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Create Transform Job

sagemaker_create_transform_job R Documentation

Starts a transform job

Description

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.

To perform batch transformations, you create a transform job and use the data that you have readily available.

In the request body, you provide the following:

  • TransformJobName - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

  • ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see create_model.

  • TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored.

  • TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

  • TransformResources - Identifies the ML compute instances for the transform job.

For more information about how batch transformation works, see Batch Transform.

Usage

sagemaker_create_transform_job(TransformJobName, ModelName,
  MaxConcurrentTransforms, ModelClientConfig, MaxPayloadInMB,
  BatchStrategy, Environment, TransformInput, TransformOutput,
  DataCaptureConfig, TransformResources, DataProcessing, Tags,
  ExperimentConfig)

Arguments

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. ModelName must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.

MaxConcurrentTransforms

The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, Amazon SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms.

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 MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB.

The value of MaxPayloadInMB cannot be greater than 100 MB. If you specify the MaxConcurrentTransforms parameter, the value of (MaxConcurrentTransforms * MaxPayloadInMB) also cannot exceed 100 MB.

For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.

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 SplitType property to Line, RecordIO, or TFRecord.

To use only one record when making an HTTP invocation request to a container, set BatchStrategy to SingleRecord and SplitType to Line.

To fit as many records in a mini-batch as can fit within the MaxPayloadInMB limit, set BatchStrategy to MultiRecord and SplitType to Line.

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

Value

A list with the following syntax: 

list(
  TransformJobArn = "string"
)

Request syntax

svc$create_transform_job(
  TransformJobName = "string",
  ModelName = "string",
  MaxConcurrentTransforms = 123,
  ModelClientConfig = list(
    InvocationsTimeoutInSeconds = 123,
    InvocationsMaxRetries = 123
  ),
  MaxPayloadInMB = 123,
  BatchStrategy = "MultiRecord"|"SingleRecord",
  Environment = list(
    "string"
  ),
  TransformInput = list(
    DataSource = list(
      S3DataSource = list(
        S3DataType = "ManifestFile"|"S3Prefix"|"AugmentedManifestFile",
        S3Uri = "string"
      )
    ),
    ContentType = "string",
    CompressionType = "None"|"Gzip",
    SplitType = "None"|"Line"|"RecordIO"|"TFRecord"
  ),
  TransformOutput = list(
    S3OutputPath = "string",
    Accept = "string",
    AssembleWith = "None"|"Line",
    KmsKeyId = "string"
  ),
  DataCaptureConfig = list(
    DestinationS3Uri = "string",
    KmsKeyId = "string",
    GenerateInferenceId = TRUE|FALSE
  ),
  TransformResources = list(
    InstanceType = "ml.m4.xlarge"|"ml.m4.2xlarge"|"ml.m4.4xlarge"|"ml.m4.10xlarge"|"ml.m4.16xlarge"|"ml.c4.xlarge"|"ml.c4.2xlarge"|"ml.c4.4xlarge"|"ml.c4.8xlarge"|"ml.p2.xlarge"|"ml.p2.8xlarge"|"ml.p2.16xlarge"|"ml.p3.2xlarge"|"ml.p3.8xlarge"|"ml.p3.16xlarge"|"ml.c5.xlarge"|"ml.c5.2xlarge"|"ml.c5.4xlarge"|"ml.c5.9xlarge"|"ml.c5.18xlarge"|"ml.m5.large"|"ml.m5.xlarge"|"ml.m5.2xlarge"|"ml.m5.4xlarge"|"ml.m5.12xlarge"|"ml.m5.24xlarge"|"ml.m6i.large"|"ml.m6i.xlarge"|"ml.m6i.2xlarge"|"ml.m6i.4xlarge"|"ml.m6i.8xlarge"|"ml.m6i.12xlarge"|"ml.m6i.16xlarge"|"ml.m6i.24xlarge"|"ml.m6i.32xlarge"|"ml.c6i.large"|"ml.c6i.xlarge"|"ml.c6i.2xlarge"|"ml.c6i.4xlarge"|"ml.c6i.8xlarge"|"ml.c6i.12xlarge"|"ml.c6i.16xlarge"|"ml.c6i.24xlarge"|"ml.c6i.32xlarge"|"ml.r6i.large"|"ml.r6i.xlarge"|"ml.r6i.2xlarge"|"ml.r6i.4xlarge"|"ml.r6i.8xlarge"|"ml.r6i.12xlarge"|"ml.r6i.16xlarge"|"ml.r6i.24xlarge"|"ml.r6i.32xlarge"|"ml.m7i.large"|"ml.m7i.xlarge"|"ml.m7i.2xlarge"|"ml.m7i.4xlarge"|"ml.m7i.8xlarge"|"ml.m7i.12xlarge"|"ml.m7i.16xlarge"|"ml.m7i.24xlarge"|"ml.m7i.48xlarge"|"ml.c7i.large"|"ml.c7i.xlarge"|"ml.c7i.2xlarge"|"ml.c7i.4xlarge"|"ml.c7i.8xlarge"|"ml.c7i.12xlarge"|"ml.c7i.16xlarge"|"ml.c7i.24xlarge"|"ml.c7i.48xlarge"|"ml.r7i.large"|"ml.r7i.xlarge"|"ml.r7i.2xlarge"|"ml.r7i.4xlarge"|"ml.r7i.8xlarge"|"ml.r7i.12xlarge"|"ml.r7i.16xlarge"|"ml.r7i.24xlarge"|"ml.r7i.48xlarge"|"ml.g4dn.xlarge"|"ml.g4dn.2xlarge"|"ml.g4dn.4xlarge"|"ml.g4dn.8xlarge"|"ml.g4dn.12xlarge"|"ml.g4dn.16xlarge"|"ml.g5.xlarge"|"ml.g5.2xlarge"|"ml.g5.4xlarge"|"ml.g5.8xlarge"|"ml.g5.12xlarge"|"ml.g5.16xlarge"|"ml.g5.24xlarge"|"ml.g5.48xlarge",
    InstanceCount = 123,
    VolumeKmsKeyId = "string"
  ),
  DataProcessing = list(
    InputFilter = "string",
    OutputFilter = "string",
    JoinSource = "Input"|"None"
  ),
  Tags = list(
    list(
      Key = "string",
      Value = "string"
    )
  ),
  ExperimentConfig = list(
    ExperimentName = "string",
    TrialName = "string",
    TrialComponentDisplayName = "string",
    RunName = "string"
  )
)