types-aiobotocore-machinelearning 2.4.2

Type annotations for aiobotocore.MachineLearning 2.4.2 service generated with mypy-boto3-builder 7.12.2

types-aiobotocore-machinelearning

Type annotations for aiobotocore.MachineLearning 2.4.2 service compatible with VSCode, PyCharm, Emacs, Sublime Text, mypy, pyright and other tools.

Generated by mypy-boto3-builder 7.12.2.

More information can be found on types-aiobotocore page and in types-aiobotocore-machinelearning docs.

See how it helps to find and fix potential bugs:

• types-aiobotocore-machinelearning
  • How to install
    • From PyPI with pip
  • How to uninstall
  • Usage
    • VSCode
    • PyCharm
    • Emacs
    • Sublime Text
    • Other IDEs
    • mypy
    • pyright
  • Explicit type annotations
    • Client annotations
    • Paginators annotations
    • Waiters annotations
    • Literals
    • Typed dictionaries
  • How it works
  • What's new
    • Implemented features
    • Latest changes
  • Versioning
  • Thank you
  • Documentation
  • Support and contributing

How to install

From PyPI with pip

Install types-aiobotocore for MachineLearning service.

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


# 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[machinelearning]'


# standalone installation
python -m pip install types-aiobotocore-machinelearning

How to uninstall

python -m pip uninstall -y types-aiobotocore-machinelearning

Usage

VSCode

• Install Python extension
• Install Pylance extension
• Set Pylance as your Python Language Server
• Install types-aiobotocore[machinelearning] in your environment:

python -m pip install 'types-aiobotocore[machinelearning]'

Both type checking and code completion should now work. No explicit type annotations required, write your aiobotocore code as usual.

PyCharm

Install types-aiobotocore-lite[machinelearning] in your environment:

python -m pip install 'types-aiobotocore-lite[machinelearning]'`

Both type checking and code completion should now work. Explicit type annotations are required.

Use types-aiobotocore package instead for implicit type discovery.

Emacs

• Install types-aiobotocore with services you use in your environment:

python -m pip install 'types-aiobotocore[machinelearning]'

• Install use-package, lsp, company and flycheck packages
• Install lsp-pyright package

(use-package lsp-pyright
  :ensure t
  :hook (python-mode . (lambda ()
                          (require 'lsp-pyright)
                          (lsp)))  ; or lsp-deferred
  :init (when (executable-find "python3")
          (setq lsp-pyright-python-executable-cmd "python3"))
  )

• Make sure emacs uses the environment where you have installed types-aiobotocore

Type checking should now work. No explicit type annotations required, write your aiobotocore code as usual.

Sublime Text

• Install types-aiobotocore[machinelearning] with services you use in your environment:

python -m pip install 'types-aiobotocore[machinelearning]'

• Install LSP-pyright package

Type checking should now work. No explicit type annotations required, write your aiobotocore code as usual.

Other IDEs

Not tested, but as long as your IDE supports mypy or pyright, everything should work.

mypy

• Install mypy: python -m pip install mypy
• Install types-aiobotocore[machinelearning] in your environment:

python -m pip install 'types-aiobotocore[machinelearning]'`

Type checking should now work. No explicit type annotations required, write your aiobotocore code as usual.

pyright

• Install pyright: npm i -g pyright
• Install types-aiobotocore[machinelearning] in your environment:

python -m pip install 'types-aiobotocore[machinelearning]'

Optionally, you can install types-aiobotocore to typings folder.

Type checking should now work. No explicit type annotations required, write your aiobotocore code as usual.

Explicit type annotations

Client annotations

MachineLearningClient provides annotations for session.create_client("machinelearning").

from aiobotocore.session import get_session

from types_aiobotocore_machinelearning import MachineLearningClient

session = get_session()
async with session.create_client("machinelearning") as client:
    client: MachineLearningClient
    # now client usage is checked by mypy and IDE should provide code completion

Paginators annotations

types_aiobotocore_machinelearning.paginator module contains type annotations for all paginators.

from aiobotocore.session import get_session

from types_aiobotocore_machinelearning import MachineLearningClient
from types_aiobotocore_machinelearning.paginator import (
    DescribeBatchPredictionsPaginator,
    DescribeDataSourcesPaginator,
    DescribeEvaluationsPaginator,
    DescribeMLModelsPaginator,
)

session = get_session()
async with session.create_client("machinelearning") as client:
    client: MachineLearningClient

