ubuntu - 如何在 Ubuntu 14.04 x64 上安装 Theano，并配置它以使用 GPU？

Question

我尝试按照 Easy Installation of an Optimized Theano on Current Ubuntu 上的说明进行操作但它不起作用:每当我使用 GPU 运行 Theano 脚本时，它都会给我错误消息:

CUDA is installed, but device gpu is not available (error: Unable to get the number of gpus available: no CUDA-capable device is detected)

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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

但是我得到:

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，我想它应该适用于大多数 Ubuntu 变体)

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(假设是全新安装的 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 应该可以运行，但运行 nvcc 将无法运行。

# 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

此时，nvidia-smi 和 nvcc 都应该可以工作了。

测试Theano是否能够使用GPU:

将以下内容复制粘贴到 gpu_test.py 中:

# 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

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

了解您的 CUDA 版本:

​nvcc -V

例子:

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:将 *.h 文件复制到 CUDA_ROOT/include 并将 *.so* 文件复制到 CUDA_ROOT/lib64(默认情况下，CUDA_ROOT 在 Linux 上是 /usr/local/cuda)。

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 会检测它是否可以使用 cuDNN。如果是这样，它将使用它。如果不是，Theano 优化将不会引入 cuDNN 操作。所以如果用户没有手动引入它们，Theano 仍然可以工作。

如果 Theano 不能使用 cuDNN，要得到一个错误，使用这个 Theano 标志:optimizer_including=cudnn。

例子:

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

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添加CNMeM

CNMeM library是一个“帮助深度学习框架管理 CUDA 内存的简单库”。

# 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。示例:

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 表示它可以使用高达 80% 的 GPU。

据报道，CNMeM 提供了一些有趣的速度改进，并得到了 Theano、Torch 和 Caffee 的支持。

Theano - source 1 :

The speed up depend of many factor, like the shapes and the model itself. The speed up go from 0 to 2x faster.

Theano - source 2 :

If you don't change the Theano flag allow_gc, you can expect 20% speed up on the GPU. In some case (small models), we saw a 50% speed up.

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在多个 CPU 内核上运行 Theano

附带说明一下，您可以使用 OMP_NUM_THREADS=[number_of_cpu_cores] flag 在多个 CPU 内核上运行 Theano。 .示例:

OMP_NUM_THREADS=4 python gpu_test.py 

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

---

运行 Theano 的测试套件:

nosetests theano

或

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

常见问题:

• > Importing theano: AttributeError: 'module' object has no attribute 'find_graphviz'