Marco Friaz 1c0826e55b Append the default_bucket_prefix to s3 paths if one exists to hyperparameter_tuning, introduction_to_amazon_algorithms and sagemaker-pipelines sample notebooks (#4807) ...

**Description**
This change checks the sagemaker session if there is a default_bucket_prefix set. If a default bucket prefix is specified, it is append it to the s3 path. This was done for hyperparameter_tuning, introduction_to_amazon_algorithms and sagemaker-pipelines sample notebooks. This ensures sample notebooks that utilize s3, works in SageMaker Unified Studio.

Co-authored-by: Marco Friaz <marfriaz@amazon.com>

2025-01-21 09:39:05 -08:00

..

nlp/amazon_comprehend_sagemaker_pipeline

Append the default_bucket_prefix to s3 paths if one exists to hyperparameter_tuning, introduction_to_amazon_algorithms and sagemaker-pipelines sample notebooks (#4807)

2025-01-21 09:39:05 -08:00

notebook-job-step

Append the default_bucket_prefix to s3 paths if one exists to hyperparameter_tuning, introduction_to_amazon_algorithms and sagemaker-pipelines sample notebooks (#4807)

2025-01-21 09:39:05 -08:00

selective-step-execution

Append the default_bucket_prefix to s3 paths if one exists to hyperparameter_tuning, introduction_to_amazon_algorithms and sagemaker-pipelines sample notebooks (#4807)

2025-01-21 09:39:05 -08:00

step-decorator

Append the default_bucket_prefix to s3 paths if one exists to hyperparameter_tuning, introduction_to_amazon_algorithms and sagemaker-pipelines sample notebooks (#4807)

2025-01-21 09:39:05 -08:00

tabular

Revert "New folder structure (#4694)" (#4701)

2024-07-09 12:54:45 -04:00

time_series_forecasting/amazon_forecast_pipeline

Update train.py (#4426)

2024-06-06 22:02:24 -07:00

index.rst

Add computer vision pipeline notebook sample (#4543)

2024-01-26 13:36:42 -08:00