cursus.steps.configs.config_package_step

class PackageConfig(*, 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='package.py', processing_script_arguments=None, processing_framework_version='1.2-1', **extra_data)

Bases: ProcessingStepConfigBase

Configuration for a model packaging step.

This configuration follows the three-tier field categorization: 1. Tier 1: Essential User Inputs - fields that users must explicitly provide 2. Tier 2: System Inputs with Defaults - fields with reasonable defaults that users can override 3. Tier 3: Derived Fields - fields calculated from other fields, stored in private attributes

processing_entry_point: 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 configuration and ensure defaults are set.

This validator ensures that: 1. Entry point is provided 2. Script contract is available and valid 3. Required input paths are defined in the script contract

get_environment_variables(declared_env_vars=None)

Packaging env vars (the single env source; moved here from the builder, FZ 31e1d3g).

declared_env_vars accepted for the builder’s names-driven contract but ignored — these are config-derived names (PIPELINE_NAME, REGION, …) emitted only when the underlying field is present, preserving the builder’s original conditional-add behavior.

inference_scripts_source()

Local source for the packaging step’s inference_scripts_input (FZ 31e1d3i).

The packaging step always mounts inference scripts from a LOCAL path (overriding any dependency-resolved value). Delegates to effective_source_dir — the single comprehensive source-dir resolver (hybrid processing_source_dir → hybrid source_dir → legacy values) on ProcessingStepConfigBase — falling back to the literal "inference" only when no source dir is configured.

NOTE: the original builder used resolved_source_dir or source_dir or "inference", which reimplemented a PARTIAL version of this resolution and silently IGNORED processing_source_dir (falling through to "inference" when only that was set — a latent bug). Using effective_source_dir fixes that and removes the duplicated chain.

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