
.. _install_windows:


Installation of Theano on Windows
==================================

These instructions show step-by-step how to install Theano and
required dependencies on a 32- or 64-bit system using freely available
tools and compilers.

Installing Dependencies
~~~~~~~~~~~~~~~~~~~~~~~

.. note::

    Command lines listed below are assumed to be run in a Windows prompt:
    To open the prompt on Windows <= 7, click ``Start`` and type the ``cmd`` command to launch a command window.
    In Windows 8, go to the Start screen and type ``command`` or ``cmd``.


Theano currently works on Windows, however it requires compilers for
C/C++ (for Python 2.7 family this has to be Microsoft Visual Studio
2008 compiler), CUDA (CUDA v5.5 is required as it is the latest
version supporting Visual Studio 2008), and GCC (for non-CUDA C code
generated by Theano).

Visual Studio and CUDA
######################

Unfortunately Microsoft recently stopped distributing Visual Studio
Express 2008 (the compilers required for Python 2.7 are provided,
though), therefore we require a temporary install of Visual Studio Express
2010 to be able to install CUDA (its installer requires a Visual
Studio installation to proceed). Afterwards, the Visual Studio 2010
can be safely removed. If someone knows how to install CUDA 5.5
without a proper Visual Studio installation, please let us know.


First we need to install Microsoft Visual Studio 2010 Express, which
is required to install CUDA. You can download it from
`Visual Studio Express
<http://www.visualstudio.com/en-us/products/visual-studio-express-vs.aspx>`_.
Please install the Visual C version. We have downloaded the
`all-in-one CD <http://go.microsoft.com/?linkid=9709969>`_, extracted
it using `7zip <http://www.7-zip.org/>`_, and ran the installer at
VCExpress\\setup.exe.

If you want a 64bit Python installation, Visual Studio 2010 Express
doesn't provide a 64bit compiler. To get one download and install the
`Windows Software Development Kit version 7.1
<http://msdn.microsoft.com/en-us/windowsserver/bb980924.aspx>`_.

Now you have a running (and free even for commercial use) installation
of MSVS2010 IDE with 32- and 64-bit compilers.

Once Visual Studio is installed, you can install CUDA. We recommend
CUDA 5.5, as it supports MSVC 2008. Download the CUDA installer from
`the CUDA archive
<https://developer.nvidia.com/cuda-toolkit-55-archive>`_. Be sure to
get 32-, or 64-bit version depending on your system configuration.

Once CUDA is installed you can remove VisualStudio Express 2010.

Finally, grab the `Microsoft Visual C++ Compiler for Python 2.7
<http://www.microsoft.com/en-us/download/details.aspx?id=44266>`_. It
provides the now-obsolete compilers form Visual Studio 2008 that are
required for compatibility with Python 2.7. To install the package for
all users please:

1. open an administrator's console (got to ``start``, then type ``cmd``,
   right-click on the command prompt icon and select ``run as
   administrator``)
2. ``cd`` to your downloads directory and execute ``msiexec /i
   VCForPython27.msi ALLUSERS=1``

The package will be installed to ``C:\Program Files
(x86)\Common Files\Microsoft\Visual C++ for Python\9.0``.

Finally download the ``stdint.h`` header from
`here <http://msinttypes.googlecode.com/svn/trunk/stdint.h>`_ and save it as
``C:\Program Files (x86)\Common Files\Microsoft\Visual C++ for
Python\9.0\VC\include\stdint.h``.

GCC
###

Theano C code compiler currently requires a GCC installation. We have
used the build `TDM GCC <http://tdm-gcc.tdragon.net/>`_ which is
provided for both 32- and 64-bit platforms. A few caveats to watch for
during installation:

1. Install to a directory without spaces (we have placed it in
   ``C:\SciSoft\TDM-GCC-64``)
2. If you don't want to clutter your system PATH un-check ``add to
   path`` option.
3. Enable OpenMP support by checking the option ``openmp support
   option``.

Scientific Python distribution
##############################

We highly recommend the Pierre Raybaut's `WinPython
<http://winpython.sourceforge.net/>`_ distribution - it is compiled
