api.py

#!/usr/bin/env python
# coding:utf8

import ctypes
from .utils import Loader
import numpy as np


mv_lib = Loader.get_lib()


def init(sync=False, updater=None):
    '''Initialize mutliverso.

    This should be called only once before training at the beginning of the
    whole project.
    If sync is True, a sync server will be created. Otherwise an async server
    will be created.
    If a sync server is created, you **must** make sure every process call
    `add` and `get` in the same order and for the same times. Otherwise some
    processes will be blocked. In sync server mode, all `get` method will
    return **exactly the same results**.
    If a async server is created, there won't be limitations like a sync
    server. But we can't make sure `get` method will return the same results.
    If you want to get the same results in async server mode, you should use
    `barrier` and `get` with the argument `sync` set to `True` to sync the
    processes.
    '''
    args = [b""]  # the first argument will be ignored. So we put a placeholder here
    if sync:
        args.append(b"-sync=true")
    if updater:
        args.append(b"-updater_type="+updater)
    n = len(args)
    args_type = ctypes.c_char_p * n
    mv_lib.MV_Init(ctypes.pointer(ctypes.c_int(n)), args_type(*[ctypes.c_char_p(arg) for arg in args]))


def shutdown():
    '''Set a barrier for all workers to wait.

    Workers will wait until all workers reach a specific barrier.
    '''
    mv_lib.MV_ShutDown()


def barrier():
    '''Shutdown multiverso.

    This should be called only once after finishing training at the end of the
    whole project.
    '''
    mv_lib.MV_Barrier()


def workers_num():
    '''Return the total number of workers.'''
    return mv_lib.MV_NumWorkers()


def worker_id():
    '''Return the id (zero-based index) for current worker.'''
    return mv_lib.MV_WorkerId()


def server_id():
    return mv_lib.MV_ServerId()


def is_master_worker():
    '''If the worker is master worker

    Some things only need one worker process, such as validation, outputing the
    result, initializing the parameters and so on. So we mark the worker 0 as
    the master worker to finish these things.
    '''
    return worker_id() == 0