# Design Doc: The Client Library of Parameter Server For an overview of trainer's role, please refer to [distributed training design doc](README.md). In this design doc, we will discuss the parameter server's client library, which will manage communication with parameter servers. The library will be implemented in [Go](https://golang.org/) and made available as a static or dynamic library with a C header file. ## C Interface ```c #define PADDLE_ELEMENT_TYPE_INT32 0 #define PADDLE_ELEMENT_TYPE_UINT32 1 #define PADDLE_ELEMENT_TYPE_INT64 2 #define PADDLE_ELEMENT_TYPE_UINT64 3 #define PADDLE_ELEMENT_TYPE_FLOAT32 4 #define PADDLE_ELEMENT_TYPE_FLOAT64 5 typedef struct { char* name; int element_type; void* content; int content_len; } paddle_parameter, paddle_gradient; typedef struct paddle_pserver_client paddle_pserver_client; paddle_pserver_client* paddle_new_pserver_client(); void paddle_pserver_client_release(paddle_pserver_client* client); /** * @brief paddle_begin_init_params begins to initialize parameters on * parameter servers. * * paddle_begin_init_params will be called from multiple trainers, * only one trainer will be selected to initialize the parameters on * parameter servers. Other trainers will be blocked until the * initialization is done, and they need to get the initialized * parameters from parameter servers using @paddle_get_params. * * @param config_proto serialized parameter server configuration in * Protocol Buffers format. * @return 1 if the trainer is selected to initialize parameter * servers, otherwise 0. */ int paddle_begin_init_params(paddle_pserver_client* client, const char* config_proto); /** * @brief paddle_init_param initializes the parameter on parameter * servers. * * @param param the parameter to initialize. * @return 0 if successful, otherwise -1. On failure, the trainer * needs to restart the entire initialization process (starting from * @paddle_begin_init_param). Or simply exit the program and wait for * the cluster management system to restart the trainer. */ int paddle_init_param(paddle_pserver_client* client, paddle_parameter params); /** * @brief paddle_finish_init_params tells parameter servers client has * sent all parameters to parameter servers as initialization. * * @return 0 if successful, otherwise -1. On failure, the trainer * needs to restart the entire initialization process (starting from * @paddle_begin_init_param). Or simply exit the program and wait for * the cluster management system to restart the trainer. */ int paddle_finish_init_params(paddle_pserver_client* client); /** * @brief paddle_send_grads sends gradients to parameter servers for * updating parameters. * * @param grads the array of gradients to send. * @param total the total number of gradient inside the gradient array. * @param learning_rate the learning rate for the gradients. * @return 0 if successful, otherwise -1. */ int paddle_send_grads(paddle_pserver_client* client, const paddle_gradient* grads, int total, double learning_rate); /** * @brief paddle_set_params sets parameters to parameter servers. * * @param params the array of parameters to set to parameter servers. * @param total the total number of parameters inside the parameter * array. * @return 0 if successful, otherwise -1. */ int paddle_set_params(paddle_pserver_client* client, const paddle_parameter* params, int total); /** * @brief paddle_get_params gets parameters from parameter servers. * * @param names the array of names of the parameters to get. * @param dst the destination array of parameters to save to. * @param total the total number of parameters to get. * @return 0 if successful, otherwise -1. */ int paddle_get_params(paddle_pserver_client* client, const char** names, paddle_parameter* dst, int total); /** * @brief paddle_save_model indicates parameters to save the parameter * to the given path * * @param path the path to save parameters. * @return 0 if successful, otherwise -1. */ int paddle_save_model(paddle_pserver_client* client, const char* path); ```