Struct HyperParameterTrainingJobDefinition

pub struct HyperParameterTrainingJobDefinition {

    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>,
}

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

impl Clone for HyperParameterTrainingJobDefinition

fn clone(&self) -> HyperParameterTrainingJobDefinition

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for HyperParameterTrainingJobDefinition

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for HyperParameterTrainingJobDefinition

fn default() -> HyperParameterTrainingJobDefinition

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for HyperParameterTrainingJobDefinition

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for HyperParameterTrainingJobDefinition

fn eq(&self, other: &HyperParameterTrainingJobDefinition) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for HyperParameterTrainingJobDefinition

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for HyperParameterTrainingJobDefinition