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Release 0.5.1 (#9533)

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This commit is contained in:
Lianmin Zheng
2025-08-23 07:09:26 -07:00
committed by GitHub
parent 86d10d220f
commit 97a38ee85b
6 changed files with 7 additions and 9 deletions
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2
benchmark/deepseek_v3/README.md
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@@ -33,7 +33,7 @@ Add [performance optimization options](#performance-optimization-options) as nee
```bash
# Installation
pip install "sglang[all]>=0.5.0rc2"
pip install "sglang[all]>=0.5.1"
# Launch
python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V3 --tp 8 --trust-remote-code

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docs/get_started/install.md
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@@ -12,20 +12,19 @@ It is recommended to use uv for faster installation:
```bash
pip install --upgrade pip
pip install uv
uv pip install "sglang[all]>=0.5.0rc2"
uv pip install "sglang[all]>=0.5.1"
```
**Quick fixes to common problems**
- 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.
- SGLang currently uses torch 2.8 and flashinfer for torch 2.8. If you want to install flashinfer separately, please refer to [FlashInfer installation doc](https://docs.flashinfer.ai/installation.html). Please note that the FlashInfer pypi package is called `flashinfer-python` instead of `flashinfer`.
## Method 2: From source
```bash
# Use the last release branch
git clone -b v0.5.0rc2 https://github.com/sgl-project/sglang.git
git clone -b v0.5.1 https://github.com/sgl-project/sglang.git
cd sglang
# Install the python packages
@@ -35,7 +34,6 @@ pip install -e "python[all]"
**Quick fixes to common problems**
- If you want to develop SGLang, it is recommended to use docker. Please refer to [setup docker container](../developer_guide/development_guide_using_docker.md#setup-docker-container). The docker image is `lmsysorg/sglang:dev`.
- SGLang currently uses torch 2.8 and flashinfer for torch 2.8. If you want to install flashinfer separately, please refer to [FlashInfer installation doc](https://docs.flashinfer.ai/installation.html). Please note that the FlashInfer pypi package is called `flashinfer-python` instead of `flashinfer`.
## Method 3: Using docker

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docs/platforms/amd_gpu.md
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@@ -44,7 +44,7 @@ You can install SGLang using one of the methods below.
```bash
# Use the last release branch
git clone -b v0.5.0rc2 https://github.com/sgl-project/sglang.git
git clone -b v0.5.1 https://github.com/sgl-project/sglang.git
cd sglang
# Compile sgl-kernel

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docs/platforms/ascend_npu.md
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@@ -99,7 +99,7 @@ We are also providing a DeepEP-compatible Library as a drop-in replacement of de
```shell
# Use the last release branch
git clone -b v0.5.0rc2 https://github.com/sgl-project/sglang.git
git clone -b v0.5.1 https://github.com/sgl-project/sglang.git
cd sglang
pip install --upgrade pip

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python/pyproject.toml
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@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "sglang"
version = "0.5.0rc2"
version = "0.5.1"
description = "SGLang is yet another fast serving framework for large language models and vision language models."
readme = "README.md"
requires-python = ">=3.10"

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python/sglang/version.py
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@@ -1 +1 @@
__version__ = "0.5.0rc2"
__version__ = "0.5.1"