strategy: Option<String>,
pub data_processing: Option<DataProcessing>,
pub environment: Option<HashMap<String, String>>,
pub experiment_config: Option<ExperimentConfig>,
pub max_concurrent_transforms: Option<i64>,
pub max_payload_in_mb: Option<i64>,
pub model_client_config: Option<ModelClientConfig>,
pub model_name: String,
pub tags: Option<Vec<Tag>>,
pub transform_input: TransformInput,
pub transform_job_name: String,
pub transform_output: TransformOutput,
pub transform_resources: TransformResources,
}
Fields
batch_strategy: Option<String>
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
.
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.
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.
experiment_config: Option<ExperimentConfig>
max_concurrent_transforms: Option<i64>
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
.
max_payload_in_mb: Option<i64>
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.
model_client_config: Option<ModelClientConfig>
Configures the timeout and maximum number of retries for processing a transform job invocation.
model_name: 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 AWS Region in an AWS account.
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
transform_input: TransformInput
Describes the input source and the way the transform job consumes it.
transform_job_name: String
The name of the transform job. The name must be unique within an AWS Region in an AWS account.
transform_output: TransformOutput
Describes the results of the transform job.
transform_resources: TransformResources
Describes the resources, including ML instance types and ML instance count, to use for the transform job.
Trait Implementations
sourceimpl Clone for CreateTransformJobRequest
impl Clone for CreateTransformJobRequest
sourcefn clone(&self) -> CreateTransformJobRequest
fn clone(&self) -> CreateTransformJobRequest
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source
. Read more
sourceimpl Debug for CreateTransformJobRequest
impl Debug for CreateTransformJobRequest
sourceimpl Default for CreateTransformJobRequest
impl Default for CreateTransformJobRequest
sourcefn default() -> CreateTransformJobRequest
fn default() -> CreateTransformJobRequest
Returns the “default value” for a type. Read more
sourceimpl PartialEq<CreateTransformJobRequest> for CreateTransformJobRequest
impl PartialEq<CreateTransformJobRequest> for CreateTransformJobRequest
sourcefn eq(&self, other: &CreateTransformJobRequest) -> bool
fn eq(&self, other: &CreateTransformJobRequest) -> bool
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
sourcefn ne(&self, other: &CreateTransformJobRequest) -> bool
fn ne(&self, other: &CreateTransformJobRequest) -> bool
This method tests for !=
.
sourceimpl Serialize for CreateTransformJobRequest
impl Serialize for CreateTransformJobRequest
impl StructuralPartialEq for CreateTransformJobRequest
Auto Trait Implementations
impl RefUnwindSafe for CreateTransformJobRequest
impl Send for CreateTransformJobRequest
impl Sync for CreateTransformJobRequest
impl Unpin for CreateTransformJobRequest
impl UnwindSafe for CreateTransformJobRequest
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
sourceimpl<T> Instrument for T
impl<T> Instrument for T
sourcefn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
sourcefn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcefn clone_into(&self, target: &mut T)
fn clone_into(&self, target: &mut T)
toowned_clone_into
)Uses borrowed data to replace owned data, usually by cloning. Read more
sourceimpl<T> WithSubscriber for T
impl<T> WithSubscriber for T
sourcefn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
sourcefn with_current_subscriber(self) -> WithDispatch<Self>
fn with_current_subscriber(self) -> WithDispatch<Self>
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more