cursus.steps.configs.config_xgboost_training_step

XGBoost Training Step Configuration with Self-Contained Derivation Logic

This module implements the configuration class for SageMaker XGBoost Training steps using a self-contained design where derived fields are private with read-only properties. Fields are organized into three tiers: 1. Tier 1: Essential User Inputs - fields that users must explicitly provide 2. Tier 2: System Inputs with Defaults - fields with reasonable defaults that can be overridden 3. Tier 3: Derived Fields - fields calculated from other fields (private with properties)

class XGBoostTrainingConfig(*, author, bucket, role, region, service_name, pipeline_version, model_class='xgboost', current_date=<factory>, framework_version='1.7-1', py_version='py3', source_dir=None, enable_caching=False, use_secure_pypi=True, max_runtime_seconds=172800, project_root_folder, training_entry_point, training_instance_type='ml.m5.4xlarge', training_instance_count=1, training_volume_size=30, skip_hyperparameters_s3_uri=True, use_precomputed_imputation=False, use_precomputed_risk_tables=False, use_precomputed_features=False, job_type=None, **extra_data)

Bases: BasePipelineConfig

Configuration specific to the SageMaker XGBoost Training Step. This version is streamlined to work with specification-driven architecture. Input/output paths are now provided via step specifications and dependencies.

Fields are organized into three tiers: 1. Tier 1: Essential User Inputs - fields that users must explicitly provide 2. Tier 2: System Inputs with Defaults - fields with reasonable defaults that can be overridden 3. Tier 3: Derived Fields - fields calculated from other fields (private with properties)

training_entry_point: str

training_instance_type: str

training_instance_count: int

training_volume_size: int

framework_version: str

py_version: str

skip_hyperparameters_s3_uri: bool

use_secure_pypi: bool

use_precomputed_imputation: bool

use_precomputed_risk_tables: bool

use_precomputed_features: bool

job_type: str | None

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 hyperparameter_file: str

Get hyperparameter file path.

model_dump(**kwargs)

Override model_dump to include derived properties.

initialize_derived_fields()

Initialize all derived fields once after validation.

classmethod validate_job_type(v)

Validate job_type is one of allowed values.

get_environment_variables()

Get environment variables for the XGBoost training script.

Returns:

Dictionary mapping environment variable names to values

Return type:

Dict[str, str]

get_public_init_fields()

Override get_public_init_fields to include XGBoost training-specific fields. Gets a dictionary of public fields suitable for initializing a child config. Includes both base fields (from parent) and XGBoost training-specific fields.

Returns:

Dictionary of field names to values for child initialization

Return type:

Dict[str, Any]

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.

author: str

bucket: str

role: str

region: str

service_name: str

pipeline_version: str

model_class: str

current_date: str

source_dir: str | None

enable_caching: bool

max_runtime_seconds: int

project_root_folder: str