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<div class="section" id="distributed-communication-package-torch-distributed">
<h1>Distributed communication package - torch.distributed<a class="headerlink" href="#distributed-communication-package-torch-distributed" title="Permalink to this headline">¶</a></h1>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Please refer to <a class="reference external" href="https://pytorch.org/tutorials/beginner/dist_overview.html">PyTorch Distributed Overview</a>
for a brief introduction to all features related to distributed training.</p>
</div>
<span class="target" id="module-torch.distributed"></span><div class="section" id="backends">
<h2>Backends<a class="headerlink" href="#backends" title="Permalink to this headline">¶</a></h2>
<p><code class="docutils literal notranslate"><span class="pre">torch.distributed</span></code> supports three built-in backends, each with
different capabilities. The table below shows which functions are available
for use with CPU / CUDA tensors.
MPI supports CUDA only if the implementation used to build PyTorch supports it.</p>
<table class="docutils colwidths-auto align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Backend</p></th>
<th class="head" colspan="2"><p><code class="docutils literal notranslate"><span class="pre">gloo</span></code></p></th>
<th class="head" colspan="2"><p><code class="docutils literal notranslate"><span class="pre">mpi</span></code></p></th>
<th class="head" colspan="2"><p><code class="docutils literal notranslate"><span class="pre">nccl</span></code></p></th>
</tr>
<tr class="row-even"><th class="head"><p>Device</p></th>
<th class="head"><p>CPU</p></th>
<th class="head"><p>GPU</p></th>
<th class="head"><p>CPU</p></th>
<th class="head"><p>GPU</p></th>
<th class="head"><p>CPU</p></th>
<th class="head"><p>GPU</p></th>
</tr>
</thead>
<tbody>
<tr class="row-odd"><td><p>send</p></td>
<td><p>✓</p></td>
<td><p>✘</p></td>
<td><p>✓</p></td>
<td><p>?</p></td>
<td><p>✘</p></td>
<td><p>✘</p></td>
</tr>
<tr class="row-even"><td><p>recv</p></td>
<td><p>✓</p></td>
<td><p>✘</p></td>
<td><p>✓</p></td>
<td><p>?</p></td>
<td><p>✘</p></td>
<td><p>✘</p></td>
</tr>
<tr class="row-odd"><td><p>broadcast</p></td>
<td><p>✓</p></td>
<td><p>✓</p></td>
<td><p>✓</p></td>
<td><p>?</p></td>
<td><p>✘</p></td>
<td><p>✓</p></td>
</tr>
<tr class="row-even"><td><p>all_reduce</p></td>
<td><p>✓</p></td>
<td><p>✓</p></td>
<td><p>✓</p></td>
<td><p>?</p></td>
<td><p>✘</p></td>
<td><p>✓</p></td>
</tr>
<tr class="row-odd"><td><p>reduce</p></td>
<td><p>✓</p></td>
<td><p>✘</p></td>
<td><p>✓</p></td>
<td><p>?</p></td>
<td><p>✘</p></td>
<td><p>✓</p></td>
</tr>
<tr class="row-even"><td><p>all_gather</p></td>
<td><p>✓</p></td>
<td><p>✘</p></td>
<td><p>✓</p></td>
<td><p>?</p></td>
<td><p>✘</p></td>
<td><p>✓</p></td>
</tr>
<tr class="row-odd"><td><p>gather</p></td>
<td><p>✓</p></td>
<td><p>✘</p></td>
<td><p>✓</p></td>
<td><p>?</p></td>
<td><p>✘</p></td>
<td><p>✘</p></td>
</tr>
<tr class="row-even"><td><p>scatter</p></td>
<td><p>✓</p></td>
<td><p>✘</p></td>
<td><p>✓</p></td>
<td><p>?</p></td>
<td><p>✘</p></td>
<td><p>✘</p></td>
</tr>
<tr class="row-odd"><td><p>reduce_scatter</p></td>
<td><p>✘</p></td>
<td><p>✘</p></td>
<td><p>✘</p></td>
<td><p>✘</p></td>
<td><p>✘</p></td>
<td><p>✓</p></td>
</tr>
<tr class="row-even"><td><p>all_to_all</p></td>
<td><p>✘</p></td>
<td><p>✘</p></td>
<td><p>✓</p></td>
<td><p>?</p></td>
<td><p>✘</p></td>
<td><p>✓</p></td>
</tr>
<tr class="row-odd"><td><p>barrier</p></td>
<td><p>✓</p></td>
<td><p>✘</p></td>
<td><p>✓</p></td>
<td><p>?</p></td>
<td><p>✘</p></td>
<td><p>✓</p></td>
</tr>
</tbody>
</table>
<div class="section" id="backends-that-come-with-pytorch">
<h3>Backends that come with PyTorch<a class="headerlink" href="#backends-that-come-with-pytorch" title="Permalink to this headline">¶</a></h3>
<p>PyTorch distributed package supports Linux (stable), MacOS (stable), and Windows (prototype).
