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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()
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Basem
asked 6 hours ago

Trying to track redshift Assert error

Since today we are having this error, the DBT/SQL was not touched I'm receiving errors in many processes with this same message. A full refresh on DBT clears it, but when the first incremental runs, it starts to fail again with the same error, "context" is not helping. I ran the query manually and had no trouble at all. It's a query that creates a table, a delete on the destination table (with a where), and inserts into it (managed by DBT that wasn't modified for more than 6 months, the query runs daily and also wasn't changed on the last months). The only thing that I think changed was the Redshift version via an auto-maintenance yesterday, but can't do a rollback to validate, or can I? I was in "current" version on maintenance, just switched to "trailing", now I'm on 1.0.38361, and can't find the changelog for that version. ``` 20:26:44 | 20:26:44 | 1 of 1 START incremental model xxxxxx......... [RUN] 20:26:51 | 1 of 1 ERROR creating incremental model xxxxxx [ERROR in 6.44s] 20:26:51 | 20:26:51 | Finished running 1 incremental model in 8.55s. Completed with 1 error and 0 warnings: Database Error in model xxxxx (models/xxxxx.sql) Assert DETAIL: ----------------------------------------------- error: Assert code: 1000 context: size >= 0 - size=-2, 0=0. query: 62706875 location: tbl_trans.cpp:867 process: padbmaster [pid=70356] ----------------------------------------------- compiled SQL at target/run/xxxxx/xxxxx.sql Done. PASS=0 WARN=0 ERROR=1 SKIP=0 TOTAL=1 ``` Any idea of what can be happening? Thanks!
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AWS-User-3279708
asked 8 hours ago