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 3.10–3.95 runs
build_error
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
MultiModalPredictor
✓ from autogluon.multimodal import MultiModalPredictor
✗ from autogluon.multimodal import MultiModalPredictor
This quickstart demonstrates how to use `MultiModalPredictor` for a text classification task. The `fit` method automatically handles feature engineering and model selection. For image or other modalities, include file paths in your DataFrame columns. The `presets` argument allows trading off training time for model quality.
import pandas as pd
from autogluon.multimodal import MultiModalPredictor
# Prepare sample data (text classification example)
# For image/video/audio, you'd provide file paths.
train_data = pd.DataFrame({
'text_feature': [
'This is a great product and I love it.',
'Terrible service, very disappointed.',
'It works as expected, nothing special.',
'Absolutely fantastic, highly recommend!'
],
'label': ['positive', 'negative', 'neutral', 'positive']
})
# Initialize and train the MultiModalPredictor
predictor = MultiModalPredictor(label='label', problem_type='classification')
predictor.fit(train_data, presets='best_quality')
# Make predictions on new data
test_data = pd.DataFrame({
'text_feature': [
'This is amazing!',
'Not happy with this at all.'
]
})
predictions = predictor.predict(test_data)
print(f"Predictions: {predictions.tolist()}")
# To save the predictor:
# predictor.save('./my_multimodal_predictor')
Debug
Known issues
breakingModels trained with one AutoGluon version are generally NOT compatible with other versions for loading and inference. Always use the same AutoGluon version (or a patch release of the same major.minor) that was used to train the model.fixEnsure your deployment environment uses the identical `autogluon-multimodal` version (and potentially other `autogluon` subpackages) as your training environment. Consider pinning dependencies in `requirements.txt`.
affects: All versions, specifically 0.8.2+
gotchaAutoGluon Multimodal relies on deep learning models and can be resource-intensive (CPU, GPU, RAM), especially for large datasets or complex multimodal tasks. Training times can be significant.fixStart with smaller datasets or simpler `presets` (e.g., `'fast_inference'`) to understand resource requirements. Ensure adequate hardware (e.g., GPU for faster training). Monitor system resources during training.
affects: All versions
breakingPython version compatibility has changed across major releases. AutoGluon 1.2.0 dropped support for Python 3.8 and added support for Python 3.12. The current version (1.5.0) supports Python >=3.10, <3.14.fixAlways check the `requires_python` field on PyPI or the official documentation for your specific AutoGluon version to ensure your environment's Python version is supported.
affects: Pre-1.2.0 to 1.2.0+, specific changes in 1.2.0, 1.3.0, 1.4.0, 1.5.0
gotchaInstallation of `autogluon.multimodal` can be complex due to its large number of deep learning dependencies (e.g., PyTorch, Transformers, Timm). This can lead to conflicts with other installed packages or slow installation times.fixIt is highly recommended to install `autogluon.multimodal` in a clean virtual environment. For specific hardware acceleration (e.g., GPU), follow the official AutoGluon installation guide to ensure correct PyTorch CUDA versions are installed.
affects: All versions
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Version history
1.6.1latest on PyPI · released Aug 6, 2026
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