By default for Linux, the Gloo and NCCL backends are built and included in PyTorch
distributed (NCCL only when building with CUDA). MPI is an optional backend that can only be
included if you build PyTorch from source. (e.g. building PyTorch on a host that has MPI
installed.)</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As of PyTorch v1.8, Windows supports all collective communications backend but NCCL,
If the <cite>init_method</cite> argument of <a class="reference internal" href="#torch.distributed.init_process_group" title="torch.distributed.init_process_group"><code class="xref py py-func docutils literal notranslate"><span class="pre">init_process_group()</span></code></a> points to a file it must adhere
to the following schema:</p>
<ul class="simple">
<li><p>Local file system, <code class="docutils literal notranslate"><span class="pre">init_method="file:///d:/tmp/some_file"</span></code></p></li>
<li><p>Shared file system, <code class="docutils literal notranslate"><span class="pre">init_method="file://////{machine_name}/{share_folder_name}/some_file"</span></code></p></li>
</ul>
<p>Same as on Linux platform, you can enable TcpStore by setting environment variables,
MASTER_ADDR and MASTER_PORT.</p>
</div>
</div>
<div class="section" id="which-backend-to-use">
<h3>Which backend to use?<a class="headerlink" href="#which-backend-to-use" title="Permalink to this headline">¶</a></h3>
<p>In the past, we were often asked: “which backend should I use?”.</p>
<ul class="simple">
<li><p>Rule of thumb</p>
<ul>
<li><p>Use the NCCL backend for distributed <strong>GPU</strong> training</p></li>
<li><p>Use the Gloo backend for distributed <strong>CPU</strong> training.</p></li>
</ul>
</li>
<li><p>GPU hosts with InfiniBand interconnect</p>
<ul>
<li><p>Use NCCL, since it’s the only backend that currently supports
InfiniBand and GPUDirect.</p></li>
</ul>
</li>
<li><p>GPU hosts with Ethernet interconnect</p>
<ul>
<li><p>Use NCCL, since it currently provides the best distributed GPU
training performance, especially for multiprocess single-node or
multi-node distributed training. If you encounter any problem with
NCCL, use Gloo as the fallback option. (Note that Gloo currently
runs slower than NCCL for GPUs.)</p></li>
</ul>
</li>
<li><p>CPU hosts with InfiniBand interconnect</p>
<ul>
<li><p>If your InfiniBand has enabled IP over IB, use Gloo, otherwise,
use MPI instead. We are planning on adding InfiniBand support for
Gloo in the upcoming releases.</p></li>
</ul>
</li>
<li><p>CPU hosts with Ethernet interconnect</p>
<ul>
<li><p>Use Gloo, unless you have specific reasons to use MPI.</p></li>
</ul>
</li>
</ul>
</div>
<div class="section" id="common-environment-variables">
<h3>Common environment variables<a class="headerlink" href="#common-environment-variables" title="Permalink to this headline">¶</a></h3>
<div class="section" id="choosing-the-network-interface-to-use">
<h4>Choosing the network interface to use<a class="headerlink" href="#choosing-the-network-interface-to-use" title="Permalink to this headline">¶</a></h4>
<p>By default, both the NCCL and Gloo backends will try to find the right network interface to use.
If the automatically detected interface is not correct, you can override it using the following
environment variables (applicable to the respective backend):</p>
<ul class="simple">
<li><p><strong>NCCL_SOCKET_IFNAME</strong>, for example <code class="docutils literal notranslate"><span class="pre">export</span> <span class="pre">NCCL_SOCKET_IFNAME=eth0</span></code></p></li>
<li><p><strong>GLOO_SOCKET_IFNAME</strong>, for example <code class="docutils literal notranslate"><span class="pre">export</span> <span class="pre">GLOO_SOCKET_IFNAME=eth0</span></code></p></li>
</ul>
<p>If you’re using the Gloo backend, you can specify multiple interfaces by separating
them by a comma, like this: <code class="docutils literal notranslate"><span class="pre">export</span> <span class="pre">GLOO_SOCKET_IFNAME=eth0,eth1,eth2,eth3</span></code>.
The backend will dispatch operations in a round-robin fashion across these interfaces.
