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Class: Aws::SageMaker::Types::TransformJob
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::TransformJob
- Defined in:
- (unknown)
Overview
A batch transform job. For information about SageMaker batch transform, see Use Batch Transform.
Returned by:
Instance Attribute Summary collapse
-
#auto_ml_job_arn ⇒ String
The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
-
#batch_strategy ⇒ String
Specifies the number of records to include in a mini-batch for an HTTP inference request.
-
#creation_time ⇒ Time
A timestamp that shows when the transform Job was created.
-
#data_processing ⇒ Types::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.
-
#environment ⇒ Hash<String,String>
The environment variables to set in the Docker container.
-
#experiment_config ⇒ Types::ExperimentConfig
Associates a SageMaker job as a trial component with an experiment and trial.
-
#failure_reason ⇒ String
If the transform job failed, the reason it failed.
-
#labeling_job_arn ⇒ String
The Amazon Resource Name (ARN) of the labeling job that created the transform job.
-
#max_concurrent_transforms ⇒ Integer
The maximum number of parallel requests that can be sent to each instance in a transform job.
-
#max_payload_in_mb ⇒ Integer
The maximum allowed size of the payload, in MB.
-
#model_client_config ⇒ Types::ModelClientConfig
Configures the timeout and maximum number of retries for processing a transform job invocation.
.
-
#model_name ⇒ String
The name of the model associated with the transform job.
-
#tags ⇒ Array<Types::Tag>
A list of tags associated with the transform job.
-
#transform_end_time ⇒ Time
Indicates when the transform job has been completed, or has stopped or failed.
-
#transform_input ⇒ Types::TransformInput
Describes the input source of a transform job and the way the transform job consumes it.
.
-
#transform_job_arn ⇒ String
The Amazon Resource Name (ARN) of the transform job.
-
#transform_job_name ⇒ String
The name of the transform job.
-
#transform_job_status ⇒ String
The status of the transform job.
-
#transform_output ⇒ Types::TransformOutput
Describes the results of a transform job.
.
-
#transform_resources ⇒ Types::TransformResources
Describes the resources, including ML instance types and ML instance count, to use for transform job.
.
-
#transform_start_time ⇒ Time
Indicates when the transform job starts on ML instances.
Instance Attribute Details
#auto_ml_job_arn ⇒ String
The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
#batch_strategy ⇒ 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.
Possible values:
- MultiRecord
- SingleRecord
#creation_time ⇒ Time
A timestamp that shows when the transform Job was created.
#data_processing ⇒ Types::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.
#environment ⇒ Hash<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 ⇒ Types::ExperimentConfig
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
#failure_reason ⇒ String
If the transform job failed, the reason it failed.
#labeling_job_arn ⇒ String
The Amazon Resource Name (ARN) of the labeling job that created the transform job.
#max_concurrent_transforms ⇒ Integer
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 ⇒ Integer
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 ⇒ Types::ModelClientConfig
Configures the timeout and maximum number of retries for processing a transform job invocation.
#model_name ⇒ String
The name of the model associated with the transform job.
#tags ⇒ Array<Types::Tag>
A list of tags associated with the transform job.
#transform_end_time ⇒ Time
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 ⇒ Types::TransformInput
Describes the input source of a transform job and the way the transform job consumes it.
#transform_job_arn ⇒ String
The Amazon Resource Name (ARN) of the transform job.
#transform_job_name ⇒ String
The name of the transform job.
#transform_job_status ⇒ 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 theFailureReason
field in the response to aDescribeTransformJob
call.Stopping
- The transform job is stopping.Stopped
- The transform job has stopped.Possible values:
- InProgress
- Completed
- Failed
- Stopping
- Stopped
#transform_output ⇒ Types::TransformOutput
Describes the results of a transform job.
#transform_resources ⇒ Types::TransformResources
Describes the resources, including ML instance types and ML instance count, to use for transform job.
#transform_start_time ⇒ Time
Indicates when the transform job starts on ML instances. You are billed
for the time interval between this time and the value of
TransformEndTime
.