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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 heading">¶</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 heading">¶</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 heading">¶</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 heading">¶</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 heading">¶</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 heading">¶</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 heading">¶</a></h4>
<p><strong>Debugging</strong> - in case of NCCL failure, you can set <code class="docutils literal notranslate"><span class="pre">NCCL_DEBUG=INFO</span></code> to print an explicit
warning message as well as basic NCCL initialization information.</p>
<p>You may also use <code class="docutils literal notranslate"><span class="pre">NCCL_DEBUG_SUBSYS</span></code> to get more details about a specific
aspect of NCCL. For example, <code class="docutils literal notranslate"><span class="pre">NCCL_DEBUG_SUBSYS=COLL</span></code> would print logs of
collective calls, which may be helpful when debugging hangs, especially those
caused by collective type or message size mismatch. In case of topology
detection failure, it would be helpful to set <code class="docutils literal notranslate"><span class="pre">NCCL_DEBUG_SUBSYS=GRAPH</span></code>
to inspect the detailed detection result and save as reference if further help
from NCCL team is needed.</p>
<p><strong>Performance tuning</strong> - NCCL performs automatic tuning based on its topology detection to save users’
tuning effort. On some socket-based systems, users may still try tuning
<code class="docutils literal notranslate"><span class="pre">NCCL_SOCKET_NTHREADS</span></code> and <code class="docutils literal notranslate"><span class="pre">NCCL_NSOCKS_PERTHREAD</span></code> to increase socket
network bandwidth. These two environment variables have been pre-tuned by NCCL
for some cloud providers, such as AWS or GCP.</p>
<p>For a 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 heading">¶</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 heading">¶</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>
or <a class="reference internal" href="#torch.distributed.device_mesh.init_device_mesh" title="torch.distributed.device_mesh.init_device_mesh"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.distributed.device_mesh.init_device_mesh()</span></code></a> function before calling any other methods.
Both block until all processes have joined.</p>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.is_available">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><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>Return <code class="docutils literal notranslate"><span class="pre">True</span></code> if the distributed package is available.</p>
<p>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>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.init_process_group">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">init_process_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</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">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">None</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">-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">-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>, <em class="sig-param"><span class="n"><span class="pre">device_id</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>Initialize the default distributed process group.</p>
<p>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 v3.12)"><em>str</em></a><em> or </em><a class="reference internal" href="#torch.distributed.Backend" title="torch.distributed.Backend"><em>Backend</em></a><em>, </em><em>optional</em>) – 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>,
<code class="docutils literal notranslate"><span class="pre">nccl</span></code>, and <code class="docutils literal notranslate"><span class="pre">ucc</span></code>. If the backend is not provided, then both a <code class="docutils literal notranslate"><span class="pre">gloo</span></code>
and <code class="docutils literal notranslate"><span class="pre">nccl</span></code> backend will be created, see notes below for how multiple
backends are managed. This field can 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. <code class="docutils literal notranslate"><span class="pre">ucc</span></code> backend is
experimental.</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 v3.12)"><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 v3.12)"><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 v3.12)"><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 is 10 minutes for NCCL and 30 minutes for other backends.
This is the duration after which collectives will be aborted asynchronously and the process will crash.
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.
When TORCH_NCCL_BLOCKING_WAIT is set, the process will block and wait for this timeout.</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 v3.12)"><em>str</em></a><em>, </em><em>optional</em><em>, </em><em>deprecated</em>) – Group name. This argument is ignored</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>
<li><p><strong>device_id</strong> (<a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><em>torch.device</em></a><em>, </em><em>optional</em>) – a single, specific device
to “bind” this process to, allowing for backend-specific
optimizations. Currently this has two effects, only under
NCCL: the communicator is immediately formed (calling
<code class="docutils literal notranslate"><span class="pre">ncclCommInit*</span></code> immediately rather than the normal lazy
call) and sub-groups will use <code class="docutils literal notranslate"><span class="pre">ncclCommSplit</span></code> when
possible to avoid unnecessary overhead of group creation. If you
want to know NCCL initialization error early, you can also use this
field.</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>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Support for multiple backends is experimental. Currently when no backend is
specified, both <code class="docutils literal notranslate"><span class="pre">gloo</span></code> and <code class="docutils literal notranslate"><span class="pre">nccl</span></code> backends will be created. The <code class="docutils literal notranslate"><span class="pre">gloo</span></code> backend
will be used for collectives with CPU tensors and the <code class="docutils literal notranslate"><span class="pre">nccl</span></code> backend will be used
for collectives with CUDA tensors. A custom backend can be specified by passing in
a string with format “<device_type>:<backend_name>,<device_type>:<backend_name>”, e.g.
“cpu:gloo,cuda:custom_backend”.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.device_mesh.init_device_mesh">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.device_mesh.</span></span><span class="sig-name descname"><span class="pre">init_device_mesh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_shape</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_dim_names</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/device_mesh.html#init_device_mesh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.init_device_mesh" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes a <cite>DeviceMesh</cite> based on <cite>device_type</cite>, <cite>mesh_shape</cite>, and <cite>mesh_dim_names</cite> parameters.</p>
<p>This creates a DeviceMesh with an n-dimensional array layout, where <cite>n</cite> is the length of <cite>mesh_shape</cite>.
If <cite>mesh_dim_names</cite> is provided, each dimension is labeled as <cite>mesh_dim_names[i]</cite>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><cite>init_device_mesh</cite> follows SPMD programming model, meaning the same PyTorch Python program
runs on all processes/ranks in the cluster. Ensure <cite>mesh_shape</cite> (the dimensions of the nD array
describing device layout) is identical across all ranks. Inconsistent <cite>mesh_shape</cite> may lead to hanging.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If no process group is found, init_device_mesh will initialize distributed process group/groups
required for distributed communications behind the scene.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>device_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The device type of the mesh. Currently supports: “cpu”, “cuda/cuda-like”.</p></li>
<li><p><strong>mesh_shape</strong> (<em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>]</em>) – A tuple defining the dimensions of the multi-dimensional array
describing the layout of devices.</p></li>
<li><p><strong>mesh_dim_names</strong> (<em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>]</em><em>, </em><em>optional</em>) – A tuple of mesh dimension names to assign to each dimension
of the multi-dimensional array describing the layout of devices. Its length must match the length
of <cite>mesh_shape</cite>. Each string in <cite>mesh_dim_names</cite> must be unique.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A <a class="reference internal" href="#torch.distributed.device_mesh.DeviceMesh" title="torch.distributed.device_mesh.DeviceMesh"><code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code></a> object representing the device layout.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.distributed.device_mesh.DeviceMesh" title="torch.distributed.device_mesh.DeviceMesh">DeviceMesh</a></p>
</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">from</span> <span class="nn">torch.distributed.device_mesh</span> <span class="kn">import</span> <span class="n">init_device_mesh</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">mesh_1d</span> <span class="o">=</span> <span class="n">init_device_mesh</span><span class="p">(</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="n">mesh_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,))</span>
<span class="gp">>>> </span><span class="n">mesh_2d</span> <span class="o">=</span> <span class="n">init_device_mesh</span><span class="p">(</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="n">mesh_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">mesh_dim_names</span><span class="o">=</span><span class="p">(</span><span class="s2">"dp"</span><span class="p">,</span> <span class="s2">"tp"</span><span class="p">))</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.is_initialized">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><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>Check if the default process group has been initialized.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.is_mpi_available">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_mpi_available</span></span><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>Check if the MPI backend is available.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.is_nccl_available">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_nccl_available</span></span><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>Check if the NCCL backend is available.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.is_gloo_available">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_gloo_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_gloo_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_gloo_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Check if the Gloo backend is available.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.is_torchelastic_launched">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_torchelastic_launched</span></span><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>Check whether this process was launched with <code class="docutils literal notranslate"><span class="pre">torch.distributed.elastic</span></code> (aka torchelastic).</p>
<p>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>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
</dd>
</dl>
</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 heading">¶</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 heading">¶</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