Skip to main content

Type annotations for aiobotocore.MachineLearning 2.5.2 service generated with mypy-boto3-builder 7.17.1

Project description

types-aiobotocore-machinelearning

PyPI - types-aiobotocore-machinelearning PyPI - Python Version Docs PyPI - Downloads

boto3.typed

Type annotations for aiobotocore.MachineLearning 2.5.2 service compatible with VSCode, PyCharm, Emacs, Sublime Text, mypy, pyright and other tools.

Generated by mypy-boto3-builder 7.17.1.

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:

boto3-stubs demo

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

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]'
(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]'

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:
    ...

Type definitions

types_aiobotocore_machinelearning.type_defs module contains structures and shapes assembled to typed dictionaries and unions 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_value() -> TagTypeDef:
    return {...}

How it works

Fully automated mypy-boto3-builder carefully generates type annotations for each service, patiently waiting for aiobotocore updates. It delivers 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 botocore schemas.
  • 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, aiobotocore and aioboto3 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.resource calls
  • Auto discovery of types for session.client and session.resource 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

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

Built Distribution

File details

Details for the file types-aiobotocore-machinelearning-2.5.2.post1.tar.gz.

File metadata

File hashes

Hashes for types-aiobotocore-machinelearning-2.5.2.post1.tar.gz
Algorithm Hash digest
SHA256 0c1fce1fc63857b8042b4bf872cc93be6420146b373f51b57d4808f5f967753f
MD5 5397b8dc89d790fa7715286a8bdf9228
BLAKE2b-256 9110feeacda6a52b986f450f534f201b0c05d0c309b560340eaf1eaf99e5352b

See more details on using hashes here.

File details

Details for the file types_aiobotocore_machinelearning-2.5.2.post1-py3-none-any.whl.

File metadata

File hashes

Hashes for types_aiobotocore_machinelearning-2.5.2.post1-py3-none-any.whl
Algorithm Hash digest
SHA256 9c409a0f5802ec863442b10454441c8f4fbeb3da6803527e4913260369c8cb8f
MD5 3e80d9debefa91f0c88cce0c8e9ce089
BLAKE2b-256 b46c07abdb6ea510fbf703f6ef431bcee80e63c580ee3f0bc24b05f9df8aaf49

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page