• MindSpore-based Inference Service Deployment
• • Overview
  • Starting Serving
  • Application Example
  • • Exporting Model
    • Starting Serving Inference
    • Client Samples
    • • Python Client Sample
      • C++ Client Sample

MindSpore-based Inference Service Deployment

• MindSpore-based Inference Service Deployment
  • Overview
  • Starting Serving
  • Application Example
    • Exporting Model
    • Starting Serving Inference
    • Client Samples
      • Python Client Sample
      • C++ Client Sample

Overview

MindSpore Serving is a lightweight and high-performance service module that helps MindSpore developers efficiently deploy online serving services in the production environment. After completing model training using MindSpore, you can export the MindSpore model and use MindSpore Serving to create an serving service for the model. Currently, only Ascend 910 is supported.

Starting Serving

After MindSpore is installed using pip, the Serving executable program is stored in /{your python path}/lib/python3.7/site-packages/mindspore/ms_serving.
Run the following command to start Serving:

ms_serving [--help] [--model_path <MODEL_PATH>] [--model_name <MODEL_NAME>]
                  [--port <PORT>] [--device_id <DEVICE_ID>]

Parameters are described as follows:

Parameter	Attribute	Function	Parameter Type	Default Value	Value Range
--help	Optional	Displays the help information about the startup command.	-	-	-
--model_path=<MODEL_PATH>	Mandatory	Path for storing the model to be loaded.	String	Null	-
--model_name=<MODEL_NAME>	Mandatory	Name of the model file to be loaded.	String	Null	-
--=port <PORT>	Optional	Specifies the external Serving port number.	Integer	5500	1–65535
--device_id=<DEVICE_ID>	Optional	Specifies device ID to be used.	Integer	0	0 to 7

Before running the startup command, add the path /{your python path}/lib:/{your python path}/lib/python3.7/site-packages/mindspore/lib to the environment variable LD_LIBRARY_PATH.

Application Example

The following uses a simple network as an example to describe how to use MindSpore Serving.

Exporting Model

Use add_model.py to build a network with only the Add operator and export the MindSpore serving deployment model.

python add_model.py

Execute the script to generate the tensor_add.mindir file. The input of the model is two one-dimensional tensors with shape [2,2], and the output is the sum of the two input tensors.

Starting Serving Inference

ms_serving --model_path={model directory} --model_name=tensor_add.mindir

If the server prints the MS Serving Listening on 0.0.0.0:5500 log, the Serving has loaded the serving model.

Client Samples

Python Client Sample

Obtain ms_client.py and start the Python client.

python ms_client.py

If the following information is displayed, the Serving has correctly executed the serving of the Add network.

ms client received:
[[2. 2.]
 [2. 2.]]

C++ Client Sample

1. Obtain an executable client sample program.

   Download the MindSpore source code. You can use either of the following methods to compile and obtain the client sample program:

   • When MindSpore is compiled using the source code, the Serving C++ client sample program is generated. You can find the ms_client executable program in the build/mindspore/serving/example/cpp_client directory.

   • Independent compilation

     Preinstall gRPC.

     Run the following command in the MindSpore source code path to compile a client sample program:

     cd mindspore/serving/example/cpp_client
     mkdir build && cd build
     cmake -D GRPC_PATH={grpc_install_dir} ..
     make
     

     In the preceding command, {grpc_install_dir} indicates the gRPC installation path. Replace it with the actual gRPC installation path.

2. Start the client.

   Execute ms_client to send an serving request to the Serving.

   ./ms_client --target=localhost:5500
   

   If the following information is displayed, the Serving has correctly executed the serving of the Add network.

   Compute [[1, 2], [3, 4]] + [[1, 2], [3, 4]]
   Add result is 2 4 6 8
   client received: RPC OK
   

The client code consists of the following parts:

1. Implement the client based on MSService::Stub and create a client instance.

   class MSClient {
    public:
     explicit MSClient(std::shared_ptr<Channel> channel) :  stub_(MSService::NewStub(channel)) {}
    private:
     std::unique_ptr<MSService::Stub> stub_;
   };MSClient client(grpc::CreateChannel(target_str, grpc::InsecureChannelCredentials()));
   
   MSClient client(grpc::CreateChannel(target_str, grpc::InsecureChannelCredentials()));
   
   

2. Build the request input parameter Request, output parameter Reply, and gRPC client Context based on the actual network input.

   PredictRequest request;
   PredictReply reply;
   ClientContext context;
   
   //construct tensor
   Tensor data;
   
   //set shape
   TensorShape shape;
   shape.add_dims(4);
   *data.mutable_tensor_shape() = shape;
   
   //set type
   data.set_tensor_type(ms_serving::MS_FLOAT32);
   std::vector<float> input_data{1, 2, 3, 4};
   
   //set datas
   data.set_data(input_data.data(), input_data.size());
   
   //add tensor to request
   *request.add_data() = data;
   *request.add_data() = data;
   

3. Call the gRPC API to communicate with the Serving that has been started, and obtain the return value.

   Status status = stub_->Predict(&context, request, &reply);
   

For details about the complete code, see ms_client.