It is imperative that all processes specify the same number of interfaces in this variable.</p>
</div>
<div class="section" id="other-nccl-environment-variables">
<h4>Other NCCL environment variables<a class="headerlink" href="#other-nccl-environment-variables" title="Permalink to this headline">¶</a></h4>
<p>NCCL has also provided a number of environment variables for fine-tuning purposes.</p>
<p>Commonly used ones include the following for debugging purposes:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">export</span> <span class="pre">NCCL_DEBUG=INFO</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">export</span> <span class="pre">NCCL_DEBUG_SUBSYS=ALL</span></code></p></li>
</ul>
<p>For the full list of NCCL environment variables, please refer to
<a class="reference external" href="https://docs.nvidia.com/deeplearning/sdk/nccl-developer-guide/docs/env.html">NVIDIA NCCL’s official documentation</a></p>
</div>
</div>
</div>
<div class="section" id="basics">
<span id="distributed-basics"></span><h2>Basics<a class="headerlink" href="#basics" title="Permalink to this headline">¶</a></h2>
<p>The <cite>torch.distributed</cite> package provides PyTorch support and communication primitives
for multiprocess parallelism across several computation nodes running on one or more
machines. The class <a class="reference internal" href="generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel" title="torch.nn.parallel.DistributedDataParallel"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.parallel.DistributedDataParallel()</span></code></a> builds on this
functionality to provide synchronous distributed training as a wrapper around any
PyTorch model. This differs from the kinds of parallelism provided by
<a class="reference internal" href="multiprocessing.html"><span class="doc">Multiprocessing package - torch.multiprocessing</span></a> and <a class="reference internal" href="generated/torch.nn.DataParallel.html#torch.nn.DataParallel" title="torch.nn.DataParallel"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.DataParallel()</span></code></a> in that it supports
multiple network-connected machines and in that the user must explicitly launch a separate
copy of the main training script for each process.</p>
<p>In the single-machine synchronous case, <cite>torch.distributed</cite> or the
<a class="reference internal" href="generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel" title="torch.nn.parallel.DistributedDataParallel"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.parallel.DistributedDataParallel()</span></code></a> wrapper may still have advantages over other
approaches to data-parallelism, including <a class="reference internal" href="generated/torch.nn.DataParallel.html#torch.nn.DataParallel" title="torch.nn.DataParallel"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.DataParallel()</span></code></a>:</p>
<ul class="simple">
<li><p>Each process maintains its own optimizer and performs a complete optimization step with each
iteration. While this may appear redundant, since the gradients have already been gathered
together and averaged across processes and are thus the same for every process, this means
that no parameter broadcast step is needed, reducing time spent transferring tensors between
nodes.</p></li>
<li><p>Each process contains an independent Python interpreter, eliminating the extra interpreter
overhead and “GIL-thrashing” that comes from driving several execution threads, model
replicas, or GPUs from a single Python process. This is especially important for models that
make heavy use of the Python runtime, including models with recurrent layers or many small
components.</p></li>
</ul>
</div>
<div class="section" id="initialization">
<h2>Initialization<a class="headerlink" href="#initialization" title="Permalink to this headline">¶</a></h2>
<p>The package needs to be initialized using the <a class="reference internal" href="#torch.distributed.init_process_group" title="torch.distributed.init_process_group"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.distributed.init_process_group()</span></code></a>
function before calling any other methods. This blocks until all processes have
joined.</p>
<dl class="py function">
<dt id="torch.distributed.is_available">
<code class="sig-prename descclassname"><span class="pre">torch.distributed.</span></code><code class="sig-name descname"><span class="pre">is_available</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if the distributed package is available. Otherwise,
<code class="docutils literal notranslate"><span class="pre">torch.distributed</span></code> does not expose any other APIs. Currently,
<code class="docutils literal notranslate"><span class="pre">torch.distributed</span></code> is available on Linux, MacOS and Windows. Set
<code class="docutils literal notranslate"><span class="pre">USE_DISTRIBUTED=1</span></code> to enable it when building PyTorch from source.
