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Set torch url index in pyproject.toml (#16802)

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This commit is contained in:
Baizhou Zhang
2026-02-01 13:23:52 +08:00
committed by GitHub
parent 429ef988bc
commit c7d53fa26a
2 changed files with 22 additions and 6 deletions
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12
docs/get_started/install.md
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@@ -15,11 +15,11 @@ uv pip install "sglang"
```
**Quick fixes to common problems**
- In some cases (e.g., GB200), the above command might install a wrong torch version (e.g., the CPU version) due to dependency resolution. To fix this, you can first run the above command and then force-reinstall the correct [PyTorch](https://pytorch.org/get-started/locally/) with the following:
```
uv pip install "torch==2.9.1" "torchvision" --extra-index-url https://download.pytorch.org/whl/cu129 --force-reinstall
```
- For CUDA 13, Docker is recommended (see the Method 3 note on B300/GB300/CUDA 13). If you do not have Docker access, installing the matching `sgl_kernel` wheel from [the sgl-project whl releases](https://github.com/sgl-project/whl/releases) after installing SGLang also works. Replace `X.Y.Z` with the `sgl_kernel` version required by your SGLang (you can find this by running `uv pip show sgl_kernel`). Examples:
- For CUDA 13, Docker is recommended (see Method 3 note on B300/GB300/CUDA 13). If you do not have Docker access, an extra index url needs to be provided when installing wheels:
```
uv pip install "sglang" --extra-index-url https://download.pytorch.org/whl/cu130
```
- The `sgl_kernel` wheel for CUDA 13 can be downloaded from [the sgl-project whl releases](https://github.com/sgl-project/whl/blob/gh-pages/cu130/sgl-kernel/index.html). Replace `X.Y.Z` with the `sgl_kernel` version required by your SGLang install (you can find this by running `uv pip show sgl_kernel`). Examples:
```bash
# x86_64
uv pip install "https://github.com/sgl-project/whl/releases/download/vX.Y.Z/sgl_kernel-X.Y.Z+cu130-cp310-abi3-manylinux2014_x86_64.whl"
@@ -27,7 +27,7 @@ uv pip install "sglang"
# aarch64
uv pip install "https://github.com/sgl-project/whl/releases/download/vX.Y.Z/sgl_kernel-X.Y.Z+cu130-cp310-abi3-manylinux2014_aarch64.whl"
```
- If you encounter `OSError: CUDA_HOME environment variable is not set`, set it to your CUDA install root with either of the following solutions:
- If you encounter `OSError: CUDA_HOME environment variable is not set`. Please set it to your CUDA install root with either of the following solutions:
1. Use `export CUDA_HOME=/usr/local/cuda-<your-cuda-version>` to set the `CUDA_HOME` environment variable.
2. Install FlashInfer first following [FlashInfer installation doc](https://docs.flashinfer.ai/installation.html), then install SGLang as described above.