bedrock_create_model_customization_job: Creates a fine-tuning job to customize a base model
In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

bedrock_create_model_customization_job

R Documentation

Creates a fine-tuning job to customize a base model

Description

Creates a fine-tuning job to customize a base model.

See https://www.paws-r-sdk.com/docs/bedrock_create_model_customization_job/ for full documentation.

Usage

bedrock_create_model_customization_job(
  jobName,
  customModelName,
  roleArn,
  clientRequestToken = NULL,
  baseModelIdentifier,
  customizationType = NULL,
  customModelKmsKeyId = NULL,
  jobTags = NULL,
  customModelTags = NULL,
  trainingDataConfig,
  validationDataConfig = NULL,
  outputDataConfig,
  hyperParameters,
  vpcConfig = NULL
)

Arguments

`jobName`	[required] A name for the fine-tuning job.
`customModelName`	[required] A name for the resulting custom model.
`roleArn`	[required] The Amazon Resource Name (ARN) of an IAM service role that Amazon Bedrock can assume to perform tasks on your behalf. For example, during model training, Amazon Bedrock needs your permission to read input data from an S3 bucket, write model artifacts to an S3 bucket. To pass this role to Amazon Bedrock, the caller of this API must have the `iam:PassRole` permission.
`clientRequestToken`	A unique, case-sensitive identifier to ensure that the API request completes no more than one time. If this token matches a previous request, Amazon Bedrock ignores the request, but does not return an error. For more information, see Ensuring idempotency.
`baseModelIdentifier`	[required] Name of the base model.
`customizationType`	The customization type.
`customModelKmsKeyId`	The custom model is encrypted at rest using this key.
`jobTags`	Tags to attach to the job.
`customModelTags`	Tags to attach to the resulting custom model.
`trainingDataConfig`	[required] Information about the training dataset.
`validationDataConfig`	Information about the validation dataset.
`outputDataConfig`	[required] S3 location for the output data.
`hyperParameters`	[required] Parameters related to tuning the model. For details on the format for different models, see Custom model hyperparameters.
`vpcConfig`	VPC configuration (optional). Configuration parameters for the private Virtual Private Cloud (VPC) that contains the resources you are using for this job.