使用pip安装后会出错

TypeError: Class advice impossible in Python3. Use the @Implementer
class decorator instead.

解决方法：

$ pip uninstall apex
$ git clone https://www.github.com/nvidia/apex
$ cd apex
$ python setup.py install

核心代码：

from apex import amp
model, optimizer = amp.initialize(model, optimizer, opt_level="O1") # “欧一”，不是“零一”
with amp.scale_loss(loss, optimizer) as scaled_loss:
		scaled_loss.backward()

原始训练代码：

import torch
ngpu=2
def traiin():
		model = torch.nn.Linear(D_in, D_out).cuda()
		model = torch.nn.DataParallel(model, device_ids=[i for i in range(ngpu)])
		optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
		for img, label in dataloader:
			out = model(img.half())
			loss = LOSS(out, label)
			loss.backward()
			optimizer.step()
			optimizer.zero_grad()

#此时采用全精度32位来训练

半精度训练：

import torch
ngpu=2
def traiin():
		model = torch.nn.Linear(D_in, D_out).cuda().half()
		model = torch.nn.DataParallel(model, device_ids=[i for i in range(ngpu)])
		optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
		for img, label in dataloader:
			out = model(img.half())
			loss = LOSS(out, label)
			loss.backward()
			optimizer.step()
			optimizer.zero_grad()
#此时采用半精度16位来训练

显存基本可以降低为原来的一半，但训练速度降低，可能原因是，CUDNN只支持float32加速，半精度后，将不能加速

混合精度训练：

import torch
ngpu=2
def train():
		model = torch.nn.Linear(D_in, D_out).cuda()
		optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
		#设置混合精度模式为O1（欧1，不是零1，后面会解释各个模式区别）
		model, optimizer = amp.initialize(model, optimizer, opt_level="O1")
		model = torch.nn.DataParallel(model, device_ids=[i for i in range(ngpu)])
		for img, label in dataloader:
			out = model(img)
			loss = LOSS(out, label)
			#将loss进行缩放，防止溢出
			with amp.scale_loss(loss, optimizer) as scaled_loss:
		    	scaled_loss.backward()
		
			optimizer.step()
			optimizer.zero_grad()
def save_model(self, epoch):
        if self.mixed_precision:
            import apex.amp as amp
            amp_state_dict = amp.state_dict()
        else:
            amp_state_dict = None
        checkpoint = {
            'epoch': epoch,
            'params': self.params,
            'model': self.model.module.state_dict() if self.ngpu > 1 else self.model.state_dict(),
            'optimizer': self.optimizer.state_dict(),
            'amp': amp_state_dict
        }
        torch.save(checkpoint, os.path.join(self.expdir,'model.pt'))

def load_model(self, checkpoint):
	  state_dict = torch.load(checkpoint)
	  self.model.load_state_dict(state_dict['model'])
	
	  if self.mixed_precision:
	      import apex.amp as amp
	      amp.load_state_dict(state_dict['amp'])

注意：
1.模型在amp.initialize前必须加载到GPU上。
2.amp.initialize前不能对模型进行任何分布式操作，如torch.nn.DataParallel必须放在之后。