TransformOutput
- class aws_cdk.aws_stepfunctions_tasks.TransformOutput(*, s3_output_path, accept=None, assemble_with=None, encryption_key=None)
- Bases: - object- S3 location where you want Amazon SageMaker to save the results from the transform job. - Parameters:
- s3_output_path ( - str) – S3 path where you want Amazon SageMaker to store the results of the transform job.
- accept ( - Optional[- str]) – MIME type used to specify the output data. Default: - None
- assemble_with ( - Optional[- AssembleWith]) – Defines how to assemble the results of the transform job as a single S3 object. Default: - None
- encryption_key ( - Optional[- IKey]) – AWS KMS key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. Default: - default KMS key for Amazon S3 for your role’s account.
 
- ExampleMetadata:
- infused 
 - Example: - tasks.SageMakerCreateTransformJob(self, "Batch Inference", transform_job_name="MyTransformJob", model_name="MyModelName", model_client_options=tasks.ModelClientOptions( invocations_max_retries=3, # default is 0 invocations_timeout=Duration.minutes(5) ), transform_input=tasks.TransformInput( transform_data_source=tasks.TransformDataSource( s3_data_source=tasks.TransformS3DataSource( s3_uri="s3://inputbucket/train", s3_data_type=tasks.S3DataType.S3_PREFIX ) ) ), transform_output=tasks.TransformOutput( s3_output_path="s3://outputbucket/TransformJobOutputPath" ), transform_resources=tasks.TransformResources( instance_count=1, instance_type=ec2.InstanceType.of(ec2.InstanceClass.M4, ec2.InstanceSize.XLARGE) ) ) - Attributes - accept
- MIME type used to specify the output data. - Default:
- None 
 
 
 - assemble_with
- Defines how to assemble the results of the transform job as a single S3 object. - Default:
- None 
 
 
 - encryption_key
- AWS KMS key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. - Default:
- default KMS key for Amazon S3 for your role’s account. 
 
 
 - s3_output_path
- S3 path where you want Amazon SageMaker to store the results of the transform job.