QuantConnect / QuantConnect/Lean

[Library Upgrade] autogluon

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library-request
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C#
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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 master branch
  • I have confirmed that this is not a duplicate issue by searching issues

Requested by a Cloud member (Intercom conversation 215475609991152).

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. 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

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