For the first question, there is way to get g++ compiled module work with
software compiled with VS. This is what we do. We use the small subset of
feature that allow this to work.

With the new gpu back-end, we don't call nvcc anymore. We compile the c
code with g++ and call the nvidia driver to compile the gpu kernel.

Fred

On Tue, Mar 28, 2017 at 1:35 PM Michael Klachko <[email protected]>
wrote:

> You can install everything using conda (use mingw's gcc instead of Visual
> Studio):
>
> http://deeplearning.net/software/theano/install_windows.html#requirements-installation-through-conda-recommended
>
> Here's the summary of the process:
>
> 1. install CUDA
> 2. copy the CuDNN files
> 3. install miniconda
> 4. install theano dependencies
> 5. install theano and pygpu
> 6. update the PATH to see miniconda, and update theanorc to see mingw
> compiler
>
>
>
>
>
>
> On Saturday, March 25, 2017 at 6:03:07 AM UTC-7, Márton Marczell wrote:
>
> Hi,
>
>  I have a number of questions about the internal architecture of Theano
> itself, simply out of a wish to understand what I'm using.
>
>  If I understand correctly, when I use the "cpu" backend, Theano generates
> a bunch of C++ code and compiles and runs it. My first question is: how is
> it possible that Theano uses g++ on Windows, when I know that Python is
> built with Visual Studio and therefore C++ code built with other compilers
> does not link correctly to Python?
>
>  When I use the "gpu" backend, Theano uses the same approach, but
> incorporates nvcc into the mix as well. My second question: when I build
> libgpuarray and switch to the "cuda" backend, how does it work? What is the
> role of libgpuarray? Does it contain all the GPU code so that no new code
> has to be generated at runtime?
>
>  Why can't libgpuarray be distributed as a prebuilt binary? Is there a
> technical reason why I can't just "pip install pygpu"?
>
> Márton
>
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