from IPython.display import YouTubeVideo
YouTubeVideo('S75EdAcXHKk')

%%bash
mkdir -p ~/Downloads/lisa/data/mnist/
cd ~/Downloads/lisa/data/mnist/
#wget http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
#wget http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
#wget http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
#wget http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
gunzip *.gz

!ls ~/Downloads/lisa/data/mnist/

import os,sys,inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0,parentdir) 
import load

load.datasets_dir = os.path.expanduser("~/Downloads/lisa/data/")

trX, teX, trY, teY = load.mnist(onehot=True)

trX.shape, teX.shape, trY.shape, teY.shape

import numpy as np
np.min(trX),np.max(trX),np.min(teX),np.max(teX)

!sudo pip install theano

import os
#os.environ['THEANO_FLAGS'] = 'mode=FAST_RUN,device=cpu,floatX=float32'
os.environ['THEANO_FLAGS'] = 'mode=FAST_RUN,device=gpu,floatX=float32'

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' % iters, t1 - t0, 'seconds'
print 'Result is', 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'