Struct rusoto_sagemaker::TransformInput
source · [−]pub struct TransformInput {
pub compression_type: Option<String>,
pub content_type: Option<String>,
pub data_source: TransformDataSource,
pub split_type: Option<String>,
}
Expand description
Describes the input source of a transform job and the way the transform job consumes it.
Fields
compression_type: Option<String>
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None
.
content_type: Option<String>
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
data_source: TransformDataSource
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
split_type: Option<String>
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType
is None
, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line
to split records on a newline character boundary. SplitType
also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
-
RecordIO
-
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy
and MaxPayloadInMB
parameters. When the value of BatchStrategy
is MultiRecord
, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB
limit. If the value of BatchStrategy
is SingleRecord
, Amazon SageMaker sends individual records in each request.
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy
is set to SingleRecord
. Padding is not removed if the value of BatchStrategy
is set to MultiRecord
.
For more information about RecordIO
, see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord
, see Consuming TFRecord data in the TensorFlow documentation.
Trait Implementations
sourceimpl Clone for TransformInput
impl Clone for TransformInput
sourcefn clone(&self) -> TransformInput
fn clone(&self) -> TransformInput
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 TransformInput
impl Debug for TransformInput
sourceimpl Default for TransformInput
impl Default for TransformInput
sourcefn default() -> TransformInput
fn default() -> TransformInput
Returns the “default value” for a type. Read more
sourceimpl<'de> Deserialize<'de> for TransformInput
impl<'de> Deserialize<'de> for TransformInput
sourcefn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
sourceimpl PartialEq<TransformInput> for TransformInput
impl PartialEq<TransformInput> for TransformInput
sourcefn eq(&self, other: &TransformInput) -> bool
fn eq(&self, other: &TransformInput) -> bool
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
sourcefn ne(&self, other: &TransformInput) -> bool
fn ne(&self, other: &TransformInput) -> bool
This method tests for !=
.
sourceimpl Serialize for TransformInput
impl Serialize for TransformInput
impl StructuralPartialEq for TransformInput
Auto Trait Implementations
impl RefUnwindSafe for TransformInput
impl Send for TransformInput
impl Sync for TransformInput
impl Unpin for TransformInput
impl UnwindSafe for TransformInput
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