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<div class="section" id="windows-faq">
<h1>Windows FAQ<a class="headerlink" href="#windows-faq" title="Permalink to this headline">¶</a></h1>
<div class="section" id="building-from-source">
<h2>Building from source<a class="headerlink" href="#building-from-source" title="Permalink to this headline">¶</a></h2>
<div class="section" id="include-optional-components">
<h3>Include optional components<a class="headerlink" href="#include-optional-components" title="Permalink to this headline">¶</a></h3>
<p>There are two supported components for Windows PyTorch:
MKL and MAGMA. Here are the steps to build with them.</p>
<div class="highlight-bat"><div class="highlight"><pre><span></span><span class="c1">REM Make sure you have 7z and curl installed.</span>
<span class="c1">REM Download MKL files</span>
curl https://s3.amazonaws.com/ossci-windows/mkl_2018.2.185.7z -k -O
7z x -aoa mkl_2018.2.185.7z -omkl
<span class="c1">REM Download MAGMA files</span>
<span class="c1">REM cuda90/cuda92/cuda100 is also available in the following line.</span>
<span class="k">set</span> <span class="nv">CUDA_PREFIX</span><span class="p">=</span>cuda80
curl -k https://s3.amazonaws.com/ossci-windows/magma_2.4.0_<span class="nv">%CUDA_PREFIX%</span>_release.7z -o magma.7z
7z x -aoa magma.7z -omagma
<span class="c1">REM Setting essential environment variables</span>
<span class="k">set</span> <span class="s2">"CMAKE_INCLUDE_PATH=</span><span class="nv">%cd%</span><span class="s2">\\mkl\\include"</span>
<span class="k">set</span> <span class="s2">"LIB=</span><span class="nv">%cd%</span><span class="s2">\\mkl\\lib;</span><span class="nv">%LIB%</span><span class="s2">"</span>
<span class="k">set</span> <span class="s2">"MAGMA_HOME=</span><span class="nv">%cd%</span><span class="s2">\\magma"</span>
</pre></div>
</div>
</div>
<div class="section" id="speeding-cuda-build-for-windows">
<h3>Speeding CUDA build for Windows<a class="headerlink" href="#speeding-cuda-build-for-windows" title="Permalink to this headline">¶</a></h3>
<p>Visual Studio doesn’t support parallel custom task currently.
As an alternative, we can use <code class="docutils literal"><span class="pre">Ninja</span></code> to parallelize CUDA
build tasks. It can be used by typing only a few lines of code.</p>
<div class="highlight-bat"><div class="highlight"><pre><span></span><span class="c1">REM Let's install ninja first.</span>
pip install ninja
<span class="c1">REM Set it as the cmake generator</span>
<span class="k">set</span> <span class="nv">CMAKE_GENERATOR</span><span class="p">=</span>Ninja
</pre></div>
</div>
</div>
<div class="section" id="one-key-install-script">
<h3>One key install script<a class="headerlink" href="#one-key-install-script" title="Permalink to this headline">¶</a></h3>
<p>You can take a look at <a class="reference external" href="https://github.com/peterjc123/pytorch-scripts">this set of scripts</a>.
It will lead the way for you.</p>
</div>
</div>
<div class="section" id="extension">
<h2>Extension<a class="headerlink" href="#extension" title="Permalink to this headline">¶</a></h2>
<div class="section" id="cffi-extension">
<h3>CFFI Extension<a class="headerlink" href="#cffi-extension" title="Permalink to this headline">¶</a></h3>
<p>The support for CFFI Extension is very experimental. There’re
generally two steps to enable it under Windows.</p>
<p>First, specify additional <code class="docutils literal"><span class="pre">libraries</span></code> in <code class="docutils literal"><span class="pre">Extension</span></code>
object to make it build on Windows.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">ffi</span> <span class="o">=</span> <span class="n">create_extension</span><span class="p">(</span>
<span class="s1">'_ext.my_lib'</span><span class="p">,</span>
<span class="n">headers</span><span class="o">=</span><span class="n">headers</span><span class="p">,</span>
<span class="n">sources</span><span class="o">=</span><span class="n">sources</span><span class="p">,</span>
<span class="n">define_macros</span><span class="o">=</span><span class="n">defines</span><span class="p">,</span>
<span class="n">relative_to</span><span class="o">=</span><span class="vm">__file__</span><span class="p">,</span>
<span class="n">with_cuda</span><span class="o">=</span><span class="n">with_cuda</span><span class="p">,</span>
<span class="n">extra_compile_args</span><span class="o">=</span><span class="p">[</span><span class="s2">"-std=c99"</span><span class="p">],</span>
<span class="n">libraries</span><span class="o">=</span><span class="p">[</span><span class="s1">'ATen'</span><span class="p">,</span> <span class="s1">'_C'</span><span class="p">]</span> <span class="c1"># Append cuda libaries when necessary, like cudart</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Second, here is a workground for “unresolved external symbol
