batch_put_metrics `async` ¶

batch_put_metrics(
    *,
    config_overrides: Optional[AsyncSageMakerMetricsClientConfig] = None,
    trial_component_name: Optional[ExperimentEntityName] = None,
    metric_data: Optional[RawMetricDataList] = None,
) -> BatchPutMetricsResponse

Used to ingest training metrics into SageMaker. These metrics can be visualized in SageMaker Studio.

Parameters:

trial_component_name (Optional[ExperimentEntityName], default: None ) –

The name of the Trial Component to associate with the metrics. The Trial Component name must be entirely lowercase.
metric_data (Optional[RawMetricDataList], default: None ) –

A list of raw metric values to put.

Raises:

UnknownServiceError –

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

Source code in docs_src/capo_sagemaker_metrics/_services/async_sage_maker_metrics.py

async def batch_put_metrics(
    self,
    *,
    config_overrides: Optional[AsyncSageMakerMetricsClientConfig] = None,
    trial_component_name: Optional[
        "capo_sagemaker_metrics.types.experiment_entity_name.ExperimentEntityName"
    ] = None,
    metric_data: Optional[
        "capo_sagemaker_metrics.types.raw_metric_data_list.RawMetricDataList"
    ] = None,
) -> "capo_sagemaker_metrics.types.batch_put_metrics_response.BatchPutMetricsResponse":
    """<p>Used to ingest training metrics into SageMaker. These metrics can be visualized in SageMaker Studio. </p>

    Args:
        trial_component_name: <p>The name of the Trial Component to associate with the metrics. The Trial Component name must be entirely lowercase.</p>
        metric_data: <p>A list of raw metric values to put.</p>

    Raises:
        capo_sagemaker_metrics.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_sagemaker_metrics.types.batch_put_metrics_request.BatchPutMetricsRequest]",
    ) -> AsyncOperationResponse[
        "capo_sagemaker_metrics.types.batch_put_metrics_response.BatchPutMetricsResponse"
    ]:
        import capo_sagemaker_metrics._operations.sage_maker_metrics_service.batch_put_metrics

        (
            output,
            http_response,
        ) = await capo_sagemaker_metrics._operations.sage_maker_metrics_service.batch_put_metrics.async_batch_put_metrics(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("BatchPutMetrics")
    )
    input_: capo_sagemaker_metrics.types.batch_put_metrics_request.BatchPutMetricsRequest = {}
    if trial_component_name is not None:
        input_["trial_component_name"] = trial_component_name
    if metric_data is not None:
        input_["metric_data"] = metric_data

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

batch_put_metrics async ¶

batch_put_metrics `async` ¶