Currently, the default value is <code class="docutils literal notranslate"><span class="pre">USE_DISTRIBUTED=1</span></code> for Linux and Windows,
<code class="docutils literal notranslate"><span class="pre">USE_DISTRIBUTED=0</span></code> for MacOS.</p>
</dd></dl>
<dl class="py function">
<dt id="torch.distributed.init_process_group">
<code class="sig-prename descclassname"><span class="pre">torch.distributed.</span></code><code class="sig-name descname"><span class="pre">init_process_group</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">datetime.timedelta(0,</span> <span class="pre">1800)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pg_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#init_process_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.init_process_group" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes the default distributed process group, and this will also
initialize the distributed package.</p>
<dl class="simple">
<dt>There are 2 main ways to initialize a process group:</dt><dd><ol class="arabic simple">
<li><p>Specify <code class="docutils literal notranslate"><span class="pre">store</span></code>, <code class="docutils literal notranslate"><span class="pre">rank</span></code>, and <code class="docutils literal notranslate"><span class="pre">world_size</span></code> explicitly.</p></li>
<li><p>Specify <code class="docutils literal notranslate"><span class="pre">init_method</span></code> (a URL string) which indicates where/how
to discover peers. Optionally specify <code class="docutils literal notranslate"><span class="pre">rank</span></code> and <code class="docutils literal notranslate"><span class="pre">world_size</span></code>,
or encode all required parameters in the URL and omit them.</p></li>
</ol>
</dd>
</dl>
<p>If neither is specified, <code class="docutils literal notranslate"><span class="pre">init_method</span></code> is assumed to be “env://”.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>backend</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> or </em><a class="reference internal" href="#torch.distributed.Backend" title="torch.distributed.Backend"><em>Backend</em></a>) – The backend to use. Depending on
build-time configurations, valid values include <code class="docutils literal notranslate"><span class="pre">mpi</span></code>, <code class="docutils literal notranslate"><span class="pre">gloo</span></code>,
and <code class="docutils literal notranslate"><span class="pre">nccl</span></code>. This field should be given as a lowercase string
(e.g., <code class="docutils literal notranslate"><span class="pre">"gloo"</span></code>), which can also be accessed via
<a class="reference internal" href="#torch.distributed.Backend" title="torch.distributed.Backend"><code class="xref py py-class docutils literal notranslate"><span class="pre">Backend</span></code></a> attributes (e.g., <code class="docutils literal notranslate"><span class="pre">Backend.GLOO</span></code>). If using
multiple processes per machine with <code class="docutils literal notranslate"><span class="pre">nccl</span></code> backend, each process
must have exclusive access to every GPU it uses, as sharing GPUs
between processes can result in deadlocks.</p></li>
<li><p><strong>init_method</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><em>optional</em>) – URL specifying how to initialize the
process group. Default is “env://” if no
<code class="docutils literal notranslate"><span class="pre">init_method</span></code> or <code class="docutils literal notranslate"><span class="pre">store</span></code> is specified.
Mutually exclusive with <code class="docutils literal notranslate"><span class="pre">store</span></code>.</p></li>
<li><p><strong>world_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>, </em><em>optional</em>) – Number of processes participating in
the job. Required if <code class="docutils literal notranslate"><span class="pre">store</span></code> is specified.</p></li>
<li><p><strong>rank</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>, </em><em>optional</em>) – Rank of the current process (it should be a
number between 0 and <code class="docutils literal notranslate"><span class="pre">world_size</span></code>-1).
Required if <code class="docutils literal notranslate"><span class="pre">store</span></code> is specified.</p></li>
<li><p><strong>store</strong> (<a class="reference internal" href="#torch.distributed.Store" title="torch.distributed.Store"><em>Store</em></a><em>, </em><em>optional</em>) – Key/value store accessible to all workers, used
to exchange connection/address information.
Mutually exclusive with <code class="docutils literal notranslate"><span class="pre">init_method</span></code>.</p></li>
<li><p><strong>timeout</strong> (<em>timedelta</em><em>, </em><em>optional</em>) – Timeout for operations executed against
the process group. Default value equals 30 minutes.
This is applicable for the <code class="docutils literal notranslate"><span class="pre">gloo</span></code> backend. For <code class="docutils literal notranslate"><span class="pre">nccl</span></code>, this is
applicable only if the environment variable <code class="docutils literal notranslate"><span class="pre">NCCL_BLOCKING_WAIT</span></code>
or <code class="docutils literal notranslate"><span class="pre">NCCL_ASYNC_ERROR_HANDLING</span></code> is set to 1. When
<code class="docutils literal notranslate"><span class="pre">NCCL_BLOCKING_WAIT</span></code> is set, this is the duration for which the
process will block and wait for collectives to complete before
throwing an exception. When <code class="docutils literal notranslate"><span class="pre">NCCL_ASYNC_ERROR_HANDLING</span></code> is set,
this is the duration after which collectives will be aborted
asynchronously and the process will crash. <code class="docutils literal notranslate"><span class="pre">NCCL_BLOCKING_WAIT</span></code>
will provide errors to the user which can be caught and handled,
but due to its blocking nature, it has a performance overhead. On
the other hand, <code class="docutils literal notranslate"><span class="pre">NCCL_ASYNC_ERROR_HANDLING</span></code> has very little
performance overhead, but crashes the process on errors. This is
done since CUDA execution is async and it is no longer safe to
continue executing user code since failed async NCCL operations
might result in subsequent CUDA operations running on corrupted
data. Only one of these two environment variables should be set.</p></li>
<li><p><strong>group_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><em>optional</em><em>, </em><em>deprecated</em>) – Group name.</p></li>
<li><p><strong>pg_options</strong> (<em>ProcessGroupOptions</em><em>, </em><em>optional</em>) – process group options
specifying what additional options need to be passed in during
the construction of specific process groups. As of now, the only
