Fixup tutorial/using_gpu.txt

doc/tutorial/using_gpu.txt

@@ -33,9 +33,6 @@ Testing Theano with GPU
 To see if your GPU is being used, cut and paste the following program into a
 file and run it.
 
-.. If you modify this code, also change :
-.. theano/tests/test_tutorial.py:T_using_gpu.test_using_gpu_1
-
 .. testcode::
 
     from theano import function, config, shared, sandbox
@@ -111,9 +108,6 @@ the graph to express a computation with a GPU-stored result.  The ``gpu_from_hos
 op means "copy the input from the host to the GPU" and it is optimized away
 after the ``T.exp(x)`` is replaced by a GPU version of ``exp()``.
 
-.. If you modify this code, also change :
-.. theano/tests/test_tutorial.py:T_using_gpu.test_using_gpu_2
-
 .. testcode::
 
     from theano import function, config, shared, sandbox
@@ -466,7 +460,6 @@ If you don't mind a loss of flexibility, you can ask theano to return
 the GPU object directly.  The following code is modifed to do just that.
 
 .. testcode::
-  :emphasize-lines: 10,17
 
   from theano import function, config, shared, tensor, sandbox
   import numpy
@@ -501,14 +494,13 @@ The output is
 
 .. testoutput::
    :hide:
-   :options: +ELLIPSIS
+   :options: +ELLIPSIS, +SKIP
    
-  $ THEANO_FLAGS=device=cuda0 python check2.py
-  Using device cuda0: GeForce GTX 275
-  [GpuElemwise{exp,no_inplace}(<GpuArray<float64>>)]
-  Looping 1000 times took ... seconds
-  Result is ...
-  Used the gpu
+   Using device cuda0: ...
+   [GpuElemwise{exp,no_inplace}(<GpuArray<float64>>)]
+   Looping 1000 times took ... seconds
+   Result is ...
+   Used the gpu
 
 .. code-block:: none
 

theano/tests/test_tutorial.py

@@ -21,154 +21,6 @@ from theano.tests import unittest_tools as utt
 from theano.sandbox.rng_mrg import MRG_RandomStreams
 from theano.tensor.shared_randomstreams import RandomStreams
 
-
-class T_using_gpu(unittest.TestCase):
-    # All tests here belog to
-    # http://deeplearning.net/software/theano/tutorial/using_gpu.html
-    # Theano/doc/tutorial/using_gpu.txt
-    # Any change you do here also add it to the tutorial !
-
-    def test_using_gpu_1(self):
-        # I'm checking if this compiles and runs
-        from theano import function, config, shared, sandbox
-        import theano.tensor as T
-        import numpy
-        import time
-
-        vlen = 10 * 30 * 70  # 10 x #cores x # threads per core
-        iters = 10
-
-        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')
-        if theano.config.device.find('gpu') > -1:
-            assert not numpy.any( [isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()])
-        else:
-            assert numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()])
-
-    def test_using_gpu_2(self):
-        if theano.config.device.find('gpu') > -1:
-
-            from theano import function, config, shared, sandbox
-            import theano.tensor as T
-            import numpy
-            import time
-
-            vlen = 10 * 30 * 70  # 10 x #cores x # threads per core
-            iters = 10
-
-            rng = numpy.random.RandomState(22)
-            x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
-            f = function([], sandbox.cuda.basic_ops.gpu_from_host(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)
-            print('Numpy result is', numpy.asarray(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')
-
-            assert not numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()])
-
-    def test_using_gpu_3(self):
-
-        if theano.config.device.find('gpu') > -1:
-
-            from theano import function, config, shared, sandbox, Out
-            import theano.tensor as T
-            import numpy
-            import time
-
-            vlen = 10 * 30 * 70  # 10 x #cores x # threads per core
-            iters = 10
-
-            rng = numpy.random.RandomState(22)
-            x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
-            f = function([],
-                    Out(sandbox.cuda.basic_ops.gpu_from_host(T.exp(x)),
-                        borrow=True))
-            # 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)
-            print('Numpy result is', numpy.asarray(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')
-
-            assert not numpy.any([isinstance(x.op, T.Elemwise)
-                                  for x in f.maker.fgraph.toposort()])
-
-    def test_using_gpu_pycudaop(self):
-        import theano.misc.pycuda_init
-        if not theano.misc.pycuda_init.pycuda_available:
-            raise SkipTest("Pycuda not installed. Skip test of theano op"
-                           " with pycuda code.")
-        from pycuda.compiler import SourceModule
-        import theano.sandbox.cuda as cuda
-
-        import theano.sandbox.cuda as cuda_ndarray
-        if not cuda_ndarray.cuda_available:
-            raise SkipTest('Optional package cuda disabled')
-
-        class PyCUDADoubleOp(theano.Op):
-            
-            __props__ = ()
-
-            def make_node(self, inp):
-                inp = cuda.basic_ops.gpu_contiguous(
-                    cuda.basic_ops.as_cuda_ndarray_variable(inp))
-                assert inp.dtype == "float32"
-                return theano.Apply(self, [inp], [inp.type()])
-
-            def make_thunk(self, node, storage_map, _, _2):
-                mod = SourceModule("""
-    __global__ void my_fct(float * i0, float * o0, int size) {
-    int i = blockIdx.x*blockDim.x + threadIdx.x;
-    if(i<size){
-        o0[i] = i0[i]*2;
-    }
-  }""")
-                pycuda_fct = mod.get_function("my_fct")
-                inputs = [storage_map[v] for v in node.inputs]
-                outputs = [storage_map[v] for v in node.outputs]
-
-                def thunk():
-                    z = outputs[0]
-                    if z[0] is None or z[0].shape != inputs[0][0].shape:
-                        z[0] = cuda.CudaNdarray.zeros(inputs[0][0].shape)
-                        grid = (int(numpy.ceil(inputs[0][0].size / 512.)), 1)
-                        pycuda_fct(inputs[0][0], z[0],
-                                   numpy.intc(inputs[0][0].size),
-                                   block=(512, 1, 1), grid=grid)
-                return thunk
-        x = theano.tensor.fmatrix()
-        f = theano.function([x], PyCUDADoubleOp()(x))
-        xv = numpy.ones((4, 5), dtype="float32")
-        assert numpy.allclose(f(xv), xv*2)
-
-
 class T_typedlist(unittest.TestCase):
     # All tests here belong to
     # http://deeplearning.net/software/theano/library/typed_list.html