for both 32- and 64-bit systems, links against the fast `MKL
<https://software.intel.com/en-us/intel-mkl>`_ BLAS
implementation, supports installation of auxiliary packages from
`Chris Gohlke <http://www.lfd.uci.edu/~gohlke/pythonlibs/>`_ and is
free.

WinPython also allows for a portable installation and doesn't clutter
your main system PATH. We have installed it to
``c:\SciSoft\WinPython-64bit-2.7.9.4``.

Alternative in academia: EPD
++++++++++++++++++++++++++++

If you are working in academia, an easy way to install most of the
dependencies is to install `Enthought Python Distribution (EPD) <http://enthought.com/products/epd.php>`_.
If you are affiliated with a university (as student or employee), you can
download the installation for free.
EPD installation includes, in particular, Python (and the development headers),
NumPy, SciPy, nose, sphinx, easy_install, pydot (but *not* `Graphviz`_, which is
necessary for it to work), g++, and the MKL
implementation of blas.

If you want to use the iPython shell, you should first try to import NumPy
in it::

    C:\Users\user>ipython
    [...]
    In [1]: import numpy

If you see an error message telling you that ``DLL load failed``, that is
probably due to a bug in the script launching ipython. If ``C:\SciSoft\Python27``
is the directory where you installed EPD, edit
``C:\SciSoft\Python27\Scripts\ipython.bat``, there should be a line saying::

    set path="C:\SciSoft\Python27";%path%

Remove the quotes around ``Python27``, leading to::

    set path=C:\SciSoft\Python27;%path%

Then, it should work in all new terminals.

pip is not included in EPD, but you can simply install it with::

    easy_install pip

Alternative: Canopy
+++++++++++++++++++

Canopy is another software that installs all Theano dependencies.
If you are affiliated with a university (as student or employee), you
can download the installation for free.

- Install Canopy x64, and update it to the latest version (`Help /
  Software updates...`), as older Canopy versions have trouble installing
  `pip`.
- Then install `pip` from Canopy Package Manager.
- In the Windows Prompt, type `pip install theano`.
- In Canopy Package Manager, search and install packages "mingw 4.5.2" and "libpython 1.2"
- (Needed only for Theano 0.6rc3 or earlier)
  The "libpython 1.2" package installs files `libpython27.a` and `libmsvcr90.a` to
  `C:\\Users\\<USER>\\AppData\\Local\\Enthought\\Canopy\\User\\libs`. Copy the two files to
  `C:\\Users\\<USER>\\AppData\\Local\\Enthought\\Canopy\\App\\appdata\\canopy-1.0.0.1160.win-x86_64\libs`.
- (Needed only for Theano 0.6rc3 or earlier) Set the Theano flags
  ``blas.ldflags=-LC:\Users\<USER>\AppData\Local\Enthought\Canopy\App\appdata\canopy-1.0.0.1160.win-x86_64\Scripts -lmk2_core -lmk2_intel_thread -lmk2_rt``.



Alternative: Anaconda
+++++++++++++++++++++++

ContinuumIO_ is providing a free Python distribution for Windows (32-bit
and 64-bit), including all dependencies of Theano. If you are not
eligible for a download of EPD or Canopy (via a commercial, or free academic
licence), this is the easiest way to install
Theano's dependencies. Simply download and execute the installer from
`Anaconda download page <https://store.continuum.io/cshop/anaconda/>`__,
and execute the following in Windows command line:

.. _ContinuumIO: http://continuum.io

.. code-block:: bash

    $ conda install mingw libpython

Alternative: Python(x,y)
++++++++++++++++++++++++

If you do not have a commercial licence of EPD, and are not eligible to a free
academic licence, and neither
Python nor MinGW is installed on your computer, you can install most
dependencies of Theano with `Python(x,y) <http://www.pythonxy.com>`__.
It is a single installation
file that contains additional packages like NumPy, SciPy, IPython, Matplotlib,
MinGW, Nose, etc.
Note however that there is no 64 bit version currently available.
You can keep the default install options, except that the installation
directory should not contain any blank space (in particular, do not install it
into ``C:\Program Files``).

Alternative: manual installation
++++++++++++++++++++++++++++++++

