[Doc] Add multinode deployment documents (#3417)

* Create multi-node_deployment.md

* Create multi-node_deployment.md

* Update mkdocs.yml

docs/features/multi-node_deployment.md

@@ -0,0 +1,71 @@
+# Multi-Node Deployment
+
+## Overview  
+Multi-node deployment addresses scenarios where a single machine's GPU memory is insufficient to support deployment of large models by enabling tensor parallelism across multiple machines.
+
+## Environment Preparation  
+#### Network Requirements  
+1. All nodes must be within the same local network  
+2. Ensure bidirectional connectivity between all nodes (test using `ping` and `nc -zv`)  
+
+#### Software Requirements  
+1. Install the same version of FastDeploy on all nodes  
+2. [Recommended] Install and configure MPI (OpenMPI or MPICH)  
+
+## Tensor Parallel Deployment  
+
+### Recommended Launch Method  
+We recommend using mpirun for one-command startup without manually starting each node.  
+
+### Usage Instructions  
+1. Execute the same command on all machines  
+2. The IP order in the `ips` parameter determines the node startup sequence  
+3. The first IP will be designated as the master node  
+4. Ensure all nodes can resolve each other's hostnames  
+
+* Online inference startup example:  
+    ```shell  
+    python -m fastdeploy.entrypoints.openai.api_server \  
+    --model baidu/ERNIE-4.5-300B-A47B-Paddle \  
+    --port 8180 \  
+    --metrics-port 8181 \  
+    --engine-worker-queue-port 8182 \  
+    --max-model-len 32768 \  
+    --max-num-seqs 32 \  
+    --tensor-parallel-size 16 \  
+    --ips 192.168.1.101,192.168.1.102  
+    ```  
+
+* Offline startup example:  
+    ```python  
+    from fastdeploy.engine.sampling_params import SamplingParams  
+    from fastdeploy.entrypoints.llm import LLM  
+      
+    model_name_or_path = "baidu/ERNIE-4.5-300B-A47B-Paddle"  
+      
+    sampling_params = SamplingParams(temperature=0.1, max_tokens=30)  
+    llm = LLM(model=model_name_or_path, tensor_parallel_size=16, ips="192.168.1.101,192.168.1.102")  
+    if llm._check_master():  
+        output = llm.generate(prompts="Who are you?", use_tqdm=True, sampling_params=sampling_params)  
+        print(output)  
+    ```  
+
+* Notes:  
+- Only the master node can receive completion requests  
+- Always send requests to the master node (the first IP in the ips list)  
+- The master node will distribute workloads across all nodes  
+
+### Parameter Description  
+
+#### `ips` Parameter  
+- **Type**: `string`  
+- **Format**: Comma-separated IPv4 addresses  
+- **Description**: Specifies the IP addresses of all nodes in the deployment group  
+- **Required**: Only for multi-node deployments  
+- **Example**: `"192.168.1.101,192.168.1.102,192.168.1.103"`  
+
+#### `tensor_parallel_size` Parameter  
+- **Type**: `integer`  
+- **Description**: Total number of GPUs across all nodes  
+- **Required**: Yes  
+- **Example**: For 2 nodes with 8 GPUs each, set to 16

docs/zh/features/multi-node_deployment.md

@@ -0,0 +1,73 @@
+# 多节点部署
+
+## 概述
+多节点部署旨在解决单个机器GPU显存不足时，支持跨多台机器的张量并行执行。
+
+## 环境准备
+#### 网络要求
+1. 所有节点必须在同一本地网络中
+2. 确保所有节点之间双向连通（可使用`ping`和`nc -zv`测试）
+
+
+#### 软件要求
+1. 所有节点安装相同版本的FastDeploy
+2. [建议安装]安装并配置MPI（OpenMPI或MPICH）
+
+## 张量并行部署
+
+### 推荐启动方式
+我们推荐使用mpirun进行一键启动，无需手动启动每个节点。
+
+### 使用说明
+1. 在所有机器上执行相同的命令
+2. `ips`参数中的IP顺序决定了节点启动顺序
+3. 第一个IP将被指定为主节点
+4. 确保所有节点能够解析彼此的主机名
+
+* 在线推理启动示例：
+    ```shell
+    python -m fastdeploy.entrypoints.openai.api_server \
+    --model baidu/ERNIE-4.5-300B-A47B-Paddle \
+    --port 8180 \
+    --metrics-port 8181 \
+    --engine-worker-queue-port 8182 \
+    --max-model-len 32768 \
+    --max-num-seqs 32 \
+    --tensor-parallel-size 16 \
+    --ips 192.168.1.101,192.168.1.102
+    ```
+
+* 离线启动示例：
+    ```python
+    from fastdeploy.engine.sampling_params import SamplingParams
+    from fastdeploy.entrypoints.llm import LLM
+
+    model_name_or_path = "baidu/ERNIE-4.5-300B-A47B-Paddle"
+
+    sampling_params = SamplingParams(temperature=0.1, max_tokens=30)
+    llm = LLM(model=model_name_or_path, tensor_parallel_size=16, ips="192.168.1.101,192.168.1.102")
+    if llm._check_master():
+        output = llm.generate(prompts="你是谁?", use_tqdm=True, sampling_params=sampling_params)
+        print(output)
+    ```
+
+* 注意：
+- 只有主节点可以接收完成请求
+- 请始终将请求发送到主节点（ips列表中的第一个IP）
+- 主节点将在所有节点间分配工作负载
+
+### 参数说明
+
+#### `ips`参数
+- **类型**: `字符串`
+- **格式**: 逗号分隔的IPv4地址
+- **描述**: 指定部署组中所有节点的IP地址
+- **必填**: 仅多节点部署时需要
+- **示例**: `"192.168.1.101,192.168.1.102,192.168.1.103"`
+
+#### `tensor_parallel_size`参数
+- **类型**: `整数`
+- **描述**: 所有节点上的GPU总数
+- **必填**: 是
+- **示例**: 对于2个节点各8个GPU，设置为16
+

mkdocs.yml

@@ -80,6 +80,7 @@ plugins:
             Early Stop: 早停功能
             Plugins: 插件机制
             Sampling: 采样策略
+            MultiNode Deployment: 多机部署
             Supported Models: 支持模型列表
             Benchmark: 基准测试
             Usage: 用法
@@ -127,6 +128,7 @@ nav:
       - 'Early Stop': features/early_stop.md
       - 'Plugins': features/plugins.md
       - 'Sampling': features/sampling.md
+      - 'MultiNode Deployment': features/multi-node_deployment.md
   - 'Supported Models': supported_models.md
   - Benchmark: benchmark.md
   - Usage: