Struct TrainingJobDefinition

pub struct TrainingJobDefinition {
    pub hyper_parameters: Option<HashMap<String, String>>,
    pub input_data_config: Vec<Channel>,
    pub output_data_config: OutputDataConfig,
    pub resource_config: ResourceConfig,
    pub stopping_condition: StoppingCondition,
    pub training_input_mode: String,
}

Defines the input needed to run a training job using the algorithm.

Fields

hyper_parameters: Option<HashMap<String, String>>

The hyperparameters used for the training job.

input_data_config: Vec<Channel>

An array of Channel objects, each of which specifies an input source.

output_data_config: OutputDataConfig

the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

resource_config: ResourceConfig

The resources, including the ML compute instances and ML storage volumes, to use for model training.

stopping_condition: StoppingCondition

Specifies a limit to how long a model 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.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.

training_input_mode: String

The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms.

If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.

Trait Implementations

impl Clone for TrainingJobDefinition

fn clone(&self) -> TrainingJobDefinition

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for TrainingJobDefinition

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

Formats the value using the given formatter.

impl Default for TrainingJobDefinition

fn default() -> TrainingJobDefinition

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for TrainingJobDefinition

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for TrainingJobDefinition

fn eq(&self, other: &TrainingJobDefinition) -> 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 TrainingJobDefinition

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 TrainingJobDefinition