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transform_jobs

Creates, updates, deletes, gets or lists a transform_jobs resource.

Overview​

Nametransform_jobs
TypeResource
Idaws.sagemaker.transform_jobs

Fields​

The following fields are returned by SELECT queries:

NameDatatypeDescription
auto_ml_job_arnstringThe Amazon Resource Name (ARN) of the AutoML transform job. (pattern: <code>arn:aws[a-z-]:sagemaker:[a-z0-9-]:[0-9]{12}:automl-job/.*</code>)
batch_strategystringSpecifies 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. (MultiRecord, SingleRecord)
creation_timestring (date-time)A timestamp that shows when the transform Job was created.
data_capture_configobjectConfiguration to control how SageMaker captures inference data.
data_processingobjectThe 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.
environmentobjectThe environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
experiment_configobjectAssociates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs: CreateProcessingJob CreateTrainingJob CreateTransformJob
failure_reasonstringIf 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_arnstringThe Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job. (pattern: <code>arn:aws[a-z-]:sagemaker:[a-z0-9-]:[0-9]{12}:labeling-job/.*</code>)
max_concurrent_transformsintegerThe 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_mbintegerThe maximum payload size, in MB, used in the transform job.
model_client_configobjectThe timeout and maximum number of retries for processing a transform job invocation.
model_namestringThe name of the model used in the transform job. (pattern: <code>[a-zA-Z0-9]([-a-zA-Z0-9]*[a-zA-Z0-9])?</code>)
transform_end_timestring (date-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_inputobjectDescribes the dataset to be transformed and the Amazon S3 location where it is stored.
transform_job_arnstringThe Amazon Resource Name (ARN) of the transform job. (pattern: <code>arn:aws[a-z-]:sagemaker:[a-z0-9-]:[0-9]{12}:transform-job/.*</code>)
transform_job_namestringThe name of the transform job. (pattern: <code>[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}</code>)
transform_job_statusstringThe status of the transform job. If the transform job failed, the reason is returned in the FailureReason field. (InProgress, Completed, Failed, Stopping, Stopped)
transform_outputobjectIdentifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
transform_resourcesobjectDescribes the resources, including ML instance types and ML instance count, to use for the transform job.
transform_start_timestring (date-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.

Methods​

The following methods are available for this resource:

NameAccessible byRequired ParamsOptional ParamsDescription
describe_transform_jobselectregionReturns information about a transform job.
list_transform_jobsselectregionLists transform jobs.
create_transform_jobinsertregion, TransformJobName, ModelName, TransformInput, TransformOutput, TransformResourcesStarts 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 CreateModel. 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 and AMI image versions for the transform job. For more information about how batch transformation works, see Batch Transform.
stop_transform_jobexecregion, TransformJobNameStops a batch transform job. When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to Stopping. After Amazon SageMaker stops the job, the status is set to Stopped. When you stop a batch transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.

Parameters​

Parameters can be passed in the WHERE clause of a query. Check the Methods section to see which parameters are required or optional for each operation.

NameDatatypeDescription
regionstringAWS region (default: us-east-1)

SELECT examples​

Returns information about a transform job.

SELECT
auto_ml_job_arn,
batch_strategy,
creation_time,
data_capture_config,
data_processing,
environment,
experiment_config,
failure_reason,
labeling_job_arn,
max_concurrent_transforms,
max_payload_in_mb,
model_client_config,
model_name,
transform_end_time,
transform_input,
transform_job_arn,
transform_job_name,
transform_job_status,
transform_output,
transform_resources,
transform_start_time
FROM aws.sagemaker.transform_jobs
WHERE region = '{{ region }}' -- required
;

INSERT examples​

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 CreateModel. 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 and AMI image versions for the transform job. For more information about how batch transformation works, see Batch Transform.

INSERT INTO aws.sagemaker.transform_jobs (
TransformJobName,
ModelName,
MaxConcurrentTransforms,
ModelClientConfig,
MaxPayloadInMB,
BatchStrategy,
Environment,
TransformInput,
TransformOutput,
DataCaptureConfig,
TransformResources,
DataProcessing,
Tags,
ExperimentConfig,
region
)
SELECT 
'{{ TransformJobName }}' /* required */,
'{{ ModelName }}' /* required */,
{{ MaxConcurrentTransforms }},
'{{ ModelClientConfig }}',
{{ MaxPayloadInMB }},
'{{ BatchStrategy }}',
'{{ Environment }}',
'{{ TransformInput }}' /* required */,
'{{ TransformOutput }}' /* required */,
'{{ DataCaptureConfig }}',
'{{ TransformResources }}' /* required */,
'{{ DataProcessing }}',
'{{ Tags }}',
'{{ ExperimentConfig }}',
'{{ region }}'
RETURNING
transform_job_arn
;

Lifecycle Methods​

Stops a batch transform job. When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to Stopping. After Amazon SageMaker stops the job, the status is set to Stopped. When you stop a batch transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.

EXEC aws.sagemaker.transform_jobs.stop_transform_job 
@region='{{ region }}' --required 
@@json=
'{
"TransformJobName": "{{ TransformJobName }}"
}'
;
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