WSL2部署LangGraph全栈指南

Ubuntu 22.04 与 Win11 的 PyCharm 本地 WSL2 部署 Gemini Fullstack LangGraph 指南

环境准备

确保 Windows 11 已启用 WSL2 功能，并安装 Ubuntu 22.04 发行版。通过管理员权限的 PowerShell 执行以下命令启用 WSL2：

wsl --install -d Ubuntu-22.04

安装完成后，在 Ubuntu 22.04 中更新系统并安装基础依赖：

sudo apt update && sudo apt upgrade -y
sudo apt install -y python3-pip git curl build-essential

配置 PyCharm 与 WSL2 集成

打开 PyCharm Professional 版（社区版不支持 WSL2 集成），进入 File > Settings > Build, Execution, Deployment > Toolchains。添加 WSL 配置，选择已安装的 Ubuntu 22.04 解释器路径（通常为 /usr/bin/python3）。在项目设置中，将 Python 解释器切换为 WSL 环境下的解释器。

安装 Gemini Fullstack LangGraph

在 WSL2 的 Ubuntu 22.04 终端中创建项目目录并克隆仓库：

git clone https://github.com/langchain-ai/langgraph.git
cd langgraph
python3 -m pip install -e .

安装额外的 GPU 支持（如需要）：

pip install torch --extra-index-url https://download.pytorch.org/whl/cu118

配置开发环境

在 PyCharm 中打开 WSL2 中的项目路径（格式为 \\wsl$\Ubuntu-22.04\path\to\langgraph）。创建虚拟环境以避免依赖冲突：

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

验证部署

运行示例脚本测试 LangGraph 功能是否正常：

from langgraph.graph import Graph
graph = Graph()
# 添加测试节点和边
print(graph.nodes)

若输出正确节点信息，则表明环境配置成功。可通过 PyCharm 的 WSL 终端直接调试代码，或使用内置的 WSL 运行配置。

性能优化

对于 GPU 加速，安装正确的 CUDA 驱动版本（建议 12.1+）。在 WSL2 中配置 NVIDIA 容器工具包：

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt update && sudo apt install -y nvidia-docker2

常见问题解决

若遇到 WSL2 网络问题，在 Windows 主机上执行：

wsl --shutdown

重新启动 WSL2 实例。对于 PyCharm 文件同步延迟，禁用 Settings > Build, Execution, Deployment > Deployment 中的自动上传功能。

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