[−][src]Struct rusoto_sagemaker::Channel
A channel is a named input source that training algorithms can consume.
Fields
channel_name: String
The name of the channel.
compression_type: Option<String>
If training data is compressed, the compression type. The default value is None
. CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
content_type: Option<String>
The MIME type of the data.
data_source: DataSource
The location of the channel data.
input_mode: Option<String>
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode
, Amazon SageMaker uses the value set for TrainingInputMode
. Use this parameter to override the TrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File
input mode. To stream data directly from Amazon S3 to the container, choose Pipe
input mode.
To use a model for incremental training, choose File
input model.
record_wrapper_type: Option<String>
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
shuffle_config: Option<ShuffleConfig>
A configuration for a shuffle option for input data in a channel. If you use S3Prefix
for S3DataType
, this shuffles the results of the S3 key prefix matches. If you use ManifestFile
, the order of the S3 object references in the ManifestFile
is shuffled. If you use AugmentedManifestFile
, the order of the JSON lines in the AugmentedManifestFile
is shuffled. The shuffling order is determined using the Seed
value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType
of ShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Trait Implementations
impl Clone for Channel
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impl Debug for Channel
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impl Default for Channel
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impl<'de> Deserialize<'de> for Channel
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pub fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
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__D: Deserializer<'de>,
impl PartialEq<Channel> for Channel
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impl Serialize for Channel
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pub fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
__S: Serializer,
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__S: Serializer,
impl StructuralPartialEq for Channel
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Auto Trait Implementations
impl RefUnwindSafe for Channel
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impl Send for Channel
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impl Sync for Channel
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impl Unpin for Channel
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impl UnwindSafe for Channel
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Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
pub fn borrow_mut(&mut self) -> &mut T
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impl<T> DeserializeOwned for T where
T: for<'de> Deserialize<'de>,
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T: for<'de> Deserialize<'de>,
impl<T> From<T> for T
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impl<T> Instrument for T
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pub fn instrument(self, span: Span) -> Instrumented<Self>
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pub fn in_current_span(self) -> Instrumented<Self>
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T> Same<T> for T
type Output = T
Should always be Self
impl<T> ToOwned for T where
T: Clone,
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T: Clone,
type Owned = T
The resulting type after obtaining ownership.
pub fn to_owned(&self) -> T
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pub fn clone_into(&self, target: &mut T)
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impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
pub fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,