如何处理火炬中的GPU内存泄漏问题？

Question

我的机器的GPU有2GB的内存。当我第一次运行以下代码时，不会出现错误。但是，当我第二次运行代码时，我会得到内存错误。作为一种短期的补救措施，我唯一能做的就是使用torch.Tensor.float()将数据转换到torch.Tensor.float()中。但是，问题仍然存在，进程完成后没有释放占用的内存，或者进程在运行时终止。这也是机器RAM的情况。如何防止火炬中的记忆泄漏或释放记忆？

require 'nn'
require 'image'
require 'cunn'
require 'paths'



collectgarbage(); collectgarbage()
if (not paths.filep("cifar10torchsmall.zip")) then
    os.execute('wget -c https://s3.amazonaws.com/torch7/data/cifar10torchsmall.zip')
    os.execute('unzip cifar10torchsmall.zip')
end
trainset = torch.load('cifar10-train.t7')
testset = torch.load('cifar10-test.t7')
classes = {'airplane', 'automobile', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck'}

setmetatable(trainset, 
    {__index = function(t, i) 
                    return {t.data[i], t.label[i]} 
                end}
);
trainset.data = trainset.data:double() -- convert the data from a ByteTensor to a DoubleTensor.

function trainset:size() 
    return self.data:size(1) 
end

mean = {} -- store the mean, to normalize the test set in the future
stdv  = {} -- store the standard-deviation for the future
for i=1,3 do -- over each image channel
    mean[i] = trainset.data[{ {}, {i}, {}, {}  }]:mean() -- mean estimation
    print('Channel ' .. i .. ', Mean: ' .. mean[i])
    trainset.data[{ {}, {i}, {}, {}  }]:add(-mean[i]) -- mean subtraction

    stdv[i] = trainset.data[{ {}, {i}, {}, {}  }]:std() -- std estimation
    print('Channel ' .. i .. ', Standard Deviation: ' .. stdv[i])
    trainset.data[{ {}, {i}, {}, {}  }]:div(stdv[i]) -- std scaling
end


testset.data = testset.data:double()   -- convert from Byte tensor to Double tensor
for i=1,3 do -- over each image channel
    testset.data[{ {}, {i}, {}, {}  }]:add(-mean[i]) -- mean subtraction    
    testset.data[{ {}, {i}, {}, {}  }]:div(stdv[i]) -- std scaling
end

trainset.data = trainset.data:cuda()
testset.data = testset.data:cuda()

net = nn.Sequential()
net:add(nn.SpatialConvolution(3, 6, 5, 5)) -- 3 input image channels, 6 output channels, 5x5 convolution kernel
net:add(nn.ReLU())                       -- non-linearity 
net:add(nn.SpatialMaxPooling(2,2,2,2))     -- A max-pooling operation that looks at 2x2 windows and finds the max.
net:add(nn.SpatialConvolution(6, 16, 5, 5))
net:add(nn.ReLU())                       -- non-linearity 
net:add(nn.SpatialMaxPooling(2,2,2,2))
net:add(nn.View(16*5*5))                    -- reshapes from a 3D tensor of 16x5x5 into 1D tensor of 16*5*5
net:add(nn.Linear(16*5*5, 120))             -- fully connected layer (matrix multiplication between input and weights)
net:add(nn.ReLU())                       -- non-linearity 
net:add(nn.Linear(120, 84))
net:add(nn.ReLU())                       -- non-linearity 
net:add(nn.Linear(84, 10))                   -- 10 is the number of outputs of the network (in this case, 10 digits)
net:add(nn.LogSoftMax())  
net = net:cuda()

criterion = nn.ClassNLLCriterion()
criterion = criterion:cuda()



pred = net:forward(trainset.data)
outputEr = criterion:forward(pred, trainset.label:cuda())
net:zeroGradParameters()
outputGrad = criterion:backward(pred, trainset.label:cuda())
collectgarbage()
inputGrad = net:backward(trainset.data, outputGrad)

附带问题:为什么火炬将网络参数初始化为双倍，尽管GPU在计算双精度操作方面非常慢，而且几乎所有的神经网络应用程序都不需要64位参数值？如何使用浮动(32位)参数向量初始化模型？

我找到了这个问题的答案。您可以在代码开始时使用以下代码轻松地将torch的默认数据类型设置为浮动：

torch.setdefaulttensortype('torch.FloatTensor')

Answer 1

我可以解决这个问题(几乎)从CUDA 6.5升级到CUDA 7.5在机器上，我正在做上述实验。现在，在大多数情况下，当程序崩溃时，运行GPU内存将被释放。然而，有时它不会发生，我不得不重新启动机器。

此外，为了确保程序在程序成功运行时清除GPU内存，我将执行以下操作：

net = nil
trainset = nil
testset = nil
pred = nil
inputGrad = nil
criterion = nil

collectgarbage()