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_2020.0.166.7z -k -O
7z x -aoa mkl_2020.0.166.7z -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)
REM 2.5.0 (CUDA 9.0 9.2 10.0 10.1) x (Debug Release)
REM 2.4.0 (CUDA 8.0 9.2) x (Release)
set CUDA_PREFIX=cuda101
set CONFIG=release
curl -k https://s3.amazonaws.com/ossci-windows/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. There're
generally two steps to enable it under Windows.
First, 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
)
Second, here is a workground for "unresolved external symbol
state caused by ``extern THCState *state;``"
Change the source code from C to C++. An example is listed below.
.. code-block:: cpp
#include <THC/THC.h>
#include <ATen/ATen.h>
THCState *state = at::globalContext().thc_state;
extern "C" int my_lib_add_forward_cuda(THCudaTensor *input1, THCudaTensor *input2,
THCudaTensor *output)
{
if (!THCudaTensor_isSameSizeAs(state, input1, input2))
return 0;
THCudaTensor_resizeAs(state, output, input1);
THCudaTensor_cadd(state, output, input1, 1.0, input2);
return 1;
}
extern "C" int my_lib_add_backward_cuda(THCudaTensor *grad_output, THCudaTensor *grad_input)
{
THCudaTensor_resizeAs(state, grad_input, grad_output);
THCudaTensor_fill(state, grad_input, 1);
return 1;
}
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://conda.anaconda.org/pytorch/win-32
- https://conda.anaconda.org/pytorch/noarch
- 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. Actually,
we include almost all the essential files that PyTorch need for the conda
package except VC2017 redistributable and some mkl libraries.
You can resolve this by typing the following command.
.. code-block:: bat
conda install -c peterjc123 vc vs2017_runtime
conda install mkl_fft intel_openmp numpy mkl
As 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
Another possible cause may be you are using GPU version without NVIDIA
graphics cards. Please replace your GPU package with the CPU one.
.. code-block:: python
from torch._C import *
ImportError: DLL load failed: The operating system cannot run %1.
This is actually an upstream issue of Anaconda. When you initialize your
environment with conda-forge channel, this issue will emerge. You may fix
the intel-openmp libraries through this command.
.. code-block:: bat
conda install -c defaults intel-openmp -f
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.