AutoGluon Core

1.5.0 · active · verified Sun Apr 12

AutoGluon is an open-source AutoML library developed by AWS AI, designed to automate machine learning tasks with minimal code. It supports various data types, including tabular, image, text, and time series, enabling users to train and deploy highly accurate models efficiently. The current version is 1.5.0, with regular updates introducing new features, performance improvements, and bug fixes, typically on a quarterly major release cadence with interim patch releases.

Warnings

breaking Models trained with an older version of AutoGluon are generally not compatible with newer versions. Users are advised to re-train models after upgrading the library.
Fix: Re-train your models using the new AutoGluon version after upgrading the library.
breaking Python 3.8 support was dropped in AutoGluon v1.2.0. Users on Python 3.8 will need to upgrade their Python environment to use v1.2.0 or newer.
Fix: Upgrade Python to version 3.9 or higher (current stable support: 3.10-3.12; 3.13 is experimental).
deprecated Several methods of `TabularPredictor` (e.g., `persist_models`, `get_model_names`, `get_pred_from_proba`) were deprecated in v1.0.0, started raising errors in v1.2.0, and were removed in v1.3.0.
Fix: Use the new method names: `persist`, `model_names`, `predict_from_proba` respectively, and consult the release notes for a full list of renames.
gotcha `autogluon-core` is a foundational package primarily containing core utilities, searchers, and schedulers for hyperparameter tuning. Most users performing machine learning tasks will interact with higher-level packages like `autogluon.tabular`, `autogluon.timeseries`, or `autogluon.multimodal` (via `pip install autogluon`).
Fix: For end-to-end ML, install the full `autogluon` package and import specific predictor classes from its submodules (e.g., `from autogluon.tabular import TabularPredictor`).
gotcha The `extreme` preset for `TabularPredictor`, introduced in v1.4.0, often requires a CUDA-compatible GPU (ideally with 32+ GB vRAM) for optimal performance and is most effective for datasets with at most 30,000 samples. Inference time can also be longer than with other presets.
Fix: Ensure adequate GPU resources or consider other presets like `best_quality` for larger datasets or CPU-only environments.
gotcha Python 3.13 support, introduced in v1.5.0, is currently experimental. Some features may not be available when running on Python 3.13, particularly on Windows.
Fix: For production or critical workloads, consider using fully supported Python versions (3.10, 3.11, 3.12). If using 3.13, be aware of potential limitations.
breaking For users previously using `"TABPFNV2"` as a model, AutoGluon v1.4.0 (and newer) strongly recommends switching to `"REALTABPFN-V2"` to avoid breaking changes related to the underlying TabPFN releases.
Fix: Update model references from `"TABPFNV2"` to `"REALTABPFN-V2"` in your `hyperparameters` configurations.

Install

pip install autogluon Recommended installation for full AutoGluon functionality (includes autogluon-core)

Imports

TabularPredictor
```
from autogluon.tabular import TabularPredictor
```
While autogluon-core is the underlying package, users typically import high-level predictor classes from specific AutoGluon modules like autogluon.tabular, autogluon.timeseries, or autogluon.multimodal for machine learning tasks.
TabularDataset
```
from autogluon.tabular import TabularDataset
```
Used for loading and managing tabular data within the AutoGluon framework, often in conjunction with TabularPredictor.

Quickstart

This quickstart demonstrates how to use AutoGluon's TabularPredictor for a classification task. It involves loading a dataset, fitting a predictor by specifying the target column, and then making predictions on new data. AutoGluon automatically handles model selection, hyperparameter tuning, and ensemble creation.

import pandas as pd
from autogluon.tabular import TabularPredictor, TabularDataset

# Create a dummy CSV for demonstration
data = {
    'feature1': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'feature2': ['A', 'B', 'A', 'C', 'B', 'A', 'C', 'B', 'A', 'C'],
    'target': [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
}
df = pd.DataFrame(data)
df.to_csv('train.csv', index=False)

# Load data using TabularDataset
train_data = TabularDataset('train.csv')

# Initialize and train a TabularPredictor
label = 'target'
predictor = TabularPredictor(label=label, path='AutogluonModels').fit(train_data)

# Make predictions (example test data)
test_data = TabularDataset(pd.DataFrame({
    'feature1': [11, 12, 13],
    'feature2': ['A', 'B', 'C']
}))
predictions = predictor.predict(test_data)

print("Predictions:")
print(predictions)

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