{"id":4441,"library":"autogluon-common","title":"AutoGluon","description":"AutoGluon is an open-source AutoML library developed by AWS AI, designed to automate machine learning tasks for tabular, image, text, and time series data. It enables users to train and deploy high-accuracy machine learning and deep learning models with minimal code, often lauded for its '3 lines of code' capability. The library is actively maintained with frequent updates and is currently at version 1.5.0, offering state-of-the-art predictive performance.","status":"active","version":"1.5.0","language":"en","source_language":"en","source_url":"https://github.com/autogluon/autogluon/","tags":["machine-learning","automl","tabular-data","time-series","computer-vision","natural-language-processing","aws"],"install":[{"cmd":"pip install autogluon","lang":"bash","label":"Recommended installation for full AutoGluon functionality"},{"cmd":"pip install autogluon-common","lang":"bash","label":"Install only common helper utilities"}],"dependencies":[{"reason":"AutoGluon 1.5.0 requires Python 3.10, 3.11, 3.12, or 3.13. Support for Python 3.13 is experimental on Windows.","package":"python","optional":false}],"imports":[{"note":"Primary class for tabular data prediction tasks.","symbol":"TabularPredictor","correct":"from autogluon.tabular import TabularPredictor"},{"note":"Utility for loading tabular data, compatible with pandas DataFrames.","symbol":"TabularDataset","correct":"from autogluon.tabular import TabularDataset"},{"note":"Class for time series forecasting tasks.","symbol":"TimeSeriesPredictor","correct":"from autogluon.timeseries import TimeSeriesPredictor"},{"note":"Class for tasks involving multiple data modalities (image, text, tabular).","symbol":"MultiModalPredictor","correct":"from autogluon.multimodal import MultiModalPredictor"}],"quickstart":{"code":"import pandas as pd\nfrom autogluon.tabular import TabularPredictor, TabularDataset\n\n# Create dummy training data\ntrain_data = pd.DataFrame({\n    'feature1': [1, 2, 3, 4, 5],\n    'feature2': ['A', 'B', 'A', 'C', 'B'],\n    'target': [0, 1, 0, 1, 0]\n})\n# Save to a CSV for TabularDataset\ntrain_data.to_csv('train.csv', index=False)\n\n# Create dummy test data\ntest_data = pd.DataFrame({\n    'feature1': [6, 7],\n    'feature2': ['C', 'A']\n})\ntest_data.to_csv('test.csv', index=False)\n\n# Load data using AutoGluon's TabularDataset\ntrain_dataset = TabularDataset('train.csv')\n# For demonstration, label is 'target'\nlabel = 'target'\n\n# Initialize and train the TabularPredictor\npredictor = TabularPredictor(label=label, path='AutoGluonModels').fit(train_dataset, presets='medium_quality')\n\n# Make predictions on new data\ntest_dataset = TabularDataset('test.csv')\npredictions = predictor.predict(test_dataset)\n\nprint(\"Predictions:\\n\", predictions)\n# Clean up generated files (optional)\nimport shutil\nshutil.rmtree('AutoGluonModels', ignore_errors=True)\nimport os\nos.remove('train.csv')\nos.remove('test.csv')","lang":"python","description":"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."},"warnings":[{"fix":"Re-train all AutoGluon models using the new version of the library after upgrading.","message":"Models 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.","severity":"breaking","affected_versions":"All versions (e.g., models from v1.4.0 incompatible with v1.5.0)"},{"fix":"Update 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.","message":"Several `TabularPredictor` methods were deprecated in v1.0.0 and subsequently removed in v1.3.0. Using these old method names will result in errors.","severity":"deprecated","affected_versions":"1.0.0 (deprecated), 1.2.0 (raised errors), 1.3.0+"},{"fix":"Ensure 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.","message":"AutoGluon 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.","severity":"breaking","affected_versions":"<1.2.0 (dropped Python 3.8), <1.5.0 (dropped Python 3.9)"},{"fix":"Install `autogluon` via `pip install autogluon` to get all necessary modules and functionality.","message":"The `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.","severity":"gotcha","affected_versions":"All versions"},{"fix":"While no direct replacement in AutoGluon is provided, users should consider external EDA libraries or custom scripts for exploratory data analysis.","message":"The `autogluon.eda` module, which provided exploratory data analysis functionality, has been deprecated.","severity":"deprecated","affected_versions":"Recent versions (e.g., 1.x)"}],"env_vars":null,"last_verified":"2026-04-12T00:00:00.000Z","next_check":"2026-07-11T00:00:00.000Z"}