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<p><a href="./">English</a></p>
<h1 id="_1">多节点部署</h1>
<h2 id="_2">概述</h2>
<p>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。</p>
<h2 id="_3">环境准备</h2>
<h3 id="_4">网络要求</h3>
<ol>
<li>所有节点必须在同一本地网络中</li>
<li>确保所有节点之间双向连通（可使用<code>ping</code>和<code>nc -zv</code>测试）</li>
</ol>
<h4 id="_5">软件要求</h4>
<ol>
<li>所有节点安装相同版本的FastDeploy</li>
<li>[建议安装]安装并配置MPI（OpenMPI或MPICH）</li>
</ol>
<h2 id="_6">张量并行部署</h2>
<h3 id="_7">推荐启动方式</h3>
<p>我们推荐使用mpirun进行一键启动，无需手动启动每个节点。</p>
<h3 id="_8">使用说明</h3>
<ol>
<li>在所有机器上执行相同的命令</li>
<li><code>ips</code>参数中的IP顺序决定了节点启动顺序</li>
<li>第一个IP将被指定为主节点</li>
<li>
<p>确保所有节点能够解析彼此的主机名</p>
</li>
<li>
<p>在线推理启动示例：</p>
<p><code>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 \
--graph-optimization-config '{"use_cudagraph":false}' \
--no-enable-prefix-caching \
--disable-custom-all-reduce \
--ips 192.168.1.101,192.168.1.102</code></p>
</li>
</ol>
<blockquote>
<p>:bulb: 多机张量并行部署暂不支持CUDAGraphs、PrefixCaching与CustomAllReduce，需在部署命令中显示关闭。</p>
</blockquote>
<ul>
<li>
<p>离线启动示例：</p>
<p>```python
from fastdeploy.engine.sampling_params import SamplingParams
from fastdeploy.entrypoints.llm import LLM</p>
<p>model_name_or_path = "baidu/ERNIE-4.5-300B-A47B-Paddle"</p>
<p>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)
```</p>
</li>
<li>
<p>注意：</p>
</li>
<li>只有主节点可以接收完成请求</li>
<li>请始终将请求发送到主节点（ips列表中的第一个IP）</li>
<li>主节点将在所有节点间分配工作负载</li>
</ul>
<h3 id="_9">参数说明</h3>
<h4 id="ips"><code>ips</code>参数</h4>
<ul>
<li><strong>类型</strong>: <code>字符串</code></li>
<li><strong>格式</strong>: 逗号分隔的IPv4地址</li>
<li><strong>描述</strong>: 指定部署组中所有节点的IP地址</li>
<li><strong>必填</strong>: 仅多节点部署时需要</li>
<li><strong>示例</strong>: <code>"192.168.1.101,192.168.1.102,192.168.1.103"</code></li>
</ul>
<h4 id="tensor_parallel_size"><code>tensor_parallel_size</code>参数</h4>
<ul>
<li><strong>类型</strong>: <code>整数</code></li>
<li><strong>描述</strong>: 所有节点上的GPU总数</li>
<li><strong>必填</strong>: 是</li>
<li><strong>示例</strong>: 对于2个节点各8个GPU，设置为16</li>
</ul>
</article>
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