start_human_loop `async` ¶

start_human_loop(
    *,
    config_overrides: Optional[AsyncSageMakerA2IRuntimeClientConfig] = None,
    human_loop_name: Optional[HumanLoopName] = None,
    flow_definition_arn: Optional[FlowDefinitionArn] = None,
    human_loop_input: Optional[HumanLoopInput] = None,
    data_attributes: Optional[HumanLoopDataAttributes] = None,
) -> StartHumanLoopResponse

Starts a human loop, provided that at least one activation condition is met.

Parameters:

human_loop_name (Optional[HumanLoopName], default: None ) –

The name of the human loop.
flow_definition_arn (Optional[FlowDefinitionArn], default: None ) –

The Amazon Resource Name (ARN) of the flow definition associated with this human loop.
human_loop_input (Optional[HumanLoopInput], default: None ) –

An object that contains information about the human loop.
data_attributes (Optional[HumanLoopDataAttributes], default: None ) –

Attributes of the specified data. Use DataAttributes to specify if your data is free of personally identifiable information and/or free of adult content.

Raises:

ConflictException –

Your request has the same name as another active human loop but has different input data. You cannot start two human loops with the same name and different input data.
InternalServerException –

We couldn't process your request because of an issue with the server. Try again later.
ServiceQuotaExceededException –

You exceeded your service quota. Service quotas, also referred to as limits, are the maximum number of service resources or operations for your AWS account. For a list of Amazon A2I service quotes, see Amazon Augmented AI Service Quotes. Delete some resources or request an increase in your service quota. You can request a quota increase using Service Quotas or the AWS Support Center. To request an increase, see AWS Service Quotas in the AWS General Reference.
ThrottlingException –

You exceeded the maximum number of requests.
ValidationException –

The request isn't valid. Check the syntax and try again.
UnknownServiceError –

The service returned an error code this client does not model.

Source code in docs_src/capo_sagemaker_a2i_runtime/_services/async_sage_maker_a2_i_runtime.py

async def start_human_loop(
    self,
    *,
    config_overrides: Optional[AsyncSageMakerA2IRuntimeClientConfig] = None,
    human_loop_name: Optional[
        "capo_sagemaker_a2i_runtime.types.human_loop_name.HumanLoopName"
    ] = None,
    flow_definition_arn: Optional[
        "capo_sagemaker_a2i_runtime.types.flow_definition_arn.FlowDefinitionArn"
    ] = None,
    human_loop_input: Optional[
        "capo_sagemaker_a2i_runtime.types.human_loop_input.HumanLoopInput"
    ] = None,
    data_attributes: Optional[
        "capo_sagemaker_a2i_runtime.types.human_loop_data_attributes.HumanLoopDataAttributes"
    ] = None,
) -> "capo_sagemaker_a2i_runtime.types.start_human_loop_response.StartHumanLoopResponse":
    """<p>Starts a human loop, provided that at least one activation condition is met.</p>

    Args:
        human_loop_name: <p>The name of the human loop.</p>
        flow_definition_arn: <p>The Amazon Resource Name (ARN) of the flow definition associated with this human loop.</p>
        human_loop_input: <p>An object that contains information about the human loop.</p>
        data_attributes: <p>Attributes of the specified data. Use <code>DataAttributes</code> to specify if your data is free of personally identifiable information and/or free of adult content.</p>

    Raises:
        capo_sagemaker_a2i_runtime.errors.conflict_exception.ConflictException: <p>Your request has the same name as another active human loop but has different input data. You cannot start two human loops with the same name and different input data.</p>
        capo_sagemaker_a2i_runtime.errors.internal_server_exception.InternalServerException: <p>We couldn't process your request because of an issue with the server. Try again later.</p>
        capo_sagemaker_a2i_runtime.errors.service_quota_exceeded_exception.ServiceQuotaExceededException: <p>You exceeded your service quota. Service quotas, also referred to as limits, are the maximum number of service resources or operations for your AWS account. For a list of Amazon A2I service quotes, see <a href="https://docs.aws.amazon.com/general/latest/gr/a2i.html">Amazon Augmented AI Service Quotes</a>. Delete some resources or request an increase in your service quota. You can request a quota increase using Service Quotas or the AWS Support Center. To request an increase, see <a href="https://docs.aws.amazon.com/general/latest/gr/aws_service_limits.html">AWS Service Quotas</a> in the <i>AWS General Reference</i>.</p>
        capo_sagemaker_a2i_runtime.errors.throttling_exception.ThrottlingException: <p>You exceeded the maximum number of requests.</p>
        capo_sagemaker_a2i_runtime.errors.validation_exception.ValidationException: <p>The request isn't valid. Check the syntax and try again.</p>
        capo_sagemaker_a2i_runtime.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_sagemaker_a2i_runtime.types.start_human_loop_request.StartHumanLoopRequest]",
    ) -> AsyncOperationResponse[
        "capo_sagemaker_a2i_runtime.types.start_human_loop_response.StartHumanLoopResponse"
    ]:
        import capo_sagemaker_a2i_runtime._operations.amazon_sage_maker_a2_i_runtime.start_human_loop

        (
            output,
            http_response,
        ) = await capo_sagemaker_a2i_runtime._operations.amazon_sage_maker_a2_i_runtime.start_human_loop.async_start_human_loop(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StartHumanLoop")
    )
    input_: capo_sagemaker_a2i_runtime.types.start_human_loop_request.StartHumanLoopRequest = {}
    if human_loop_name is not None:
        input_["human_loop_name"] = human_loop_name
    if flow_definition_arn is not None:
        input_["flow_definition_arn"] = flow_definition_arn
    if human_loop_input is not None:
        input_["human_loop_input"] = human_loop_input
    if data_attributes is not None:
        input_["data_attributes"] = data_attributes

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output

start_human_loop async ¶

start_human_loop `async` ¶