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<h1 id="benchmark">Benchmark</h1>
<p>FastDeploy extends the <a href="https://github.com/vllm-project/vllm/blob/main/benchmarks/">vLLM benchmark</a> script with additional metrics, enabling more detailed performance benchmarking for FastDeploy.</p>
<h2 id="benchmark-dataset">Benchmark Dataset</h2>
<p>The following dataset is sourced from open-source data (original data from <a href="https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json">HuggingFace Datasets</a>):</p>
<table>
<thead>
<tr>
<th style="text-align: left;">Dataset</th>
<th style="text-align: left;">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;">https://fastdeploy.bj.bcebos.com/eb_query/filtered_sharedgpt_2000_input_1136_output_200_fd.json</td>
<td style="text-align: left;">Open-source dataset</td>
</tr>
</tbody>
</table>
<h2 id="how-to-run">How to Run</h2>
<pre><code>cd FastDeploy/benchmarks
python -m pip install -r requirements.txt
# Start service
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-0.3B-Base-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 8192
# Run benchmark
python benchmark_serving.py \
--backend openai-chat \
--model baidu/ERNIE-4.5-0.3B-Base-Paddle \
--endpoint /v1/chat/completions \
--host 0.0.0.0 \
--port 8188 \
--dataset-name EBChat \
--dataset-path ./filtered_sharedgpt_2000_input_1136_output_200_fd.json \
--percentile-metrics ttft,tpot,itl,e2el,s_ttft,s_itl,s_e2el,s_decode,input_len,s_input_len,output_len \
--metric-percentiles 80,95,99,99.9,99.95,99.99 \
--num-prompts 1 \
--max-concurrency 1 \
--save-result
</code></pre>
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