Windows Theano GPU 版配置
因为自己在上Coursera的Advanced Machine Learning, 里面第四周的Assignment要用到PYMC3，然后这个似乎是基于theano后端的。然而CPU版TMD太慢了，跑个马尔科夫蒙特卡洛要10个小时，简直不能忍了。所以妥妥换gpu版。
为了不把环境搞坏，我在Anaconda里面新建了一个环境。(关于Anaconda，可以看我之前翻译的文章)
Conda Create -n theano-gpu python=3.4
（theano GPU版貌似不支持最新版，保险起见装了旧版）
conda install theano pygpu
这里面会涉及很多依赖，应该conda会给你搞好，缺什么的话自己按官方文档去装。
然后至于Cuda和Cudnn的安装，可以看我写的关于TF安装的教程
和TF不同的是，Theano不分gpu和cpu版，用哪个看配置文件设置，这一点是翻博客了解到的：
配置好Theano环境之后，只要 C:Users你的用户名 的路径下添加 .theanorc.txt 文件。
.theanorc.txt 文件内容:
[global] openmp=False device = cuda floatX = float32 base_compiler = C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin allow_input_downcast=True [lib] cnmem = 0.75 [blas] ldflags= [gcc] cxxflags=-IC:\Users\lyh\Anaconda2\MinGW [nvcc] fastmath = True flags = -LC:\Users\lyh\Anaconda2\libs compiler_bindir = C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin flags = -arch=sm_30
注意在新版本中，声明用gpu从device=gpu改为device=cuda
然后测试是否成功：
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')输出：
[GpuElemwise{exp,no_inplace}(<GpuArrayType<None>(float32, vector)>), HostFromGpu(gpuarray)(GpuElemwise{exp,no_inplace}.0)]
Looping 1000 times took 0.377000 seconds
Result is [ 1.23178029 1.61879349 1.52278066 ..., 2.20771813 2.29967761
1.62323296]
Used the gpu到这里就算配好了
然后在作业里面，显示Quadro卡启用
但是还是有个warning
WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.
这个真不知道怎么处理
然后后面运行到：
with pm.Model() as logistic_model:
# Since it is unlikely that the dependency between the age and salary is linear, we will include age squared
# into features so that we can model dependency that favors certain ages.
# Train Bayesian logistic regression model on the following features: sex, age, age^2, educ, hours
# Use pm.sample to run MCMC to train this model.
# To specify the particular sampler method (Metropolis-Hastings) to pm.sample,
# use `pm.Metropolis`.
# Train your model for 400 samples.
# Save the output of pm.sample to a variable: this is the trace of the sampling procedure and will be used
# to estimate the statistics of the posterior distribution.
#### YOUR CODE HERE ####
pm.glm.GLM.from_formula('income_more_50K ~ sex+age + age_square + educ + hours', data, family=pm.glm.families.Binomial())
with logistic_model:
trace = pm.sample(400, step=[pm.Metropolis()]) #nchains=1 works for gpu model
### END OF YOUR CODE ###这里出现的报错：
GpuArrayException: cuMemcpyDtoHAsync(dst, src->ptr + srcoff, sz, ctx->mem_s): CUDA_ERROR_INVALID_VALUE: invalid argument
这个问题最后github大神解决了：
So njobs will spawn multiple chains to run in parallel. If the model uses the GPU there will be a conflict. We recently added nchains where you can still run multiple chains. So I think running pm.sample(niter, nchains=4, njobs=1) should give you what you want.
我把：
trace = pm.sample(400, step=[pm.Metropolis()]) #nchains=1 works for gpu model
加上nchains就好了，应该是并行方面的问题
trace = pm.sample(400, step=[pm.Metropolis()],nchains=1, njobs=1) #nchains=1 works for gpu model
另外
plot_traces(trace, burnin=200)
出现pm.df_summary报错，把pm.df_summary 换成 pm.summary就好了，也是github搜出来的。