options we support is <code class="docutils literal notranslate"><span class="pre">ProcessGroupNCCL.Options</span></code> for the <code class="docutils literal notranslate"><span class="pre">nccl</span></code>
backend, <code class="docutils literal notranslate"><span class="pre">is_high_priority_stream</span></code> can be specified so that
the nccl backend can pick up high priority cuda streams when
there’re compute kernels waiting.</p></li>
</ul>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>To enable <code class="docutils literal notranslate"><span class="pre">backend</span> <span class="pre">==</span> <span class="pre">Backend.MPI</span></code>, PyTorch needs to be built from source
on a system that supports MPI.</p>
</div>
</dd></dl>
<dl class="py function">
<dt id="torch.distributed.is_initialized">
<code class="sig-prename descclassname"><span class="pre">torch.distributed.</span></code><code class="sig-name descname"><span class="pre">is_initialized</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_initialized" title="Permalink to this definition">¶</a></dt>
<dd><p>Checking if the default process group has been initialized</p>
</dd></dl>
<dl class="py function">
<dt id="torch.distributed.is_mpi_available">
<code class="sig-prename descclassname"><span class="pre">torch.distributed.</span></code><code class="sig-name descname"><span class="pre">is_mpi_available</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_mpi_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_mpi_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Checks if the MPI backend is available.</p>
</dd></dl>
<dl class="py function">
<dt id="torch.distributed.is_nccl_available">
<code class="sig-prename descclassname"><span class="pre">torch.distributed.</span></code><code class="sig-name descname"><span class="pre">is_nccl_available</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_nccl_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_nccl_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Checks if the NCCL backend is available.</p>
</dd></dl>
<dl class="py function">
<dt id="torch.distributed.is_torchelastic_launched">
<code class="sig-prename descclassname"><span class="pre">torch.distributed.</span></code><code class="sig-name descname"><span class="pre">is_torchelastic_launched</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_torchelastic_launched"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_torchelastic_launched" title="Permalink to this definition">¶</a></dt>
<dd><p>Checks whether this process was launched with <code class="docutils literal notranslate"><span class="pre">torch.distributed.elastic</span></code>
(aka torchelastic). The existence of <code class="docutils literal notranslate"><span class="pre">TORCHELASTIC_RUN_ID</span></code> environment
variable is used as a proxy to determine whether the current process
was launched with torchelastic. This is a reasonable proxy since
<code class="docutils literal notranslate"><span class="pre">TORCHELASTIC_RUN_ID</span></code> maps to the rendezvous id which is always a
non-null value indicating the job id for peer discovery purposes..</p>
</dd></dl>
<hr class="docutils" />
<p>Currently three initialization methods are supported:</p>
<div class="section" id="tcp-initialization">
<h3>TCP initialization<a class="headerlink" href="#tcp-initialization" title="Permalink to this headline">¶</a></h3>
<p>There are two ways to initialize using TCP, both requiring a network address
reachable from all processes and a desired <code class="docutils literal notranslate"><span class="pre">world_size</span></code>. The first way
requires specifying an address that belongs to the rank 0 process. This
initialization method requires that all processes have manually specified ranks.</p>
<p>Note that multicast address is not supported anymore in the latest distributed
package. <code class="docutils literal notranslate"><span class="pre">group_name</span></code> is deprecated as well.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
<span class="c1"># Use address of one of the machines</span>
<span class="n">dist</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span><span class="n">backend</span><span class="p">,</span> <span class="n">init_method</span><span class="o">=</span><span class="s1">'tcp://10.1.1.20:23456'</span><span class="p">,</span>
<span class="n">rank</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="shared-file-system-initialization">
<h3>Shared file-system initialization<a class="headerlink" href="#shared-file-system-initialization" title="Permalink to this headline">¶</a></h3>
<p>Another initialization method makes use of a file system that is shared and
visible from all machines in a group, along with a desired <code class="docutils literal notranslate"><span class="pre">world_size</span></code>. The URL should start
with <code class="docutils literal notranslate"><span class="pre">file://</span></code> and contain a path to a non-existent file (in an existing
directory) on a shared file system. File-system initialization will automatically
create that file if it doesn’t exist, but will not delete the file. Therefore, it
is your responsibility to make sure that the file is cleaned up before the next
<a class="reference internal" href="#torch.distributed.init_process_group" title="torch.distributed.init_process_group"><code class="xref py py-func docutils literal notranslate"><span class="pre">init_process_group()</span></code></a> call on the same file path/name.</p>
<p>Note that automatic rank assignment is not supported anymore in the latest
distributed package and <code class="docutils literal notranslate"><span class="pre">group_name</span></code> is deprecated as well.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This method assumes that the file system supports locking using <code class="docutils literal notranslate"><span class="pre">fcntl</span></code> - most
local systems and NFS support it.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This method will always create the file and try its best to clean up and remove
the file at the end of the program. In other words, each initialization with
the file init method will need a brand new empty file in order for the initialization
to succeed. If the same file used by the previous initialization (which happens not
to get cleaned up) is used again, this is unexpected behavior and can often cause
deadlocks and failures. Therefore, even though this method will try its best to clean up
the file, if the auto-delete happens to be unsuccessful, it is your responsibility
to ensure that the file is removed at the end of the training to prevent the same
file to be reused again during the next time. This is especially important
if you plan to call <a class="reference internal" href="#torch.distributed.init_process_group" title="torch.distributed.init_process_group"><code class="xref py py-func docutils literal notranslate"><span class="pre">init_process_group()</span></code></a> multiple times on the same file name.
