PySDK Version
Describe the bug
While running sagemaker.train.sft_trainer.SFTTrainer, internally, the function sagemaker.train.common_utils.finetune_utils._validate_s3_path_exists is called but if the s3 bucket referred has SSE (Server Side Encryption) enabled and the path doesn't exist, s3.put_object fails.
To reproduce
- Add SSE using kms key to your target s3 bucket for SFT job
- For any dataset, try running SFTTrainer job, it fails saying access denied.
Expected behavior
Since kms_key_id is an accepted parameter in SFTTrainer, _validate_s3_path_exists should succeed and proceed to launch the job. Note that after the actual training is done, the subsequent put_object for model files does not fail.
System information
A description of your system. Please provide:
- SageMaker Python SDK version: 3.12.0
- Python version: 3.12
- CPU or GPU: CPU
- Custom Docker image (Y/N): N
Additional context
Add any other context about the problem here.
PySDK Version
Describe the bug
While running
sagemaker.train.sft_trainer.SFTTrainer, internally, the functionsagemaker.train.common_utils.finetune_utils._validate_s3_path_existsis called but if the s3 bucket referred has SSE (Server Side Encryption) enabled and the path doesn't exist,s3.put_objectfails.To reproduce
Expected behavior
Since kms_key_id is an accepted parameter in SFTTrainer,
_validate_s3_path_existsshould succeed and proceed to launch the job. Note that after the actual training is done, the subsequentput_objectfor model files does not fail.System information
A description of your system. Please provide:
Additional context
Add any other context about the problem here.