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<p><a href="../zh/features/data_parallel_service.md">简体中文</a></p>
<h1 id="data-parallelism-dp">Data Parallelism (DP)</h1>
<p>Data Parallelism (DP) is a distributed inference approach in which incoming requests are distributed across multiple <strong>identical model replicas</strong>, with each replica independently handling inference for its assigned requests.</p>
<p>In practice, especially when deploying <strong>Mixture-of-Experts (MoE)</strong> models, <strong>Data Parallelism (DP)</strong> is often combined with <strong>Expert Parallelism (EP)</strong>.
Each DP service independently performs the Attention computation, while all DP services collaboratively participate in the MoE computation, thereby improving overall inference performance.</p>
<p>FastDeploy supports DP-based inference and provides the <code>multi_api_server</code> interface to launch multiple inference services simultaneously.</p>
<p><img alt="Architecture" src="../images/no_scheduler_img.png" /></p>
<hr />
<h2 id="launching-fastdeploy-services">Launching FastDeploy Services</h2>
<p>Taking the <strong>ERNIE-4.5-300B</strong> model as an example, the following command launches a service with <strong>DP=8, TP=1, EP=8</strong>:</p>
<pre><code class="language-shell">export FD_ENABLE_MULTI_API_SERVER=1
python -m fastdeploy.entrypoints.openai.multi_api_server \
--num-servers 8 \
--ports &quot;1811,1822,1833,1844,1855,1866,1877,1888&quot; \
--metrics-ports &quot;3101,3201,3301,3401,3501,3601,3701,3801&quot; \
--args --model ERNIE-4_5-300B-A47B-FP8-Paddle \
--engine-worker-queue-port &quot;25611,25621,25631,25641,25651,25661,25671,25681&quot; \
--tensor-parallel-size 1 \
--data-parallel-size 8 \
--max-model-len 12288 \
--max-num-seqs 64 \
--num-gpu-blocks-override 256 \
--enable-expert-parallel
</code></pre>
<h3 id="parameter-description">Parameter Description</h3>
<ul>
<li><code>num-servers</code>: Number of DP service instances to launch.</li>
<li><code>ports</code>: API server ports for the DP service instances. The number of ports must match <code>num-servers</code>.</li>
<li><code>metrics-ports</code>: Metrics server ports for the DP service instances. The number must match <code>num-servers</code>. If not specified, available ports will be allocated automatically.</li>
<li><code>args</code>: Arguments passed to each DP service instance. Refer to the <a href="../../parameters/">parameter documentation</a> for details.</li>
</ul>
<hr />
<h2 id="request-scheduling">Request Scheduling</h2>
<p>After launching multiple DP services using the data parallel strategy, incoming user requests must be distributed across the services by a scheduler to achieve load balancing.</p>
<h3 id="web-serverbased-scheduling">Web Server–Based Scheduling</h3>
<p>Once the IP addresses and ports of the DP service instances are known, common web servers (such as <strong>Nginx</strong>) can be used to implement request scheduling. Details are omitted here.</p>
<h3 id="fastdeploy-router">FastDeploy Router</h3>
<p>FastDeploy provides a Python-based <a href="https://github.com/PaddlePaddle/FastDeploy/tree/develop/fastdeploy/router">Router</a> to handle request reception and scheduling.
A high-performance Router implementation is currently under development.</p>
<p>The usage and request scheduling workflow is as follows:</p>
<ul>
<li>Start the Router</li>
<li>Start FastDeploy service instances (either single-DP or multi-DP), which register themselves with the Router</li>
<li>User requests are sent to the Router</li>
<li>The Router selects an appropriate service instance based on the global load status</li>
<li>The Router forwards the request to the selected instance for inference</li>
<li>The Router receives the generated result from the instance and returns it to the user</li>
</ul>
<hr />
<h2 id="quick-start-example">Quick Start Example</h2>
<h3 id="launching-the-router">Launching the Router</h3>
<p>Start the Router service. Logs are written to <code>log_router/router.log</code>. <code>fd-router</code> installation instructions can be found in the <a href="../../online_serving/router/">Router documentation</a>.</p>
<pre><code class="language-shell">export FD_LOG_DIR=&quot;log_router&quot;
/usr/local/bin/fd-router \
--port 30000
</code></pre>
<h3 id="launching-dp-services-with-router">Launching DP Services with Router</h3>
<p>Again using the <strong>ERNIE-4.5-300B</strong> model as an example, the following command launches <strong>DP=8, TP=1, EP=8</strong> services and registers them with the Router via the <code>--router</code> argument:</p>
<pre><code class="language-shell">export FD_ENABLE_MULTI_API_SERVER=1
python -m fastdeploy.entrypoints.openai.multi_api_server \
--num-servers 8 \
--ports &quot;1811,1822,1833,1844,1855,1866,1877,1888&quot; \
--metrics-ports &quot;3101,3201,3301,3401,3501,3601,3701,3801&quot; \
--args --model ERNIE-4_5-300B-A47B-FP8-Paddle \
--tensor-parallel-size 1 \
--data-parallel-size 8 \
--max-model-len 12288 \
--max-num-seqs 64 \
--num-gpu-blocks-override 256 \
--enable-expert-parallel \
--router &quot;0.0.0.0:30000&quot;
</code></pre>
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