在Ubuntu MATE 16.04中我尝试使用GPU运行深度学习python示例:
我确实运行了示例代码,
THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python check1.py
但它似乎是使用CPU而不是GPU。这是终端输出的最后一部分:
WARNING (theano.sandbox.cuda): CUDA is installed, but device gpu0 is not available (error: cuda unavailable)
...
Used the cpu
我也尝试运行此代码:
THEANO_FLAGS=device=cuda0 python check1.py
但输出是:
ERROR (theano.sandbox.gpuarray): pygpu was configured but could not be imported
Traceback (most recent call last):
File "/usr/local/lib/python2.7/dist-packages/theano/sandbox/gpuarray/__init__.py", line 20, in <module>
import pygpu
ImportError: No module named pygpu
...
used cpu
我从apt安装了cuda工具包。 这里有(希望)有用的数据:
python --version
Python 2.7.12
g++ -v
gcc version 5.4.0
nvcc --version
Cuda compilation tools, release 7.5, V7.5.17
lspci
NVIDIA Corporation GM107 [GeForce GTX 750 Ti] (rev a2)
nvidia-smi
+------------------------------------------------------+
| NVIDIA-SMI 361.42 Driver Version: 361.42 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 750 Ti Off | 0000:01:00.0 On | N/A |
| 29% 35C P8 1W / 38W | 100MiB / 2044MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 2861 G /usr/lib/xorg/Xorg 90MiB |
+-----------------------------------------------------------------------------+
答案 0 :(得分:8)
最后我解决了! 这个帖子 Ubuntu 16.04, Theano and Cuda
建议添加标志
nvcc.flags=-D_FORCE_INLINES
到命令行,命令行变为:
THEANO_FLAGS=floatX=float32,device=gpu,nvcc.flags=-D_FORCE_INLINES python check1.py
似乎修复了使用glibc 2.23的错误
现在程序正确使用GPU,这是正确的输出:
THEANO_FLAGS=floatX=float32,device=gpu,nvcc.flags=-D_FORCE_INLINES python check1.py
Using gpu device 0: GeForce GTX 750 Ti (CNMeM is disabled, cuDNN not available)
[GpuElemwise{exp,no_inplace}(<CudaNdarrayType(float32, vector)>), HostFromGpu(GpuElemwise{exp,no_inplace}.0)]
Looping 1000 times took 0.317012 seconds
Result is [ 1.23178029 1.61879349 1.52278066 ..., 2.20771813 2.29967761
1.62323296]
Used the gpu
请注意,在尝试此解决方案之前,我删除了nvidia-cuda-toolkit并从Nvidia网站安装了CUDA,遵循此处的部分说明:
这正是我所做的:
1)我从这里下载了CUDA CUDA 7.5 download 选择LINUX,x86_64,Ubuntu 15.04,deb local
2)我安装了deb文件
dpkg -i cuda_repo-ubuntu1504-7-5-local_7.5-18_amd64.deb
3)然后运行
apt-get update
这会产生一些错误!我修复了用\ n以下行覆盖\ var \ cuda-repo-7.5-local中的文件Release:
Origin: NVIDIA
Label: NVIDIA CUDA
Architecture: repogenstagetemp
MD5Sum:
51483bc34577facd49f0fbc8c396aea0 75379 Packages
4ef963dfa4276be01db8e7bf7d8a4f12 21448 Packages.gz
SHA256:
532b1bb3b392b9083de4445dab2639b36865d7df1f610aeef8961a3c6f304d8a 75379 Packages
2e48cc13b6cc5856c9c6f628c6fe8088ef62ed664e9e0046fc72819269f7432c 21448 Packages.gz
(抱歉,我不记得我在哪里读到这个解决方案)。
4)我成功地跑了
apt-get-update
apt-get install cuda
5)所有内容都被打包在\ usr \ local \ cuda-7.5
中6)我评论了文件\ usr \ local \ cuda-7.5 \ include \ host-config.h
中的第n 115行 #if __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ > 9)
//#error -- unsupported GNU version! gcc versions later than 4.9 are not supported!
#endif /* __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ > 9) */
这似乎阻止了CUDA使用gcc 5.4 完成所有这些操作后,我更新了.theanorc文件,添加了cuda root
[cuda]
root = /usr/local/cuda-7.5
全部:)
PS:我不知道即使使用nvidia-cuda-toolkit它是否也能正常工作!
答案 1 :(得分:0)
在我的系统中,只需重新启动系统即可解决此问题。也许你可以尝试一下。
答案 2 :(得分:0)
我修复了这个问题,将cuda路径添加到〜/ .bashrc,如下所示,
export LD_LIBRARY_PATH=/usr/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH