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<span id="distributed-communication-package-torch-distributed"></span><h1>Distributed communication package - torch.distributed<a class="headerlink" href="#module-torch.distributed" title="Permalink to this headline">¶</a></h1>
<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 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 align-default">
<colgroup>
<col style="width: 29%" />
<col style="width: 12%" />
<col style="width: 12%" />
<col style="width: 12%" />
<col style="width: 12%" />
<col style="width: 12%" />
<col style="width: 12%" />
</colgroup>
<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>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 currently only supports Linux. By default, 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>
<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="nn.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="nn.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="nn.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="nn.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="function">
<dt id="torch.distributed.init_process_group">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">init_process_group</code><span class="sig-paren">(</span><em class="sig-param">backend</em>, <em class="sig-param">init_method=None</em>, <em class="sig-param">timeout=datetime.timedelta(0</em>, <em class="sig-param">1800)</em>, <em class="sig-param">world_size=-1</em>, <em class="sig-param">rank=-1</em>, <em class="sig-param">store=None</em>, <em class="sig-param">group_name=''</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#init_process_group"><span class="viewcode-link">[source]</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> (<em>str</em><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> (<em>str</em><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> (<em>python:int</em><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> (<em>python:int</em><em>, </em><em>optional</em>) – Rank of the current process.
Required if <code class="docutils literal notranslate"><span class="pre">store</span></code> is specified.</p></li>
<li><p><strong>store</strong> (<em>Store</em><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>
is set to 1.</p></li>
<li><p><strong>group_name</strong> (<em>str</em><em>, </em><em>optional</em><em>, </em><em>deprecated</em>) – Group name.</p></li>
</ul>
</dd>
</dl>
<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 built from source
on a system that supports MPI. The same applies to NCCL as well.</p>
</dd></dl>
<dl class="class">
<dt id="torch.distributed.Backend">
<em class="property">class </em><code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">Backend</code><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#Backend"><span class="viewcode-link">[source]</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, and MPI.</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="function">
<dt id="torch.distributed.get_backend">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">get_backend</code><span class="sig-paren">(</span><em class="sig-param">group=<object object></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_backend"><span class="viewcode-link">[source]</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="function">
<dt id="torch.distributed.get_rank">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">get_rank</code><span class="sig-paren">(</span><em class="sig-param">group=<object object></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_rank"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.get_rank" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the rank of current process group</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</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="function">
<dt id="torch.distributed.get_world_size">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">get_world_size</code><span class="sig-paren">(</span><em class="sig-param">group=<object object></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_world_size"><span class="viewcode-link">[source]</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</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>
<dl class="function">
<dt id="torch.distributed.is_initialized">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">is_initialized</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">[source]</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="function">
<dt id="torch.distributed.is_mpi_available">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">is_mpi_available</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">[source]</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="function">
<dt id="torch.distributed.is_nccl_available">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">is_nccl_available</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">[source]</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>
<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 everytime <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="groups">
<h2>Groups<a class="headerlink" href="#groups" title="Permalink to this headline">¶</a></h2>
<p>By default collectives operate on the default group (also called the world) and
require all processes to enter the distributed function call. However, some workloads can benefit
from more fine-grained communication. This is where distributed groups come
into play. <a class="reference internal" href="#torch.distributed.new_group" title="torch.distributed.new_group"><code class="xref py py-func docutils literal notranslate"><span class="pre">new_group()</span></code></a> function can be
used to create new groups, with arbitrary subsets of all processes. It returns
an opaque group handle that can be given as a <code class="docutils literal notranslate"><span class="pre">group</span></code> argument to all collectives
(collectives are distributed functions to exchange information in certain well-known programming patterns).</p>
<dl class="function">
<dt id="torch.distributed.new_group">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">new_group</code><span class="sig-paren">(</span><em class="sig-param">ranks=None</em>, <em class="sig-param">timeout=datetime.timedelta(0</em>, <em class="sig-param">1800)</em>, <em class="sig-param">backend=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#new_group"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.new_group" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a new distributed group.</p>
<p>This function requires that all processes in the main group (i.e. all
processes that are part of the distributed job) enter this function, even
if they are not going to be members of the group. Additionally, groups
should be created in the same order in all processes.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>ranks</strong> (<em>list</em><em>[</em><em>python:int</em><em>]</em>) – List of ranks of group members.</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 only applicable for the <code class="docutils literal notranslate"><span class="pre">gloo</span></code> backend.</p></li>
<li><p><strong>backend</strong> (<em>str</em><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 are <code class="docutils literal notranslate"><span class="pre">gloo</span></code> and <code class="docutils literal notranslate"><span class="pre">nccl</span></code>.
