Struct rusoto_sagemaker::CreateTransformJobRequest

pub struct CreateTransformJobRequest {
    pub batch_strategy: Option<String>,
    pub environment: Option<HashMap<String, String>>,
    pub max_concurrent_transforms: Option<i64>,
    pub max_payload_in_mb: Option<i64>,
    pub model_name: String,
    pub tags: Option<Vec<Tag>>,
    pub transform_input: TransformInput,
    pub transform_job_name: String,
    pub transform_output: TransformOutput,
    pub transform_resources: TransformResources,
}

Fields

batch_strategy: Option<String>

Determines the number of records to include in a mini-batch. If you want to include only one record in a mini-batch, specify SingleRecord.. If you want mini-batches to contain a maximum of the number of records specified in the MaxPayloadInMB parameter, specify MultiRecord.

If you set SplitType to Line and BatchStrategy to MultiRecord, a batch transform automatically splits your input data into the specified payload size. There's no need to split the dataset into smaller files or to use larger payload sizes unless the records in your dataset are very large.

environment: Option<HashMap<String, String>>

The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

max_concurrent_transforms: Option<i64>

The maximum number of parallel requests that can be sent to an algorithm container on an instance. This is good for algorithms that implement multiple workers on larger instances . The default value is 1. To allow Amazon SageMaker to determine the appropriate number for MaxConcurrentTransforms, do not set the value in the API.

max_payload_in_mb: Option<i64>

The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater or equal to the size of a single record. You can approximate the size of a record by dividing the size of your dataset by the number of records. Then multiply this value by the number of records you want in a mini-batch. We recommend to enter a slightly larger value than this to ensure the records fit within the maximum payload size. The default value is 6 MB.

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

model_name: String

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 AWS Region in an AWS account.

tags: Option<Vec<Tag>>

(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

transform_input: TransformInput

Describes the input source and the way the transform job consumes it.

transform_job_name: String

The name of the transform job. The name must be unique within an AWS Region in an AWS account.

transform_output: TransformOutput

Describes the results of the transform job.

transform_resources: TransformResources

Describes the resources, including ML instance types and ML instance count, to use for the transform job.

Trait Implementations

impl PartialEq<CreateTransformJobRequest> for CreateTransformJobRequest

fn eq(&self, other: &CreateTransformJobRequest) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &CreateTransformJobRequest) -> bool

This method tests for !=.

impl Clone for CreateTransformJobRequest

fn clone(&self) -> CreateTransformJobRequest

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Default for CreateTransformJobRequest

fn default() -> CreateTransformJobRequest

Returns the "default value" for a type.

impl Debug for CreateTransformJobRequest

fn fmt(&self, f: &mut Formatter) -> Result

Formats the value using the given formatter.

impl Serialize for CreateTransformJobRequest

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.