aws / aws/sagemaker-python-sdk

How to set hyperparameters for warm start jobs created from helper methods?

Open
#1,816 2 comments 0 reactions 0 assignees View on GitHub
type: documentation type: feature request
Dominant language
Python
Stars
2.3k
Forks
1.3k
Avg merge
1d 22h
Merged PRs (30d)
35

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

Open the contributing 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

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.