    # Explicit type annotations are optional here
    # Types should be correctly discovered by mypy and IDEs
    describe_batch_predictions_paginator: DescribeBatchPredictionsPaginator = client.get_paginator(
        "describe_batch_predictions"
    )
    describe_data_sources_paginator: DescribeDataSourcesPaginator = client.get_paginator(
        "describe_data_sources"
    )
    describe_evaluations_paginator: DescribeEvaluationsPaginator = client.get_paginator(
        "describe_evaluations"
    )
    describe_ml_models_paginator: DescribeMLModelsPaginator = client.get_paginator(
        "describe_ml_models"
    )

Waiters annotations

types_aiobotocore_machinelearning.waiter module contains type annotations for all waiters.

from aiobotocore.session import get_session

from types_aiobotocore_machinelearning.client import MachineLearningClient
from types_aiobotocore_machinelearning.waiter import (
    BatchPredictionAvailableWaiter,
    DataSourceAvailableWaiter,
    EvaluationAvailableWaiter,
    MLModelAvailableWaiter,
)

session = get_session()
async with session.create_client("machinelearning") as client:
    client: MachineLearningClient

    # Explicit type annotations are optional here
    # Types should be correctly discovered by mypy and IDEs
    batch_prediction_available_waiter: BatchPredictionAvailableWaiter = client.get_waiter(
        "batch_prediction_available"
    )
    data_source_available_waiter: DataSourceAvailableWaiter = client.get_waiter(
        "data_source_available"
    )
    evaluation_available_waiter: EvaluationAvailableWaiter = client.get_waiter(
        "evaluation_available"
    )
    ml_model_available_waiter: MLModelAvailableWaiter = client.get_waiter("ml_model_available")

Literals

types_aiobotocore_machinelearning.literals module contains literals extracted from shapes that can be used in user code for type checking.

from types_aiobotocore_machinelearning.literals import (
    AlgorithmType,
    BatchPredictionAvailableWaiterName,
    BatchPredictionFilterVariableType,
    DataSourceAvailableWaiterName,
    DataSourceFilterVariableType,
    DescribeBatchPredictionsPaginatorName,
    DescribeDataSourcesPaginatorName,
    DescribeEvaluationsPaginatorName,
    DescribeMLModelsPaginatorName,
    DetailsAttributesType,
    EntityStatusType,
    EvaluationAvailableWaiterName,
    EvaluationFilterVariableType,
    MLModelAvailableWaiterName,
    MLModelFilterVariableType,
    MLModelTypeType,
    RealtimeEndpointStatusType,
    SortOrderType,
    TaggableResourceTypeType,
    MachineLearningServiceName,
    ServiceName,
    ResourceServiceName,
    PaginatorName,
    WaiterName,
    RegionName,
)


def check_value(value: AlgorithmType) -> bool:
    ...

Typed dictionaries

types_aiobotocore_machinelearning.type_defs module contains structures and shapes assembled to typed dictionaries for additional type checking.

