Install & Compatibility
Where this runs
tested against v? · pip install
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.915 runs
build_error
glibcpy 3.10–3.915 runs
timeout
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
TabularPredictor, TabularDataset
✓ from autogluon.tabular import TabularPredictor, TabularDataset
TimeSeriesPredictor, TimeSeriesDataFrame
✓ from autogluon.timeseries import TimeSeriesPredictor, TimeSeriesDataFrame
MultiModalPredictor
✓ from autogluon.multimodal import MultiModalPredictor
This quickstart demonstrates how to use AutoGluon's `TabularPredictor` to train a high-accuracy classification model and make predictions on new data with just a few lines of code. It automatically handles feature engineering, model selection, and hyperparameter tuning.
import pandas as pd
from autogluon.tabular import TabularPredictor, TabularDataset
# Load example data (using a public dataset URL)
data_url = 'https://raw.githubusercontent.com/mli/ag-docs/main/knot_theory/'
train_data = TabularDataset(f'{data_url}train.csv')
test_data = TabularDataset(f'{data_url}test.csv')
label = 'signature'
predictor = TabularPredictor(label=label, path='AutogluonModels').fit(train_data, presets='high_quality')
predictions = predictor.predict(test_data)
print("Top 5 predictions:")
print(predictions.head())
print(f"Predictor leaderboards:\n{predictor.leaderboard(test_data, silent=True)}")
ag --version
Debug
Known issues
breakingModels trained with an older version of AutoGluon are not guaranteed to be compatible with newer versions. Users must re-train models after upgrading AutoGluon to prevent issues.fixRe-train all AutoGluon models using the new AutoGluon version.
affects: All major and minor version upgrades (e.g., 0.x to 1.x, 1.0 to 1.1).
breakingPython 3.8 support was dropped in AutoGluon v1.2.0. Additionally, Python 3.13 support is currently experimental in v1.5.0, with potential limitations on Windows.fixEnsure your Python environment is between 3.10 and 3.12 (inclusive) for stable usage. If using Python 3.13, be aware of potential experimental features or platform-specific issues.
affects: v1.2.0+
breakingSeveral `TabularPredictor` methods were deprecated in v1.0.0 (e.g., `persist_models`, `get_model_names`), began raising errors in v1.2.0, and were completely removed in v1.3.0.fixUpdate method calls to their new names: `persist_models` -> `persist`, `unpersist_models` -> `unpersist`, `get_model_names` -> `model_names`, `get_model_best` -> `model_best`, `get_pred_from_proba` -> `predict_from_proba`, etc. Refer to the AutoGluon v1.3.0 release notes for a complete list.
affects: v1.3.0+
deprecatedThe default behavior of `TabularPredictor.delete_models()` will change from `dry_run=True` to `dry_run=False` in a future release. A `FutureWarning` is logged since v1.3.0.fixExplicitly specify the `dry_run` argument (e.g., `predictor.delete_models(dry_run=True)`) to maintain current behavior or `predictor.delete_models(dry_run=False)` to enable actual deletion.
affects: v1.3.0+
gotchaAutoGluon (especially `TabularPredictor`) can be memory-intensive, particularly with large datasets or complex ensembles. Out-of-memory errors are common.fixUse machines with ample RAM. For tabular data, GPUs are often not critical, but more memory is. Consider reducing `presets` quality, `time_limit`, or subsampling data for initial runs. Set `max_memory` parameter in `predictor.persist()` carefully.
affects: All versions
gotchaOn macOS, LightGBM and XGBoost (used by AutoGluon) can experience segmentation faults or instability if `libomp` is installed via `brew install libomp`.fixIf experiencing issues, uninstall `libomp` via brew and follow AutoGluon's specific instructions for installing a compatible version of `libomp`.
affects: All versions on macOS
Errors
Common errors & fixes
ModuleNotFoundError: No module named 'autogluon'
AutoGluon is not installed in the Python environment.
AttributeError: 'TabularDataset' object has no attribute 'unique'
The 'label' parameter in TabularPredictor is incorrectly set as a list instead of a string.
fixEnsure the 'label' parameter is a string representing the target column name.
AttributeError: 'XGBClassifier' object has no attribute 'n_classes_'
Incompatibility between the current XGBoost version and AutoGluon.
fixDowngrade XGBoost to a compatible version, such as 1.5.0.
AttributeError: 'super' object has no attribute '__sklearn_tags__'
Incompatibility between XGBoost and scikit-learn during model deserialization.
fixEnsure both XGBoost and scikit-learn are updated to compatible versions.
AttributeError: module 'google._upb._message' has no attribute 'Message'
Incompatibility between protobuf versions and AutoGluon.
fixDowngrade protobuf to version 3.20.1.
Upgrade
Version history
1.6.1latest on PyPI · released Aug 6, 2026
Audit
Dependencies
pandasrequiredCore data structure (TabularDataset inherits from DataFrame).
numpyrequiredFundamental numerical computing library.
torchoptionalRequired for neural network models and GPU acceleration. Often included with `[all]` or specific module installs.
lightgbm, catboost, xgboostoptionalGradient Boosting models, often included with `[all]` or specific module installs.
rayoptionalUsed for parallel processing and distributed training, often included with `[all]`.