如何在Ubuntu 14.04 x64上安装Theano,并配置它以便它使用GPU？

Question

我尝试按照关于在当前Ubuntu上轻松安装优化Theano的说明但它不起作用:每当我使用GPU运行Theano脚本时,它都会给出错误消息:

已安装CUDA,但设备gpu不可用(错误:无法获取可用的gpus数量:未检测到支持CUDA的设备)

---

更具体地说,按照链接网页中的说明,我执行了以下步骤:

# Install Theano
sudo apt-get install python-numpy python-scipy python-dev python-pip python-nose g++ libopenblas-dev git
sudo pip install Theano

# Install Nvidia drivers and CUDA
sudo apt-get install nvidia-current
sudo apt-get install nvidia-cuda-toolkit

然后我重新启动并尝试运行: 

THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python gpu_test.py # gpu_test.py comes from http://deeplearning.net/software/theano/tutorial/using_gpu.html

但我得到:

f@f-Aurora-R4:~$ THEANO_FLAGS=’mode=FAST_RUN,device=gpu,floatX=float32,cuda.root=/usr/lib/nvidia-cuda-toolkit’ python gpu_test.py WARNING (theano.sandbox.cuda): CUDA is installed, but device gpu is not available (error: Unable to get the number of gpus available: no CUDA-capable device is detected) [Elemwise{exp,no_inplace}(<TensorType(float32, vector)>)] Looping 1000 times took 2.199992 seconds Result is [ 1.23178029 1.61879337 1.52278066 ..., 2.20771813 2.29967761 1.62323284] Used the cpu

Answer 1

(我在Ubuntu 14.04.4 LTS x64和Kubuntu 14.04.4 LTS x64上测试了以下内容,我猜它应该适用于大多数Ubuntu变种)

安装Theano并配置GPU(CUDA)

官方网站上的说明已过时.相反,您可以使用以下说明(假设新安装的Kubuntu 14.04 LTS x64):

# Install Theano
sudo apt-get install python-numpy python-scipy python-dev python-pip python-nose g++ libopenblas-dev git
sudo pip install Theano

# Install Nvidia drivers, CUDA and CUDA toolkit, following some instructions from http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html
wget http://developer.download.nvidia.com/compute/cuda/7.5/Prod/local_installers/cuda-repo-ubuntu1404-7-5-local_7.5-18_amd64.deb # Got the link at https://developer.nvidia.com/cuda-downloads
sudo dpkg -i cuda-repo-ubuntu1404-7-5-local_7.5-18_amd64.deb
sudo apt-get update
sudo apt-get install cuda

sudo reboot

此时,运行nvidia-smi应该工作,但运行nvcc不起作用.

# Execute in console, or (add in ~/.bash_profile then run "source ~/.bash_profile"):
export PATH=/usr/local/cuda-7.5/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib64:$LD_LIBRARY_PATH

在这一点上,两者nvidia-smi并nvcc应工作.

要测试Theano是否能够使用GPU:

复制粘贴以下内容gpu_test.py:

# 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

并运行它:

THEANO_FLAGS='mode=FAST_RUN,device=gpu,floatX=float32' python gpu_test.py

哪个应该返回:

f@f-Aurora-R4:~$ THEANO_FLAGS='mode=FAST_RUN,device=gpu,floatX=float32' python gpu_test.py
Using gpu device 0: GeForce GTX 690
[GpuElemwise{exp,no_inplace}(<CudaNdarrayType(float32, vector)>), HostFromGpu(GpuElemwise{exp,no_inplace}.0)]
Looping 1000 times took 0.658292 seconds
Result is [ 1.23178029  1.61879349  1.52278066 ...,  2.20771813  2.29967761
  1.62323296]
Used the gpu

要了解您的CUDA版本:

?nvcc -V

例:

username@server:~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2015 NVIDIA Corporation
Built on Tue_Aug_11_14:27:32_CDT_2015
Cuda compilation tools, release 7.5, V7.5.17

添加cuDNN

要添加cuDNN(来自http://deeplearning.net/software/theano/library/sandbox/cuda/dnn.html的说明):

1. 从https://developer.nvidia.com/rdp/cudnn-download下载cuDNN (需要注册,免费)
2. tar -xvf cudnn-7.0-linux-x64-v3.0-prod.tgz
3. 执行以下操作之一

选项1:复制*.h文件CUDA_ROOT/include和*.so*文件CUDA_ROOT/lib64(默认情况下,CUDA_ROOT是/usr/local/cuda在Linux上).

sudo cp cuda/lib64/* /usr/local/cuda/lib64/
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/

选项2:

export LD_LIBRARY_PATH=/home/user/path_to_CUDNN_folder/lib64:$LD_LIBRARY_PATH
export CPATH=/home/user/path_to_CUDNN_folder/include:$CPATH
export LIBRARY_PATH=/home/user/path_to_CUDNN_folder/lib64:$LD_LIBRARY_PATH

默认情况下,Theano将检测它是否可以使用cuDNN.如果是这样,它将使用它.如果没有,Theano优化将不会引入cuDNN操作.因此,如果用户没有手动引入它们,Theano仍然可以工作.

要在Theano无法使用cuDNN时出现错误,请使用此Theano标志:optimizer_including=cudnn.

例:

THEANO_FLAGS='mode=FAST_RUN,device=gpu,floatX=float32,optimizer_including=cudnn' python gpu_test.py

要了解您的cuDNN版本:

cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2

添加CNMeM

该CNMeM库是一个"简单的库,以帮助深学习框架管理CUDA记忆."

# Build CNMeM without the unit tests
git clone https://github.com/NVIDIA/cnmem.git cnmem
cd cnmem
mkdir build
cd build
sudo apt-get install -y cmake
cmake ..
make

# Copy files to proper location
sudo cp ../include/cnmem.h /usr/local/cuda/include
sudo cp *.so /usr/local/cuda/lib64/
cd ../..

要与Theano一起使用,您需要添加lib.cnmem标志.例:

THEANO_FLAGS='mode=FAST_RUN,device=gpu,floatX=float32,lib.cnmem=0.8,optimizer_including=cudnn' python gpu_test.py

脚本的第一个输出应该是:

Using gpu device 0: GeForce GTX TITAN X (CNMeM is enabled with initial size: 80.0% of memory, cuDNN 5005)

lib.cnmem=0.8 意味着它可以使用高达80%的GPU.

据报道,CNMeM提供了一些有趣的速度提升,并得到了Theano,Torch和Caffee的支持.

Theano - 来源1:

加速取决于许多因素,如形状和模型本身.速度从0加快到2倍.

Theano - 来源2:

如果你不改变Theano标志allow_gc,你可以期望GPU加速20%.在某些情况下(小型号),我们看到了50%的加速.

在多个CPU核心上运行Theano

作为旁注,您可以使用OMP_NUM_THREADS=[number_of_cpu_cores]标志在多个CPU核心上运行Theano .例:

OMP_NUM_THREADS=4 python gpu_test.py 

该脚本theano/misc/check_blas.py输出有关使用哪个BLAS的信息:

cd [theano_git_directory]
OMP_NUM_THREADS=4 python theano/misc/check_blas.py

要运行Theano的测试套件:

nosetests theano

要么

sudo pip install nose-parameterized
import theano
theano.test()

常见问题:

• 导入theano:AttributeError:'module'对象没有属性'find_graphviz'