In other words, if the file is not removed/cleaned up and you call
<a class="reference internal" href="#torch.distributed.init_process_group" title="torch.distributed.init_process_group"><code class="xref py py-func docutils literal notranslate"><span class="pre">init_process_group()</span></code></a> again on that file, failures are expected.
The rule of thumb here is that, make sure that the file is non-existent or
empty every time <a class="reference internal" href="#torch.distributed.init_process_group" title="torch.distributed.init_process_group"><code class="xref py py-func docutils literal notranslate"><span class="pre">init_process_group()</span></code></a> is called.</p>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
<span class="c1"># rank should always be specified</span>
<span class="n">dist</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span><span class="n">backend</span><span class="p">,</span> <span class="n">init_method</span><span class="o">=</span><span class="s1">'file:///mnt/nfs/sharedfile'</span><span class="p">,</span>
<span class="n">world_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">rank</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="environment-variable-initialization">
<h3>Environment variable initialization<a class="headerlink" href="#environment-variable-initialization" title="Permalink to this headline">¶</a></h3>
<p>This method will read the configuration from environment variables, allowing
one to fully customize how the information is obtained. The variables to be set
are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">MASTER_PORT</span></code> - required; has to be a free port on machine with rank 0</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">MASTER_ADDR</span></code> - required (except for rank 0); address of rank 0 node</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">WORLD_SIZE</span></code> - required; can be set either here, or in a call to init function</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">RANK</span></code> - required; can be set either here, or in a call to init function</p></li>
</ul>
<p>The machine with rank 0 will be used to set up all connections.</p>
<p>This is the default method, meaning that <code class="docutils literal notranslate"><span class="pre">init_method</span></code> does not have to be specified (or
can be <code class="docutils literal notranslate"><span class="pre">env://</span></code>).</p>
</div>
</div>
<div class="section" id="post-initialization">
<h2>Post-Initialization<a class="headerlink" href="#post-initialization" title="Permalink to this headline">¶</a></h2>
<p>Once <a class="reference internal" href="#torch.distributed.init_process_group" title="torch.distributed.init_process_group"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.distributed.init_process_group()</span></code></a> was run, the following functions can be used. To
check whether the process group has already been initialized use <a class="reference internal" href="#torch.distributed.is_initialized" title="torch.distributed.is_initialized"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.distributed.is_initialized()</span></code></a>.</p>
<dl class="py class">
<dt id="torch.distributed.Backend">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.</span></code><code class="sig-name descname"><span class="pre">Backend</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#Backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.Backend" title="Permalink to this definition">¶</a></dt>
<dd><p>An enum-like class of available backends: GLOO, NCCL, MPI, and other registered
backends.</p>
<p>The values of this class are lowercase strings, e.g., <code class="docutils literal notranslate"><span class="pre">"gloo"</span></code>. They can
be accessed as attributes, e.g., <code class="docutils literal notranslate"><span class="pre">Backend.NCCL</span></code>.</p>
<p>This class can be directly called to parse the string, e.g.,
<code class="docutils literal notranslate"><span class="pre">Backend(backend_str)</span></code> will check if <code class="docutils literal notranslate"><span class="pre">backend_str</span></code> is valid, and
return the parsed lowercase string if so. It also accepts uppercase strings,
e.g., <code class="docutils literal notranslate"><span class="pre">Backend("GLOO")</span></code> returns <code class="docutils literal notranslate"><span class="pre">"gloo"</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The entry <code class="docutils literal notranslate"><span class="pre">Backend.UNDEFINED</span></code> is present but only used as
initial value of some fields. Users should neither use it directly
nor assume its existence.</p>
</div>
</dd></dl>
<dl class="py function">
<dt id="torch.distributed.get_backend">
<code class="sig-prename descclassname"><span class="pre">torch.distributed.</span></code><code class="sig-name descname"><span class="pre">get_backend</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_backend" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the backend of the given process group.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>group</strong> (<em>ProcessGroup</em><em>, </em><em>optional</em>) – The process group to work on. The
default is the general main process group. If another specific group
is specified, the calling process must be part of <code class="xref py py-attr docutils literal notranslate"><span class="pre">group</span></code>.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>The backend of the given process group as a lower case string.</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="torch.distributed.get_rank">
