aws / aws/sagemaker-python-sdk
How to set hyperparameters for warm start jobs created from helper methods?
- Dominant language
- Python
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Description
**What did you find confusing? Please describe.**
I'm using:
```
IDENTICAL_TUNER = PARENT_TUNER.identical_dataset_and_algorithm_tuner(
additional_parents = ADDITIONAL_PARENT_TUNING_JOB_NAMES
)
```
But cloud not find a way to set `hyperparameter_ranges` for the auto-created tuner. Could not find it in the docs nor in the code.
**Describe how documentation can be improved**
How should I warm start a tuning job with a new set of hyperparameter ranges?
**Additional context**
Add any other context or screenshots about the documentation request here.
Contributor guide
Research direction
Start by reviewing the documented warm-start flow and the helper method `identical_dataset_and_algorithm_tuner`, focusing on how `hyperparameter_ranges` is handled for the auto-created tuner. The documentation is done when it explains whether and how a new set of hyperparameter ranges can be supplied, with an example using the shown helper pattern.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- aws, python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100