The following instructions provide steps for manual installation of all Theano
dependencies.
Note that it should be
possible to run Theano with `Cygwin <http://www.cygwin.com/>`__ instead of
MinGW, but this has not been tested yet.

- For 32 bit MinGW: from `the MinGW files <http://sourceforge.net/projects/mingw/files/>`__,
  download the latest version of the ``Automated MinGW Installer``
  (``mingw-get-inst``) and install it (you should install all optional components,
  except the Objective C and Ada compilers which are not needed).

- For 64 bit MinGW (**note that manual installation for 64 bit is experimental**):
  download the latest version of MinGW-w64 from the project's
  `releases page <http://sourceforge.net/projects/mingw-w64/files/>`__, and extract
  it for instance to ``C:\mingw64``. Also download MSYS from
  `this page <http://sourceforge.net/projects/mingw-w64/files/External%20binary%20packages%20%28Win64%20hosted%29/MSYS%20%2832-bit%29/>`__
  (although it is a 32-bit version of MSYS, this does not matter since it is only
  a convenience tool). Extract MSYS into the same folder, so that, for instance, you
  end up with ``C:\mingw64\msys``. Run ``C:\mingw64\msys\msys.bat`` and in the MSYS
  shell, type

   .. code-block:: bash

       sh /postinstall/pi.sh

  and answer the few questions so that MSYS is properly linked to your MinGW install.

- It is recommended to set your MSYS home to be the same as your Windows home
  directory. This will avoid inconsistent behavior between running Theano
  in a Windows command prompt vs. a MSYS shell. One way to do this without
  setting a global Windows ``HOME`` environment variable (which may affect
  other programs) is to edit your ``msys.bat`` file (found e.g. under
  ``C:\MinGW\msys\1.0`` or ``C:\mingw64\msys``) and add the following line at
  the beginning (note that you may need to use e.g. Wordpad to edit this file,
  since Notepad gets confused by Unix-style line breaks):

    .. code-block:: bash

        set HOME=%USERPROFILE%

- If you do not have them already, install the latest versions of
  `Python 2.x <http://www.python.org/download/windows>`__ and
  corresponding `NumPy <http://sourceforge.net/projects/numpy/files/>`__
  then `SciPy <http://sourceforge.net/projects/scipy/files/>`__
  packages (simply use the executable installers).
  Note that there are currently no official 64 bit releases of NumPy and
  SciPy, but you can find unofficial builds
  `here <http://www.lfd.uci.edu/~gohlke/pythonlibs/>`__.

- Ensure that the Python installation directory and its ``Scripts``
  sub-directory are in your system path. This may be done by
  modifying the global ``PATH`` Windows environment variables, or by creating
  a ``.profile`` file in your MinGW home, containing a line like
  ``export PATH=$PATH:/c/Python27:/c/Python27/Scripts`` (note that the latter
  will work only when you run Theano from an MSYS shell).

- If you are installing the 64 bit version, you will need the following hack
  to be able to compile Theano files with GCC (skip this step if you are using
  the 32 bit version). In a temporary work directory, copy ``python27.dll``
  (found in ``C:\\Windows\\System32``) as well as
  `python27.def <http://wiki.cython.org/InstallingOnWindows?action=AttachFile&do=get&target=python27.def>`__.
  Edit ``python27.def`` and replace ``Py_InitModule4`` with ``Py_InitModule4_64``.
  Then open an MSYS shell, go to this temporary directory, and run:

    .. code-block:: bash

        dlltool --dllname python27.dll --def python27.def --output-lib libpython27.a

  Finally, copy the libpython27.a file that was generated into your
  ``C:\\Python27\\libs`` folder.

- In order to run Theano's test-suite, you will need `nose
  <http://somethingaboutorange.com/mrl/projects/nose>`__.
  After unpacking its source code (you may use `7-zip
  <http://www.7-zip.org/>`__), you can build and install it from within
  its code directory by running the following command (either from a Windows
  command prompt or an MSYS shell):

    .. code-block:: bash

        python setup.py install





Configuring the Environment
~~~~~~~~~~~~~~~~~~~~~~~~~~~

At this point, you should have installed all Theano dependencies.
By default neither Python, GCC, nor Visual Studio was added to the
PATH. Save the following shell script as ``c:\scisoft\env.bat`` to
configure the system path:

.. code-block:: cmd

   REM configuration of paths
   set VSFORPYTHON="C:\Program Files (x86)\Common Files\Microsoft\Visual C++ for Python\9.0"
   set SCISOFT=%~dp0

   REM add tdm gcc stuff
   set PATH=%SCISOFT%\TDM-GCC-64\bin;%SCISOFT%\TDM-GCC-64\x86_64-w64-mingw32\bin;%PATH%

   REM add winpython stuff
   CALL %SCISOFT%\WinPython-64bit-2.7.9.4\scripts\env.bat

   REM configure path for msvc compilers
   REM for a 32 bit installation change this line to
   REM CALL %VSFORPYTHON%\vcvarsall.bat
   CALL %VSFORPYTHON%\vcvarsall.bat amd64

   REM return a shell
   cmd.exe /k

The script assumes that you installed WinPython distribution, update the winpython line otherwise.
For a 32 bit installation please change the indicated line to load
32-bit Microsoft Compilers.

You can access the Python shell by double-clicking on
``c:\scisoft\env.bat``. Please do so, and verify that the following
programs are found:

1. where gcc
2. where gendef
3. where cl
4. where nvcc

Finally we need to create a link library for GCC. Open up the Python
shell and ``cd`` to ``c:\SciSoft``. Then execute:

.. code-block:: cmd

   gendef WinPython-64bit-2.7.9.4\python-2.7.9.amd64\python27.dll

   dlltool --dllname python27.dll --def python27.def --output-lib WinPython-64bit-2.7.9.4\python-2.7.9.amd64\libs\libpython27.a

