Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations
| Date | Stars |
|---|---|
| 2026-07-24 | 1104 |
| 2026-07-25 | 1104 |
| 2026-07-28 | 1104 |
| 2026-07-30 | 1104 |
| 2026-08-06 | 1104 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
[pypi-image]: https://badge.fury.io/py/torch-sparse.svg
[pypi-url]: https://pypi.python.org/pypi/torch-sparse
[testing-image]: https://github.com/rusty1s/pytorch_sparse/actions/workflows/testing.yml/badge.svg
[testing-url]: https://github.com/rusty1s/pytorch_sparse/actions/workflows/testing.yml
[linting-image]: https://github.com/rusty1s/pytorch_sparse/actions/workflows/linting.yml/badge.svg
[linting-url]: https://github.com/rusty1s/pytorch_sparse/actions/workflows/linting.yml
[coverage-image]: https://codecov.io/gh/rusty1s/pytorch_sparse/branch/master/graph/badge.svg
[coverage-url]: https://codecov.io/github/rusty1s/pytorch_sparse?branch=master
# PyTorch Sparse
[![PyPI Version][pypi-image]][pypi-url]
[![Testing Status][testing-image]][testing-url]
[![Linting Status][linting-image]][linting-url]
[![Code Coverage][coverage-image]][coverage-url]
--------------------------------------------------------------------------------
This package consists of a small extension library of optimized sparse matrix operations with autograd support.
This package currently consists of the following methods:
* **[Coalesce](#coalesce)**
* **[Transpose](#transpose)**
* **[Sparse Dense Matrix Multiplication](#sparse-dense-matrix-multiplication)**
* **[Sparse Sparse Matrix Multiplication](#sparse-sparse-matrix-multiplication)**
All included operations work on varying data types and are implemented both for CPU and GPU.
To avoid the hazzle of creating [`torch.sparse_coo_tensor`](https://pytorch.org/docs/stable/torch.html?highlight=sparse_coo_tensor#torch.sparse_coo_tensor), this package defines operations on sparse tensors by simply passing `index` and `value` tensors as arguments ([with same shapes as defined in PyTorch](https://pytorch.org/docs/stable/sparse.html)).
Note that only `value` comes with autograd support, as `index` is discrete and therefore not differentiable.
## Installation
### Binaries
We provide pip wheels for all major OS/PyTorch/CUDA combinations, see [here](https://data.pyg.org/whl).
#### PyTorch 2.12
To install the binaries for PyTorch 2.12, simply run
```
pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.12.0+${CUDA}.html
```
where `${CUDA}` should be replaced by either `cpu`, `cu126`, `cu130`, or `cu132` depending on your PyTorch installation.
| | `cpu` | `cu126` | `cu130` | `cu132` |
|-------------|-------|---------|---------|---------|
| **Linux** | ✅ | ✅ | ✅ | ✅ |
| **Windows** | ✅ | ✅ | | |
| **macOS** | ✅ | | | |
#### PyTorch 2.11
To install the binaries for PyTorch 2.11, simply run
```
pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.11.0+${CUDA}.html
```
where `${CUDA}` should be replaced by either `cpu`, `cu126`, `cu128`, or `cu130` depending on your PyTorch installation.
| | `cpu` | `cu126` | `cu128` | `cu130` |
|-------------|-------|---------|---------|---------|
| **Linux** | ✅ | ✅ | ✅ | ✅ |
| **Windows** | ✅ | ✅ | ✅ | ✅ |
| **macOS** | ✅ | | | |
#### PyTorch 2.10
To install the binaries for PyTorch 2.10, simply run
```
pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.10.0+${CUDA}.html
```
where `${CUDA}` should be replaced by either `cpu`, `cu126`, `cu128`, or `cu130` depending on your PyTorch installation.
| | `cpu` | `cu126` | `cu128` | `cu130` |
|-------------|-------|---------|---------|---------|
| **Linux** | ✅ | ✅ | ✅ | ✅ |
| **Windows** | ✅ | ✅ | ✅ | ✅ |
| **macOS** | ✅ | | | |
**Note:** Binaries of older versions are also provided for PyTorch 1.4.0, PyTorch 1.5.0, PyTorch 1.6.0, PyTorch 1.7.0/1.7.1, PyTorch 1.8.0/1.8.1, PyTorch 1.9.0, PyTorch 1.10.0/1.10.1/1.10.2, PyTorch 1.11.0, PyTorch 1.12.0/1.12.1, PyTorch 1.13.0/1.13.1, PyTorch 2.0.0/2.0.1, PyTorch 2.1.0/2.1.1/Excerpt of 10,759 characters
Read on GitHub
Matthias Fey · NVIDIA · Germany
657
16
6
Ekagra Ranjan
6
4
Lindsey Gray · United States
3
Akihiro Nitta · @nvidia · United States
3
3
3
2
Damian Szwichtenberg · Intel · Poland
2
Shagun Sodhani · @facebook
2
2
1
1
1
Hongyu, Chiu · Taiwan
1
1
Padarn Wilson · @myteksi · Singapore
1
Kinga Gajdamowicz · Intel · Poland
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:888bc3e29b7f97d7, topic:pytorch, desc:autograd, readme:autograd