如何在 Ubuntu 16.04 上将 Theano 与 GPU 一起使用?

How can I use Theano with GPU on Ubuntu 16.04?

我使用以下脚本来测试 GPU 是否正常工作:

#!/usr/bin/env python
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' % iters, t1 - t0, 'seconds'
print 'Result is', 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')

当我 运行 它时,我得到:

http://pastebin.com/wM9jaGMF

有趣的部分在最后:

ERROR (theano.sandbox.cuda): Failed to compile cuda_ndarray.cu: ('nvcc return status', 1, 'for cmd', 'nvcc -shared -O3 -m64 -Xcompiler -DCUDA_NDARRAY_CUH=c72d035fdf91890f3b36710688069b2e,-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION,-fPIC,-fvisibility=hidden -Xlinker -rpath,/home/moose/.theano/compiledir_Linux-4.4--generic-x86_64-with-Ubuntu-16.04-xenial-x86_64-2.7.11+-64/cuda_ndarray -I/home/moose/.local/lib/python2.7/site-packages/theano/sandbox/cuda -I/usr/lib/python2.7/dist-packages/numpy/core/include -I/usr/include/python2.7 -I/home/moose/.local/lib/python2.7/site-packages/theano/gof -o /home/moose/.theano/compiledir_Linux-4.4--generic-x86_64-with-Ubuntu-16.04-xenial-x86_64-2.7.11+-64/cuda_ndarray/cuda_ndarray.so mod.cu -L/usr/lib -lcublas -lpython2.7 -lcudart')
WARNING (theano.sandbox.cuda): CUDA is installed, but device gpu is not available  (error: cuda unavailable)

我的系统

我的~/.theanorc

[global]
exception_verbosity=high
device=gpu
floatX=float32

[cuda]
root=/usr/bin/

路径

我认为从标准存储库安装可能与手动安装有所不同。以下是一些可能会发现一些问题的路径:

/usr/bin/nvcc
/usr/lib/x86_64-linux-gnu/libcuda.so
/usr/lib/x86_64-linux-gnu/libcudart.so
/usr/lib/nvidia-cuda-toolkit
/usr/include/cudnn.h

问题

我怎样才能让它发挥作用?

我不太确定是什么解决了这个问题,但是以下一项或两项 (source)

sudo apt-get install python-numpy python-scipy python-dev python-pip python-nose g++ libopenblas-dev libblas-dev git
echo -e "\n[nvcc]\nflags=-D_FORCE_INLINES\n" >> ~/.theanorc

我在这里写了一个更笼统的答案,以防其他人发现自己处于类似情况。 首先看到安装了theano依赖,说明here. you should install nvidia driver as described here,用sudo ubuntu-drivers devices判断推荐使用哪个驱动,用sudo apt-get install nvidia-xxx安装(此时xxx=375)。然后通过打开 "additional drivers" window(从终端 software-properties-gtk --open-tab=4)看到正在使用 nvidia 驱动程序。设置一个 ~/.theanorc 文本文件如下:

[global]
exception_verbosity=high
device=gpu
floatX=float32

[cuda]
root=/usr/bin/

[nvcc]
flags=-D_FORCE_INLINES

[lib]
cnmem = 1

[lib] 部分不是必需的,但在我的笔记本电脑上,将其添加到 .theanorc 后性能大约快 2 倍。