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<div class="section" id="multiprocessing-best-practices">
<span id="id1"></span><h1>Multiprocessing best practices<a class="headerlink" href="#multiprocessing-best-practices" title="Permalink to this heading">¶</a></h1>
<p><a class="reference internal" href="../multiprocessing.html#module-torch.multiprocessing" title="torch.multiprocessing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.multiprocessing</span></code></a> is a drop in replacement for Python’s
<a class="reference external" href="https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">multiprocessing</span></code></a> module. It supports the exact same operations,
but extends it, so that all tensors sent through a
<a class="reference external" href="https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">multiprocessing.Queue</span></code></a>, will have their data moved into shared
memory and will only send a handle to another process.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When a <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> is sent to another process, the
<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> data is shared. If <a class="reference internal" href="../generated/torch.Tensor.grad.html#torch.Tensor.grad" title="torch.Tensor.grad"><code class="xref py py-attr docutils literal notranslate"><span class="pre">torch.Tensor.grad</span></code></a> is
not <code class="docutils literal notranslate"><span class="pre">None</span></code>, it is also shared. After a <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> without
a <a class="reference internal" href="../generated/torch.Tensor.grad.html#torch.Tensor.grad" title="torch.Tensor.grad"><code class="xref py py-attr docutils literal notranslate"><span class="pre">torch.Tensor.grad</span></code></a> field is sent to the other process, it
creates a standard process-specific <code class="docutils literal notranslate"><span class="pre">.grad</span></code> <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> that
is not automatically shared across all processes, unlike how the
<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a>’s data has been shared.</p>
</div>
<p>This allows to implement various training methods, like Hogwild, A3C, or any
others that require asynchronous operation.</p>
<div class="section" id="cuda-in-multiprocessing">
<span id="multiprocessing-cuda-note"></span><h2>CUDA in multiprocessing<a class="headerlink" href="#cuda-in-multiprocessing" title="Permalink to this heading">¶</a></h2>
<p>The CUDA runtime does not support the <code class="docutils literal notranslate"><span class="pre">fork</span></code> start method; either the <code class="docutils literal notranslate"><span class="pre">spawn</span></code> or <code class="docutils literal notranslate"><span class="pre">forkserver</span></code> start method are
required to use CUDA in subprocesses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The start method can be set via either creating a context with
<code class="docutils literal notranslate"><span class="pre">multiprocessing.get_context(...)</span></code> or directly using
<code class="docutils literal notranslate"><span class="pre">multiprocessing.set_start_method(...)</span></code>.</p>
</div>
<p>Unlike CPU tensors, the sending process is required to keep the original tensor
as long as the receiving process retains a copy of the tensor. It is implemented
under the hood but requires users to follow the best practices for the program
to run correctly. For example, the sending process must stay alive as long as
the consumer process has references to the tensor, and the refcounting can not
save you if the consumer process exits abnormally via a fatal signal. See
<a class="reference internal" href="../multiprocessing.html#multiprocessing-cuda-sharing-details"><span class="std std-ref">this section</span></a>.</p>
<p>See also: <a class="reference internal" href="cuda.html#cuda-nn-ddp-instead"><span class="std std-ref">Use nn.parallel.DistributedDataParallel instead of multiprocessing or nn.DataParallel</span></a></p>
</div>
<div class="section" id="best-practices-and-tips">
<h2>Best practices and tips<a class="headerlink" href="#best-practices-and-tips" title="Permalink to this heading">¶</a></h2>
<div class="section" id="avoiding-and-fighting-deadlocks">
<h3>Avoiding and fighting deadlocks<a class="headerlink" href="#avoiding-and-fighting-deadlocks" title="Permalink to this heading">¶</a></h3>
<p>There are a lot of things that can go wrong when a new process is spawned, with
the most common cause of deadlocks being background threads. If there’s any
thread that holds a lock or imports a module, and <code class="docutils literal notranslate"><span class="pre">fork</span></code> is called, it’s very
likely that the subprocess will be in a corrupted state and will deadlock or
fail in a different way. Note that even if you don’t, Python built in
libraries do - no need to look further than <a class="reference external" href="https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">multiprocessing</span></code></a>.