Installing Theano
~~~~~~~~~~~~~~~~~

Once the dependencies are installed, you can download and install
Theano. We have found that in the long run, the Git install is the
most useful, because you can update it with a single ``git pull``
command. Therefore we recommend it. However, a manual install without
Git is also possible.

Git Install
###########


Theano is hosted on GitHub, you need Git to download it. For Windows,
download and install the `MSYSGIT <http://msysgit.github.io/>`_ build.
Open up the `Git Shell` in the directory in which you want to install
Theano. For the bleeding-edge version execute

.. code-block:: bash

  git clone https://github.com/Theano/Theano.git

For the latest stable release 0.7 (as of March 2015) run instead:

.. code-block:: bash

  git clone https://github.com/Theano/Theano.git --branch rel-0.7

Either way, a folder `Theano` will be created with the library
downloaded to it.


Manual Installation
###################

To get the latest bleeding edge version got to `Theano on GitHub
<https://github.com/Theano/Theano>`_ and download the `latest zip
<https://github.com/Theano/Theano/archive/master.zip>`_. Then unzip it
somewhere.

Alternatively, you can check the latest release release 0.7 (as of March
2015) by going to
https://github.com/Theano/Theano/releases/tag/rel-0.7 and
downloading the `zip
<https://github.com/Theano/Theano/archive/rel-0.7.zip>`_.

Configuring Theano
##################

Once you have installed Theano, open the Python Shell
(e.g. ``c:\scisoft\env.bat`` if you follow the installation directories
from this tutorial) and ``cd`` to it. Then run::

  python setup.py develop

this step will add the Theano directory to you ``PYTHON_PATH``
environment variable.

At this stage you can check whether Theano works and is able to
compile C code for CPU execution.

Create a test file containing:

.. code-block:: python

   import numpy as np
   import time
   import theano
   A = np.random.rand(1000,10000).astype(theano.config.floatX)
   B = np.random.rand(10000,1000).astype(theano.config.floatX)
   np_start = time.time()
   AB = A.dot(B)
   np_end = time.time()
   X,Y = theano.tensor.matrices('XY')
   mf = theano.function([X,Y],X.dot(Y))
   t_start = time.time()
   tAB = mf(A,B)
   t_end = time.time()
   print "NP time: %f[s], theano time: %f[s] (times should be close when run on CPU!)" %(
                                              np_end-np_start, t_end-t_start)
   print "Result difference: %f" % (np.abs(AB-tAB).max(), )

Then run it. It should execute without problems and the Theano function
should run at a speed similar to the regular NumPy
multiplication. (Both Numpy and Theano should call the same BLAS
routine for matrix multiplication)


Configure Theano for GPU use
############################

Theano can be configured with a ``.theanorc`` text file (or
``.theanorc.txt``, whichever is easier for you to create under
Windows). It should be placed in the directory pointed to by the
``%USERPROFILE%`` variable. Please note, that WinPython changes it to
``WinPythonDir\settings`` (so in our system this corresponds to
``c:\scisoft\WinPython-64bit-2.7.9.4\settings``.

To use the GPU please write the following configuration file:

.. code-block:: cfg

   [global]
   device = gpu
   floatX = float32

   [nvcc]
   flags = --use-local-env  --cl-version=2008

Rerun the simple test file and verify that it runs. Depending on you
GPU, the theano function should run on the GPU much faster than the
CPU matrix multiplication performed by NumPy.
You can also find additional test code and useful GPU tips on the
:ref:`using_gpu` page.

Running Theano's test-suite
###########################

Currently, due to memory fragmentation issue in Windows, the
test-suite breaks at some point when using ``theano-nose``, with many error
messages looking
like: ``DLL load failed: Not enough storage is available to process this
command``. As a workaround, you can instead run:

    .. code-block:: bash

        theano-nose --batch

This will run tests in batches of 100, which should avoid memory errors.
Note that this script calls ``nosetests``, which may require being run from
within an MSYS shell if you installed Nose manually as described above.

.. note::

    In Theano versions <= 0.5, ``theano-nose`` was not included.  If you
    are working with such a version, you can call this command instead:

    .. code-block:: bash

        python theano/tests/run_tests_in_batch.py

Compiling a faster BLAS
~~~~~~~~~~~~~~~~~~~~~~~

If you installed Python through WinPython or EPD, Theano will automatically 
link with the MKL library, so you should not need to compile your own BLAS.

.. note::

   The instructions below have not been tested in a Windows 64 bit environment.

If you want a faster and/or multi-threaded BLAS library, you can
compile OpenBLAS (ATLAS may work too, but was not tested, and is
usually reported to be slower and more difficult to compile -- especially
on Windows).
OpenBLAS can be downloaded as a zip file from
`its website <http://xianyi.github.io/OpenBLAS/>`__
(we tested v0.2.6).
To compile it, you will also need MSYS and wget (installation steps are
described below).

If you already have a full install of MinGW, you should
have MSYS included in it, and thus should be able to start a MinGW shell.
If that is the case, you can skip the following MSYS installation steps.
Note that these steps were written for Python(x,y), but should also work
for other bundle Python distributions like EPD (changing paths accordingly,
for instance in EPD 7.3.2 the MinGW folder is
``EPD7.3.2\EGG-INFO\mingw\usr\i686-w64-mingw32``).
To install MSYS on top of the MinGW installation included within Python(x,y),
do as follows:

- Download the `mingw-get command-line installer binary
  <http://sourceforge.net/projects/mingw/files/Installer/mingw-get/>`__.

