[−][src]Struct rusoto_sagemaker::HyperParameterTrainingJobDefinition
Defines the training jobs launched by a hyperparameter tuning job.
Fields
algorithm_specification: HyperParameterAlgorithmSpecification
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
checkpoint_config: Option<CheckpointConfig>
definition_name: Option<String>
The job definition name.
enable_inter_container_traffic_encryption: Option<bool>
To encrypt all communications between ML compute instances in distributed training, choose True
. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
enable_managed_spot_training: Option<bool>
A Boolean indicating whether managed spot training is enabled (True
) or not (False
).
enable_network_isolation: Option<bool>
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
hyper_parameter_ranges: Option<ParameterRanges>
input_data_config: Option<Vec<Channel>>
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
output_data_config: OutputDataConfig
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
resource_config: ResourceConfig
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose File
as the TrainingInputMode
in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
role_arn: String
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
static_hyper_parameters: Option<HashMap<String, String>>
Specifies the values of hyperparameters that do not change for the tuning job.
stopping_condition: StoppingCondition
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long you are willing to wait for a managed spot training job to complete. When the job reaches the a limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
tuning_objective: Option<HyperParameterTuningJobObjective>
vpc_config: Option<VpcConfig>
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
Trait Implementations
impl Clone for HyperParameterTrainingJobDefinition
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pub fn clone(&self) -> HyperParameterTrainingJobDefinition
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pub fn clone_from(&mut self, source: &Self)
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impl Debug for HyperParameterTrainingJobDefinition
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impl Default for HyperParameterTrainingJobDefinition
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pub fn default() -> HyperParameterTrainingJobDefinition
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impl<'de> Deserialize<'de> for HyperParameterTrainingJobDefinition
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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<HyperParameterTrainingJobDefinition> for HyperParameterTrainingJobDefinition
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pub fn eq(&self, other: &HyperParameterTrainingJobDefinition) -> bool
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pub fn ne(&self, other: &HyperParameterTrainingJobDefinition) -> bool
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impl Serialize for HyperParameterTrainingJobDefinition
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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 HyperParameterTrainingJobDefinition
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Auto Trait Implementations
impl RefUnwindSafe for HyperParameterTrainingJobDefinition
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impl Send for HyperParameterTrainingJobDefinition
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impl Sync for HyperParameterTrainingJobDefinition
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impl Unpin for HyperParameterTrainingJobDefinition
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impl UnwindSafe for HyperParameterTrainingJobDefinition
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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> 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>,