为什么floatX的标志会影响是否在Theano中使用GPU?

时间:2016-03-14 21:32:53

标签: python gpu theano

我正在使用script provided in the tutorial for that purpose

使用GPU测试Theano
# Start gpu_test.py
# From http://deeplearning.net/software/theano/tutorial/using_gpu.html#using-gpu
from theano import function, config, shared, sandbox
import theano.tensor as T
import numpy
import time

vlen = 10 * 30 * 768  # 10 x #cores x # threads per core
iters = 1000

rng = numpy.random.RandomState(22)
x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
f = function([], T.exp(x))
print(f.maker.fgraph.toposort())
t0 = time.time()
for i in xrange(iters):
    r = f()
t1 = time.time()
print("Looping %d times took %f seconds" % (iters, t1 - t0))
print("Result is %s" % (r,))
if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):
    print('Used the cpu')
else:
    print('Used the gpu')
# End gpu_test.py

如果我指定floatX=float32,则它在GPU上运行:

francky@here:/fun$ THEANO_FLAGS='mode=FAST_RUN,device=gpu2,floatX=float32' python gpu_test.py
Using gpu device 2: GeForce GTX TITAN X (CNMeM is disabled)
[GpuElemwise{exp,no_inplace}(<CudaNdarrayType(float32, vector)>), HostFromGpu(Gp
Looping 1000 times took 1.458473 seconds
Result is [ 1.23178029  1.61879349  1.52278066 ...,  2.20771813  2.29967761
  1.62323296]
Used the gpu

如果我没有指定floatX=float32,它将在CPU上运行:

francky@here:/fun$ THEANO_FLAGS='mode=FAST_RUN,device=gpu2'
Using gpu device 2: GeForce GTX TITAN X (CNMeM is disabled)
[Elemwise{exp,no_inplace}(<TensorType(float64, vector)>)]
Looping 1000 times took 3.086261 seconds
Result is [ 1.23178032  1.61879341  1.52278065 ...,  2.20771815  2.29967753
  1.62323285]
Used the cpu

如果我指定floatX=float64,它将在CPU上运行:

francky@here:/fun$ THEANO_FLAGS='mode=FAST_RUN,device=gpu2,floatX=float64' python gpu_test.py
Using gpu device 2: GeForce GTX TITAN X (CNMeM is disabled)
[Elemwise{exp,no_inplace}(<TensorType(float64, vector)>)]
Looping 1000 times took 3.148040 seconds
Result is [ 1.23178032  1.61879341  1.52278065 ...,  2.20771815  2.29967753
  1.62323285]
Used the cpu

为什么floatX标志会影响Theano中是否使用GPU?

我用:

  • Theano 0.7.0(根据pip freeze),
  • Python 2.7.6 64位(根据import platform; platform.architecture()),
  • Nvidia-smi 361.28(根据nvidia-smi),
  • CUDA 7.5.17(根据nvcc --version),
  • GeForce GTX Titan X(根据nvidia-smi),
  • Ubuntu 14.04.4 LTS x64(根据lsb_release -auname -i)。

我阅读floatX上的文档,但它没有帮助。它只是说:

  

config.floatX
字符串值:'float64'或'float32'
  默认值:'float64'

     

这设置了tensor.matrix()返回的默认dtype,   tensor.vector()和类似的函数。它还设置默认值   作为Python浮点传递的参数的theano位宽   号。

2 个答案:

答案 0 :(得分:2)

http://deeplearning.net/software/theano/tutorial/using_gpu.html#gpuarray-backend我读到可以在GPU上执行float64计算,但您必须从源代码安装libgpuarray

我设法安装它,请参阅this script,我使用virtualenv,您甚至不必拥有sudo

安装完成后,您可以将旧后端与config flag device=gpu一起使用,将新后端与device=cuda一起使用。

新的后端可以执行64位计算,但对我来说它的工作方式不同。一些操作停止了工作。 ABSOLUTELY NO WARRANTY, to the extent permitted by applicable law:)

答案 1 :(得分:1)

据我所知,这是因为他们还没有为GPU实现float64。

http://deeplearning.net/software/theano/tutorial/using_gpu.html

  

只能加速使用float32数据类型的计算。预计即将推出的硬件对float64提供更好的支持,但float64计算仍然相对较慢(2010年1月)。