aws / aws/sagemaker-python-sdk
Add additional dependencies for 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**
When migrating from sagemaker v2 to v3, the [Estimator](https://sagemaker.readthedocs.io/en/v2.256.0/api/training/estimators.html?highlight=tensorboardoutputconfig#sagemaker.estimator.Estimator) can be replaced by ModelTrainer. However, the ~~`tensorboard_output_config`and~~ `dependencies` parameters in Estimator has no equivalent in the ModelTrainer. This functionality would be useful to have in ModelTrainer to maintain feature parity with the v2 Estimator.
**How would this feature be used? Please describe.**
* Allows the import of additional source files and dependencies into the model training container.
~~* Allows debugging visualization using TensorBoard, with customization on the location in Amazon S3 to store the output, as well as the local path in the container.~~ (addressed by [ModelTrainer.with_tensorboard_output_config](https://sagemaker.readthedocs.io/en/v2.253.1/api/training/model_trainer.html#sagemaker.modules.train.model_trainer.ModelTrainer.with_tensorboard_output_config))
**Describe alternatives you've considered**
None
**Additional context**
None
Contributor guide
Research direction
Start at the ModelTrainer entry point and compare its handling with the v2 Estimator's `dependencies` parameter. Trace how additional source files and dependencies enter the training container, then verify that ModelTrainer supports the requested imports and preserves the documented training behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- aws, python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 55/100