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Update README architecture and platform support #1476

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Oct 17, 2024
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Update README architecture and platform support #1476

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7 changes: 3 additions & 4 deletions README.md
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Expand Up @@ -102,8 +102,7 @@ gitGraph
## Using cuSpatial
**CUDA/GPU requirements**
- CUDA 11.2+ with a [compatible, supported driver](https://docs.nvidia.com/datacenter/tesla/drivers/#cuda-drivers)
- Linux native: Pascal architecture or newer ([Compute Capability >=6.0](https://developer.nvidia.com/cuda-gpus))
- WSL2: Volta architecture or newer ([Compute Capability >=7.0](https://developer.nvidia.com/cuda-gpus))
- Volta architecture or newer ([Compute Capability >=7.0](https://developer.nvidia.com/cuda-gpus))

### Quick start: Docker
Use the [RAPIDS Release Selector](https://docs.rapids.ai/install#selector), selecting `Docker` as the installation method. All RAPIDS Docker images contain cuSpatial.
Expand All @@ -113,15 +112,15 @@ An example command from the Release Selector:
docker run --gpus all --pull always --rm -it \
--shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \
-p 8888:8888 -p 8787:8787 -p 8786:8786 \
nvcr.io/nvidia/rapidsai/notebooks:24.12-cuda11.8-py3.10
nvcr.io/nvidia/rapidsai/notebooks:24.12-cuda11.8-py3.12
```

### Install with Conda

To install via conda:
> **Note** cuSpatial is supported only on Linux or [through WSL](https://rapids.ai/wsl2.html), and with Python versions 3.10, 3.11, and 3.12.

cuSpatial can be installed with conda (miniconda, or the full Anaconda distribution) from the rapidsai channel:
cuSpatial can be installed with conda from the rapidsai channel:

```shell
conda install -c rapidsai -c conda-forge -c nvidia \
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