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Class: Aws::SageMaker::Types::DescribeTransformJobResponse
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::DescribeTransformJobResponse
- Defined in:
- (unknown)
Overview
Returned by:
Instance Attribute Summary collapse
-
#auto_ml_job_arn ⇒ String
The Amazon Resource Name (ARN) of the AutoML 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,
FailureReason
describes why it failed. -
#labeling_job_arn ⇒ String
The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
-
#max_concurrent_transforms ⇒ Integer
The maximum number of parallel requests on each instance node that can be launched in a transform job.
-
#max_payload_in_mb ⇒ Integer
The maximum payload size, in MB, used in the transform job.
-
#model_client_config ⇒ Types::ModelClientConfig
The timeout and maximum number of retries for processing a transform job invocation.
-
#model_name ⇒ String
The name of the model used in 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 dataset to be transformed and the Amazon S3 location where it is stored.
-
#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
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
-
#transform_resources ⇒ Types::TransformResources
Describes the resources, including ML instance types and ML instance count, to use for the 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 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.
To enable the batch strategy, you must set SplitType
to Line
,
RecordIO
, or TFRecord
.
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, FailureReason
describes why it failed. A
transform job creates a log file, which includes error messages, and
stores it as an Amazon S3 object. For more information, see Log Amazon
SageMaker Events with Amazon CloudWatch.
#labeling_job_arn ⇒ String
The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
#max_concurrent_transforms ⇒ Integer
The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.
#max_payload_in_mb ⇒ Integer
The maximum payload size, in MB, used in the transform job.
#model_client_config ⇒ Types::ModelClientConfig
The timeout and maximum number of retries for processing a transform job invocation.
#model_name ⇒ String
The name of the model used in 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 dataset to be transformed and the Amazon S3 location where it is stored.
#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. If the transform job failed, the reason
is returned in the FailureReason
field.
Possible values:
- InProgress
- Completed
- Failed
- Stopping
- Stopped
#transform_output ⇒ Types::TransformOutput
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
#transform_resources ⇒ Types::TransformResources
Describes the resources, including ML instance types and ML instance count, to use for the 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
.