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+# FastDeploy Runtime User Guideline
+
+`Runtime`, the module for model inference in FastDeploy, currently integrates a variety of backends. It allows users to quickly complete inference in different model formats on different hardware, platforms and backends through a unified backend. This demo shows the inference on each hardware and backend.
+
+## CPU Inference
+
+Python demo
+
+```python
+import fastdeploy as fd
+import numpy as np
+option = fd.RuntimeOption()
+# Set model path
+option.set_model_path("resnet50/inference.pdmodel", "resnet50/inference.pdiparams")
+# Use OpenVINO backend
+option.use_openvino_backend()
+# Initialize runtime
+runtime = fd.Runtime(option)
+# Get input info
+input_name = runtime.get_input_info(0).name
+# Constructing data for inference
+results = runtime.infer({input_name: np.random.rand(1, 3, 224, 224).astype("float32")})
+```
+
+## GPU Inference
+
+```python
+import fastdeploy as fd
+import numpy as np
+option = fd.RuntimeOption()
+# Set model path
+option.set_model_path("resnet50/inference.pdmodel", "resnet50/inference.pdiparams")
+# Use the GPU (0th GPU card)
+option.use_gpu(0)
+# Use Paddle Inference backend
+option.use_paddle_backend()
+# Initialize runtime
+runtime = fd.Runtime(option)
+# Get input info
+input_name = runtime.get_input_info(0).name
+# Constructing data for inference
+results = runtime.infer({input_name: np.random.rand(1, 3, 224, 224).astype("float32")})
+```
+
+More Python/C++ inference demo, please refer to [FastDeploy/examples/runtime](../../examples/runtime)