state caused by <code class="docutils literal"><span class="pre">extern</span> <span class="pre">THCState</span> <span class="pre">*state;</span></code>”</p>
<p>Change the source code from C to C++. An example is listed below.</p>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="cp">#include</span> <span class="cpf"><THC/THC.h></span><span class="cp"></span>
<span class="cp">#include</span> <span class="cpf"><ATen/ATen.h></span><span class="cp"></span>
<span class="n">THCState</span> <span class="o">*</span><span class="n">state</span> <span class="o">=</span> <span class="n">at</span><span class="o">::</span><span class="n">globalContext</span><span class="p">().</span><span class="n">thc_state</span><span class="p">;</span>
<span class="k">extern</span> <span class="s">"C"</span> <span class="kt">int</span> <span class="n">my_lib_add_forward_cuda</span><span class="p">(</span><span class="n">THCudaTensor</span> <span class="o">*</span><span class="n">input1</span><span class="p">,</span> <span class="n">THCudaTensor</span> <span class="o">*</span><span class="n">input2</span><span class="p">,</span>
<span class="n">THCudaTensor</span> <span class="o">*</span><span class="n">output</span><span class="p">)</span>
<span class="p">{</span>
<span class="k">if</span> <span class="p">(</span><span class="o">!</span><span class="n">THCudaTensor_isSameSizeAs</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="p">))</span>
<span class="k">return</span> <span class="mi">0</span><span class="p">;</span>
<span class="n">THCudaTensor_resizeAs</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">input1</span><span class="p">);</span>
<span class="n">THCudaTensor_cadd</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">input1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">input2</span><span class="p">);</span>
<span class="k">return</span> <span class="mi">1</span><span class="p">;</span>
<span class="p">}</span>
<span class="k">extern</span> <span class="s">"C"</span> <span class="kt">int</span> <span class="n">my_lib_add_backward_cuda</span><span class="p">(</span><span class="n">THCudaTensor</span> <span class="o">*</span><span class="n">grad_output</span><span class="p">,</span> <span class="n">THCudaTensor</span> <span class="o">*</span><span class="n">grad_input</span><span class="p">)</span>
<span class="p">{</span>
<span class="n">THCudaTensor_resizeAs</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="n">grad_input</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">);</span>
<span class="n">THCudaTensor_fill</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="n">grad_input</span><span class="p">,</span> <span class="mi">1</span><span class="p">);</span>
<span class="k">return</span> <span class="mi">1</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
<div class="section" id="cpp-extension">
<h3>Cpp Extension<a class="headerlink" href="#cpp-extension" title="Permalink to this headline">¶</a></h3>
<p>This type of extension has better support compared with
the previous one. However, it still needs some manual
configuration. First, you should open the
<strong>x86_x64 Cross Tools Command Prompt for VS 2017</strong>.
And then, you can open the Git-Bash in it. It is
usually located in <code class="docutils literal"><span class="pre">C:\Program</span> <span class="pre">Files\Git\git-bash.exe</span></code>.
Finally, you can start your compiling process.</p>
</div>
</div>
<div class="section" id="installation">
<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h2>
<div class="section" id="package-not-found-in-win-32-channel">
<h3>Package not found in win-32 channel.<a class="headerlink" href="#package-not-found-in-win-32-channel" title="Permalink to this headline">¶</a></h3>
<div class="highlight-bat"><div class="highlight"><pre><span></span>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
</pre></div>
</div>
<p>PyTorch doesn’t work on 32-bit system. Please use Windows and
Python 64-bit version.</p>
</div>
<div class="section" id="why-are-there-no-python-2-packages-for-windows">
<h3>Why are there no Python 2 packages for Windows?<a class="headerlink" href="#why-are-there-no-python-2-packages-for-windows" title="Permalink to this headline">¶</a></h3>
<p>Because it’s not stable enough. There’re some issues that need to
be solved before we officially release it. You can build it by yourself.</p>
</div>
<div class="section" id="import-error">
<h3>Import error<a class="headerlink" href="#import-error" title="Permalink to this headline">¶</a></h3>
<div class="highlight-py3tb"><div class="highlight"><pre><span></span>from torch._C import *
ImportError: DLL load failed: The specified module could not be found.
