AWS Marketplace AMI install doc section

This is my proposal for GpuMaxAndArgmax (issue #1399).

Pci bus ID now printed for GPU cards

to get PCI Bus ID.

PCI Bus ID ( see issue #5176:
https://github.com/Theano/Theano/issues/5176 ).

Tested both in Python 2 and Python 3.

Works currently with CUDA, not with OpenCL
( see libgpuarray pull request #283:
https://github.com/Theano/libgpuarray/pull/283 ).

Removed comment about Boost because does not come with this AMI.

Added small section on AWS Marketplace AMI for Theano by Bitfusion. This was okayed by nouiz a couple months ago in the google group: https://groups.google.com/forum/#!searchin/theano-users/AMI%7Csort:relevance/theano-users/bZczMYoqdFU/gXEdvohhBAAJ

(error message, profile output message, and comment).

add doc version switcher to top of sidebar

Make corrMM work in float16/64

correction of casting from new MaxAndArgmax
to new Argmax.

Ensure code works both on Python 2 and Python 3.
test_reduction rewritten.

CPU op MaxAndArgmax rewritten so that it now takes the axes
as a parameter op. Same update for GPU op GpuMaxAndArgmax.

max_and_argmax wrapper rewritten to fully check the axis before
passing it to MaxAndArgmax.

Some other files have also been updated to ensure that the
optimization which replace CPU op by GPU op works well and
that all tests involving MaxAndArgmax still work well after
the updates.

GPU op rewritten to handle the last libgpuarray update.

test_reduction rewritten. It now tests also 0-d arrays (scalars).

I have run the following tests with success. The new
update of libgpuarray has been downloaded, compiled
and fully installed before running these tests.

nosetests -v theano/gpuarray/tests/test_opt.py
    # There is 1 fail here, but not related to MaxAndArgmax:
    # ERROR: theano.gpuarray.tests.test_opt.test_local_lift_abstractconv_gpu_shape
    # RuntimeError: cuDNN is required for convolution and pooling
nosetests -v theano/tensor/nnet/tests/test_nnet.py
nosetests -v theano/tensor/tests/test_opt_uncanonicalize.py
nosetests -v theano/tensor/tests/test_basic.py
THEANO_FLAGS=floatX=float32,profile=True,profiling.n_ops=1000,profiling.n_apply=1000,profiling.ignore_first_call=True,profiling.destination=profiling.log nosetests --nocapture --verbose theano/gpuarray/tests/test_reduction.py

Prevent Flake8!

Tests have been rewritten, and they run so that
a CPU computation and a GPU computation are always
performed (separately) on the same input.

This allow to run the tests with Theano profiling
flags and then compare the execution time of
MaxAndArgmax (CPU) and GpuMaxAndArgmax (GPU).

Some code has also been modified in theano/tensor/basic.py,
related to MaxAndArgmax, to make the API more uniform
and to put most of axis checking in maxandargmax wrapper
instead of in make_node functions of (Gpu)MaxAndArgmax.

GpuMaxAndArgmax op is implemented in a separate file
in theano/gpuarray.

A test script has also been written.

To integrate this op in Theano, an optimization has been
added to ttheano/gpuarray/opt.py.

Added checks for taps values.

Fix remaining tests in debugmode

Update to fix python 3 error

Option to evaluate part of the max-and-argmax on cpu

Numpy imports

Fix h_softmax example data types

Fix grad of elemwise with boolean inputs.

Commented code removed, and assertion added to check that call to sys.getrefcount does nots changes the refcount.

Update test function test_pycuda_memory_to_theano() to fix Python 3 error.

The refcount of a variable seems to be different between Python 2 and Python 3. So it seems better to stock an initial refcount in a variable and then do the checkings with that variable instead of directly using comparing to constant values.

Tests passed with Python 3 (Anaconda):

```
$ nosetests --verbose --nocapture theano/misc/tests/test_pycuda_theano_simple.py
Run pycuda only example to test that pycuda works. ... ok
Simple example with pycuda function and Theano CudaNdarray object. ... ok
theano.misc.tests.test_pycuda_theano_simple.test_pycuda_memory_to_theano ... 4
gpuarray ref count before creating a CudaNdarray 4
strides (20, 5, 1)
gpuarray ref count after creating a CudaNdarray 5
gpuarray ref count after deleting the CudaNdarray 4
ok

----------------------------------------------------------------------
Ran 3 tests in 0.297s

OK
```