本文最后更新于 426 天前,其中的信息可能已经过时,如有错误请发送邮件到 wuxianglongblog@163.com
注意:不建议在 windows
进行 theano
的配置。
务必确认你的显卡支持 CUDA
。
我个人的电脑搭载的是 Windows 10 x64
系统,显卡是 Nvidia GeForce GTX 850M
。
首先是用 anaconda
安装 theano
:
| conda install mingw libpython |
| pip install theano |
按顺序安装这两个软件:
- 安装 Visual Studio 2010/2012/2013
- 安装 对应的 x64 或 x86 CUDA
Cuda 的版本与电脑的显卡兼容。
我安装的是 Visual Studio 2012 和 CUDA v7.0v。
CUDA
会自动帮你添加一个 CUDA_PATH
环境变量(环境变量在 控制面板 -> 系统与安全 -> 系统 -> 高级系统设置 中),表示你的 CUDA
安装位置,我的电脑上为:
CUDA_PATH
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0
我们配置两个相关变量:
CUDA_BIN_PATH
CUDA_LIB_PATH
接下来在 Path
环境变量的后面加上:
-
Minicoda
中关于 mingw
的项:
C:\Miniconda\MinGW\bin;
C:\Miniconda\MinGW\x86_64-w64-mingw32\lib;
-
VS
中的 cl
编译命令:
C:\Program Files (x86)\Microsoft Visual Studio 11.0\VC\bin;
C:\Program Files (x86)\Microsoft Visual Studio 11.0\Common7\IDE;
生成测试文件:
| %%file test_theano.py |
| from theano import config |
| print 'using device:', config.device |
我们可以通过临时设置环境变量 THEANO_FLAGS
来改变 theano
的运行模式,在 linux 下,临时环境变量直接用:
就可以完成,设置完成之后,该环境变量只在当前的命令窗口有效,你可以这样运行你的代码:
| THEANO_FLAGS=xxx python .py |
在 Windows
下,需要使用 set
命令来临时设置环境变量,所以运行方式为:
| set THEANO_FLAGS=xxx && python .py |
| import sys |
| |
| if sys.platform == 'win32': |
| !set THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 && python test_theano.py |
| else: |
| !THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python test_theano.py |
| if sys.platform == 'win32': |
| !set THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 && python test_theano.py |
| else: |
| !THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python test_theano.py |
| Using gpu device 0: Tesla C2075 (CNMeM is disabled) |
| using device: gpu |
测试 CPU
和 GPU
的差异:
| %%file test_theano.py |
| |
| from theano import function, config, shared, sandbox |
| import theano.tensor as T |
| import numpy |
| import time |
| |
| vlen = 10 * 30 * 768 |
| iters = 1000 |
| |
| rng = numpy.random.RandomState(22) |
| x = shared(numpy.asarray(rng.rand(vlen), config.floatX)) |
| f = function([], T.exp(x)) |
| |
| 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') |
| Overwriting test_theano.py |
| if sys.platform == 'win32': |
| !set THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 && python test_theano.py |
| else: |
| !THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python test_theano.py |
| Looping 1000 times took 3.498123 seconds |
| |
| Result is [ 1.23178029 1.61879337 1.52278066 ..., 2.20771813 2.29967761 |
| |
| 1.62323284] |
| |
| Used the cpu |
| if sys.platform == 'win32': |
| !set THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 && python test_theano.py |
| else: |
| !THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python test_theano.py |
| Using gpu device 0: Tesla C2075 (CNMeM is disabled) |
| Looping 1000 times took 0.847006 seconds |
| Result is [ 1.23178029 1.61879349 1.52278066 ..., 2.20771813 2.29967761 |
| 1.62323296] |
| Used the gpu |
可以看到 GPU
明显要比 CPU
快。
使用 GPU
模式的 T.exp(x)
可以获得更快的加速效果:
| %%file test_theano.py |
| |
| from theano import function, config, shared, sandbox |
| import theano.sandbox.cuda.basic_ops |
| import theano.tensor as T |
| import numpy |
| import time |
| |
| vlen = 10 * 30 * 768 |
| iters = 1000 |
| |
| rng = numpy.random.RandomState(22) |
| x = shared(numpy.asarray(rng.rand(vlen), 'float32')) |
| f = function([], sandbox.cuda.basic_ops.gpu_from_host(T.exp(x))) |
| |
| 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,)) |
| print("Numpy result is %s" % (numpy.asarray(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') |
| Overwriting test_theano.py |
| if sys.platform == 'win32': |
| !set THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 && python test_theano.py |
| else: |
| !THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python test_theano.py |
| Using gpu device 0: Tesla C2075 (CNMeM is disabled) |
| Looping 1000 times took 0.318359 seconds |
| Result is |
| Numpy result is [ 1.23178029 1.61879349 1.52278066 ..., 2.20771813 2.29967761 |
| 1.62323296] |
| Used the gpu |
我们可以在个人文件夹下配置 .theanorc.txt 文件来省去每次都使用环境变量设置的麻烦:
例如我现在的 .theanorc.txt 配置为:
| [global] |
| device = gpu |
| floatX = float32 |
| |
| [nvcc] |
| fastmath = True |
| flags = -LC:\Miniconda\libs |
| compiler_bindir=C:\Program Files (x86)\Microsoft Visual Studio 11.0\VC\bin |
| |
| [gcc] |
| cxxflags = -LC:\Miniconda\MinGW |
具体这些配置有什么作用之后可以查看官网上的教程。