<a class="reference external" href="https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">multiprocessing.Queue</span></code></a> is actually a very complex class, that
spawns multiple threads used to serialize, send and receive objects, and they
can cause aforementioned problems too. If you find yourself in such situation
try using a <code class="xref py py-class docutils literal notranslate"><span class="pre">SimpleQueue</span></code>, that doesn’t
use any additional threads.</p>
<p>We’re trying our best to make it easy for you and ensure these deadlocks don’t
happen but some things are out of our control. If you have any issues you can’t
cope with for a while, try reaching out on forums, and we’ll see if it’s an
issue we can fix.</p>
</div>
<div class="section" id="reuse-buffers-passed-through-a-queue">
<h3>Reuse buffers passed through a Queue<a class="headerlink" href="#reuse-buffers-passed-through-a-queue" title="Permalink to this heading">¶</a></h3>
<p>Remember that each time you put a <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> into a
<a class="reference external" href="https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">multiprocessing.Queue</span></code></a>, it has to be moved into shared memory.
If it’s already shared, it is a no-op, otherwise it will incur an additional
memory copy that can slow down the whole process. Even if you have a pool of
processes sending data to a single one, make it send the buffers back - this
is nearly free and will let you avoid a copy when sending next batch.</p>
</div>
<div class="section" id="asynchronous-multiprocess-training-e-g-hogwild">
<h3>Asynchronous multiprocess training (e.g. Hogwild)<a class="headerlink" href="#asynchronous-multiprocess-training-e-g-hogwild" title="Permalink to this heading">¶</a></h3>
<p>Using <a class="reference internal" href="../multiprocessing.html#module-torch.multiprocessing" title="torch.multiprocessing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.multiprocessing</span></code></a>, it is possible to train a model
asynchronously, with parameters either shared all the time, or being
periodically synchronized. In the first case, we recommend sending over the whole
model object, while in the latter, we advise to only send the
<a class="reference internal" href="../generated/torch.nn.Module.html#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a>.</p>
<p>We recommend using <a class="reference external" href="https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">multiprocessing.Queue</span></code></a> for passing all kinds
of PyTorch objects between processes. It is possible to e.g. inherit the tensors
and storages already in shared memory, when using the <code class="docutils literal notranslate"><span class="pre">fork</span></code> start method,
however it is very bug prone and should be used with care, and only by advanced
users. Queues, even though they’re sometimes a less elegant solution, will work
properly in all cases.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>You should be careful about having global statements, that are not guarded
with an <code class="docutils literal notranslate"><span class="pre">if</span> <span class="pre">__name__</span> <span class="pre">==</span> <span class="pre">'__main__'</span></code>. If a different start method than
<code class="docutils literal notranslate"><span class="pre">fork</span></code> is used, they will be executed in all subprocesses.</p>
</div>
<div class="section" id="hogwild">
<h4>Hogwild<a class="headerlink" href="#hogwild" title="Permalink to this heading">¶</a></h4>
<p>A concrete Hogwild implementation can be found in the <a class="reference external" href="https://github.com/pytorch/examples/tree/master/mnist_hogwild">examples repository</a>,
but to showcase the overall structure of the code, there’s also a minimal
example below as well:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
<span class="kn">from</span> <span class="nn">model</span> <span class="kn">import</span> <span class="n">MyModel</span>
<span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
<span class="c1"># Construct data_loader, optimizer, etc.</span>
<span class="k">for</span> <span class="n">data</span><span class="p">,</span> <span class="n">labels</span> <span class="ow">in</span> <span class="n">data_loader</span><span class="p">:</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="n">loss_fn</span><span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span> <span class="c1"># This will update the shared parameters</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">num_processes</span> <span class="o">=</span> <span class="mi">4</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span>
<span class="c1"># NOTE: this is required for the ``fork`` method to work</span>
<span class="n">model</span><span class="o">.</span><span class="n">share_memory</span><span class="p">()</span>
<span class="n">processes</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">rank</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_processes</span><span class="p">):</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">Process</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">model</span><span class="p">,))</span>
<span class="n">p</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
<span class="n">processes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">processes</span><span class="p">:</span>
<span class="n">p</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="cpu-in-multiprocessing">
<h2>CPU in multiprocessing<a class="headerlink" href="#cpu-in-multiprocessing" title="Permalink to this heading">¶</a></h2>
<p>Inappropriate multiprocessing can lead to CPU oversubscription, causing
different processes to compete for CPU resources, resulting in low
