PaddlePaddle

 paddle.distributed / spawn


spawn

paddle.distributed. spawn ( func, args=(), nprocs=- 1, join=True, daemon=False, **options ) [源代码]

使用 spawn 方法启动多进程任务。

注解

spawn 目前仅支持 GPU collective模式.

参数

  • func (function) - 由 spawn 方法启动的进程所调用的目标函数。该目标函数需要能够被 pickled (序列化),所以目标函数必须定义为模块的一级函数,不能是内部子函数或者类方法。

  • args (tuple, 可选) - 传入目标函数 func 的参数。

  • nprocs (int, 可选) - 启动进程的数目。默认值为-1。当 nproc 为-1时,模型执行时将会从环境变量中获取当前可用的所有设备进行使用:如果使用GPU执行任务,将会从环境变量 CUDA_VISIBLE_DEVICES 中获取当前所有可用的设备ID;如果使用CPU执行任务,将会从环境变量 CPU_NUM 中获取当前可用的CPU设备数,例如,可以通过指令 export CPU_NUM=4 配置默认可用CPU设备数,如果此环境变量没有设置,将会默认设置该环境变量的值为1。

  • join (bool, 可选) - 对所有启动的进程执行阻塞的 join ,等待进程执行结束。默认为True。

  • daemon (bool, 可选) - 配置启动进程的 daemon 属性。默认为False。

  • **options (dict, 可选) - 其他初始化并行执行环境的配置选项。目前支持以下选项: (1) start_method (string) - 启动子进程的方法。进程的启动方法可以是 spawnfork , forkserver 。 因为CUDA运行时环境不支持 fork 方法,当在子进程中使用CUDA时,需要使用 spawn 或者 forkserver 方法启动进程。默认方法为 spawn ; (2) gpus (string) - 指定训练使用的GPU ID, 例如 "0,1,2,3" , 默认值为None ; (3) ips (string) - 运行集群的节点(机器)IP,例如 "192.168.0.16,192.168.0.17" ,默认值为 "127.0.0.1" 。

返回

MultiprocessContext 对象,持有创建的多个进程。

代码示例

from __future__ import print_function

import paddle
import paddle.nn as nn
import paddle.optimizer as opt
import paddle.distributed as dist

class LinearNet(nn.Layer):
    def __init__(self):
        super(LinearNet, self).__init__()
        self._linear1 = nn.Linear(10, 10)
        self._linear2 = nn.Linear(10, 1)

    def forward(self, x):
        return self._linear2(self._linear1(x))

def train(print_result=False):
    # 1. initialize parallel environment
    dist.init_parallel_env()

    # 2. create data parallel layer & optimizer
    layer = LinearNet()
    dp_layer = paddle.DataParallel(layer)

    loss_fn = nn.MSELoss()
    adam = opt.Adam(
        learning_rate=0.001, parameters=dp_layer.parameters())

    # 3. run layer
    inputs = paddle.randn([10, 10], 'float32')
    outputs = dp_layer(inputs)
    labels = paddle.randn([10, 1], 'float32')
    loss = loss_fn(outputs, labels)

    if print_result is True:
        print("loss:", loss.numpy())

    loss.backward()

    adam.step()
    adam.clear_grad()

# Usage 1: only pass function.
# If your training method no need any argument, and
# use all visible devices for parallel training.
if __name__ == '__main__':
    dist.spawn(train)

# Usage 2: pass function and arguments.
# If your training method need some arguments, and
# use all visible devices for parallel training.
if __name__ == '__main__':
    dist.spawn(train, args=(True,))

# Usage 3: pass function, arguments and nprocs.
# If your training method need some arguments, and
# only use part of visible devices for parallel training.
# If your machine hold 8 cards {0,1,2,3,4,5,6,7},
# this case will use cards {0,1}; If you set
# CUDA_VISIBLE_DEVICES=4,5,6,7, this case will use
# cards {4,5}
if __name__ == '__main__':
    dist.spawn(train, args=(True,), nprocs=2)

# Usage 4: pass function, arguments, nprocs and gpus.
# If your training method need some arguments, and
# only use part of visible devices for parallel training,
# but you can't set your machine's environment variable
# CUDA_VISIBLE_DEVICES, such as it is None or all cards
# {0,1,2,3,4,5,6,7}, you can pass `gpus` to
# select the GPU cards you want to use. For example,
# this case will use cards {4,5} if your machine hold 8 cards.
if __name__ == '__main__':
    dist.spawn(train, args=(True,), nprocs=2, gpus='4,5')

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