Install & Compatibility
Where this runs
tested against v1.6.1 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
py 3.10
✕ build_error
1/2 runs
py 3.11
✕ build_error
1/2 runs
py 3.12
✕ build_error
1/2 runs
py 3.13
✕ build_error
1/2 runs
py 3.9
✕ build_error
1/2 runs
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
TabularPredictor
✓ from autogluon.tabular import TabularPredictor
Primary class for tabular data prediction tasks.
TabularDataset
✓ from autogluon.tabular import TabularDataset
Utility for loading tabular data, compatible with pandas DataFrames.
TimeSeriesPredictor
✓ from autogluon.timeseries import TimeSeriesPredictor
Class for time series forecasting tasks.
MultiModalPredictor
✓ from autogluon.multimodal import MultiModalPredictor
Class for tasks involving multiple data modalities (image, text, tabular).
This quickstart demonstrates how to use AutoGluon's TabularPredictor to train a model on tabular data and make predictions. It showcases the common workflow for classification or regression tasks with minimal code.
import pandas as pd
from autogluon.tabular import TabularPredictor, TabularDataset
# Create dummy training data
train_data = pd.DataFrame({
'feature1': [1, 2, 3, 4, 5],
'feature2': ['A', 'B', 'A', 'C', 'B'],
'target': [0, 1, 0, 1, 0]
})
# Save to a CSV for TabularDataset
train_data.to_csv('train.csv', index=False)
# Create dummy test data
test_data = pd.DataFrame({
'feature1': [6, 7],
'feature2': ['C', 'A']
})
test_data.to_csv('test.csv', index=False)
# Load data using AutoGluon's TabularDataset
train_dataset = TabularDataset('train.csv')
# For demonstration, label is 'target'
label = 'target'
# Initialize and train the TabularPredictor
predictor = TabularPredictor(label=label, path='AutoGluonModels').fit(train_dataset, presets='medium_quality')
# Make predictions on new data
test_dataset = TabularDataset('test.csv')
predictions = predictor.predict(test_dataset)
print("Predictions:\n", predictions)
# Clean up generated files (optional)
import shutil
shutil.rmtree('AutoGluonModels', ignore_errors=True)
import os
os.remove('train.csv')
os.remove('test.csv')
Debug
Known issues
breakingModels trained with older versions of AutoGluon are not compatible with newer versions. Users must retrain models after upgrading AutoGluon to a new major or minor release.fixRe-train all AutoGluon models using the new version of the library after upgrading.
affects: All versions (e.g., models from v1.4.0 incompatible with v1.5.0)
deprecatedSeveral `TabularPredictor` methods were deprecated in v1.0.0 and subsequently removed in v1.3.0. Using these old method names will result in errors.fixUpdate method calls to their new names (e.g., `persist_models` -> `persist`, `get_model_names` -> `model_names`). Refer to the AutoGluon documentation for the full list of changes.
affects: 1.0.0 (deprecated), 1.2.0 (raised errors), 1.3.0+
breakingAutoGluon regularly updates its supported Python versions. For instance, Python 3.8 support was dropped in v1.2.0, and newer versions (e.g., v1.5.0) require Python 3.10-3.13. Running AutoGluon on an unsupported Python version will lead to installation or runtime failures.fixEnsure your Python environment matches the requirements for your AutoGluon version. For v1.5.0, use Python 3.10, 3.11, 3.12, or 3.13.
affects: <1.2.0 (dropped Python 3.8), <1.5.0 (dropped Python 3.9)
gotchaThe `autogluon-common` PyPI package provides helper functionality and is not intended for standalone use to access the full AutoML features. Users seeking the complete AutoGluon experience (e.g., `TabularPredictor`, `TimeSeriesPredictor`) should install the `autogluon` meta-package.fixInstall `autogluon` via `pip install autogluon` to get all necessary modules and functionality.
affects: All versions
deprecatedThe `autogluon.eda` module, which provided exploratory data analysis functionality, has been deprecated.fixWhile no direct replacement in AutoGluon is provided, users should consider external EDA libraries or custom scripts for exploratory data analysis.
affects: Recent versions (e.g., 1.x)
Errors
Common errors & fixes
ModuleNotFoundError: No module named 'autogluon.common'
The 'autogluon.common' module is not installed or not found in the Python environment.
fixInstall the module using pip: 'pip install autogluon.common'.
ImportError: cannot import name 'TabularPredictor' from 'autogluon.common'
Attempting to import 'TabularPredictor' from 'autogluon.common', but it resides in 'autogluon.tabular'.
fixCorrect the import statement: 'from autogluon.tabular import TabularPredictor'.
FileNotFoundError: [Errno 2] No such file or directory: 'ag-model/models/KNeighborsDist/info.pkl'
The specified file 'info.pkl' is missing, possibly due to incomplete model saving or loading.
fixEnsure the model was saved correctly and all necessary files are present before loading.
ModuleNotFoundError: No module named 'autogluon.vision'
Specific AutoGluon submodules like 'autogluon.vision' or 'autogluon.eda' are not included in the default `autogluon` meta-package installation and need to be installed separately.
fixpip install autogluon.vision
OSError: [Errno 28] No space left on device
This error commonly occurs when training AutoGluon models on platforms like SageMaker Notebooks, where the default disk size (e.g., 5 GiB) is insufficient for the artifacts generated by AutoGluon, especially with large datasets or complex models.
fixIncrease the disk size of your SageMaker Notebook instance or the environment where AutoGluon is running. Alternatively, manage disk usage by deleting unnecessary files or setting a `path` parameter to an external storage location if available.
Upgrade
Version history
1.6.1latest on PyPI · released Aug 6, 2026
Audit
Dependencies
pythonrequiredAutoGluon 1.5.0 requires Python 3.10, 3.11, 3.12, or 3.13. Support for Python 3.13 is experimental on Windows.