cursus.steps.configs.config_batch_transform_step

class BatchTransformStepConfig(*, author, bucket, role, region, service_name, pipeline_version, model_class='xgboost', current_date=<factory>, framework_version='2.1.0', py_version='py310', source_dir=None, enable_caching=False, use_secure_pypi=False, max_runtime_seconds=172800, project_root_folder, job_type, transform_instance_type='ml.m5.large', transform_instance_count=1, content_type='text/csv', accept='text/csv', split_type='Line', assemble_with='Line', input_filter='$[1:]', output_filter='$[-1]', join_source='Input', **extra_data)

Bases: BasePipelineConfig

Configuration for a generic SageMaker BatchTransform step. Inherits all the BasePipelineConfig attributes (bucket, region, etc.) and adds just what’s needed to drive a TransformStep.

job_type: str

transform_instance_type: str

transform_instance_count: int

content_type: str

accept: str

split_type: str

assemble_with: str | None

input_filter: str | None

output_filter: str | None

join_source: str

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'allow', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

validate_config()

Validate join and assemble configurations.

model_post_init(context, /)

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self (BaseModel) – The BaseModel instance.

• context (Any) – The context.

author: str

bucket: str

role: str

region: str

service_name: str

pipeline_version: str

model_class: str

current_date: str

framework_version: str

py_version: str

source_dir: str | None

enable_caching: bool

use_secure_pypi: bool

max_runtime_seconds: int

project_root_folder: str