From b0983c656a9e52cb1c8d78b94f73c42c53517949 Mon Sep 17 00:00:00 2001 From: Akihiro Nitta Date: Sat, 21 Dec 2024 01:26:49 +0000 Subject: [PATCH 1/2] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e54879db..1bd2f1e9 100644 --- a/README.md +++ b/README.md @@ -80,7 +80,7 @@ PyTorch Frame builds directly upon PyTorch, ensuring a smooth transition for exi Comes with a collection of readily-usable tabular datasets. Also supports custom datasets to solve your own problem. We [benchmark](https://github.com/pyg-team/pytorch-frame/blob/master/benchmark) deep tabular models against GBDTs. - **PyTorch integration**: - Integrates effortlessly with other PyTorch libraries, facilitating end-to-end training of PyTorch Frame with downstream PyTorch models. For example, by integrating with [PyG](https://pyg.org/), a PyTorch library for GNNs, we can perform deep learning over relational databases. Learn more in [RelBench](https://relbench.stanford.edu/) and [example code (WIP)](https://github.com/snap-stanford/relbench/blob/main/examples/gnn.py). + Integrates effortlessly with other PyTorch libraries, facilitating end-to-end training of PyTorch Frame with downstream PyTorch models. For example, by integrating with [PyG](https://pyg.org/), a PyTorch library for GNNs, we can perform deep learning over relational databases. Learn more in [RelBench](https://relbench.stanford.edu/) and [example code)](https://github.com/snap-stanford/relbench/blob/main/examples/). ## Architecture Overview From da62d0454761ea3c1b0c0a3d878bf7f1dca91cda Mon Sep 17 00:00:00 2001 From: Akihiro Nitta Date: Sat, 21 Dec 2024 01:27:35 +0000 Subject: [PATCH 2/2] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1bd2f1e9..a16b3a15 100644 --- a/README.md +++ b/README.md @@ -80,7 +80,7 @@ PyTorch Frame builds directly upon PyTorch, ensuring a smooth transition for exi Comes with a collection of readily-usable tabular datasets. Also supports custom datasets to solve your own problem. We [benchmark](https://github.com/pyg-team/pytorch-frame/blob/master/benchmark) deep tabular models against GBDTs. - **PyTorch integration**: - Integrates effortlessly with other PyTorch libraries, facilitating end-to-end training of PyTorch Frame with downstream PyTorch models. For example, by integrating with [PyG](https://pyg.org/), a PyTorch library for GNNs, we can perform deep learning over relational databases. Learn more in [RelBench](https://relbench.stanford.edu/) and [example code)](https://github.com/snap-stanford/relbench/blob/main/examples/). + Integrates effortlessly with other PyTorch libraries, facilitating end-to-end training of PyTorch Frame with downstream PyTorch models. For example, by integrating with [PyG](https://pyg.org/), a PyTorch library for GNNs, we can perform deep learning over relational databases. Learn more in [RelBench](https://relbench.stanford.edu/) and [example code](https://github.com/snap-stanford/relbench/blob/main/examples/). ## Architecture Overview