mypy-boto3-sagemaker-edge Type Annotations

Warnings

• breaking Support for Python 3.8 has been removed in `mypy-boto3-builder` (version 8.12.0) and consequently for all generated `mypy-boto3-*` packages. Users must use Python 3.9 or higher.
  Fix: Upgrade your Python environment to 3.9 or newer.
• breaking The `mypy-boto3` ecosystem migrated to PEP 561 compliant packages in `mypy-boto3-builder` 8.12.0. This change affects how type checkers locate stubs. If you experience issues, ensure your type checker (e.g., MyPy, Pyright) is up-to-date and configured correctly to discover installed stub packages.
  Fix: Update your static type checker (e.g., `pip install --upgrade mypy`) and verify its configuration. In most cases, installing the stub package with pip is sufficient for modern type checkers.
• gotcha For optimal type checking, the `mypy-boto3-sagemaker-edge` package version should align closely with your installed `boto3` version. Mismatching versions (e.g., using `mypy-boto3-sagemaker-edge==1.42.x` with `boto3==1.34.x`) can lead to incorrect or missing type hints.
  Fix: Ensure `mypy-boto3-sagemaker-edge` and `boto3` are updated to compatible versions. The stub's version typically reflects the `boto3` version it types (e.g., `1.42.3` for `boto3` `1.42.x`).
• gotcha To prevent `mypy-boto3-*` packages from becoming runtime dependencies, it is best practice to import the type hints only within an `if TYPE_CHECKING:` block. This avoids shipping type stubs with your production code.
  Fix: Wrap type-only imports within `from typing import TYPE_CHECKING` and `if TYPE_CHECKING:` blocks, as demonstrated in the `imports` and `quickstart` sections.
• deprecated The `sms-voice` service was removed and is no longer supported starting with `mypy-boto3-builder` 8.11.0. Users of `sms-voice` should migrate to `pinpoint-sms-voice`. While not directly related to `sagemaker-edge`, this highlights potential service deprecations across the `mypy-boto3` ecosystem.
  Fix: If using the deprecated `sms-voice` service, switch to `pinpoint-sms-voice` and install the corresponding `mypy-boto3-pinpoint-sms-voice` stub.
• gotcha PyCharm users might experience performance issues due to Literal overloads. For better IDE performance, consider installing `boto3-stubs-lite` (if available for the specific service) or disable PyCharm's built-in type checker and rely on `mypy` or `pyright` instead.
  Fix: For PyCharm: `pip install boto3-stubs-lite[sagemaker-edge]` (if available) or adjust PyCharm's type checking settings.

Install

• pip install mypy-boto3-sagemaker-edge boto3 Install with pip

Imports

• SagemakerEdgeManagerClient

  from typing import TYPE_CHECKING
  import boto3
  
  if TYPE_CHECKING:
      from mypy_boto3_sagemaker_edge.client import SagemakerEdgeManagerClient
      from mypy_boto3_sagemaker_edge.type_defs import SendHeartbeatRequestRequestTypeDef
  
  def get_client() -> SagemakerEdgeManagerClient:
      return boto3.client("sagemaker-edge")

  Without `mypy-boto3-sagemaker-edge` or `TYPE_CHECKING` guards, `boto3.client` will return a `BaseClient` without service-specific type hints. Explicitly importing `SagemakerEdgeManagerClient` under `TYPE_CHECKING` provides full type fidelity without adding runtime dependency.

Quickstart

This quickstart demonstrates how to initialize a `boto3` SagemakerEdgeManager client with type annotations provided by `mypy-boto3-sagemaker-edge` and perform a basic operation like `send_heartbeat`. The `TYPE_CHECKING` block ensures that the stub types are only used during static analysis, preventing them from becoming a runtime dependency. Replace placeholder AWS credentials and region as needed.

import boto3
from typing import TYPE_CHECKING
import os

if TYPE_CHECKING:
    from mypy_boto3_sagemaker_edge.client import SagemakerEdgeManagerClient
    from mypy_boto3_sagemaker_edge.type_defs import SendHeartbeatRequestRequestTypeDef

# Configure AWS credentials and region (replace with your actual configuration)
# For quickstart, using environment variables or placeholder for simplicity
os.environ['AWS_ACCESS_KEY_ID'] = os.environ.get('AWS_ACCESS_KEY_ID', 'YOUR_ACCESS_KEY')
os.environ['AWS_SECRET_ACCESS_KEY'] = os.environ.get('AWS_SECRET_ACCESS_KEY', 'YOUR_SECRET_KEY')
os.environ['AWS_REGION_NAME'] = os.environ.get('AWS_REGION_NAME', 'us-east-1')

def main():
    # Initialize the Sagemaker Edge Manager client with type hints
    client: SagemakerEdgeManagerClient = boto3.client("sagemaker-edge")

    # Example: Prepare a request for sending heartbeat with type definition
    heartbeat_request: SendHeartbeatRequestRequestTypeDef = {
        "AgentMetrics": [],  # List of AgentMetricTypeDef
        "Models": []        # List of ModelTypeDef
    }

    try:
        # Call an operation on the client
        response = client.send_heartbeat(**heartbeat_request)
        print("Heartbeat sent successfully.")
        print(f"Response Metadata: {response['ResponseMetadata']}")
    except client.exceptions.InternalServiceException as e:
        print(f"Error sending heartbeat: {e}")
    except Exception as e:
        print(f"An unexpected error occurred: {e}")

if __name__ == "__main__":
    main()