from types_aiobotocore_machinelearning.type_defs import (
    TagTypeDef,
    ResponseMetadataTypeDef,
    BatchPredictionTypeDef,
    CreateBatchPredictionInputRequestTypeDef,
    S3DataSpecTypeDef,
    CreateEvaluationInputRequestTypeDef,
    CreateMLModelInputRequestTypeDef,
    CreateRealtimeEndpointInputRequestTypeDef,
    RealtimeEndpointInfoTypeDef,
    DeleteBatchPredictionInputRequestTypeDef,
    DeleteDataSourceInputRequestTypeDef,
    DeleteEvaluationInputRequestTypeDef,
    DeleteMLModelInputRequestTypeDef,
    DeleteRealtimeEndpointInputRequestTypeDef,
    DeleteTagsInputRequestTypeDef,
    WaiterConfigTypeDef,
    PaginatorConfigTypeDef,
    DescribeBatchPredictionsInputRequestTypeDef,
    DescribeDataSourcesInputRequestTypeDef,
    DescribeEvaluationsInputRequestTypeDef,
    DescribeMLModelsInputRequestTypeDef,
    DescribeTagsInputRequestTypeDef,
    PerformanceMetricsTypeDef,
    GetBatchPredictionInputRequestTypeDef,
    GetDataSourceInputRequestTypeDef,
    GetEvaluationInputRequestTypeDef,
    GetMLModelInputRequestTypeDef,
    PredictInputRequestTypeDef,
    PredictionTypeDef,
    RDSDatabaseCredentialsTypeDef,
    RDSDatabaseTypeDef,
    RedshiftDatabaseCredentialsTypeDef,
    RedshiftDatabaseTypeDef,
    UpdateBatchPredictionInputRequestTypeDef,
    UpdateDataSourceInputRequestTypeDef,
    UpdateEvaluationInputRequestTypeDef,
    UpdateMLModelInputRequestTypeDef,
    AddTagsInputRequestTypeDef,
    AddTagsOutputTypeDef,
    CreateBatchPredictionOutputTypeDef,
    CreateDataSourceFromRDSOutputTypeDef,
    CreateDataSourceFromRedshiftOutputTypeDef,
    CreateDataSourceFromS3OutputTypeDef,
    CreateEvaluationOutputTypeDef,
    CreateMLModelOutputTypeDef,
    DeleteBatchPredictionOutputTypeDef,
    DeleteDataSourceOutputTypeDef,
    DeleteEvaluationOutputTypeDef,
    DeleteMLModelOutputTypeDef,
    DeleteTagsOutputTypeDef,
    DescribeTagsOutputTypeDef,
    GetBatchPredictionOutputTypeDef,
    UpdateBatchPredictionOutputTypeDef,
    UpdateDataSourceOutputTypeDef,
    UpdateEvaluationOutputTypeDef,
    UpdateMLModelOutputTypeDef,
    DescribeBatchPredictionsOutputTypeDef,
    CreateDataSourceFromS3InputRequestTypeDef,
    CreateRealtimeEndpointOutputTypeDef,
    DeleteRealtimeEndpointOutputTypeDef,
    GetMLModelOutputTypeDef,
    MLModelTypeDef,
    DescribeBatchPredictionsInputBatchPredictionAvailableWaitTypeDef,
    DescribeDataSourcesInputDataSourceAvailableWaitTypeDef,
    DescribeEvaluationsInputEvaluationAvailableWaitTypeDef,
    DescribeMLModelsInputMLModelAvailableWaitTypeDef,
    DescribeBatchPredictionsInputDescribeBatchPredictionsPaginateTypeDef,
    DescribeDataSourcesInputDescribeDataSourcesPaginateTypeDef,
    DescribeEvaluationsInputDescribeEvaluationsPaginateTypeDef,
    DescribeMLModelsInputDescribeMLModelsPaginateTypeDef,
    EvaluationTypeDef,
    GetEvaluationOutputTypeDef,
    PredictOutputTypeDef,
    RDSDataSpecTypeDef,
    RDSMetadataTypeDef,
    RedshiftDataSpecTypeDef,
    RedshiftMetadataTypeDef,
    DescribeMLModelsOutputTypeDef,
    DescribeEvaluationsOutputTypeDef,
    CreateDataSourceFromRDSInputRequestTypeDef,
    CreateDataSourceFromRedshiftInputRequestTypeDef,
    DataSourceTypeDef,
    GetDataSourceOutputTypeDef,
    DescribeDataSourcesOutputTypeDef,
)


def get_structure() -> TagTypeDef:
    return {...}

How it works

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

• All available aiobotocore services are covered.
• Each public class and method of every aiobotocore 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 and isort for readability.

What's new

Implemented features

• Fully type annotated boto3, botocore and aiobotocore libraries
• mypy, pyright, VSCode, PyCharm, Sublime Text and Emacs compatibility
• Client, ServiceResource, Resource, Waiter Paginator type annotations for each service
• Generated TypeDefs for each service
• Generated Literals for each service
• Auto discovery of types for boto3.client and boto3.session calls
• Auto discovery of types for session.client and session.session calls
• Auto discovery of types for client.get_waiter and client.get_paginator calls
• Auto discovery of types for ServiceResource and Resource collections
• Auto discovery of types for aiobotocore.Session.create_client calls

Latest changes

Builder changelog can be found in Releases.

Versioning

types-aiobotocore-machinelearning version is the same as related aiobotocore version and follows PEP 440 format.

Thank you

• Allie Fitter for boto3-type-annotations, this package is based on top of his work
• black developers for an awesome formatting tool
• Timothy Edmund Crosley for isort and how flexible it is
• mypy developers for doing all dirty work for us
• pyright team for the new era of typed Python

Documentation

All services type annotations can be found in aiobotocore docs

Support and contributing

This package is auto-generated. Please reports any bugs or request new features in mypy-boto3-builder repository.