Struct rusoto_sagemaker::TransformJob

pub struct TransformJob {

    pub auto_ml_job_arn: Option<String>,
    pub batch_strategy: Option<String>,
    pub creation_time: Option<f64>,
    pub data_processing: Option<DataProcessing>,
    pub environment: Option<HashMap<String, String>>,
    pub experiment_config: Option<ExperimentConfig>,
    pub failure_reason: Option<String>,
    pub labeling_job_arn: Option<String>,
    pub max_concurrent_transforms: Option<i64>,
    pub max_payload_in_mb: Option<i64>,
    pub model_client_config: Option<ModelClientConfig>,
    pub model_name: Option<String>,
    pub tags: Option<Vec<Tag>>,
    pub transform_end_time: Option<f64>,
    pub transform_input: Option<TransformInput>,
    pub transform_job_arn: Option<String>,
    pub transform_job_name: Option<String>,
    pub transform_job_status: Option<String>,
    pub transform_output: Option<TransformOutput>,
    pub transform_resources: Option<TransformResources>,
    pub transform_start_time: Option<f64>,
}

A batch transform job. For information about SageMaker batch transform, see Use Batch Transform.

Fields

auto_ml_job_arn: Option<String>

The Amazon Resource Name (ARN) of the AutoML job that created the transform job.

batch_strategy: Option<String>

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.

creation_time: Option<f64>

A timestamp that shows when the transform Job was created.

data_processing: Option<DataProcessing>

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.

experiment_config: Option<ExperimentConfig>

failure_reason: Option<String>

If the transform job failed, the reason it failed.

labeling_job_arn: Option<String>

The Amazon Resource Name (ARN) of the labeling job that created the transform job.

max_concurrent_transforms: Option<i64>

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, 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 built-in algorithms, you don't need to set a value for MaxConcurrentTransforms.

max_payload_in_mb: Option<i64>

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. 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, SageMaker built-in algorithms do not support HTTP chunked encoding.

model_client_config: Option<ModelClientConfig>

model_name: Option<String>

The name of the model associated with the transform job.

tags: Option<Vec<Tag>>

A list of tags associated with the transform job.

transform_end_time: Option<f64>

Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime.

transform_input: Option<TransformInput>

transform_job_arn: Option<String>

The Amazon Resource Name (ARN) of the transform job.

transform_job_name: Option<String>

The name of the transform job.

transform_job_status: Option<String>

The status of the transform job.

Transform job statuses are:

• InProgress - The job is in progress.

• Completed - The job has completed.

• Failed - The transform job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTransformJob call.

• Stopping - The transform job is stopping.

• Stopped - The transform job has stopped.

transform_output: Option<TransformOutput>

transform_resources: Option<TransformResources>

transform_start_time: Option<f64>

Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.

Trait Implementations

impl Clone for TransformJob

fn clone(&self) -> TransformJob

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for TransformJob

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

Formats the value using the given formatter.

impl Default for TransformJob

fn default() -> TransformJob

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for TransformJob

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.

impl PartialEq<TransformJob> for TransformJob

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

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

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

This method tests for !=.

impl StructuralPartialEq for TransformJob