aws / aws/sagemaker-training-toolkit
Relying on potentially uninitialized environment variables.
- Dominant language
- Python
- Stars
- 530
- Forks
- 140
- Avg merge
- 1h 12m
- Merged PRs (30d)
- 2
Description
- https://github.com/aws/sagemaker-containers/blob/be2ac3746e0afeceb53d2f91261d1586ff290cfc/src/sagemaker_containers/_files.py#L156
- https://github.com/aws/sagemaker-containers/blob/0c0bfcd4afa54f2a7fd71ba6d5c814a692a636f0/src/sagemaker_containers/_trainer.py#L73
```
os.environ.get('AWS_REGION', os.environ.get(_params.REGION_NAME_ENV))
```
I don't think 'AWS_REGION' is set the platform in Training.
Looks like 'REGION_NAME_ENV' can be used independently but in reality it gets set when training environment is initialized.
Contributor guide
Research direction
Start at the referenced lines in src/sagemaker_containers/_files.py and src/sagemaker_containers/_trainer.py, then trace how the training environment initializes REGION_NAME_ENV. Check the behavior when AWS_REGION is absent and determine the expected independent use of REGION_NAME_ENV; done means the environment lookup no longer relies on a potentially unset variable.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- aws, python
- Domain
- cloud, machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100