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1. 启动服务
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<h1 id="10-ernie-45-03b-paddle">10分钟完成 ERNIE-4.5-0.3B-Paddle 模型部署</h1>
<p>本文档讲解如何部署ERNIE-4.5-0.3B-Base-Paddle模型，在开始部署前，请确保你的硬件环境满足如下条件：</p>
<ul>
<li>GPU驱动 &gt;= 535</li>
<li>CUDA &gt;= 12.3</li>
<li>CUDNN &gt;= 9.5</li>
<li>Linux X86_64</li>
<li>Python &gt;= 3.10</li>
<li>运行模型满足最低硬件配置要求，参考<a href="../../supported_models/">支持模型列表文档</a></li>
</ul>
<p>为了快速在各类硬件部署，本文档采用 <code>ERNIE-4.5-0.3B-Base-Paddle</code> 模型作为示例，可在大部分硬件上完成部署。</p>
<p>安装FastDeploy方式参考<a href="../installation/">安装文档</a>。</p>
<h2 id="1">1. 启动服务</h2>
<p>安装FastDeploy后，在终端执行如下命令，启动服务，其中启动命令配置方式参考<a href="../../parameters/">参数说明</a></p>
<pre><code class="language-shell">export ENABLE_V1_KVCACHE_SCHEDULER=1
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-0.3B-Paddle \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--max-model-len 32768 \
--max-num-seqs 32
</code></pre>
<blockquote>
<p>💡 注意：在 <code>--model</code> 指定的路径中，若当前目录下不存在该路径对应的子目录，则会尝试根据指定的模型名称（如 <code>baidu/ERNIE-4.5-0.3B-Paddle</code>）查询AIStudio是否存在预置模型，若存在，则自动启动下载。默认的下载路径为：<code>~/xx</code>。关于模型自动下载的说明和配置参阅<a href="../../supported_models/">模型下载</a>。
<code>--max-model-len</code> 表示当前部署的服务所支持的最长Token数量。
<code>--max-num-seqs</code> 表示当前部署的服务所支持的最大并发处理数量。</p>
</blockquote>
<p><strong>相关文档</strong></p>
<ul>
<li><a href="../../online_serving/">服务部署配置</a></li>
<li><a href="../../online_serving/metrics/">服务监控metrics</a></li>
</ul>
<h2 id="2">2. 用户发起服务请求</h2>
<p>执行启动服务指令后，当终端打印如下信息，说明服务已经启动成功。</p>
<pre><code>api_server.py[line:91] Launching metrics service at http://0.0.0.0:8181/metrics
api_server.py[line:94] Launching chat completion service at http://0.0.0.0:8180/v1/chat/completions
api_server.py[line:97] Launching completion service at http://0.0.0.0:8180/v1/completions
INFO: Started server process [13909]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8180 (Press CTRL+C to quit)
</code></pre>
<p>FastDeploy提供服务探活接口，用以判断服务的启动状态，执行如下命令返回 <code>HTTP/1.1 200 OK</code> 即表示服务启动成功。</p>
<pre><code class="language-shell">curl -i http://0.0.0.0:8180/health
</code></pre>
<p>通过如下命令发起服务请求</p>
<pre><code class="language-shell">curl -X POST &quot;http://0.0.0.0:8180/v1/chat/completions&quot; \
-H &quot;Content-Type: application/json&quot; \
-d '{
&quot;messages&quot;: [
{&quot;role&quot;: &quot;user&quot;, &quot;content&quot;: &quot;把李白的静夜思改写为现代诗&quot;}
]
}'
</code></pre>
<p>FastDeploy服务接口兼容OpenAI协议，可以通过如下Python代码发起服务请求。</p>
<pre><code class="language-python">import openai
host = &quot;0.0.0.0&quot;
port = &quot;8180&quot;
client = openai.Client(base_url=f&quot;http://{host}:{port}/v1&quot;, api_key=&quot;null&quot;)
response = client.chat.completions.create(
model=&quot;null&quot;,
messages=[
{&quot;role&quot;: &quot;system&quot;, &quot;content&quot;: &quot;I'm a helpful AI assistant.&quot;},
{&quot;role&quot;: &quot;user&quot;, &quot;content&quot;: &quot;把李白的静夜思改写为现代诗&quot;},
],
stream=True,
)
for chunk in response:
if chunk.choices[0].delta:
print(chunk.choices[0].delta.content, end='')
print('\n')
</code></pre>
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