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SageMakerRuntime module#

Index > SageMakerRuntime

Auto-generated documentation for SageMakerRuntime type annotations stubs module types-boto3-sagemaker-runtime.

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 SageMakerRuntime 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 SageMakerRuntime.

From PyPI with pip#

Install types-boto3 for SageMakerRuntime service.

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

# 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-runtime]'

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

How to uninstall#

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

Usage#

Code samples can be found in Examples.

SageMakerRuntimeClient#

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

# SageMakerRuntimeClient usage example

from boto3.session import Session

from types_boto3_sagemaker_runtime.client import SageMakerRuntimeClient

def get_client() -> SageMakerRuntimeClient:
    return Session().client("sagemaker-runtime")

Literals#

Type annotations for literals used in methods and schema.

# SageMakerRuntimeServiceName usage example

from types_boto3_sagemaker_runtime.literals import SageMakerRuntimeServiceName

def get_value() -> SageMakerRuntimeServiceName:
    return "sagemaker-runtime"

Type definitions#

Type annotations for type definitions used in methods and schema.