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[](https://badge.fury.io/py/relbench)
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[](https://github.com/snap-stanford/relbench/actions/workflows/testing.yml)
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[](https://opensource.org/licenses/MIT)
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[](https://twitter.com/RelBench)
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[**Website**](https://relbench.stanford.edu) | [**Vision Paper**](https://relbench.stanford.edu/paper.pdf) | [**Mailing List**](https://groups.google.com/forum/#!forum/relbench/join)
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Relational Deep Learning is a new approach for end-to-end representation learning on data spread across multiple tables, such as in a _relational database_ (see our [vision paper](https://relbench.stanford.edu/paper.pdf)). RelBench is the accompanying benchmark which seeks to facilitate efficient, robust and reproducible research in this direction. It comprises of a collection of realistic, large-scale, and diverse datasets structured as relational tables, along with machine learning tasks defined on them. It provides full support for data downloading, task specification and standardized evaluation in an ML-framework-agnostic manner. Additionally, there is seamless integration with [PyTorch Geometric](https://github.com/pyg-team/pytorch_geometric) to load the data as a graph and train GNN models, and with [PyTorch Frame](https://github.com/pyg-team/pytorch-frame) to encode the various types of table columns. Finally, there is a leaderboard for tracking progress.
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[**Website**](https://relbench.stanford.edu) | [**Vision Paper**](https://relbench.stanford.edu/paper.pdf) | [**Mailing List**](https://groups.google.com/forum/#!forum/relbench/join) | [](https://twitter.com/RelBench)
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Relational Deep Learning is a new approach for end-to-end representation learning on data spread across multiple tables, such as in a _relational database_ (see our [vision paper](https://relbench.stanford.edu/paper.pdf)). RelBench is the accompanying benchmark which seeks to facilitate efficient, robust and reproducible research in this direction. It comprises of a collection of realistic, large-scale, and diverse datasets structured as relational tables, along with machine learning tasks defined on them. It provides full support for data downloading, task specification and standardized evaluation in an ML-framework-agnostic manner. Additionally, there is seamless integration with [PyTorch Geometric](https://github.com/pyg-team/pytorch_geometric) to load the data as a graph and train GNN models, and with [PyTorch Frame](https://github.com/pyg-team/pytorch-frame) to encode the various types of table columns. Finally, there is a leaderboard for tracking progress.
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