modAL provides a modular active learning framework for Python built on top of scikit-learn.
Open source
24 alternativespytorch tabular
pytorch tabular provides a unified framework for deep learning models on tabular data.
About pytorch tabular
PyTorch Tabular is an open-source Python library that provides a common high-level API for deep learning models on tabular data. It is built on PyTorch and PyTorch Lightning, supports training on GPU or CPU with automatic logging, and includes architectures such as feedforward networks with categorical embeddings, TabNet, TabTransformer, AutoInt, and Mixture Density Networks.
The recommended installation is to install PyTorch separately with the matching CUDA version first, then install the package with pip, with an optional extra install that adds Weights & Biases and Plotly dependencies.
Key features
- High-level API for tabular deep-learning architectures
- Scales training on GPU or CPU via PyTorch Lightning
- Automatic logging of training runs
- Feedforward network with embeddings for categorical columns
- TabNet with sparse attention over decision steps
- Mixture Density Networks for probabilistic regression output
Best for: Python users doing tabular modeling with PyTorch for research or applied cases.
Good for
- Open-source codebase built on PyTorch and PyTorch Lightning
- Single high-level API covers multiple tabular architectures
- Supports GPU or CPU training with automatic logging
Watch out for
- Recommended install requires separate PyTorch install with matching CUDA version
pytorch tabular at a glance
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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pytorch tabular FAQ
How do I install PyTorch Tabular?
Install PyTorch first with the correct CUDA version, then run pip install -U "pytorch_tabular" for essentials or pip install -U "pytorch_tabular[extra]" for the version with Weights&Biases and Plotly.
What hardware can it train on?
Training can run on GPU or CPU through PyTorch Lightning, with automatic logging.
What models does it include?
Listed models include feedforward network with category embedding, NODE, TabNet, Mixture Density Networks, AutoInt, TabTransformer, FT Transformer, GATE, GANDALF, DANETs, and Denoising AutoEncoder.
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