efficiency.</p>
<p>This tutorial will explain what CPU oversubscription is and how to
avoid it.</p>
<div class="section" id="cpu-oversubscription">
<h3>CPU oversubscription<a class="headerlink" href="#cpu-oversubscription" title="Permalink to this heading">¶</a></h3>
<p>CPU oversubscription is a technical term that refers to a situation
where the total number of vCPUs allocated to a system exceeds the total
number of vCPUs available on the hardware.</p>
<p>This leads to severe contention for CPU resources. In such cases, there
is frequent switching between processes, which increases processes
switching overhead and decreases overall system efficiency.</p>
<p>See CPU oversubscription with the code examples in the Hogwild
implementation found in the <a class="reference external" href="https://github.com/pytorch/examples/tree/main/mnist_hogwild">example
repository</a>.</p>
<p>When running the training example with the following command on CPU
using 4 processes:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>main.py<span class="w"> </span>--num-processes<span class="w"> </span><span class="m">4</span>
</pre></div>
</div>
<p>Assuming there are N vCPUs available on the machine, executing the above
command will generate 4 subprocesses. Each subprocess will allocate N
vCPUs for itself, resulting in a requirement of 4*N vCPUs. However, the
machine only has N vCPUs available. Consequently, the different
processes will compete for resources, leading to frequent process
switching.</p>
<p>The following observations indicate the presence of CPU over
subscription:</p>
<ol class="arabic simple">
<li><p>High CPU Utilization: By using the <code class="docutils literal notranslate"><span class="pre">htop</span></code> command, you can observe
that the CPU utilization is consistently high, often reaching or
exceeding its maximum capacity. This indicates that the demand for
CPU resources exceeds the available physical cores, causing
contention and competition among processes for CPU time.</p></li>
<li><p>Frequent Context Switching with Low System Efficiency: In an
oversubscribed CPU scenario, processes compete for CPU time, and the
operating system needs to rapidly switch between different processes
to allocate resources fairly. This frequent context switching adds
overhead and reduces the overall system efficiency.</p></li>
</ol>
</div>
<div class="section" id="avoid-cpu-oversubscription">
<h3>Avoid CPU oversubscription<a class="headerlink" href="#avoid-cpu-oversubscription" title="Permalink to this heading">¶</a></h3>
<p>A good way to avoid CPU oversubscription is proper resource allocation.
Ensure that the number of processes or threads running concurrently does
not exceed the available CPU resources.</p>
<p>In this case, a solution would be to specify the appropriate number of
threads in the subprocesses. This can be achieved by setting the number
of threads for each process using the <code class="docutils literal notranslate"><span class="pre">torch.set_num_threads(int)</span></code>
function in subprocess.</p>
<p>Assuming there are N vCPUs on the machine and M processes will be
generated, the maximum <code class="docutils literal notranslate"><span class="pre">num_threads</span></code> value used by each process would
be <code class="docutils literal notranslate"><span class="pre">floor(N/M)</span></code>. To avoid CPU oversubscription in the mnist_hogwild
example, the following changes are needed for the file <code class="docutils literal notranslate"><span class="pre">train.py</span></code> in
<a class="reference external" href="https://github.com/pytorch/examples/tree/main/mnist_hogwild">example
repository</a>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">dataloader_kwargs</span><span class="p">):</span>
<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">seed</span> <span class="o">+</span> <span class="n">rank</span><span class="p">)</span>
<span class="c1">#### define the num threads used in current sub-processes</span>
<span class="n">torch</span><span class="o">.</span><span class="n">set_num_threads</span><span class="p">(</span><span class="n">floor</span><span class="p">(</span><span class="n">N</span><span class="o">/</span><span class="n">M</span><span class="p">))</span>
<span class="n">train_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">dataloader_kwargs</span><span class="p">)</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">lr</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">momentum</span><span class="p">)</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">epochs</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">train_epoch</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">)</span>
</pre></div>
</div>
<p>Set <code class="docutils literal notranslate"><span class="pre">num_thread</span></code> for each process using
<code class="docutils literal notranslate"><span class="pre">torch.set_num_threads(floor(N/M))</span></code>. where you replace N with the
number of vCPUs available and M with the chosen number of processes. The
appropriate <code class="docutils literal notranslate"><span class="pre">num_thread</span></code> value will vary depending on the specific
task at hand. However, as a general guideline, the maximum value for the
<code class="docutils literal notranslate"><span class="pre">num_thread</span></code> should be <code class="docutils literal notranslate"><span class="pre">floor(N/M)</span></code> to avoid CPU oversubscription.
In the <a class="reference external" href="https://github.com/pytorch/examples/tree/main/mnist_hogwild">mnist_hogwild</a> training example, after avoiding CPU over
subscription, you can achieve a 30x performance boost.</p>
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