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45 lines
1.4 KiB
Markdown
45 lines
1.4 KiB
Markdown
# FastDeploy Runtime使用文档
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`Runtime`作为FastDeploy中模型推理的模块，目前集成了多种后端，用户通过统一的后端即可快速完成不同格式的模型，在各硬件、平台、后端上的推理。本文档通过如下示例展示各硬件、后端上的推理
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## CPU推理
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Python示例
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```python
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import fastdeploy as fd
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import numpy as np
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option = fd.RuntimeOption()
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# 设定模型路径
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option.set_model_path("resnet50/inference.pdmodel", "resnet50/inference.pdiparams")
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# 使用OpenVINO后端
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option.use_openvino_backend()
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# 初始化runtime
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runtime = fd.Runtime(option)
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# 获取输入名
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input_name = runtime.get_input_info(0).name
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# 构造数据进行推理
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results = runtime.infer({input_name: np.random.rand(1, 3, 224, 224).astype("float32")})
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```
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## GPU推理
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```python
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import fastdeploy as fd
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import numpy as np
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option = fd.RuntimeOption()
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# 设定模型路径
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option.set_model_path("resnet50/inference.pdmodel", "resnet50/inference.pdiparams")
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# 使用GPU，并且使用第0张GPU卡
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option.use_gpu(0)
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# 使用Paddle Inference后端
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option.use_paddle_backend()
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# 初始化runtime
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runtime = fd.Runtime(option)
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# 获取输入名
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input_name = runtime.get_input_info(0).name
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# 构造数据进行推理
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results = runtime.infer({input_name: np.random.rand(1, 3, 224, 224).astype("float32")})
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```
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更多Python/C++推理示例请直接参考[FastDeploy/examples/runtime](../../examples/runtime)
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