Multi-AutoML Interface v5.4.0

Multi-AutoML Interface 5.4.0

Fixed

• AutoGluon threw away every text, multimodal and computer-vision result. The reporting step
  called predictor.leaderboard(...), which MultiModalPredictor does not implement, so the run
  died after training had completed and logged nothing. That path now evaluates the fitted model
  with evaluate(), logs the numeric metrics it returns, and skips the ONNX attempt (the
  multimodal predictor has no export_onnx). Verified end to end on CPU: Text classification
  (216 s), Multimodal classification (298 s) and CV image classification (128 s) each produced an
  MLflow run with metrics.

• The macOS x64 disk image shipped an interpreter its target machines cannot execute.
  release.yml built --mac --x64 --arm64 while prepare_python_runtime.js installs the CPython
  of the runner's own architecture, so both images carried the same arm64 interpreter. macOS is
  built arm64-only now, and the packaging smoke test reads the bundled interpreter with lipo and
  fails when the image directory declares a different architecture - verified green on a real
  Apple Silicon runner.

• TPOT could not reach training at all. detect_problem_type tested the whole column on every
  loop step (all(y % 1 == 0 for val in ...)) and pandas raised "The truth value of a Series is
  ambiguous" the moment a numeric target arrived; it now checks (values % 1 == 0).all().
  The estimator is also built from the signature of whichever TPOT is installed, because
  generations/population_size/scoring/verbosity/config_dict were dropped from the 1.x estimator
  and raised TypeError deep inside the search, after the UI had reported the run as started -
  the ignored knobs are logged instead of silently swallowed. setuptools==80.9.0 is pinned:
  tpot -> stopit -> import pkg_resources, which setuptools >= 81 no longer ships, so TPOT could
  not even be imported in a fresh interpreter.

• The availability check asked about the engine, not the module a row needs. With
  autogluon.tabular installed and no autogluon.multimodal, the vision/text/multimodal rows were
  still offered and died inside the engine; availability is now resolved per
  (engine, data category), cached per module, and the "how do I install this" hint names the
  extra (pip install autogluon.multimodal) instead of the base package.

Changed

• The catalog is 14 pairs across 5 engines. Object Detection and Image Segmentation are no
  longer offered: the CV upload infers labels from the directory structure, so there is no COCO
  box or mask annotation for the engine to read, and AutoGluon's detection pipeline also needs
  mmcv with PyTorch <=2.1. train_model still honours those problem types for a caller that
  brings an annotated frame. TPOT is no longer offered either - pip install tpot gives 1.1.0,
  which raises TypeError: TPOTEstimator.__init__() got an unexpected keyword argument 'scoring'
  from inside its own fit template, while 0.12.2 trains correctly against scikit-learn 1.4 but
  fails on this project's scikit-learn 1.9 with "Expected an estimator instance ... got estimator
  class instead". src/tpot_utils.py and its orchestrator entry stay for an environment that
  pins its own scikit-learn.
• Computer Vision offers Image Classification only. Multi-Label was removed with the same
  argument as detection: an image lives in exactly one class folder, so there is no multi-hot
  target to learn, and AutoGluon was quietly getting a plain multi-class problem while the UI
  said multi-label.
• AutoKeras leaves the catalog too. pip install autokeras gives 3.0.0 against keras 3.x, whose
  classification head rejects the single-unit output ("Received an invalid value for units,
  expected a positive integer. Received: units=1"), and multi-label fails on target shape; it
  needs a keras<3 environment the project does not pin. Both CV rows stay available through
  AutoGluon, which was trained end to end on synthetic images.
• The support matrices list only engines some row can actually run, so TPOT no longer has a column
  of promises the catalog does not keep, and the docs stop counting the Hugging Face Hub as an
  eighth engine.

What is inside

Each installer bundles a standalone CPython 3.12 with everything in
requirements.txt already installed, so no Python setup is needed on
the target machine. Runs, models and the data lake are written to the app's
per-user workspace (the Electron userData directory):

Operating system	Location
Windows	%APPDATA%\multi-automl-desktop\workspace\
macOS	~/Library/Application Support/multi-automl-desktop/workspace/
Linux	~/.config/multi-automl-desktop/workspace/

The heavy AutoML backends (AutoGluon, PyCaret, TPOT, Lale, H2O, AutoKeras)
stay optional and are lazy-imported; the bundled runtime
contains the core stack (Streamlit, MLflow, FLAML, scikit-learn, XGBoost,
LightGBM, ONNX export with skl2onnx, SHAP explanations), so those features work
in the desktop app out of the box. The heavy engines stay optional: the
installers offer FLAML until you install whichever
engines you need into it. H2O additionally requires Java 11+.

Signing

These builds are not code-signed or notarized, so SmartScreen and Gatekeeper
will warn on first launch.