Installing Theano on Windows 64 bit (x86_64) with GPU capabilities

Since Theano team works under Linux, those of us that bought a laptop with a fancy Windows version pre-installed and decided that we wanted some compatibility with technology-reluctant friends and family (therefore assuming difficulties with everything else), we are doomed to hack our way into getting Theano up and running.

In this post I assume you are going with Cristoph Gohlke's packages (for reasons, read a previous post)

Make sure you also have MS Visual C++ and the NVidia CUDA Toolkit. If you don't have it, add the Visual C++ cl.exe compiler's directory to the path. Mine was under C:\Program Files (x86)\Microsoft Visual Studio 10\VC\bin.

First think you need, after installing Theano, is the nose package, since Gohlke's build needs it at initialization time. Download it and install it from Gohlke's site along with Theano.

Next, you need this .theanorc to be put under your home directory under C:\USER\<yourname>

[global]device = gpu
[nvcc]compiler_bindir=C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\bin# flags=-m32 # we have this hard coded for now
[blas]ldflags =# ldflags = -lopenblas # placeholder for openblas support

I am not very sure how to use OpenBLAS from here. I assume that if all CPU operations are done via Numpy and SciPy, then their default BLAS routines are used, and no direct call to a third BLAS implementation is made, but who knows! (Well, I looked into it a little bit and it seems Theano calls BLAS directly, I guess you may want to install OpenBLAS).

OK, we have the NVidia compiler and tools, the MS compiler that nvcc needs and the configuration. The last thing we need is to install a GNU C and C++ compiler that supports 64 bit Windows binary creation. There is a project called MinGW-w64 that does that. I recommend to download a private build from the user rubenvb that does not come along with the Python environment embedded as the more official build does. Put the bin directory (where GCC is located) of that installation in the Path (Control panel, etc). Theano needs this to compile the symbolic operations to object code and then to CUDA kernels if applicable, I presume.

If you run into errors of type "GCC: sorry, unimplemented: 64-bit mode not compiled in", then your MinGW is not x86_64 compliant. The NVidia compiler nvcc can also complain if it finds no cl.exe in the path.

By the way, all of this was to use deep learning techniques for Kaggle competitions, so the intended consequence was to install PyLearn2. This is not listed under Gohlke's libraries, but it is not low level and all is based on Theano and maybe other numerical packages such as Numpy. Being a pure Python package, you need to clone it from Github:

git clone git://github.com/lisa-lab/pylearn2.git

And then perform

cd pylearn2
python setup.py install

There is an easier procedure that will not require you to manually perform the git operations, and it is through pip

pip install git+git://github.com/lisa-lab/pylearn2.git

You have pip under your Python installation, within the Scripts directory, in the case it came with Python, or if you got Gohlke's installer.

This will also leave the module correctly accessible through Python.

Edit: Pylearn2's tutorial test is a little bit complicated to be a "hello world" test, so I looked for another quick example to see if my installation was finished. A very nice one popped up in this link, which I reproduce here. But first I have to tell that this made me realize that Gohlke's Theano is missing three files, something very, very strange since they are called from within Theano. In particular, the module missing is everything under theano.compat. In this case, just copy the contents from Theano's Github repository directory compat to a compat directory created on your local theano installation under Python 2.7 (mine C:\Python27\Lib\site-packages\theano).

After that, run the code in this link, which is a neural network solving the XOR problem. And we are done.

MinGW-w64: rubenvb build.
Python libraries and builds for Windows: Cristoph Gohlke.
Link to a "truer" hello world Pylearn2 program: here.