I started trying Theano today and wanted to use the GPU (NVIDIA GeForce GT 750M 2048 MB) on my Mac. Here’s a brief instruction on how to use the GPU on Mac, largely following the instructions from http://deeplearning.net/software/theano/install.html#mac-os.
Install Theano:
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$ pip install Theano |
Download and install CUDA: https://developer.nvidia.com/cuda-downloads
Put the following lines into your ~/.bash_profile:
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# Theano and CUDA PATH="/Developer/NVIDIA/CUDA-7.5/bin/:$PATH" export LD_LIBRARY_PATH=/Developer/NVIDIA/CUDA-7.5/lib/ export CUDA_ROOT=/Developer/NVIDIA/CUDA-7.5/ export THEANO_FLAGS='mode=FAST_RUN,device=gpu,floatX=float32' |
Note that the PATH line is necessary. Otherwise you may see the following message:
ERROR (theano.sandbox.cuda): nvcc compiler not found on $PATH. Check your nvcc installation and try again.
Configure Theano:
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$ cat .theanorc [gcc] cxxflags = -L/usr/local/lib -L/Developer/NVIDIA/CUDA-7.5/lib/ |
Test if GPU is used:
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$ cat check.py from theano import function, config, shared, sandbox import theano.tensor as T 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([], T.exp(x)) print(f.maker.fgraph.toposort()) 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') $ THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 time python check.py [Elemwise{exp,no_inplace}(<TensorType(float32, vector)>)] Looping 1000 times took 1.743682 seconds Result is [ 1.23178029 1.61879337 1.52278066 ..., 2.20771813 2.29967761 1.62323284] Used the cpu 2.47 real 2.19 user 0.27 sys $ THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 time python check.py Using gpu device 0: GeForce GT 750M [GpuElemwise{exp,no_inplace}(<CudaNdarrayType(float32, vector)>), HostFromGpu(GpuElemwise{exp,no_inplace}.0)] Looping 1000 times took 1.186971 seconds Result is [ 1.23178029 1.61879349 1.52278066 ..., 2.20771813 2.29967761 1.62323296] Used the gpu 2.09 real 1.59 user 0.41 sys |
A more realistic example:
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$ cat lr.py import numpy import theano import theano.tensor as T rng = numpy.random N = 400 feats = 784 D = (rng.randn(N, feats).astype(theano.config.floatX), rng.randint(size=N,low=0, high=2).astype(theano.config.floatX)) training_steps = 10000 # Declare Theano symbolic variables x = T.matrix("x") y = T.vector("y") w = theano.shared(rng.randn(feats).astype(theano.config.floatX), name="w") b = theano.shared(numpy.asarray(0., dtype=theano.config.floatX), name="b") x.tag.test_value = D[0] y.tag.test_value = D[1] # Construct Theano expression graph p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b)) # Probability of having a one prediction = p_1 > 0.5 # The prediction that is done: 0 or 1 xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1) # Cross-entropy cost = xent.mean() + 0.01*(w**2).sum() # The cost to optimize gw,gb = T.grad(cost, [w,b]) # Compile expressions to functions train = theano.function( inputs=[x,y], outputs=[prediction, xent], updates=[(w, w-0.01*gw), (b, b-0.01*gb)], name = "train") predict = theano.function(inputs=[x], outputs=prediction, name = "predict") if any([x.op.__class__.__name__ in ['Gemv', 'CGemv', 'Gemm', 'CGemm'] for x in train.maker.fgraph.toposort()]): print('Used the cpu') elif any([x.op.__class__.__name__ in ['GpuGemm', 'GpuGemv'] for x in train.maker.fgraph.toposort()]): print('Used the gpu') else: print('ERROR, not able to tell if theano used the cpu or the gpu') print(train.maker.fgraph.toposort()) for i in range(training_steps): pred, err = train(D[0], D[1]) print("target values for D") print(D[1]) print("prediction on D") print(predict(D[0])) $ THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 time python lr.py Used the cpu target values for D [ 1. 1. 0. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 1. 0. 0. 1. 1. 0. 1. 1. 1. 1. 0. 1. 1. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 1. 0. 1. 1. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 1. 1. 0. 0. 1. 1. 1. 1. 0. 0. 0. 1. 0. 0. 1. 1. 0. 0. 1. 1. 1. 1. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 1. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 0. 0. 1. 0. 0. 0. 1. 0. 1. 1. 1. 1. 0. 0. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 1. 1. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 1. 1. 1. 0. 0. 1. 1. 1. 1. 0. 0. 0. 1. 1. 0. 0. 1. 0. 0. 0. 0. 1. 1. 1. 0. 1. 1. 1. 0. 1. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 0. 1. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 1. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 1. 0. 0. 1. 1. 0. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 1. 1. 0. 1. 1. 1. 0. 1. 1. 1. 1. 1. 0. 0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 1. 0. 1. 1. 1. 0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 0. 1. 0. 0. 1. 1. 0. 1.] prediction on D [1 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 1 0 0 1 1 0 1 1 1 1 0 1 1 0 1 0 0 0 0 0 1 0 0 0 1 1 0 1 1 1 0 1 0 0 0 0 0 0 1 0 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 1 1 1 0 1 0 0 0 0 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 0 0 0 1 0 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 1 1 1 0 1 0 0 1 0 0 0 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0 1 1 1 0 0 1 1 1 1 0 0 0 1 1 0 0 1 0 0 0 0 1 1 1 0 1 1 1 0 1 0 0 0 0 0 1 1 1 1 1 1 0 0 1 1 1 0 1 0 1 0 1 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0 1 1 0 0 1 1 0 1 0 1 0 0 1 1 0 1 1 0 0 1 1 0 0 1 0 1 1 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 1 1 1 0 1 1 1 1 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 1 1 1 0 1 1 1 0 0 0 0 1 1 1 0 0 0 0 1 0 0 1 1 0 1] 8.92 real 8.24 user 1.14 sys $ THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 time python lr.py Using gpu device 0: GeForce GT 750M Used the gpu target values for D [ 1. 0. 0. 0. 0. 1. 0. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 1. 0. 0. 0. 1. 1. 0. 1. 1. 1. 0. 0. 1. 1. 1. 1. 1. 1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 1. 1. 0. 1. 0. 1. 1. 0. 1. 1. 1. 0. 1. 1. 0. 0. 0. 1. 1. 1. 1. 1. 0. 0. 1. 1. 0. 1. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 0. 0. 0. 1. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 1. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 1. 0. 1. 1. 1. 0. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1. 1. 1. 1. 0. 0. 0. 1. 1. 1. 0. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 1. 1. 1. 0. 1. 0. 1. 0. 1. 1. 1. 1. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 1. 1. 0. 0. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 1. 1. 0. 1. 0. 1. 1. 1. 0. 0. 1. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 0. 0. 0. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 0. 1. 0. 1. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 1. 1. 0. 1. 1. 1. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 1. 1. 0. 1. 1. 0. 1. 1. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 0. 1. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1. 1. 0. 1. 0. 0. 1. 1. 0. 1. 1. 0. 1. 1. 0. 0. 1. 0.] prediction on D [1 0 0 0 0 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 1 1 1 0 0 1 0 0 1 1 0 0 1 1 0 1 0 1 1 0 1 1 1 0 1 1 0 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 1 1 0 1 1 1 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 1 1 1 0 0 0 1 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 1 1 1 0 0 0 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 0 0 0 1 0 1 1 1 0 1 1 1 0 0 0 0 1 0 1 0 0 1 0 0 1 1 0 1 0 1 1 1 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 1 1 1 1 1 1 0 1 1 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 1 0 1 1 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0 1 1 0 1 1 0 1 1 1 0 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 1 1 0 0 1 1 0 0 0 0 0 1 0 0 1 1 1 0 1 0 0 1 1 0 1 1 0 1 1 0 0 1 0] 19.78 real 17.61 user 1.24 sys |
So it seems this GPU does not outperform the CPU. Well,GT 750M may not be the best GPU you can get… Someone else here has a similar experience.