pub struct HyperParameterTrainingJobDefinition {Show 16 fields
pub algorithm_specification: HyperParameterAlgorithmSpecification,
pub checkpoint_config: Option<CheckpointConfig>,
pub definition_name: Option<String>,
pub enable_inter_container_traffic_encryption: Option<bool>,
pub enable_managed_spot_training: Option<bool>,
pub enable_network_isolation: Option<bool>,
pub hyper_parameter_ranges: Option<ParameterRanges>,
pub input_data_config: Option<Vec<Channel>>,
pub output_data_config: OutputDataConfig,
pub resource_config: ResourceConfig,
pub retry_strategy: Option<RetryStrategy>,
pub role_arn: String,
pub static_hyper_parameters: Option<HashMap<String, String>>,
pub stopping_condition: StoppingCondition,
pub tuning_objective: Option<HyperParameterTuningJobObjective>,
pub vpc_config: Option<VpcConfig>,
}
Expand description
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.
retry_strategy: Option<RetryStrategy>
The number of times to retry the job when the job fails due to an InternalServerError
.
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 a managed spot training job has to complete. When the job reaches the time 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
sourceimpl Clone for HyperParameterTrainingJobDefinition
impl Clone for HyperParameterTrainingJobDefinition
sourcefn clone(&self) -> HyperParameterTrainingJobDefinition
fn clone(&self) -> HyperParameterTrainingJobDefinition
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 Default for HyperParameterTrainingJobDefinition
impl Default for HyperParameterTrainingJobDefinition
sourcefn default() -> HyperParameterTrainingJobDefinition
fn default() -> HyperParameterTrainingJobDefinition
Returns the “default value” for a type. Read more
sourceimpl<'de> Deserialize<'de> for HyperParameterTrainingJobDefinition
impl<'de> Deserialize<'de> for HyperParameterTrainingJobDefinition
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<HyperParameterTrainingJobDefinition> for HyperParameterTrainingJobDefinition
impl PartialEq<HyperParameterTrainingJobDefinition> for HyperParameterTrainingJobDefinition
sourcefn eq(&self, other: &HyperParameterTrainingJobDefinition) -> bool
fn eq(&self, other: &HyperParameterTrainingJobDefinition) -> bool
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
sourcefn ne(&self, other: &HyperParameterTrainingJobDefinition) -> bool
fn ne(&self, other: &HyperParameterTrainingJobDefinition) -> bool
This method tests for !=
.
impl StructuralPartialEq for HyperParameterTrainingJobDefinition
Auto Trait Implementations
impl RefUnwindSafe for HyperParameterTrainingJobDefinition
impl Send for HyperParameterTrainingJobDefinition
impl Sync for HyperParameterTrainingJobDefinition
impl Unpin for HyperParameterTrainingJobDefinition
impl UnwindSafe for HyperParameterTrainingJobDefinition
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