cursus.steps.configs.config_xgboost_model_step

class XGBoostModelStepConfig(*, author, bucket, role, region, service_name, pipeline_version, model_class='xgboost', current_date=<factory>, framework_version='1.5-1', py_version='py3', source_dir=None, enable_caching=False, use_secure_pypi=False, max_runtime_seconds=172800, project_root_folder, instance_type='ml.m5.large', entry_point='inference.py', accelerator_type=None, model_name=None, tags=None, initial_instance_count=1, container_startup_health_check_timeout=300, container_memory_limit=6144, data_download_timeout=900, inference_memory_limit=6144, max_concurrent_invocations=10, max_payload_size=6, **extra_data)

Bases: BasePipelineConfig

Configuration specific to the SageMaker XGBoost Model creation (for inference).

instance_type: str

entry_point: str

framework_version: str

py_version: str

accelerator_type: str | None

model_name: str | None

tags: List[Dict[str, str]] | None

initial_instance_count: int

container_startup_health_check_timeout: int

container_memory_limit: int

data_download_timeout: int

inference_memory_limit: int

max_concurrent_invocations: int

max_payload_size: int

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_configuration()

Validate the complete configuration

classmethod validate_memory_limits(v, info)

get_model_name()

Generate a unique model name if not provided

get_endpoint_config_name()

Generate endpoint configuration name

get_endpoint_name()

Generate endpoint name

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

use_secure_pypi: bool

max_runtime_seconds: int

project_root_folder: str