- Unpack its content into your ``pythonxy\mingw`` directory.

- In a prompt (``cmd``), install MSYS with

    .. code-block:: bash

        mingw-get install msys-base

  If ``mingw-get`` cannot be found automatically, just navigate first into the
  folder were it was extracted (it is found in the ``bin`` subfolder).

- Edit ``pythonxy\mingw\msys\1.0\msys.bat`` (e.g. in Wordpad) and add as first
  line ``set HOME=%USERPROFILE%``. Then create an easily accessible shortcut
  (e.g. on your desktop) to this file, run it and within the MSYS
  console, run the MSYS post-install script:

    .. code-block:: bash

        /postinstall/pi.sh

  It will ask for your MinGW installation directory (e.g.
  ``c:/pythonxy/mingw``; note the forward slashes).

Once you have a working MinGW/MSYS shell environment, you can go on as
follows:

  a) Install ``wget`` by running the setup program you can download on the
     `wget website <http://gnuwin32.sourceforge.net/packages/wget.htm>`__.
     Note that this setup does not add ``wget`` into the system PATH, so you
     will need to modify the ``PATH`` environment variable accordingly (either in
     Windows or in a ``.profile`` startup file in your MinGW home). Once this is done,
     type ``wget --version`` in a MinGW shell to verify that it is running
     properly. Note also that if you are behind a proxy, you should set up your
     ``HTTP_PROXY`` environment variable, or use a custom ``wgetrc`` config file
     for wget to be able to download files.

  b) Unzip OpenBLAS and, in a MinGW shell, go into the corresponding directory.

  c) Compile OpenBLAS with:

    .. code-block:: bash

      quickbuild.win32 1>log.txt 2>err.txt

    (use ``quickbuild.win64`` for 64-bit Windows).
    Compilation can take a while, so now is a good time to take a break.
    When it is done, you should have ``libopenblas.dll`` in your OpenBLAS
    folder. If that is not the case, check the ``err.txt`` log for build errors.

  d) Make sure that ``libopenblas.dll`` is in a folder that is in your ``PATH``.

  e) Modify your .theanorc (or .theanorc.txt) with
     ``ldflags = -LX:\\YYY\\ZZZ -lopenblas`` where ``X:\\YYY\\ZZZ`` is the path
     to the folder containing ``libopenblas.dll``.
     This setting can also be changed in Python for testing purpose (in which
     case it will remain only for the duration of your Python session):

    .. code-block:: python

        theano.config.blas.ldflags = "-LX:\\YYY\\YYY -lopenblas"

  f) To test the BLAS performance, you can run the script
     ``theano/misc/check_blas.py``.
     Note that you may control the number of threads used by OpenBLAS with
     the ``OPENBLAS_NUM_THREADS`` environment variable (default behavior is to use
     all available cores).
     Here are some performance results on an Intel Core2 Duo 1.86 GHz,
     compared to using NumPy's BLAS or the un-optimized standard BLAS
     (compiled manually from its source code).
     Note that we report here results for GotoBLAS2 which is the ancestor of
     OpenBLAS (this benchmark still needs to be updated with OpenBLAS results):

         * GotoBLAS2 (2 threads): 16s
         * NumPy (1 thread): 48s
         * Standard BLAS (un-optimized, 1 thread): 166s

     Conclusions:
        * The unoptimized standard BLAS is very slow and should not be used.
        * The Windows binaries of NumPy were compiled with ATLAS and are surprisingly fast.
        * GotoBLAS2 is even faster, in particular if you can use multiple cores.

.. note::

   If you get a ``DLL load failed`` error message, it typically means that
   a required DLL was not found in the PATH. If it happens only when you are
   using OpenBLAS, it means it is either ``libopenblas.dll`` itself or one of its
   dependencies. In the case where it is a dependency, you can use the
   `Dependency Walker <http://www.dependencywalker.com/>`__ utility to figure out
   which one.


.. _gpu_windows:
