[−][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.
enable_inter_container_traffic_encryption: Option<bool>
To encrypt all communications between ML compute instances in distributed training, specify True
. Encryption provides greater security for distributed training, but training take longer because of the additional communications between ML compute instances.
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.
The Semantic Segmentation built-in algorithm does not support network isolation.
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
Sets a maximum duration for the training jobs that the tuning job launches. Use this parameter to limit model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM
signal. This delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.
When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job.
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 PartialEq<HyperParameterTrainingJobDefinition> for HyperParameterTrainingJobDefinition
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fn eq(&self, other: &HyperParameterTrainingJobDefinition) -> bool
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fn ne(&self, other: &HyperParameterTrainingJobDefinition) -> bool
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impl Clone for HyperParameterTrainingJobDefinition
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fn clone(&self) -> HyperParameterTrainingJobDefinition
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fn clone_from(&mut self, source: &Self)
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Performs copy-assignment from source
. Read more
impl Default for HyperParameterTrainingJobDefinition
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impl Debug for HyperParameterTrainingJobDefinition
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impl Serialize for HyperParameterTrainingJobDefinition
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fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
__S: Serializer,
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__S: Serializer,
impl<'de> Deserialize<'de> for HyperParameterTrainingJobDefinition
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fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
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__D: Deserializer<'de>,
Auto Trait Implementations
impl Send for HyperParameterTrainingJobDefinition
impl Sync for HyperParameterTrainingJobDefinition
Blanket Implementations
impl<T> ToOwned for T where
T: Clone,
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T: Clone,
type Owned = T
The resulting type after obtaining ownership.
fn to_owned(&self) -> T
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fn clone_into(&self, target: &mut T)
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impl<T> From<T> for T
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
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.
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>,
type Error = <U as TryFrom<T>>::Error
The type returned in the event of a conversion error.
fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>
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impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
fn borrow_mut(&mut self) -> &mut T
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impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> DeserializeOwned for T where
T: Deserialize<'de>,
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T: Deserialize<'de>,
impl<T> Erased for T
impl<T> Same<T> for T
type Output = T
Should always be Self