aws / aws/sagemaker-python-sdk
Feature support pipeline name in base_job_name at ModelTrainer
- Dominant language
- Python
- Stars
- 2.3k
- Forks
- 1.3k
- Avg merge
- 1d 22h
- Merged PRs (30d)
- 35
Description
**Describe the feature you'd like**
i want to store the all files for jobs in a pattern like pipeline_name/job_name at this moment this is not possible for ModelTrainer class because train() function will call _create_training_job_args function which creates a s3 path like this:
s3://mybucket/my_pipeline_name/my_job_name/my-pipeline-name/my-job-name-20260318100943/input/sm_drivers/
or if the length of the base_job_name is longer then 63 the path could look like this:
s3://mybucket/my_pipeline_name/my_job_name/my-pipeline-name-extra-long-name/my-job-/input/sm_drivers/
**How would this feature be used? Please describe.**
This feature should be designed to help to store ModelTrainer files more consistently.
One suggestion for an S3 path would be {self.base_job_name}/input/20260318103152/sm_drivers/
**Additional context**
The Following Screenshot shows which code needs to be modified.
Contributor guide
Research direction
Start in ModelTrainer.train() and follow its call to _create_training_job_args(), where the issue shows the S3 path is assembled. Review how base_job_name length is handled and identify related tests or path-building entry points. Done means ModelTrainer stores job files in the requested consistent pattern without producing malformed paths for long names.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- aws, python
- Domain
- cloud, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100