QuantConnect / QuantConnect/Lean
[Library Upgrade] autogluon
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Description
AutoGluon automates machine learning tasks, enabling you to train and deploy high-accuracy machine learning and deep learning models on image, text, time series and tabular data with just a few lines of code. It ships in the dedicated autogluon package environment.
Expected Behavior
The autogluon package environment ships the latest stable AutoGluon release, 1.6.1 (released 2026-08-06).
Actual Behavior
The autogluon package environment still ships AutoGluon 1.5.0 (released 2025-12-19). autogluon, autogluon.common, autogluon.core, autogluon.features, autogluon.multimodal, autogluon.tabular and autogluon.timeseries are all pinned to 1.5.0 (see the Supported Libraries page, autogluon environment).
Potential Solution
Bump the seven autogluon* packages to 1.6.1 in the autogluon package environment.
The upgrade also moves the ray floor, so ray has to be bumped in the same environment:
| AutoGluon 1.5.0 | AutoGluon 1.6.1 | |
|---|---|---|
ray[default] requirement |
>=2.43.0,<2.53 |
>=2.55.0,<2.57 |
The autogluon environment currently has ray 2.52.1, which is below the 1.6.1 floor. This overlaps with #9222.
Checklist
- I have completely filled out this template
- I have confirmed that this issue exists on the current
masterbranch - I have confirmed that this is not a duplicate issue by searching issues
Requested by a Cloud member (Intercom conversation 215475609991152).
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start at the autogluon package environment and the Supported Libraries page referenced in the issue. Update the seven autogluon* packages to 1.6.1 and ray to a compatible 2.55.0-or-newer version below 2.57, then verify the environment resolves those versions and satisfies the stated dependency floor.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- build-system, machine-learning
- Issue type
- Feature
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 74/100