# Inference on a GPU
`GPU` `Inference Application`
[](https://gitee.com/mindspore/docs/blob/r1.5/docs/mindspore/programming_guide/source_en/multi_platform_inference_gpu.md)
## Use C++ Interface to Load a MindIR File for Inferencing
### Inference Directory Structure
Create a directory to store the inference code project, for example, `/home/mindspore_sample/gpu_resnet50_inference_sample`. You can download the [sample code](https://gitee.com/mindspore/docs/tree/r1.5/docs/sample_code/gpu_resnet50_inference_sample) from the official website. The `model` directory is used to store the exported `MindIR` [model file](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/sample_resources/ascend310_resnet50_preprocess_sample/resnet50_imagenet.mindir). The directory structure of the inference code project is as follows:
```text
└─gpu_resnet50_inference_sample
├── build.sh // Build script
├── CMakeLists.txt // CMake script
├── README.md // Usage description
├── src
│ └── main.cc // Main function
└── model
└── resnet50_imagenet.mindir // MindIR model file
```
### Inference Code
Namespaces that reference `mindspore`.
```c++
using mindspore::Context;
using mindspore::Serialization;
using mindspore::Model;
using mindspore::Status;
using mindspore::ModelType;
using mindspore::GraphCell;
using mindspore::kSuccess;
using mindspore::MSTensor;
```
Initialize the environment, specify the hardware platform used for inference, and set DeviceID.
Set the hardware to GPU, set DeviceID to 0 and Precision Mode to "fp16". The code example is as follows:
```c++
auto gpu_device_info = std::make_shared<mindspore::GPUDeviceInfo>();
gpu_device_info->SetDeviceID(device_id);
gpu_device_info->SetPrecisionMode("fp16");
context->MutableDeviceInfo().push_back(gpu_device_info);
```
Load the model file.
```c++
// Load the MindIR model.
mindspore::Graph graph;
Serialization::Load(mindir_path, ModelType::kMindIR, &graph);
// Build a model using a graph.
ms::Model model;
model.Build(ms::GraphCell(graph), context);
```
Obtain the input information required by the model.
```c++
std::vector<ms::MSTensor> model_inputs = model->GetInputs();
```
Start inference.
```c++
// Create an output vector.
std::vector<ms::MSTensor> outputs;
// Create an input vector.
std::vector<ms::MSTensor> inputs;
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
image.Data().get(), image.DataSize());
// Call the Predict function of the model for inference.
ret = model.Predict(inputs, &outputs);
```
### Introduce to Building Script
Add the header file search path for the compiler:
```cmake
option(MINDSPORE_PATH "mindspore install path" "")
include_directories(${MINDSPORE_PATH})
include_directories(${MINDSPORE_PATH}/include)
```
Search for the required dynamic library in MindSpore.
```cmake
find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
```
Use the specified source file to generate the target executable file and link the target file to the MindSpore library.
```cmake
add_executable(main src/main.cc)
target_link_libraries(main ${MS_LIB})
```
>For details, see
><https://gitee.com/mindspore/docs/blob/r1.5/docs/sample_code/gpu_resnet50_inference_sample/CMakeLists.txt>
### Building Inference Code
Go to the project directory `gpu_resnet50_inference_sample` and modify the `pip3` in the `build.sh` based on the actual situation. And then execute the building script.
```bash
bash build.sh
```
After building, the executable `main` file is generated in `gpu_resnet50_inference_sample/out`.
### Performing Inference and Viewing the Result
After completing the preceding operations, you can learn how to perform inference.
Log in to the GPU environment, and create the `model` directory to store the `resnet50_imagenet.mindir` file, for example, `/home/mindspore_sample/gpu_resnet50_inference_sample/model`.
Set the environment variable base on the actual situation, where the `TensorRT` is an optional configuration item. It is recommended to add `TensorRT` path to `LD_LIBRARY_PATH` to improve mode inference performance.
```bash
export LD_PRELOAD=/home/miniconda3/lib/libpython37m.so
export LD_LIBRARY_PATH=/usr/local/TensorRT-7.2.2.3/lib/:$LD_LIBRARY_PATH
```
Then, perform inference for 1000 times after 10 times warmup.
```bash
cd out/
./main ../model/resnet50_imagenet.mindir 1000 10
```
In this example, we print the inference delay for per step and average step.
```text
Start to load model..
Load model successuflly
Start to warmup..
Warmup finished
Start to infer..
step 0 cost 1.54004ms
step 1 cost 1.5271ms
... ...
step 998 cost 1.30688ms
step 999 cost 1.30493ms
infer finished.
=================Average inference time: 1.35195 ms
```
### Notices
- During the training process, some networks set operator precision to FP16 artificially. For example, the [Bert mode](https://gitee.com/mindspore/models/blob/r1.5/official/nlp/bert/src/bert_model.py) set the `Dense` and `LayerNorm` to FP16:
```python
class BertOutput(nn.Cell):
def __init__(self,
in_channels,
out_channels,
initializer_range=0.02,
dropout_prob=0.1,
compute_type=mstype.float32):
super(BertOutput, self).__init__()
# Set the nn.Dense to fp16.
self.dense = nn.Dense(in_channels, out_channels,
weight_init=TruncatedNormal(initializer_range)).to_float(compute_type)
self.dropout = nn.Dropout(1 - dropout_prob)
self.dropout_prob = dropout_prob
self.add = P.Add()
# Set the nn.LayerNorm to fp16.
self.layernorm = nn.LayerNorm((out_channels,)).to_float(compute_type)
self.cast = P.Cast()
... ...
```
It is recommended that export the MindIR model with fp32 precision mode before deploying inference. If you want to further improve the inference performance, you can set `precision_mode is` to "fp16".
- Some inference scripts may introduce some unique network structures in the training process. For example, the model requires the image label, which are transmitted to the network output directly. It is suggested to delete this part of operators and then export MindIR model to improve inference performance.
## Inference Using an ONNX File
1. Generate a model in ONNX format on the training platform. For details, see [Export ONNX Model](https://www.mindspore.cn/docs/programming_guide/en/r1.5/save_model.html#export-onnx-model).
2. Perform inference on a GPU by referring to the runtime or SDK document. For example, use TensorRT to perform inference on the NVIDIA GPU. For details, see [TensorRT backend for ONNX](https://github.com/onnx/onnx-tensorrt).