Skip to content

SageMakerMetrics module#

Index > SageMakerMetrics

Auto-generated documentation for SageMakerMetrics type annotations stubs module types-aiobotocore-sagemaker-metrics.

How to install#

You can generate type annotations for aiobotocore package locally with mypy-boto3-builder. Use uv for build isolation.

  1. Run mypy-boto3-builder in your package root directory: uvx --with 'aiobotocore==2.25.0' mypy-boto3-builder
  2. Select aiobotocore AWS SDK.
  3. Add SageMakerMetrics service.
  4. Use provided commands to install generated packages.

From PyPI with pip#

Install types-aiobotocore for SageMakerMetrics service.

# install with aiobotocore type annotations
python -m pip install 'types-aiobotocore[sagemaker-metrics]'

# Lite version does not provide session.client/resource overloads
# it is more RAM-friendly, but requires explicit type annotations
python -m pip install 'types-aiobotocore-lite[sagemaker-metrics]'

# standalone installation
python -m pip install types-aiobotocore-sagemaker-metrics

How to uninstall#

python -m pip uninstall -y types-aiobotocore-sagemaker-metrics

Usage#

Code samples can be found in Examples.

SageMakerMetricsClient#

Type annotations and code completion for session.create_client("sagemaker-metrics") as SageMakerMetricsClient boto3 documentation

# SageMakerMetricsClient usage example

from aiobotocore.session import get_session

from types_aiobotocore_sagemaker_metrics.client import SageMakerMetricsClient


session = get_session()
async with session.create_client("sagemaker-metrics") as client:
    client: SageMakerMetricsClient

Literals#

Type annotations for literals used in methods and schema.

# MetricQueryResultStatusType usage example

from types_aiobotocore_sagemaker_metrics.literals import MetricQueryResultStatusType

def get_value() -> MetricQueryResultStatusType:
    return "Complete"

Type definitions#

Type annotations for type definitions used in methods and schema.