Waiters

Index > MachineLearning > Waiters

Auto-generated documentation for MachineLearning type annotations stubs module types-boto3-machinelearning.

BatchPredictionAvailableWaiter

Type annotations and code completion for boto3.client("machinelearning").get_waiter("batch_prediction_available"). boto3 documentation

# BatchPredictionAvailableWaiter usage example

from boto3.session import Session

from types_boto3_machinelearning.waiter import BatchPredictionAvailableWaiter


session = Session()

client = session.client("machinelearning")  # (1)
waiter: BatchPredictionAvailableWaiter = client.get_waiter("batch_prediction_available")  # (2)
await waiter.wait(...)

1. client: MachineLearningClient
2. waiter: BatchPredictionAvailableWaiter

wait

Type annotations and code completion for BatchPredictionAvailableWaiter.wait method.

# wait method definition

def wait(
    self,
    *,
    FilterVariable: BatchPredictionFilterVariableType = ...,  # (1)
    EQ: str = ...,
    GT: str = ...,
    LT: str = ...,
    GE: str = ...,
    LE: str = ...,
    NE: str = ...,
    Prefix: str = ...,
    SortOrder: SortOrderType = ...,  # (2)
    NextToken: str = ...,
    Limit: int = ...,
    WaiterConfig: WaiterConfigTypeDef = ...,  # (3)
) -> None:
    ...

1. See BatchPredictionFilterVariableType
2. See SortOrderType
3. See WaiterConfigTypeDef

# wait method usage example with argument unpacking

kwargs: DescribeBatchPredictionsInputWaitTypeDef = {  # (1)
    "FilterVariable": ...,
}

parent.wait(**kwargs)

1. See DescribeBatchPredictionsInputWaitTypeDef

DataSourceAvailableWaiter

Type annotations and code completion for boto3.client("machinelearning").get_waiter("data_source_available"). boto3 documentation

# DataSourceAvailableWaiter usage example

from boto3.session import Session

from types_boto3_machinelearning.waiter import DataSourceAvailableWaiter


session = Session()

client = session.client("machinelearning")  # (1)
waiter: DataSourceAvailableWaiter = client.get_waiter("data_source_available")  # (2)
await waiter.wait(...)

1. client: MachineLearningClient
2. waiter: DataSourceAvailableWaiter

wait

Type annotations and code completion for DataSourceAvailableWaiter.wait method.

# wait method definition

def wait(
    self,
    *,
    FilterVariable: DataSourceFilterVariableType = ...,  # (1)
    EQ: str = ...,
    GT: str = ...,
    LT: str = ...,
    GE: str = ...,
    LE: str = ...,
    NE: str = ...,
    Prefix: str = ...,
    SortOrder: SortOrderType = ...,  # (2)
    NextToken: str = ...,
    Limit: int = ...,
    WaiterConfig: WaiterConfigTypeDef = ...,  # (3)
) -> None:
    ...

1. See DataSourceFilterVariableType
2. See SortOrderType
3. See WaiterConfigTypeDef

# wait method usage example with argument unpacking

kwargs: DescribeDataSourcesInputWaitTypeDef = {  # (1)
    "FilterVariable": ...,
}

parent.wait(**kwargs)

1. See DescribeDataSourcesInputWaitTypeDef

EvaluationAvailableWaiter

Type annotations and code completion for boto3.client("machinelearning").get_waiter("evaluation_available"). boto3 documentation

# EvaluationAvailableWaiter usage example

from boto3.session import Session

from types_boto3_machinelearning.waiter import EvaluationAvailableWaiter


session = Session()

client = session.client("machinelearning")  # (1)
waiter: EvaluationAvailableWaiter = client.get_waiter("evaluation_available")  # (2)
await waiter.wait(...)

1. client: MachineLearningClient
2. waiter: EvaluationAvailableWaiter

wait

Type annotations and code completion for EvaluationAvailableWaiter.wait method.

# wait method definition

def wait(
    self,
    *,
    FilterVariable: EvaluationFilterVariableType = ...,  # (1)
    EQ: str = ...,
    GT: str = ...,
    LT: str = ...,
    GE: str = ...,
    LE: str = ...,
    NE: str = ...,
    Prefix: str = ...,
    SortOrder: SortOrderType = ...,  # (2)
    NextToken: str = ...,
    Limit: int = ...,
    WaiterConfig: WaiterConfigTypeDef = ...,  # (3)
) -> None:
    ...

1. See EvaluationFilterVariableType
2. See SortOrderType
3. See WaiterConfigTypeDef

# wait method usage example with argument unpacking

kwargs: DescribeEvaluationsInputWaitTypeDef = {  # (1)
    "FilterVariable": ...,
}

parent.wait(**kwargs)

1. See DescribeEvaluationsInputWaitTypeDef

MLModelAvailableWaiter

Type annotations and code completion for boto3.client("machinelearning").get_waiter("ml_model_available"). boto3 documentation

# MLModelAvailableWaiter usage example

from boto3.session import Session

from types_boto3_machinelearning.waiter import MLModelAvailableWaiter


session = Session()

client = session.client("machinelearning")  # (1)
waiter: MLModelAvailableWaiter = client.get_waiter("ml_model_available")  # (2)
await waiter.wait(...)

1. client: MachineLearningClient
2. waiter: MLModelAvailableWaiter

wait

Type annotations and code completion for MLModelAvailableWaiter.wait method.

# wait method definition

def wait(
    self,
    *,
    FilterVariable: MLModelFilterVariableType = ...,  # (1)
    EQ: str = ...,
    GT: str = ...,
    LT: str = ...,
    GE: str = ...,
    LE: str = ...,
    NE: str = ...,
    Prefix: str = ...,
    SortOrder: SortOrderType = ...,  # (2)
    NextToken: str = ...,
    Limit: int = ...,
    WaiterConfig: WaiterConfigTypeDef = ...,  # (3)
) -> None:
    ...

1. See MLModelFilterVariableType
2. See SortOrderType
3. See WaiterConfigTypeDef

# wait method usage example with argument unpacking

kwargs: DescribeMLModelsInputWaitTypeDef = {  # (1)
    "FilterVariable": ...,
}

parent.wait(**kwargs)

1. See DescribeMLModelsInputWaitTypeDef