aws / aws/amazon-sagemaker-examples
Sagemaker Processors base_job_name argument not working
- Dominant language
- Jupyter Notebook
- Stars
- 11k
- Forks
- 7k
- Avg merge
- 8h 29m
- Merged PRs (30d)
- 8
Description
**Describe the bug**
Even though the base_job_name argument is set in the Processor definition, for instance sagemaker.sklearn.processing.SKLearnProcessor, the resulting processing job created has a totally different name.
**To reproduce**
To simplify, it's possible to use the abalone pipeline example and give a custom base_job_name to the SKLearnProcessor.
The result should be a ProcessingJob created with a name not compliant with the given job name, such as pipelines-kytlemm1lvpq-PreprocessingStep-cIpzShs3Qp
Contributor guide
Research direction
Start with the abalone pipeline example and its SKLearnProcessor definition, then reproduce the processing job using a custom base_job_name. Trace how that argument reaches the created ProcessingJob and verify that the resulting job name complies with the requested base name.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100