<code class="sig-prename descclassname"><span class="pre">torch.distributed.</span></code><code class="sig-name descname"><span class="pre">get_rank</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_rank" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the rank of the current process in the provided <code class="docutils literal notranslate"><span class="pre">group</span></code> or the
default group if none was provided.</p>
<p>Rank is a unique identifier assigned to each process within a distributed
process group. They are always consecutive integers ranging from 0 to
<code class="docutils literal notranslate"><span class="pre">world_size</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>group</strong> (<em>ProcessGroup</em><em>, </em><em>optional</em>) – The process group to work on. If None,
the default process group will be used.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>The rank of the process group
-1, if not part of the group</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="torch.distributed.get_world_size">
<code class="sig-prename descclassname"><span class="pre">torch.distributed.</span></code><code class="sig-name descname"><span class="pre">get_world_size</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_world_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_world_size" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the number of processes in the current process group</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>group</strong> (<em>ProcessGroup</em><em>, </em><em>optional</em>) – The process group to work on. If None,
the default process group will be used.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>The world size of the process group
-1, if not part of the group</p>
</dd>
</dl>
</dd></dl>
</div>
<hr class="docutils" />
<div class="section" id="distributed-key-value-store">
<h2>Distributed Key-Value Store<a class="headerlink" href="#distributed-key-value-store" title="Permalink to this headline">¶</a></h2>
<p>The distributed package comes with a distributed key-value store, which can be
used to share information between processes in the group as well as to
initialize the distributed package in
<a class="reference internal" href="#torch.distributed.init_process_group" title="torch.distributed.init_process_group"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.distributed.init_process_group()</span></code></a> (by explicitly creating the store
as an alternative to specifying <code class="docutils literal notranslate"><span class="pre">init_method</span></code>.) There are 3 choices for
Key-Value Stores: <a class="reference internal" href="#torch.distributed.TCPStore" title="torch.distributed.TCPStore"><code class="xref py py-class docutils literal notranslate"><span class="pre">TCPStore</span></code></a>,
<a class="reference internal" href="#torch.distributed.FileStore" title="torch.distributed.FileStore"><code class="xref py py-class docutils literal notranslate"><span class="pre">FileStore</span></code></a>, and <a class="reference internal" href="#torch.distributed.HashStore" title="torch.distributed.HashStore"><code class="xref py py-class docutils literal notranslate"><span class="pre">HashStore</span></code></a>.</p>
<dl class="py class">
<dt id="torch.distributed.Store">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.</span></code><code class="sig-name descname"><span class="pre">Store</span></code><a class="headerlink" href="#torch.distributed.Store" title="Permalink to this definition">¶</a></dt>
<dd><p>Base class for all store implementations, such as the 3 provided by PyTorch
distributed: (<a class="reference internal" href="#torch.distributed.TCPStore" title="torch.distributed.TCPStore"><code class="xref py py-class docutils literal notranslate"><span class="pre">TCPStore</span></code></a>, <a class="reference internal" href="#torch.distributed.FileStore" title="torch.distributed.FileStore"><code class="xref py py-class docutils literal notranslate"><span class="pre">FileStore</span></code></a>,
and <a class="reference internal" href="#torch.distributed.HashStore" title="torch.distributed.HashStore"><code class="xref py py-class docutils literal notranslate"><span class="pre">HashStore</span></code></a>).</p>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.TCPStore">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.</span></code><code class="sig-name descname"><span class="pre">TCPStore</span></code><a class="headerlink" href="#torch.distributed.TCPStore" title="Permalink to this definition">¶</a></dt>
<dd><p>A TCP-based distributed key-value store implementation. The server store holds
the data, while the client stores can connect to the server store over TCP and
perform actions such as <code class="xref py py-meth docutils literal notranslate"><span class="pre">set()</span></code> to insert a key-value
pair, <code class="xref py py-meth docutils literal notranslate"><span class="pre">get()</span></code> to retrieve a key-value pair, etc. There
should always be one server store initialized because the client store(s) will wait for
the server to establish a connection.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>host_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – The hostname or IP Address the server store should run on.</p></li>
<li><p><strong>port</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – The port on which the server store should listen for incoming requests.</p></li>
<li><p><strong>world_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>, </em><em>optional</em>) – The total number of store users (number of clients + 1 for the server). Default is -1 (a negative value indicates a non-fixed number of store users).</p></li>
<li><p><strong>is_master</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>, </em><em>optional</em>) – True when initializing the server store and False for client stores. Default is False.</p></li>
