cursus.steps.configs.config_tabular_lookup_model_building_step

Configuration for the TabularLookupModelBuilding step.

TabularLookupModelBuilding is a non-parametric model-building step: it reads a tabular dataset from an upstream data-loading step and turns the dataset itself into the model — either a key -> [values] lookup map (model_kind="lookup") or a de-duplicated key set (model_kind="set_membership") — then packages it into model.tar.gz for downstream MIMS Package + Payload. There is no training and there are no learned parameters (“the dataset IS the model”).

The per-project inference handler is bundled separately by the Package step (via its inference_scripts_input channel); this step does NOT generate an inference handler at runtime.

This configuration follows the Three-Tier Config Design pattern:

• Tier 1 (Essential Fields): required user inputs (model_kind, key_columns).

• Tier 2 (System Fields): fields with sensible defaults (value_columns, dedup, shard_count, processing_entry_point).

• Tier 3 (Derived Fields): inherited from ProcessingStepConfigBase (effective_source_dir, effective_instance_type, script_path).

class TabularLookupModelBuildingConfig(*, author, bucket, role, region, service_name, pipeline_version, model_class='xgboost', current_date=<factory>, framework_version='2.1.0', py_version='py310', image_uri=None, subnets=None, security_group_ids=None, enable_network_isolation=None, 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_lookup_model_building.py', processing_script_arguments=None, processing_framework_version='1.2-1', model_kind, key_columns, value_columns=<factory>, dedup=True, shard_count=1, inference_fields=<factory>, **extra_data)

Bases: ProcessingStepConfigBase

Configuration for the TabularLookupModelBuilding step (three-tier design).

Inherits region/author/bucket/role/pipeline + processing_* fields from ProcessingStepConfigBase -> BasePipelineConfig.

model_kind: str

key_columns: List[str]

value_columns: List[str]

dedup: bool

shard_count: int

inference_fields: List[list]

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].

get_environment_variables(declared_env_vars=None)

Source the container environment for the model-building script.

The universal builder detects a config-owned get_environment_variables and calls it to build the container env, then merges interface defaults for any declared-optional var not produced here. declared_env_vars is accepted for signature compatibility with the base resolver and intentionally ignored.

classmethod validate_model_kind(v)

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

classmethod validate_key_columns(v)

Require at least one non-empty key column.

classmethod validate_entry_point_relative(v)

Ensure processing_entry_point is a non-empty relative path.

validate_config()

Validate the INTERNAL-node configuration.

• lookup mode needs value_columns to have something to collect;

• entry point present + script contract loadable;

• the contract must declare the required ‘model_output’ output path.

get_public_init_fields()

Include TabularLookupModelBuilding-specific fields for child initialization.

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

image_uri: str | None

subnets: List[str] | None

security_group_ids: List[str] | None

enable_network_isolation: bool | None

source_dir: str | None

enable_caching: bool

use_secure_pypi: bool

max_runtime_seconds: int

project_root_folder: str