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+# Runtime
+
+After configuring `RuntimeOption`, developers can create Runtime for model inference on different hardware based on different backends.
+
+## Python Class
+
+```
+class Runtime(runtime_option)
+```
+
+**Parameters**
+
+> * **runtime_option**(fastdeploy.RuntimeOption): Configured RuntimeOption class and instance.
+
+### Member function
+
+```
+infer(data)
+```
+
+Model inference based on input data
+
+**Parameters**
+
+> * **data**(dict({str: np.ndarray}): Input dict data, and key is input name, value is np.ndarray
+
+**Return Value**
+
+Returns a list, whose length equals the number of elements in the original model; elements in the list are np.ndarray
+
+```
+num_inputs()
+```
+
+Input number that returns to the model
+
+```
+num_outputs()
+```
+
+Output number that returns to the model
+
+## C++  Class
+
+```
+class Runtime
+```
+
+### Member function
+
+```
+bool Init(const RuntimeOption& runtime_option)
+```
+
+Model loading initialization
+
+**Parameters**
+
+> * **runtime_option**: Configured RuntimeOption class and instance
+
+**Return Value**
+
+Returns TRUE for successful initialisation, FALSE otherwise
+
+```
+bool Infer(vector<FDTensor>& inputs, vector<FDTensor>* outputs)
+```
+
+Inference from the input and write the result to outputs
+
+**Parameters**
+
+> * **inputs**: Input data
+> * **outputs**: Output data
+
+**Return Value**
+
+Returns TRUE for successful inference, FALSE otherwise
+
+```
+int NumInputs()
+```
+
+Input number that returns to the model
+
+```
+input NumOutputs()
+```
+
+Output number that returns to the model