Skip to content

SageMakerMetrics module#

Index > SageMakerMetrics

Auto-generated documentation for SageMakerMetrics type annotations stubs module types-boto3-sagemaker-metrics.

How to install#

You can generate type annotations for boto3 package locally with mypy_boto3_builder. Use uv for build isolation.

  1. Run mypy-boto3-builder in your package root directory: uvx --with 'boto3==1.35.86' mypy_boto3_builder
  2. Select boto3 AWS SDK.
  3. Add SageMakerMetrics service.
  4. Use provided commands to install generated packages.

VSCode extension#

Add AWS Boto3 extension to your VSCode and run AWS boto3: Quick Start command.

Click Modify and select boto3 common and SageMakerMetrics.

From PyPI with pip#

Install types-boto3 for SageMakerMetrics service.

# install with boto3 type annotations
python -m pip install 'types-boto3[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-boto3-lite[sagemaker-metrics]'

# standalone installation
python -m pip install types-boto3-sagemaker-metrics

How to uninstall#

python -m pip uninstall -y types-boto3-sagemaker-metrics

Usage#

Code samples can be found in Examples.

SageMakerMetricsClient#

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

# SageMakerMetricsClient usage example

from boto3.session import Session

from types_boto3_sagemaker_metrics.client import SageMakerMetricsClient

def get_client() -> SageMakerMetricsClient:
    return Session().client("sagemaker-metrics")

Literals#

Type annotations for literals used in methods and schema.

# MetricQueryResultStatusType usage example

from types_boto3_sagemaker_metrics.literals import MetricQueryResultStatusType

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

Type definitions#

Type annotations for type definitions used in methods and schema.