我正在使用 Ubuntu 16.04，我有一块稍旧的 Nvidia 9600 GT 显卡。它启用了 CUDA（1.1 计算能力），虽然是旧版。我试图在使用 Keras 时利用它，为此我遵循了本指南安装 CUDA 和这个安装 cuDNN。我的显卡驱动程序是 304.104 版本，支持我的显卡的最后一个 CUDA 版本是 6.5。安装后，我在控制台中输入以下内容进行验证：
$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2014 NVIDIA Corporation
Built on Thu_Jul_17_21:41:27_CDT_2014
Cuda compilation tools, release 6.5, V6.5.12
$ nvidia-smi
Fri Dec 22 23:02:08 2017
+------------------------------------------------------+
| NVIDIA-SMI 340.104 Driver Version: 340.104 |
|-------------------------------+----------------------+----------------------+
| 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 9600 GT Off | 0000:01:00.0 N/A | N/A |
| 40% 46C P0 N/A / N/A | 77MiB / 1023MiB | N/A Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Compute processes: GPU Memory |
| GPU PID Process name Usage |
|=============================================================================|
| 0 Not Supported |
+-----------------------------------------------------------------------------+
样本编译也deviceQuery成功了：
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce 9600 GT"
CUDA Driver Version / Runtime Version 6.5 / 6.5
CUDA Capability Major/Minor version number: 1.1
Total amount of global memory: 1024 MBytes (1073414144 bytes)
( 8) Multiprocessors, ( 8) CUDA Cores/MP: 64 CUDA Cores
GPU Clock rate: 1625 MHz (1.62 GHz)
Memory Clock rate: 400 Mhz
Memory Bus Width: 256-bit
Maximum Texture Dimension Size (x,y,z) 1D=(8192), 2D=(65536, 32768), 3D=(2048, 2048, 2048)
Maximum Layered 1D Texture Size, (num) layers 1D=(8192), 512 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(8192, 8192), 512 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 16384 bytes
Total number of registers available per block: 8192
Warp size: 32
Maximum number of threads per multiprocessor: 768
Maximum number of threads per block: 512
Max dimension size of a thread block (x,y,z): (512, 512, 64)
Max dimension size of a grid size (x,y,z): (65535, 65535, 1)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 256 bytes
Concurrent copy and kernel execution: Yes with 1 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): No
Device PCI Bus ID / PCI location ID: 1 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 6.5, CUDA Runtime Version = 6.5, NumDevs = 1, Device0 = GeForce 9600 GT
Result = PASS
然后我跟着此推荐的安装方法和本文档安装 Theano，因为与 TensorFlow 相比，它能够在我的显卡上运行。我创建了.theanorc文件
[global]
device = cuda0
floatX = float32
[cuda]
root=/usr/local/cuda-6.5/bin/
[dnn]
include_path = /usr/local/cuda-6.5/include/
library_path = /usr/local/cuda-6.5/lib64/
我还将适当的变量导出到.profile：
export PATH=/usr/local/cuda-6.5/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-6.5/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
但是当我尝试运行这个简单的测试脚本时：
from theano import function, config, shared, tensor
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([], tensor.exp(x))
print(f.maker.fgraph.toposort())
t0 = time.time()
for i in range(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, tensor.Elemwise) and
('Gpu' not in type(x.op).__name__)
for x in f.maker.fgraph.toposort()]):
print('Used the cpu')
else:
print('Used the gpu')
我收到以下错误：
ERROR (theano.gpuarray): Could not initialize pygpu, support disabled
Traceback (most recent call last):
File "/home/kuba/anaconda3/lib/python3.6/site-packages/theano/gpuarray/__init__.py", line 227, in <module>
use(config.device)
File "/home/kuba/anaconda3/lib/python3.6/site-packages/theano/gpuarray/__init__.py", line 214, in use
init_dev(device, preallocate=preallocate)
File "/home/kuba/anaconda3/lib/python3.6/site-packages/theano/gpuarray/__init__.py", line 99, in init_dev
**args)
File "pygpu/gpuarray.pyx", line 651, in pygpu.gpuarray.init
File "pygpu/gpuarray.pyx", line 587, in pygpu.gpuarray.pygpu_init
pygpu.gpuarray.GpuArrayException: b'Could not find symbol "cuDevicePrimaryCtxGetState": /usr/lib/libcuda.so.1: undefined symbol: cuDevicePrimaryCtxGetState'
我不明白这个错误，因为在 Nvidia 的文档中这个函数确实存在。有人知道吗？问题可能是因为我使用的是 Python 3.6，而在上述文档中<3.6 之前有一个符号？路径是否有错误？
答案1
一般来说，所有这些组件至少需要 CUDA 7 和 cuDNN 3 或更新版本，而这又需要 GPU 的 CUDA 计算能力至少为 2.0。