cursus.steps.configs.config_tabular_preprocessing_step

Tabular Preprocessing Configuration with Self-Contained Derivation Logic

This module implements the configuration class for SageMaker Processing steps for tabular data preprocessing, using a self-contained design where each field is properly categorized according to the three-tier design: 1. Essential User Inputs (Tier 1) - Required fields that must be provided by users 2. System Fields (Tier 2) - Fields with reasonable defaults that can be overridden 3. Derived Fields (Tier 3) - Fields calculated from other fields, private with read-only properties

class TabularPreprocessingConfig(*, 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, processing_instance_count=1, processing_volume_size=500, processing_instance_type_large='ml.m5.4xlarge', processing_instance_type_small='ml.m5.2xlarge', use_large_processing_instance=False, skip_volume_kms=None, processing_source_dir=None, processing_entry_point='tabular_preprocessing.py', processing_script_arguments=None, processing_framework_version='1.2-1', job_type, label_name=None, train_ratio=0.7, test_val_ratio=0.5, output_format='CSV', max_workers=0, batch_size=5, optimize_memory=False, streaming_batch_size=0, enable_true_streaming=False, **extra_data)

Bases: ProcessingStepConfigBase

Configuration for the Tabular Preprocessing step with three-tier field categorization. Inherits from ProcessingStepConfigBase.

Fields are categorized into: - Tier 1: Essential User Inputs - Required from users - Tier 2: System Fields - Default values that can be overridden - Tier 3: Derived Fields - Private with read-only property access

job_type: str

label_name: str | None

processing_entry_point: str

train_ratio: float

test_val_ratio: float

output_format: str

max_workers: int

batch_size: int

optimize_memory: bool

streaming_batch_size: int

enable_true_streaming: bool

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].

property full_script_path: str | None

Get full path to the preprocessing script.

Returns:

Full path to the script

property preprocessing_environment_variables: Dict[str, str]

Get preprocessing-specific environment variables.

Returns:

Dictionary mapping environment variable names to values

classmethod validate_entry_point_relative(v)

Ensure processing_entry_point is a non‐empty relative path.

classmethod validate_data_type(v)

Ensure job_type is one of the allowed values.

classmethod validate_ratios(v)

Ensure the ratio is strictly between 0 and 1 (not including 0 or 1).

classmethod validate_output_format(v)

Ensure output_format is one of the allowed values (case-insensitive).

Matching is case-insensitive and the stored value is normalized to the canonical-cased allowed value, so the persisted config never drifts.

initialize_derived_fields()

Initialize all derived fields once after validation.

get_public_init_fields()

Override get_public_init_fields to include tabular preprocessing specific fields.

Returns:

Dictionary of field names to values for child initialization

Return type:

Dict[str, Any]

model_dump(**kwargs)

Override model_dump to include derived properties.

get_job_arguments()

CLI args — config is the single source (FZ 31e1d3h).

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.

processing_instance_count: int

processing_volume_size: int

processing_instance_type_large: str

processing_instance_type_small: str

use_large_processing_instance: bool

skip_volume_kms: bool | None

processing_source_dir: str | None

processing_script_arguments: List[str] | None

processing_framework_version: str

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