By default uses the same backend as the global group. 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>).</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A handle of distributed group that can be given to collective calls.</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="point-to-point-communication">
<h2>Point-to-point communication<a class="headerlink" href="#point-to-point-communication" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.distributed.send">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">send</code><span class="sig-paren">(</span><em class="sig-param">tensor</em>, <em class="sig-param">dst</em>, <em class="sig-param">group=<object object></em>, <em class="sig-param">tag=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#send"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.send" title="Permalink to this definition">¶</a></dt>
<dd><p>Sends a tensor synchronously.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Tensor to send.</p></li>
<li><p><strong>dst</strong> (<em>python:int</em>) – Destination rank.</p></li>
<li><p><strong>group</strong> (<em>ProcessGroup</em><em>, </em><em>optional</em>) – The process group to work on</p></li>
<li><p><strong>tag</strong> (<em>python:int</em><em>, </em><em>optional</em>) – Tag to match send with remote recv</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.distributed.recv">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">recv</code><span class="sig-paren">(</span><em class="sig-param">tensor</em>, <em class="sig-param">src=None</em>, <em class="sig-param">group=<object object></em>, <em class="sig-param">tag=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#recv"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.recv" title="Permalink to this definition">¶</a></dt>
<dd><p>Receives a tensor synchronously.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Tensor to fill with received data.</p></li>
<li><p><strong>src</strong> (<em>python:int</em><em>, </em><em>optional</em>) – Source rank. Will receive from any
process if unspecified.</p></li>
<li><p><strong>group</strong> (<em>ProcessGroup</em><em>, </em><em>optional</em>) – The process group to work on</p></li>
<li><p><strong>tag</strong> (<em>python:int</em><em>, </em><em>optional</em>) – Tag to match recv with remote send</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Sender rank
-1, if not part of the group</p>
</dd>
</dl>
</dd></dl>
<p><a class="reference internal" href="#torch.distributed.isend" title="torch.distributed.isend"><code class="xref py py-func docutils literal notranslate"><span class="pre">isend()</span></code></a> and <a class="reference internal" href="#torch.distributed.irecv" title="torch.distributed.irecv"><code class="xref py py-func docutils literal notranslate"><span class="pre">irecv()</span></code></a>
return distributed request objects when used. In general, the type of this object is unspecified
as they should never be created manually, but they are guaranteed to support two methods:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">is_completed()</span></code> - returns True if the operation has finished</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">wait()</span></code> - will block the process until the operation is finished.
<code class="docutils literal notranslate"><span class="pre">is_completed()</span></code> is guaranteed to return True once it returns.</p></li>
</ul>
<dl class="function">
<dt id="torch.distributed.isend">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">isend</code><span class="sig-paren">(</span><em class="sig-param">tensor</em>, <em class="sig-param">dst</em>, <em class="sig-param">group=<object object></em>, <em class="sig-param">tag=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#isend"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.isend" title="Permalink to this definition">¶</a></dt>
<dd><p>Sends a tensor asynchronously.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Tensor to send.</p></li>
<li><p><strong>dst</strong> (<em>python:int</em>) – Destination rank.</p></li>
<li><p><strong>group</strong> (<em>ProcessGroup</em><em>, </em><em>optional</em>) – The process group to work on</p></li>
<li><p><strong>tag</strong> (<em>python:int</em><em>, </em><em>optional</em>) – Tag to match send with remote recv</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A distributed request object.
None, if not part of the group</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.distributed.irecv">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">irecv</code><span class="sig-paren">(</span><em class="sig-param">tensor</em>, <em class="sig-param">src</em>, <em class="sig-param">group=<object object></em>, <em class="sig-param">tag=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#irecv"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.irecv" title="Permalink to this definition">¶</a></dt>
<dd><p>Receives a tensor asynchronously.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Tensor to fill with received data.</p></li>
<li><p><strong>src</strong> (<em>python:int</em>) – Source rank.</p></li>
<li><p><strong>group</strong> (<em>ProcessGroup</em><em>, </em><em>optional</em>) – The process group to work on</p></li>
<li><p><strong>tag</strong> (<em>python:int</em><em>, </em><em>optional</em>) – Tag to match recv with remote send</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A distributed request object.
