ClientError: An error occurred (UnknownOperationException) when calling the CreateHyperParameterTuningJob operation: The requested operation is not supported in the called region.

Hi Dears,

I am building ML model using DeepAR Algorithm.

I faced this error while i reached to this point : Error :

ClientError: An error occurred (UnknownOperationException) when calling the CreateHyperParameterTuningJob operation: The requested operation is not supported in the called region.

Code: from sagemaker.tuner import ( IntegerParameter, CategoricalParameter, ContinuousParameter, HyperparameterTuner, ) from sagemaker import image_uris

container = image_uris.retrieve(region= 'af-south-1', framework="forecasting-deepar")

deepar = sagemaker.estimator.Estimator( container, role, instance_count=1, instance_type="ml.m5.2xlarge", use_spot_instances=True, # use spot instances max_run=1800, # max training time in seconds max_wait=1800, # seconds to wait for spot instance output_path="s3://{}/{}".format(bucket, output_path), sagemaker_session=sess, ) freq = "D" context_length = 300

deepar.set_hyperparameters( time_freq=freq, context_length=str(context_length), prediction_length=str(prediction_length) )

Can you please help in solving the error? I have to do that in af-south-1 region.

Thanks Basem

hyperparameter_ranges = { "mini_batch_size": IntegerParameter(100, 400), "epochs": IntegerParameter(200, 400), "num_cells": IntegerParameter(30, 100), "likelihood": CategoricalParameter(["negative-binomial", "student-T"]), "learning_rate": ContinuousParameter(0.0001, 0.1), }

objective_metric_name = "test:RMSE"

tuner = HyperparameterTuner( deepar, objective_metric_name, hyperparameter_ranges, max_jobs=10, strategy="Bayesian", objective_type="Minimize", max_parallel_jobs=10, early_stopping_type="Auto", )

s3_input_train = sagemaker.inputs.TrainingInput( s3_data="s3://{}/{}/train/".format(bucket, prefix), content_type="json" ) s3_input_test = sagemaker.inputs.TrainingInput( s3_data="s3://{}/{}/test/".format(bucket, prefix), content_type="json" )

tuner.fit({"train": s3_input_train, "test": s3_input_test}, include_cls_metadata=False) tuner.wait()