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。
[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。