None, if not part of the group</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="synchronous-and-asynchronous-collective-operations">
<h2>Synchronous and asynchronous collective operations<a class="headerlink" href="#synchronous-and-asynchronous-collective-operations" title="Permalink to this headline">¶</a></h2>
<p>Every collective operation function supports the following two kinds of operations:</p>
<p>synchronous operation - the default mode, when <code class="docutils literal notranslate"><span class="pre">async_op</span></code> is set to False.
when the function returns, it is guaranteed that
the collective operation is performed (not necessarily completed if it’s a CUDA op since all
CUDA ops are asynchronous), and any further function calls depending on the data of the
collective operation can be called. In the synchronous mode, the collective function does not
return anything</p>
<p>asynchronous operation - when <code class="docutils literal notranslate"><span class="pre">async_op</span></code> is set to True. The collective operation function
returns a distributed request object. In general, you don’t need to create it manually and it
is guaranteed to support two methods:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">is_completed()</span></code> - returns True if the operation has finished</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">wait()</span></code> - will block the process until the operation is finished.</p></li>
</ul>
</div>
<div class="section" id="collective-functions">
<h2>Collective functions<a class="headerlink" href="#collective-functions" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.distributed.broadcast">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">broadcast</code><span class="sig-paren">(</span><em class="sig-param">tensor</em>, <em class="sig-param">src</em>, <em class="sig-param">group=<object object></em>, <em class="sig-param">async_op=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#broadcast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.broadcast" title="Permalink to this definition">¶</a></dt>
<dd><p>Broadcasts the tensor to the whole group.</p>
<p><code class="docutils literal notranslate"><span class="pre">tensor</span></code> must have the same number of elements in all processes
participating in the collective.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Data to be sent if <code class="docutils literal notranslate"><span class="pre">src</span></code> is the rank of current
process, and tensor to be used to save received data otherwise.</p></li>
<li><p><strong>src</strong> (<em>python:int</em>) – Source rank.</p></li>
<li><p><strong>group</strong> (<em>ProcessGroup</em><em>, </em><em>optional</em>) – The process group to work on</p></li>
<li><p><strong>async_op</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether this op should be an async op</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.distributed.all_reduce">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">all_reduce</code><span class="sig-paren">(</span><em class="sig-param">tensor</em>, <em class="sig-param">op=ReduceOp.SUM</em>, <em class="sig-param">group=<object object></em>, <em class="sig-param">async_op=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_reduce"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.all_reduce" title="Permalink to this definition">¶</a></dt>
<dd><p>Reduces the tensor data across all machines in such a way that all get
the final result.</p>
<p>After the call <code class="docutils literal notranslate"><span class="pre">tensor</span></code> is going to be bitwise identical in all processes.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Input and output of the collective. The function
operates in-place.</p></li>
<li><p><strong>op</strong> (<em>optional</em>) – One of the values from
<code class="docutils literal notranslate"><span class="pre">torch.distributed.ReduceOp</span></code>
enum. Specifies an operation used for element-wise reductions.</p></li>
<li><p><strong>group</strong> (<em>ProcessGroup</em><em>, </em><em>optional</em>) – The process group to work on</p></li>
<li><p><strong>async_op</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether this op should be an async op</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.distributed.reduce">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">reduce</code><span class="sig-paren">(</span><em class="sig-param">tensor</em>, <em class="sig-param">dst</em>, <em class="sig-param">op=ReduceOp.SUM</em>, <em class="sig-param">group=<object object></em>, <em class="sig-param">async_op=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#reduce"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.reduce" title="Permalink to this definition">¶</a></dt>
<dd><p>Reduces the tensor data across all machines.</p>
<p>Only the process with rank <code class="docutils literal notranslate"><span class="pre">dst</span></code> is going to receive the final result.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Input and output of the collective. The function
operates in-place.</p></li>
<li><p><strong>dst</strong> (<em>python:int</em>) – Destination rank</p></li>
<li><p><strong>op</strong> (<em>optional</em>) – One of the values from
<code class="docutils literal notranslate"><span class="pre">torch.distributed.ReduceOp</span></code>
enum. Specifies an operation used for element-wise reductions.</p></li>
<li><p><strong>group</strong> (<em>ProcessGroup</em><em>, </em><em>optional</em>) – The process group to work on</p></li>
<li><p><strong>async_op</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether this op should be an async op</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.distributed.all_gather">
<code class="sig-prename descclassname">torch.distributed.</code><code class="sig-name descname">all_gather</code><span class="sig-paren">(</span><em class="sig-param">tensor_list</em>, <em class="sig-param">tensor</em>, <em class="sig-param">group=<object object></em>, <em class="sig-param">async_op=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_gather"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributed.all_gather" title="Permalink to this definition">¶</a></dt>
<dd><p>Gathers tensors from the whole group in a list.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor_list</strong> (<em>list</em><em>[</em><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>]</em>) – Output list. It should contain
correctly-sized tensors to be used for output of the collective.</p></li>
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Tensor to be broadcast from current process.</p></li>
<li><p><strong>group</strong> (<em>ProcessGroup</em><em>, </em><em>optional</em>) – The process group to work on</p></li>
<li><p><strong>async_op</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether this op should be an async op</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group</p>
</dd>
</dl>
</dd></dl>