</pre></div>
</div>
<p>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.</p>
<div class="highlight-bat"><div class="highlight"><pre><span></span>conda install -c peterjc123 vc vs2017_runtime
conda install mkl_fft intel_openmp numpy mkl
</pre></div>
</div>
<p>As for the wheels package, since we didn’t pack some libaries and VS2017
redistributable files in, please make sure you install them manually.
The <a class="reference external" href="https://aka.ms/vs/15/release/VC_redist.x64.exe">VS 2017 redistributable installer</a> 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.</p>
<div class="highlight-bat"><div class="highlight"><pre><span></span>pip install numpy mkl intel-openmp mkl_fft
</pre></div>
</div>
<p>Another possible cause may be you are using GPU version without NVIDIA
graphics cards. Please replace your GPU package with the CPU one.</p>
<div class="highlight-py3tb"><div class="highlight"><pre><span></span>from torch._C import *
ImportError: DLL load failed: The operating system cannot run %1.
</pre></div>
</div>
<p>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.</p>
<div class="highlight-bat"><div class="highlight"><pre><span></span>conda install -c defaults intel-openmp -f
</pre></div>
</div>
</div>
</div>
<div class="section" id="usage-multiprocessing">
<h2>Usage (multiprocessing)<a class="headerlink" href="#usage-multiprocessing" title="Permalink to this headline">¶</a></h2>
<div class="section" id="multiprocessing-error-without-if-clause-protection">
<h3>Multiprocessing error without if-clause protection<a class="headerlink" href="#multiprocessing-error-without-if-clause-protection" title="Permalink to this headline">¶</a></h3>
<div class="highlight-py3tb"><div class="highlight"><pre><span></span>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.
</pre></div>
</div>
<p>The implementation of <code class="docutils literal"><span class="pre">multiprocessing</span></code> is different on Windows, which
uses <code class="docutils literal"><span class="pre">spawn</span></code> instead of <code class="docutils literal"><span class="pre">fork</span></code>. 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.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="k">def</span> <span class="nf">main</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span>
<span class="c1"># do something here</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
<span class="n">main</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="multiprocessing-error-broken-pipe">
<h3>Multiprocessing error “Broken pipe”<a class="headerlink" href="#multiprocessing-error-broken-pipe" title="Permalink to this headline">¶</a></h3>
<div class="highlight-py3tb"><div class="highlight"><pre><span></span>ForkingPickler(file, protocol).dump(obj)
BrokenPipeError: [Errno 32] Broken pipe
</pre></div>
</div>
<p>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 <code class="docutils literal"><span class="pre">num_worker</span></code> of
<a class="reference internal" href="../data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader"><code class="xref py py-class docutils literal"><span class="pre">DataLoader</span></code></a> to zero and see if the issue persists.</p>
</div>
<div class="section" id="multiprocessing-error-driver-shut-down">
<h3>Multiprocessing error “driver shut down”<a class="headerlink" href="#multiprocessing-error-driver-shut-down" title="Permalink to this headline">¶</a></h3>
<div class="highlight-py3tb"><div class="highlight"><pre><span></span>Couldn’t open shared file mapping: <torch_14808_1591070686>, error code: <1455> at torch\lib\TH\THAllocator.c:154
[windows] driver shut down
</pre></div>
</div>
<p>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 <a class="reference external" href="https://www.pugetsystems.com/labs/hpc/Working-around-TDR-in-Windows-for-a-better-GPU-computing-experience-777/">post</a>.</p>
</div>
<div class="section" id="cuda-ipc-operations">
<h3>CUDA IPC operations<a class="headerlink" href="#cuda-ipc-operations" title="Permalink to this headline">¶</a></h3>
<div class="highlight-py3tb"><div class="highlight"><pre><span></span>THCudaCheck FAIL file=torch\csrc\generic\StorageSharing.cpp line=252 error=63 : OS call failed or operation not supported on this OS
</pre></div>
</div>
<p>They are not supported on Windows. Something like doing multiprocessing on CUDA
tensors cannot succeed, there are two alternatives for this.</p>
<p>1. Don’t use <code class="docutils literal"><span class="pre">multiprocessing</span></code>. Set the <code class="docutils literal"><span class="pre">num_worker</span></code> of
<a class="reference internal" href="../data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader"><code class="xref py py-class docutils literal"><span class="pre">DataLoader</span></code></a> to zero.</p>
<p>2. Share CPU tensors instead. Make sure your custom
<code class="xref py py-class docutils literal"><span class="pre">DataSet</span></code> returns CPU tensors.</p>
</div>
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