```spconv``` is a project that provide heavily-optimized sparse convolution implementation with tensor core support. check [benchmark](docs/BENCHMARK.md) to see how fast spconv 2.x runs.
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@@ -131,7 +142,7 @@ For Linux users, you need to install pip >= 20.3 first to install prebuilt.
**NOTE** It's safe to have different **minor** cuda version between system and conda (pytorch) in **CUDA >= 11.0** because of [CUDA Minor Version Compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/#minor-version-compatibility). For example, you can use spconv-cu114 with anaconda version of pytorch cuda 11.1 in a OS with CUDA 11.2 installed.
**NOTE** In Linux, you can install spconv-cuxxx without install CUDA to system! only suitable NVIDIA driver is required. for CUDA 11, we need driver >= 450.82.
**NOTE** In Linux, you can install spconv-cuxxx without install CUDA to system! only suitable NVIDIA driver is required. for CUDA 11, we need driver >= 450.82. You may need newer driver if you use newer CUDA. for cuda 11.8, you need to have driver >= 520 installed.
#### Prebuilt GPU Support Matrix
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@@ -141,8 +152,8 @@ If you use a GPU architecture that isn't compiled in prebuilt, spconv will use N
| CUDA version | GPU Arch List |
| -------------- |:---------------------:|
| 11.x | 52,60,61,70,75,80,86 |
| 12.x | 70,75,80,86,90 |
| 11.1~11.7 | 52,60,61,70,75,80,86 |
| 11.8+ | 60,70,75,80,86,89,90 |
### Build from source for development (JIT, recommend)
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@@ -191,6 +202,18 @@ You need to rebuild ```cumm``` first if you are build along a CUDA version that
5. run ```pip install pccm cumm wheel```
6. run ```python setup.py bdist_wheel```+```pip install dists/xxx.whl```
## Citation
If you find this project useful in your research, please consider cite:
