View source: R/sagemaker_operations.R
sagemaker_create_transform_job | R Documentation |
Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.
See https://www.paws-r-sdk.com/docs/sagemaker_create_transform_job/ for full documentation.
sagemaker_create_transform_job(
TransformJobName,
ModelName,
MaxConcurrentTransforms = NULL,
ModelClientConfig = NULL,
MaxPayloadInMB = NULL,
BatchStrategy = NULL,
Environment = NULL,
TransformInput,
TransformOutput,
DataCaptureConfig = NULL,
TransformResources,
DataProcessing = NULL,
Tags = NULL,
ExperimentConfig = NULL
)
TransformJobName |
[required] The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. |
ModelName |
[required] The name of the model that you want to use for the transform job.
|
MaxConcurrentTransforms |
The maximum number of parallel requests that can be sent to each
instance in a transform job. If |
ModelClientConfig |
Configures the timeout and maximum number of retries for processing a transform job invocation. |
MaxPayloadInMB |
The maximum allowed size of the payload, in MB. A payload is the data
portion of a record (without metadata). The value in The value of For cases where the payload might be arbitrarily large and is
transmitted using HTTP chunked encoding, set the value to |
BatchStrategy |
Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record. To enable the batch strategy, you must set the To use only one record when making an HTTP invocation request to a
container, set To fit as many records in a mini-batch as can fit within the
|
Environment |
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map. |
TransformInput |
[required] Describes the input source and the way the transform job consumes it. |
TransformOutput |
[required] Describes the results of the transform job. |
DataCaptureConfig |
Configuration to control how SageMaker captures inference data. |
TransformResources |
[required] Describes the resources, including ML instance types and ML instance count, to use for the transform job. |
DataProcessing |
The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records. |
Tags |
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide. |
ExperimentConfig |
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