TrainingJobDefinition

import type { TrainingJobDefinition } from "https://aws-api.deno.dev/v0.4/services/sagemaker.ts?docs=full";

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

interface TrainingJobDefinition {

HyperParameters?: {

[key: string]: string | null | undefined;

} | null;

InputDataConfig: Channel[];

OutputDataConfig: OutputDataConfig;

ResourceConfig: ResourceConfig;

StoppingCondition: StoppingCondition;

TrainingInputMode: TrainingInputMode;

}

§Properties

HyperParameters?: {

[key: string]: string | null | undefined;

} | null

[src]

The hyperparameters used for the training job.

InputDataConfig: Channel[]

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

OutputDataConfig: OutputDataConfig

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

ResourceConfig: ResourceConfig

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

StoppingCondition: StoppingCondition

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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, SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, 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.

TrainingInputMode: TrainingInputMode

[src]