mypy-boto3-machinelearning 1.17.63.0

Type annotations for boto3.MachineLearning 1.17.63 service, generated by mypy-boto3-buider 4.8.1

mypy-boto3-machinelearning

Type annotations for boto3.MachineLearning 1.17.63 service compatible with VSCode, PyCharm, Emacs, Sublime Text, mypy, pyright and other tools.

Generated by mypy-boto3-buider 4.8.1.

More information can be found on boto3-stubs page and in auto-generated docs

See how it helps to find and fix potential bugs:

• mypy-boto3-machinelearning
  • How to install
  • Usage
    • VSCode
    • PyCharm
    • Other IDEs
    • mypy
    • pyright
  • Explicit type annotations
    • Client annotations
    • Paginators annotations
    • Waiters annotations
    • Literals
    • Typed dictionaries

How to install

Install boto3-stubs for MachineLearning service.

python -m pip install boto3-stubs[machinelearning]

Usage

VSCode

• Install Python extension
• Install Pylance extension
• Set Pylance as your Python Language Server
• Install boto-stubs[machinelearning] in your environment: python -m pip install 'boto3-stubs[machinelearning]'

Both type checking and auto-complete should work for MachineLearning service. No explicit type annotations required, write your boto3 code as usual.

PyCharm

• Install boto-stubs[machinelearning] in your environment: python -m pip install 'boto3-stubs[machinelearning]'

Both type checking and auto-complete should work for MachineLearning service. No explicit type annotations required, write your boto3 code as usual. Auto-complete can be slow on big projects or if you have a lot of installed boto3-stubs submodules.

Other IDEs

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

mypy

• Install mypy: python -m pip install mypy
• Install boto-stubs[machinelearning] in your environment: python -m pip install 'boto3-stubs[machinelearning]'
• Run mypy as usual

Type checking should work for MachineLearning service. No explicit type annotations required, write your boto3 code as usual.

pyright

• Install pyright: yarn global add pyright
• Install boto-stubs[machinelearning] in your environment: python -m pip install 'boto3-stubs[machinelearning]'
• Optionally, you can install boto3-stubs to typings folder.

Type checking should work for MachineLearning service. No explicit type annotations required, write your boto3 code as usual.

Explicit type annotations

Client annotations

MachineLearningClient provides annotations for boto3.client("machinelearning").

import boto3

from mypy_boto3_machinelearning import MachineLearningClient

client: MachineLearningClient = boto3.client("machinelearning")

# now client usage is checked by mypy and IDE should provide code auto-complete

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

Paginators annotations

mypy_boto3_machinelearning.paginator module contains type annotations for all paginators.

from mypy_boto3_machinelearning import MachineLearningClient
from mypy_boto3_machinelearning.paginator import (
    DescribeBatchPredictionsPaginator,
    DescribeDataSourcesPaginator,
    DescribeEvaluationsPaginator,
    DescribeMLModelsPaginator,
)

client: MachineLearningClient = boto3.client("machinelearning")

# Explicit type annotations are optional here
# Type should be correctly discovered by mypy and IDEs
# VSCode requires explicit type annotations
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

mypy_boto3_machinelearning.waiter module contains type annotations for all waiters.

from mypy_boto3_machinelearning import MachineLearningClient
from mypy_boto3_machinelearning.waiter import (
    BatchPredictionAvailableWaiter,
    DataSourceAvailableWaiter,
    EvaluationAvailableWaiter,
    MLModelAvailableWaiter,
)

client: MachineLearningClient = boto3.client("machinelearning")

# Explicit type annotations are optional here
# Type should be correctly discovered by mypy and IDEs
# VSCode requires explicit type annotations
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

mypy_boto3_machinelearning.literals module contains literals extracted from shapes that can be used in user code for type checking.

from mypy_boto3_machinelearning.literals import (
    Algorithm,
    BatchPredictionAvailableWaiterName,
    BatchPredictionFilterVariable,
    DataSourceAvailableWaiterName,
    DataSourceFilterVariable,
    DescribeBatchPredictionsPaginatorName,
    DescribeDataSourcesPaginatorName,
    DescribeEvaluationsPaginatorName,
    DescribeMLModelsPaginatorName,
    DetailsAttributes,
    EntityStatus,
    EvaluationAvailableWaiterName,
    EvaluationFilterVariable,
    MLModelAvailableWaiterName,
    MLModelFilterVariable,
    MLModelType,
    RealtimeEndpointStatus,
    SortOrder,
    TaggableResourceType,
)

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

Typed dictionaries

mypy_boto3_machinelearning.type_defs module contains structures and shapes assembled to typed dictionaries for additional type checking.

from mypy_boto3_machinelearning.type_defs import (
    AddTagsOutputTypeDef,
    BatchPredictionTypeDef,
    CreateBatchPredictionOutputTypeDef,
    CreateDataSourceFromRDSOutputTypeDef,
    CreateDataSourceFromRedshiftOutputTypeDef,
    CreateDataSourceFromS3OutputTypeDef,
    CreateEvaluationOutputTypeDef,
    CreateMLModelOutputTypeDef,
    CreateRealtimeEndpointOutputTypeDef,
    DataSourceTypeDef,
    DeleteBatchPredictionOutputTypeDef,
    DeleteDataSourceOutputTypeDef,
    DeleteEvaluationOutputTypeDef,
    DeleteMLModelOutputTypeDef,
    DeleteRealtimeEndpointOutputTypeDef,
    DeleteTagsOutputTypeDef,
    DescribeBatchPredictionsOutputTypeDef,
    DescribeDataSourcesOutputTypeDef,
    DescribeEvaluationsOutputTypeDef,
    DescribeMLModelsOutputTypeDef,
    DescribeTagsOutputTypeDef,
    EvaluationTypeDef,
    GetBatchPredictionOutputTypeDef,
    GetDataSourceOutputTypeDef,
    GetEvaluationOutputTypeDef,
    GetMLModelOutputTypeDef,
    MLModelTypeDef,
    PaginatorConfigTypeDef,
    PerformanceMetricsTypeDef,
    PredictOutputTypeDef,
    PredictionTypeDef,
    RDSDataSpecTypeDef,
    RDSDatabaseCredentialsTypeDef,
    RDSDatabaseTypeDef,
    RDSMetadataTypeDef,
    RealtimeEndpointInfoTypeDef,
    RedshiftDataSpecTypeDef,
    RedshiftDatabaseCredentialsTypeDef,
    RedshiftDatabaseTypeDef,
    RedshiftMetadataTypeDef,
    ResponseMetadata,
    S3DataSpecTypeDef,
    TagTypeDef,
    UpdateBatchPredictionOutputTypeDef,
    UpdateDataSourceOutputTypeDef,
    UpdateEvaluationOutputTypeDef,
    UpdateMLModelOutputTypeDef,
    WaiterConfigTypeDef,
)

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