Source code for sagemaker.core.metric_definitions

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# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
#     http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
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"""Accessors to retrieve metric definition for training jobs."""

from __future__ import absolute_import

import logging
from typing import Dict, Optional, List

from sagemaker.core.jumpstart import utils as jumpstart_utils
from sagemaker.core.jumpstart import artifacts
from sagemaker.core.jumpstart.constants import DEFAULT_JUMPSTART_SAGEMAKER_SESSION
from sagemaker.core.jumpstart.enums import JumpStartModelType
from sagemaker.core.helper.session_helper import Session

logger = logging.getLogger(__name__)




def retrieve_default(
    region: Optional[str] = None,
    model_id: Optional[str] = None,
    model_version: Optional[str] = None,
    hub_arn: Optional[str] = None,
    instance_type: Optional[str] = None,
    tolerate_vulnerable_model: bool = False,
    tolerate_deprecated_model: bool = False,
    sagemaker_session: Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
    config_name: Optional[str] = None,
    model_type: JumpStartModelType = JumpStartModelType.OPEN_WEIGHTS,
) -> Optional[List[Dict[str, str]]]:
    """Retrieves the default training metric definitions for the model matching the given arguments.

    Args:
        region (str): The AWS Region for which to retrieve the default default training metric
            definitions. Defaults to ``None``.
        model_id (str): The model ID of the model for which to
            retrieve the default training metric definitions. (Default: None).
        model_version (str): The version of the model for which to retrieve the
            default training metric definitions. (Default: None).
        hub_arn (str): The arn of the SageMaker Hub for which to retrieve
            model details from. (default: None).
        instance_type (str): An instance type to optionally supply in order to get
            metric definitions specific for the instance type.
        tolerate_vulnerable_model (bool): True if vulnerable versions of model
            specifications should be tolerated (exception not raised). If False, raises an
            exception if the script used by this version of the model has dependencies with known
            security vulnerabilities. (Default: False).
        tolerate_deprecated_model (bool): True if deprecated models should be tolerated
            (exception not raised). False if these models should raise an exception.
            (Default: False).
        sagemaker_session (sagemaker.session.Session): A SageMaker Session
            object, used for SageMaker interactions. If not
            specified, one is created using the default AWS configuration
            chain. (Default: sagemaker.jumpstart.constants.DEFAULT_JUMPSTART_SAGEMAKER_SESSION).
        config_name (Optional[str]): Name of the JumpStart Model config to apply. (Default: None).
        model_type (JumpStartModelType): The type of the model, can be open weights model
            or proprietary model. (Default: JumpStartModelType.OPEN_WEIGHTS).
    Returns:
        list: The default metric definitions to use for the model or None.

    Raises:
        ValueError: If the combination of arguments specified is not supported.
    """
    if not jumpstart_utils.is_jumpstart_model_input(model_id, model_version):
        raise ValueError(
            "Must specify JumpStart `model_id` and `model_version` "
            "when retrieving default training metric definitions."
        )

    return artifacts._retrieve_default_training_metric_definitions(
        model_id=model_id,
        model_version=model_version,
        hub_arn=hub_arn,
        instance_type=instance_type,
        region=region,
        tolerate_vulnerable_model=tolerate_vulnerable_model,
        tolerate_deprecated_model=tolerate_deprecated_model,
        sagemaker_session=sagemaker_session,
        config_name=config_name,
        model_type=model_type,
    )