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pub struct TrainingSpecification {
    pub metric_definitions: Option<Vec<MetricDefinition>>,
    pub supported_hyper_parameters: Option<Vec<HyperParameterSpecification>>,
    pub supported_training_instance_types: Vec<String>,
    pub supported_tuning_job_objective_metrics: Option<Vec<HyperParameterTuningJobObjective>>,
    pub supports_distributed_training: Option<bool>,
    pub training_channels: Vec<ChannelSpecification>,
    pub training_image: String,
    pub training_image_digest: Option<String>,
}
Expand description

Defines how the algorithm is used for a training job.

Fields

metric_definitions: Option<Vec<MetricDefinition>>

A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.

supported_hyper_parameters: Option<Vec<HyperParameterSpecification>>

A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>

supported_training_instance_types: Vec<String>

A list of the instance types that this algorithm can use for training.

supported_tuning_job_objective_metrics: Option<Vec<HyperParameterTuningJobObjective>>

A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.

supports_distributed_training: Option<bool>

Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.

training_channels: Vec<ChannelSpecification>

A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.

training_image: String

The Amazon ECR registry path of the Docker image that contains the training algorithm.

training_image_digest: Option<String>

An MD5 hash of the training algorithm that identifies the Docker image used for training.

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