mypy-boto3-machinelearning 1.12.29.0

Type annotations for boto3.MachineLearning 1.12.29 service, generated by mypy-boto3-buider 1.0.6

mypy-boto3-machinelearning

Type annotations for boto3.MachineLearning 1.12.29 service compatible with mypy, VSCode, PyCharm and other tools.

Generated by mypy-boto3-buider 1.0.6.

More information can be found on boto3-stubs page.

• mypy-boto3-machinelearning
  • How to use
    • Type checking
  • How it works

How to use

Type checking

Make sure you have mypy installed and activated in your IDE.

Install boto3-stubs for MachineLearning service.

python -m pip install boto3-stubs[mypy-boto3-machinelearning]

Use boto3 with mypy_boto3 in your project and enjoy type checking and auto-complete.

import boto3

from mypy_boto3 import machinelearning
# alternative import if you do not want to install mypy_boto3 package
# import mypy_boto3_machinelearning as machinelearning

# Use this client as usual, now mypy can check if your code is valid.
# Check if your IDE supports function overloads,
# you probably do not need explicit type annotations
# client = boto3.client("machinelearning")
client: machinelearning.MachineLearningClient = boto3.client("machinelearning")

# works for session as well
session = boto3.session.Session(region="us-west-1")
session_client: machinelearning.MachineLearningClient = session.client("machinelearning")


# Waiters need type annotation on creation
batch_prediction_available_waiter: machinelearning.BatchPredictionAvailableWaiter = client.get_waiter("batch_prediction_available")
data_source_available_waiter: machinelearning.DataSourceAvailableWaiter = client.get_waiter("data_source_available")
evaluation_available_waiter: machinelearning.EvaluationAvailableWaiter = client.get_waiter("evaluation_available")
ml_model_available_waiter: machinelearning.MLModelAvailableWaiter = client.get_waiter("ml_model_available")

# Paginators need type annotation on creation
describe_batch_predictions_paginator: machinelearning.DescribeBatchPredictionsPaginator = client.get_paginator("describe_batch_predictions")
describe_data_sources_paginator: machinelearning.DescribeDataSourcesPaginator = client.get_paginator("describe_data_sources")
describe_evaluations_paginator: machinelearning.DescribeEvaluationsPaginator = client.get_paginator("describe_evaluations")
describe_ml_models_paginator: machinelearning.DescribeMLModelsPaginator = client.get_paginator("describe_ml_models")

How it works

Fully automated builder carefully generates type annotations for each service, patiently waiting for boto3 updates. It delivers a drop-in type annotations for you and makes sure that:

• All available boto3 services are covered.
• Each public class and method of every boto3 service gets valid type annotations extracted from the documentation (blame botocore docs if types are incorrect).
• Type annotations include up-to-date documentation.
• Link to documentation is provided for every method.
• Code is processed by black for readability.