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283 lines
6.0 KiB
Markdown
283 lines
6.0 KiB
Markdown
# Theano 在 Windows 上的配置
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<font color="red">注意：不建议在 `windows` 进行 `theano` 的配置。</font>
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<font color="red">务必确认你的显卡支持 `CUDA`。</font>
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我个人的电脑搭载的是 `Windows 10 x64` 系统，显卡是 `Nvidia GeForce GTX 850M`。
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## 安装 theano
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首先是用 `anaconda` 安装 `theano`：
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```py
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conda install mingw libpython
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pip install theano
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```
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## 安装 VS 和 CUDA
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按顺序安装这两个软件：
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* 安装 Visual Studio 2010/2012/2013
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* 安装 对应的 x64 或 x86 CUDA
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Cuda 的版本与电脑的显卡兼容。
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我安装的是 Visual Studio 2012 和 CUDA v7.0v。
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## 配置环境变量
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`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：
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* `CUDA_PATH`
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* `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0`
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我们配置两个相关变量：
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* `CUDA_BIN_PATH`
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* `%CUDA_PATH%\bin`
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* `CUDA_LIB_PATH`
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* `%CUDA_PATH%\lib\Win32`
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接下来在 `Path` 环境变量的后面加上：
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* `Minicoda` 中关于 `mingw` 的项：
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* `C:\Miniconda\MinGW\bin;`
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* `C:\Miniconda\MinGW\x86_64-w64-mingw32\lib;`
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* `VS` 中的 `cl` 编译命令：
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* `C:\Program Files (x86)\Microsoft Visual Studio 11.0\VC\bin;`
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* `C:\Program Files (x86)\Microsoft Visual Studio 11.0\Common7\IDE;`
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生成测试文件：
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In [1]:
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```py
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%%file test_theano.py
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from theano import config
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print 'using device:', config.device
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```
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```py
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Writing test_theano.py
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```
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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：
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```py
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THEANO_FLAGS=xxx
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```
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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：
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```py
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THEANO_FLAGS=xxx python <your script>.py
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```
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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：
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```py
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set THEANO_FLAGS=xxx && python <your script>.py
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```
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In [2]:
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```py
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import sys
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if sys.platform == 'win32':
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!set THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 && python test_theano.py
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else:
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!THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python test_theano.py
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```
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```py
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using device: cpu
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```
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In [3]:
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```py
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if sys.platform == 'win32':
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!set THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 && python test_theano.py
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else:
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!THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python test_theano.py
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```
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```py
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Using gpu device 0: Tesla C2075 (CNMeM is disabled)
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using device: gpu
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```
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测试 `CPU` 和 `GPU` 的差异：
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In [4]:
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```py
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%%file test_theano.py
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from theano import function, config, shared, sandbox
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import theano.tensor as T
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import numpy
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import time
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vlen = 10 * 30 * 768 # 10 x #cores x # threads per core
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iters = 1000
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rng = numpy.random.RandomState(22)
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x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
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f = function([], T.exp(x))
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t0 = time.time()
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for i in xrange(iters):
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r = f()
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t1 = time.time()
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print("Looping %d times took %f seconds" % (iters, t1 - t0))
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print("Result is %s" % (r,))
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if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):
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print('Used the cpu')
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else:
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print('Used the gpu')
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```
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```py
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Overwriting test_theano.py
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```
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In [5]:
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```py
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if sys.platform == 'win32':
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!set THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 && python test_theano.py
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else:
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!THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python test_theano.py
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```
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```py
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Looping 1000 times took 3.498123 seconds
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Result is [ 1.23178029 1.61879337 1.52278066 ..., 2.20771813 2.29967761
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1.62323284]
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Used the cpu
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```
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In [6]:
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```py
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if sys.platform == 'win32':
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!set THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 && python test_theano.py
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else:
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!THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python test_theano.py
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```
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```py
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Using gpu device 0: Tesla C2075 (CNMeM is disabled)
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Looping 1000 times took 0.847006 seconds
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Result is [ 1.23178029 1.61879349 1.52278066 ..., 2.20771813 2.29967761
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1.62323296]
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Used the gpu
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```
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可以看到 `GPU` 明显要比 `CPU` 快。
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使用 `GPU` 模式的 `T.exp(x)` 可以获得更快的加速效果：
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In [7]:
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```py
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%%file test_theano.py
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from theano import function, config, shared, sandbox
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import theano.sandbox.cuda.basic_ops
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import theano.tensor as T
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import numpy
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import time
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vlen = 10 * 30 * 768 # 10 x #cores x # threads per core
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iters = 1000
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rng = numpy.random.RandomState(22)
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x = shared(numpy.asarray(rng.rand(vlen), 'float32'))
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f = function([], sandbox.cuda.basic_ops.gpu_from_host(T.exp(x)))
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t0 = time.time()
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for i in xrange(iters):
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r = f()
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t1 = time.time()
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print("Looping %d times took %f seconds" % (iters, t1 - t0))
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print("Result is %s" % (r,))
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print("Numpy result is %s" % (numpy.asarray(r),))
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if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):
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print('Used the cpu')
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else:
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print('Used the gpu')
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```
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```py
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Overwriting test_theano.py
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```
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In [8]:
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```py
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if sys.platform == 'win32':
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!set THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 && python test_theano.py
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else:
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!THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python test_theano.py
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```
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```py
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Using gpu device 0: Tesla C2075 (CNMeM is disabled)
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Looping 1000 times took 0.318359 seconds
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Result is <CudaNdarray object at 0x7f7bb701fb70>
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Numpy result is [ 1.23178029 1.61879349 1.52278066 ..., 2.20771813 2.29967761
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1.62323296]
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Used the gpu
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```
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In [9]:
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```py
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!rm test_theano.py
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```
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## 配置 .theanorc.txt
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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：
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例如我现在的 .theanorc.txt 配置为：
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```py
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[global]
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device = gpu
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floatX = float32
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[nvcc]
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fastmath = True
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flags = -LC:\Miniconda\libs
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compiler_bindir=C:\Program Files (x86)\Microsoft Visual Studio 11.0\VC\bin
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[gcc]
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cxxflags = -LC:\Miniconda\MinGW
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```
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具体这些配置有什么作用之后可以查看官网上的教程。 |