Windows FAQ
==========================

Building from source
--------------------

Include optional components
^^^^^^^^^^^^^^^^^^^^^^^^^^^

There are two supported components for Windows PyTorch:
MKL and MAGMA. Here are the steps to build with them.

.. code-block:: bat

    REM Make sure you have 7z and curl installed.

    REM Download MKL files
    curl https://s3.amazonaws.com/ossci-windows/mkl_2020.2.254.7z -k -O
    7z x -aoa mkl_2020.2.254.7z -omkl

    REM Download MAGMA files
    REM version available:
    REM 2.5.4 (CUDA 10.1 10.2 11.0 11.1) x (Debug Release)
    REM 2.5.3 (CUDA 10.1 10.2 11.0) x (Debug Release)
    REM 2.5.2 (CUDA 9.2 10.0 10.1 10.2) x (Debug Release)
    REM 2.5.1 (CUDA 9.2 10.0 10.1 10.2) x (Debug Release)
    set "CUDA_PREFIX=cuda102"
    set "CONFIG=release"
    set "HOST=https://s3.amazonaws.com/ossci-windows"
    curl -k "%HOST%/magma_2.5.4_%CUDA_PREFIX%_%CONFIG%.7z" -o magma.7z
    7z x -aoa magma.7z -omagma

    REM Setting essential environment variables
    set "CMAKE_INCLUDE_PATH=%cd%\mkl\include"
    set "LIB=%cd%\mkl\lib;%LIB%"
    set "MAGMA_HOME=%cd%\magma"

Speeding CUDA build for Windows
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Visual Studio doesn't support parallel custom task currently.
As an alternative, we can use ``Ninja`` to parallelize CUDA
build tasks. It can be used by typing only a few lines of code.

.. code-block:: bat

    REM Let's install ninja first.
    pip install ninja

    REM Set it as the cmake generator
    set CMAKE_GENERATOR=Ninja


One key install script
^^^^^^^^^^^^^^^^^^^^^^

You can take a look at `this set of scripts
<https://github.com/peterjc123/pytorch-scripts>`_.
It will lead the way for you.

Extension
---------

CFFI Extension
^^^^^^^^^^^^^^

The support for CFFI Extension is very experimental. You must specify
additional ``libraries`` in ``Extension`` object to make it build on
Windows.

.. code-block:: python

   ffi = create_extension(
       '_ext.my_lib',
       headers=headers,
       sources=sources,
       define_macros=defines,
       relative_to=__file__,
       with_cuda=with_cuda,
       extra_compile_args=["-std=c99"],
       libraries=['ATen', '_C'] # Append cuda libraries when necessary, like cudart
   )

Cpp Extension
^^^^^^^^^^^^^

This type of extension has better support compared with
the previous one. However, it still needs some manual
configuration. First, you should open the
**x86_x64 Cross Tools Command Prompt for VS 2017**.
And then, you can start your compiling process.

Installation
------------

Package not found in win-32 channel.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: bat

    Solving environment: failed

    PackagesNotFoundError: The following packages are not available from current channels:

    - pytorch

    Current channels:
    - https://repo.continuum.io/pkgs/main/win-32
    - https://repo.continuum.io/pkgs/main/noarch
    - https://repo.continuum.io/pkgs/free/win-32
    - https://repo.continuum.io/pkgs/free/noarch
    - https://repo.continuum.io/pkgs/r/win-32
    - https://repo.continuum.io/pkgs/r/noarch
    - https://repo.continuum.io/pkgs/pro/win-32
    - https://repo.continuum.io/pkgs/pro/noarch
    - https://repo.continuum.io/pkgs/msys2/win-32
    - https://repo.continuum.io/pkgs/msys2/noarch

PyTorch doesn't work on 32-bit system. Please use Windows and
Python 64-bit version.


Import error
^^^^^^^^^^^^

.. code-block:: python

    from torch._C import *

    ImportError: DLL load failed: The specified module could not be found.


The problem is caused by the missing of the essential files.
For the wheels package, since we didn't pack some libraries and VS2017
redistributable files in, please make sure you install them manually.
The `VS 2017 redistributable installer
<https://aka.ms/vs/15/release/VC_redist.x64.exe>`_ can be downloaded.
And you should also pay attention to your installation of Numpy. Make sure it
uses MKL instead of OpenBLAS. You may type in the following command.

.. code-block:: bat

    pip install numpy mkl intel-openmp mkl_fft


Usage (multiprocessing)
-------------------------------------------------------

Multiprocessing error without if-clause protection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: python

    RuntimeError:
           An attempt has been made to start a new process before the
           current process has finished its bootstrapping phase.

       This probably means that you are not using fork to start your
       child processes and you have forgotten to use the proper idiom
       in the main module:

           if __name__ == '__main__':
               freeze_support()
               ...

       The "freeze_support()" line can be omitted if the program
       is not going to be frozen to produce an executable.

The implementation of ``multiprocessing`` is different on Windows, which
uses ``spawn`` instead of ``fork``. So we have to wrap the code with an
if-clause to protect the code from executing multiple times. Refactor
your code into the following structure.

.. code-block:: python

    import torch

    def main()
        for i, data in enumerate(dataloader):
            # do something here

    if __name__ == '__main__':
        main()


Multiprocessing error "Broken pipe"
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: python

    ForkingPickler(file, protocol).dump(obj)

    BrokenPipeError: [Errno 32] Broken pipe

This issue happens when the child process ends before the parent process
finishes sending data. There may be something wrong with your code. You
can debug your code by reducing the ``num_worker`` of
:class:`~torch.utils.data.DataLoader` to zero and see if the issue persists.

Multiprocessing error "driver shut down"
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

::

    Couldn’t open shared file mapping: <torch_14808_1591070686>, error code: <1455> at torch\lib\TH\THAllocator.c:154

    [windows] driver shut down

Please update your graphics driver. If this persists, this may be that your
graphics card is too old or the calculation is too heavy for your card. Please
update the TDR settings according to this `post
<https://www.pugetsystems.com/labs/hpc/Working-around-TDR-in-Windows-for-a-better-GPU-computing-experience-777/>`_.

CUDA IPC operations
^^^^^^^^^^^^^^^^^^^

.. code-block:: python

   THCudaCheck FAIL file=torch\csrc\generic\StorageSharing.cpp line=252 error=63 : OS call failed or operation not supported on this OS

They are not supported on Windows. Something like doing multiprocessing on CUDA
tensors cannot succeed, there are two alternatives for this.

1. Don't use ``multiprocessing``. Set the ``num_worker`` of
:class:`~torch.utils.data.DataLoader` to zero.

2. Share CPU tensors instead. Make sure your custom
:class:`~torch.utils.data.DataSet` returns CPU tensors.
