Remove prefered_place document (#2205)

_all_pages/develop/cpp_demo.md

@@ -209,9 +209,11 @@ void RunModel() {
   // 1. Set CxxConfig
   CxxConfig config;
   config.set_model_dir(FLAGS_model_dir);
-  std::vector<Place> valid_places{Place{TARGET(kARM), PRECISION(kFloat)},
-                                  Place{TARGET(kHost), PRECISION(kFloat)}};
-  config.set_preferred_place(Place{TARGET(kARM), PRECISION(kFloat)});
+  std::vector<Place> valid_places({Place{TARGET(kARM), PRECISION(kFloat)}});
+  if (FLAGS_prefer_int8_kernel) {
+    valid_places.insert(valid_places.begin(),
+                        Place{TARGET(kARM), PRECISION(kInt8)});
+  }
   config.set_valid_places(valid_places);
 
   // 2. Create PaddlePredictor by CxxConfig

_all_pages/develop/fpga.md

@@ -83,18 +83,16 @@ chmod +x test_resnet50_fpga
 
 在Lite中使用fpga与ARM相似，具体的区别如下：
 
-- 由于fpga运行模式为fp16精度、nhwc布局，所以需要修改相应的`valid_place`和`preferred_place`
+- 由于fpga运行模式为fp16精度、nhwc布局，所以需要修改相应的`valid_place`
 - fpga不需要device的初始化和运行模式设置
 
 代码示例：
 ```cpp
 lite::Predictor predictor;
 std::vector<Place> valid_places(
-      {Place{TARGET(kFPGA), PRECISION(kFP16), DATALAYOUT(kNHWC)},
-       Place{TARGET(kHost), PRECISION(kFloat), DATALAYOUT(kNHWC)}});
-Place preferred_place = Place{TARGET(kFPGA), PRECISION(kFP16), DATALAYOUT(kNHWC)};
+      {Place{TARGET(kFPGA), PRECISION(kFP16), DATALAYOUT(kNHWC)}});
 
-predictor.Build(model_dir, preferred_place, valid_places);
+predictor.Build(model_dir, "", "", valid_places);
 
 auto* input_tensor = predictor.GetInput(0);
 input_tensor->Resize(DDim(std::vector<DDim::value_type>({1, 3, 224, 224})));

_all_pages/develop/npu.md

@@ -92,15 +92,14 @@ Note：
 
 代码示例：
 ```cpp
-std::vector<Place> valid_places({Place{TARGET(kHost), PRECISION(kFloat)},
-                                   Place{TARGET(kARM), PRECISION(kFloat)}});
 // if want to use NPU
-valid_places.push_back(Place{TARGET(kNPU), PRECISION(kFloat)});
+std::vector<Place> valid_places({Place{TARGET(kNPU), PRECISION(kFloat)},
+                                 Place{TARGET(kARM), PRECISION(kFloat)}});
 
 DeviceInfo::Init();
 DeviceInfo::Global().SetRunMode(LITE_POWER_HIGH, FLAGS_threads);
 lite::Predictor predictor;
-predictor.Build(model_dir, preferred_place, valid_places);
+predictor.Build(model_dir, "", "", valid_places);
 
 auto* input_tensor = predictor.GetInput(0);
 input_tensor->Resize(DDim(std::vector<DDim::value_type>({1, 3, 224, 224})));

_all_pages/develop/opencl.md

@@ -86,16 +86,14 @@ Lite支持对ARM CPU和ARM GPU的混调执行，具体描述如下：
 DeviceInfo::Init();
 DeviceInfo::Global().SetRunMode(LITE_POWER_HIGH, FLAGS_threads);
 lite::Predictor predictor;
-// 设置Lite推断执行的有效Places为{kHost, kARM, kOpenCL}
+// 设置Lite推断执行的有效Places为{kOpenCL, kARM}
 std::vector<Place> valid_places({
-      Place{TARGET(kHost), PRECISION(kFloat)},
-      Place{TARGET(kARM), PRECISION(kFloat)},
-      Place{TARGET(kOpenCL), PRECISION(kFloat)},
+      Place({TARGET(kOpenCL), PRECISION(kFloat)}),
+      Place({TARGET(kARM), PRECISION(kFloat)})
   });
-// 设置Lite推断执行的偏好Place为kOpenCL
-auto preferred_place = Place({TARGET(kOpenCL), PRECISION(kFloat)});
+
 // 根据有效Places和偏好Place构建模型
-predictor.Build(model_dir, preferred_place, valid_places);
+predictor.Build(model_dir, "", "", valid_places);
 // 设置模型的输入
 auto* input_tensor = predictor.GetInput(0);
 input_tensor->Resize(DDim(std::vector<DDim::value_type>({1, 3, 224, 224})));
@@ -107,4 +105,4 @@ for (int i = 0; i < item_size; i++) {
 // 执行模型推断并获取模型的预测结果
 predictor.Run();
 auto* out = predictor.GetOutput(0);
-```
+```
\ No newline at end of file