<li><p><strong>timeout</strong> (<em>timedelta</em><em>, </em><em>optional</em>) – Timeout used by the store during initialization and for methods such as <code class="xref py py-meth docutils literal notranslate"><span class="pre">get()</span></code> and <code class="xref py py-meth docutils literal notranslate"><span class="pre">wait()</span></code>. Default is timedelta(seconds=300)</p></li>
<li><p><strong>wait_for_worker</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to wait for all the workers to connect with the server store. This is only applicable when world_size is a fixed value. Default is True.</p></li>
</ul>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">timedelta</span>
<span class="gp">>>> </span><span class="c1"># Run on process 1 (server)</span>
<span class="gp">>>> </span><span class="n">server_store</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">TCPStore</span><span class="p">(</span><span class="s2">"127.0.0.1"</span><span class="p">,</span> <span class="mi">1234</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">seconds</span><span class="o">=</span><span class="mi">30</span><span class="p">))</span>
<span class="gp">>>> </span><span class="c1"># Run on process 2 (client)</span>
<span class="gp">>>> </span><span class="n">client_store</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">TCPStore</span><span class="p">(</span><span class="s2">"127.0.0.1"</span><span class="p">,</span> <span class="mi">1234</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># Use any of the store methods from either the client or server after initialization</span>
<span class="gp">>>> </span><span class="n">server_store</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s2">"first_key"</span><span class="p">,</span> <span class="s2">"first_value"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">client_store</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"first_key"</span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.HashStore">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.</span></code><code class="sig-name descname"><span class="pre">HashStore</span></code><a class="headerlink" href="#torch.distributed.HashStore" title="Permalink to this definition">¶</a></dt>
<dd><p>A thread-safe store implementation based on an underlying hashmap. This store can be used
within the same process (for example, by other threads), but cannot be used across processes.</p>
<dl>
<dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
<span class="gp">>>> </span><span class="n">store</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">HashStore</span><span class="p">()</span>
<span class="gp">>>> </span><span class="c1"># store can be used from other threads</span>
<span class="gp">>>> </span><span class="c1"># Use any of the store methods after initialization</span>
<span class="gp">>>> </span><span class="n">store</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s2">"first_key"</span><span class="p">,</span> <span class="s2">"first_value"</span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.FileStore">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.</span></code><code class="sig-name descname"><span class="pre">FileStore</span></code><a class="headerlink" href="#torch.distributed.FileStore" title="Permalink to this definition">¶</a></dt>
<dd><p>A store implementation that uses a file to store the underlying key-value pairs.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>file_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – path of the file in which to store the key-value pairs</p></li>
<li><p><strong>world_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>, </em><em>optional</em>) – The total number of processes using the store. Default is -1 (a negative value indicates a non-fixed number of store users).</p></li>
</ul>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
<span class="gp">>>> </span><span class="n">store1</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">FileStore</span><span class="p">(</span><span class="s2">"/tmp/filestore"</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">store2</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">FileStore</span><span class="p">(</span><span class="s2">"/tmp/filestore"</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># Use any of the store methods from either the client or server after initialization</span>
<span class="gp">>>> </span><span class="n">store1</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s2">"first_key"</span><span class="p">,</span> <span class="s2">"first_value"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">store2</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"first_key"</span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.PrefixStore">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.</span></code><code class="sig-name descname"><span class="pre">PrefixStore</span></code><a class="headerlink" href="#torch.distributed.PrefixStore" title="Permalink to this definition">¶</a></dt>
<dd><p>A wrapper around any of the 3 key-value stores (<a class="reference internal" href="#torch.distributed.TCPStore" title="torch.distributed.TCPStore"><code class="xref py py-class docutils literal notranslate"><span class="pre">TCPStore</span></code></a>,
<a class="reference internal" href="#torch.distributed.FileStore" title="torch.distributed.FileStore"><code class="xref py py-class docutils literal notranslate"><span class="pre">FileStore</span></code></a>, and <a class="reference internal" href="#torch.distributed.HashStore" title="torch.distributed.HashStore"><code class="xref py py-class docutils literal notranslate"><span class="pre">HashStore</span></code></a>)
that adds a prefix to each key inserted to the store.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>