Question

我的机器的GPU有2 GB的内存。当我第一次运行以下代码时，我没有错误。但是，第二次运行代码时出现内存错误。作为一种短时间的补救措施，我唯一能做的就是使用cmake -DSDL_ROOT=path/to/sdl将数据转换为float32。但是，问题仍然存在，并且在完成该过程后未释放占用的内存，或者在运行时终止该过程。这也是机器RAM的情况。如何防止Torch中的内存泄漏或释放内存？

torch.Tensor.float()

附带问题：为什么Torch会将网络参数初始化为double，尽管GPU在计算双精度运算时速度很慢，而且几乎所有神经网络应用程序实际上都不需要64位参数值？如何使用float（32位）参数向量初始化模型？

我找到了问题的答案。您可以使用代码开头的以下内容轻松地将火炬的默认数据类型设置为浮点数：

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)

Answer 1

我可以通过在我正在进行上述实验的机器上从CUDA 6.5升级到CUDA 7.5来解决这个问题（差不多）。现在，大多数时候程序在运行GPU内存时崩溃了。但是，有时它仍然没有发生，我必须重新启动机器。

此外，我会执行以下操作以确保程序在程序成功运行时清除GPU内存：

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

collectgarbage()

如何处理Torch中的GPU内存泄漏问题？

1 个答案: