1 確保環境

確保已經正確安裝了keras, tensorflow/theano, cuda

在MacOS下面安裝CUDA請參考：

Ubuntu下面安裝CUDA請參考：

2 切換gpu

If you are running on the TensorFlow backend, your code will automatically run on GPU if any available GPU is detected. If you are running on the Theano backend, you can use one of the following methods:

Method 1: use Theano flags.

THEANO_FLAGS=device=gpu,floatX=float32 python my_keras_script.py
The name ‘gpu’ might have to be changed depending on your device’s identifier (e.g. gpu0, gpu1, etc).

Method 2: set up your .theanorc: Instructions

sudo vim ~/.theanorc

add these content
[global]
device=gpu
floatX=float32

Method 3: manually set theano.config.device, theano.config.floatX at the beginning of your code:

import theano
theano.config.device = ‘gpu’
theano.config.floatX = ‘float32’

使用下面這個指令碼來驗證是否啟動GPU:

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')