Is your feature request related to a problem? Please describe.
A SageMaker Session object comes with the following two methods which allow me to retrieve the status of training and AutoML jobs: describe_training_job(), describe_auto_ml_job(). Example:
importsagemakersession=sagemaker.Session()
session.describe_training_job("[YOUR_JOB_NAME]")["TrainingJobStatus"]However, if I like to get the status of a batch transform job (like, e.g., a batch interence job) in the same workflow, I need to create an extra low-level SageMaker client using boto3:
importboto3sm=boto3.client("sagemaker")
sm.describe_transform_job(TransformJobName="[YOUR_JOB_NAME]")["TransformJobStatus"]Describe the solution you'd like
Even though the above workflow works it could become much more concise and smoother by adding a describe_transform_job() method to the SageMaker Session class. This would eliminate the need to use the extra low-level SageMaker client in the same workflow.
Is your feature request related to a problem? Please describe.
A SageMaker Session object comes with the following two methods which allow me to retrieve the status of training and AutoML jobs:
describe_training_job(),describe_auto_ml_job(). Example:However, if I like to get the status of a batch transform job (like, e.g., a batch interence job) in the same workflow, I need to create an extra low-level SageMaker client using boto3:
Describe the solution you'd like
Even though the above workflow works it could become much more concise and smoother by adding a
describe_transform_job()method to the SageMaker Session class. This would eliminate the need to use the extra low-level SageMaker client in the same workflow.