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.
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 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.
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. It will lead the way for you.
The support for CFFI Extension is very experimental. You must specify
Extension object to make it build on
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 )
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.
Package not found in win-32 channel.¶
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.
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.
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 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.
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.
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.
conda install -c defaults intel-openmp -f
Multiprocessing error without if-clause protection¶
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
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.
import torch def main() for i, data in enumerate(dataloader): # do something here if __name__ == '__main__': main()
Multiprocessing error “Broken pipe”¶
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
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.
CUDA IPC operations¶
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
DataLoader to zero.
2. Share CPU tensors instead. Make sure your custom
DataSet returns CPU tensors.