aws / aws/sagemaker-python-sdk
Respect default_bucket_prefix by default for Estimator code_location
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Descripción
**Describe the feature you'd like**
Currently the `code_location` attribute of the Estimator class defaults to using the `output_bucket` parameter [docs link](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.estimator.EstimatorBase):
> If not specified, the default code location is ‘s3://output_bucket/job-name/’.
The [Session object also has a parameter](https://sagemaker.readthedocs.io/en/stable/api/utility/session.html#sagemaker.session.Session) `default_bucket_prefix` that can be configured.
Ideally, if
1. The `output_bucket` part of `output_path` is the `default_bucket`
2. `_is_output_path_set_from_default_bucket_and_prefix` is False
Then the default location should respect both the `default_bucket` as well as the `default_bucket_prefix`.
e.g. `s3://default_bucket/default_bucket_prefix/job-name/`
This change would be implemented in [_stage_user_code_in_s3](https://github.com/aws/sagemaker-python-sdk/blob/23109671f6262269ab54cdd9aeb5ebe4ea640d25/src/sagemaker/estimator.py#L1042).
Otherwise, the default behavior creates artifacts at the root of the bucket. This means that default behavior for environments where IAM bucket write access is limited by prefix (i.e. SageMaker Unified Studio) will fail.
**How would this feature be used? Please describe.**
If this behavior is implemented, model code assets would be uploaded by default to a prefix where write access is allowed.
**Describe alternatives you've considered**
Currently `code_location` needs to be manually configured to work in SageMaker Unified Studio. This is poorly documented as part of features like [ModelStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.model_step.ModelStep) where it needs to be configured in `repack_model_step_settings` as the model.register output populates `output_path` by default in a pipeline.
If this change cannot be implemented in code, explicit documentation should be provided about configuring parameters to output code in the SageMaker Unified Studio project prefix.
Guía de contribución
Línea de trabajo
Comienza en src/sagemaker/estimator.py, en _stage_user_code_in_s3, y revisa después la documentación enlazada de Estimator y Session, así como las referencias a ModelStep/repack_model_step_settings. Se considera terminado cuando la ubicación predeterminada del código usa default_bucket_prefix bajo las condiciones indicadas para la ruta de salida, y la configuración está documentada si no es posible realizar el cambio en el código.
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Evaluación
- Stack tecnológico
- aws, python
- Área
- cloud, machine-learning
- Tipo de issue
- Nueva funcionalidad
- Dificultad
- 3/5
- Tiempo estimado
- 1-2 días
- Estado de actividad
- Estancado
- Claridad
- Bastante claro
- Aptitud para principiantes
- 45/100