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add qps
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@@ -156,7 +156,9 @@ The recognition model is the same.
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Multiple service requests can be sent at the same time if necessary.
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The predicted performance data will be automatically written into the `PipelineServingLogs/pipeline.tracer` file:
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The predicted performance data will be automatically written into the `PipelineServingLogs/pipeline.tracer` file.
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Tested on 200 real pictures, and limited the detection long side to 960. The average QPS on T4 GPU can reach around 13:
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```
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2021-05-12 10:03:24,812 ==================== TRACER ======================
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@@ -153,7 +153,9 @@ python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_rec_in
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```
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有需要的话可以同时发送多个服务请求
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预测性能数据会被自动写入 `PipelineServingLogs/pipeline.tracer` 文件中:
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预测性能数据会被自动写入 `PipelineServingLogs/pipeline.tracer` 文件中。
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在200张真实图片上测试,把检测长边限制为960。T4 GPU 上 QPS 均值可达到13左右:
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```
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2021-05-12 10:03:24,812 ==================== TRACER ======================
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@@ -191,6 +193,8 @@ python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_rec_in
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2021-05-12 10:03:24,910 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
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```
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<a name="FAQ"></a>
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## FAQ
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**Q1**: 发送请求后没有结果返回或者提示输出解码报错
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