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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">CUDA Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
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<div class="section" id="distributed-rpc-framework">
<span id="id1"></span><h1>Distributed RPC Framework<a class="headerlink" href="#distributed-rpc-framework" title="Permalink to this heading">¶</a></h1>
<p>The distributed RPC framework provides mechanisms for multi-machine model
training through a set of primitives to allow for remote communication, and a
higher-level API to automatically differentiate models split across several
machines.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>APIs in the RPC package are stable. There are multiple ongoing work items
to improve performance and error handling, which will ship in future releases.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>CUDA support was introduced in PyTorch 1.9 and is still a <strong>beta</strong> feature.
Not all features of the RPC package are yet compatible with CUDA support and
thus their use is discouraged. These unsupported features include: RRefs,
JIT compatibility, dist autograd and dist optimizer, and profiling. These
shortcomings will be addressed in future releases.</p>
</div>
<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>
<div class="section" id="basics">
<h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading">¶</a></h2>
<p>The distributed RPC framework makes it easy to run functions remotely, supports
referencing remote objects without copying the real data around, and provides
autograd and optimizer APIs to transparently run backward and update parameters
across RPC boundaries. These features can be categorized into four sets of APIs.</p>
<ol class="arabic simple">
<li><p><strong>Remote Procedure Call (RPC)</strong> supports running a function on the specified
destination worker with the given arguments and getting the return value back
or creating a reference to the return value. There are three main RPC APIs:
<a class="reference internal" href="#torch.distributed.rpc.rpc_sync" title="torch.distributed.rpc.rpc_sync"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rpc_sync()</span></code></a> (synchronous),
<a class="reference internal" href="#torch.distributed.rpc.rpc_async" title="torch.distributed.rpc.rpc_async"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rpc_async()</span></code></a> (asynchronous), and
<a class="reference internal" href="#torch.distributed.rpc.remote" title="torch.distributed.rpc.remote"><code class="xref py py-meth docutils literal notranslate"><span class="pre">remote()</span></code></a> (asynchronous and returns a reference
to the remote return value). Use the synchronous API if the user code cannot
proceed without the return value. Otherwise, use the asynchronous API to get
a future, and wait on the future when the return value is needed on the
caller. The <a class="reference internal" href="#torch.distributed.rpc.remote" title="torch.distributed.rpc.remote"><code class="xref py py-meth docutils literal notranslate"><span class="pre">remote()</span></code></a> API is useful when the
requirement is to create something remotely but never need to fetch it to
the caller. Imagine the case that a driver process is setting up a parameter
server and a trainer. The driver can create an embedding table on the
parameter server and then share the reference to the embedding table with the
trainer, but itself will never use the embedding table locally. In this case,
<a class="reference internal" href="#torch.distributed.rpc.rpc_sync" title="torch.distributed.rpc.rpc_sync"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rpc_sync()</span></code></a> and
<a class="reference internal" href="#torch.distributed.rpc.rpc_async" title="torch.distributed.rpc.rpc_async"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rpc_async()</span></code></a> are no longer appropriate, as they
always imply that the return value will be returned to the caller
immediately or in the future.</p></li>
<li><p><strong>Remote Reference (RRef)</strong> serves as a distributed shared pointer to a local
or remote object. It can be shared with other workers and reference counting
will be handled transparently. Each RRef only has one owner and the object
only lives on that owner. Non-owner workers holding RRefs can get copies of
the object from the owner by explicitly requesting it. This is useful when
a worker needs to access some data object, but itself is neither the creator
(the caller of <a class="reference internal" href="#torch.distributed.rpc.remote" title="torch.distributed.rpc.remote"><code class="xref py py-meth docutils literal notranslate"><span class="pre">remote()</span></code></a>) or the owner of the
object. The distributed optimizer, as we will discuss below, is one example
of such use cases.</p></li>
<li><p><strong>Distributed Autograd</strong> stitches together local autograd engines on all the
workers involved in the forward pass, and automatically reach out to them
during the backward pass to compute gradients. This is especially helpful if
the forward pass needs to span multiple machines when conducting, e.g.,
distributed model parallel training, parameter-server training, etc. With
this feature, user code no longer needs to worry about how to send gradients
across RPC boundaries and in which order should the local autograd engines
be launched, which can become quite complicated where there are nested and
inter-dependent RPC calls in the forward pass.</p></li>
<li><p><strong>Distributed Optimizer</strong>’s constructor takes a
<a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Optimizer()</span></code></a> (e.g., <a class="reference internal" href="generated/torch.optim.SGD.html#torch.optim.SGD" title="torch.optim.SGD"><code class="xref py py-meth docutils literal notranslate"><span class="pre">SGD()</span></code></a>,
<a class="reference internal" href="generated/torch.optim.Adagrad.html#torch.optim.Adagrad" title="torch.optim.Adagrad"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Adagrad()</span></code></a>, etc.) and a list of parameter RRefs, creates an
<a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Optimizer()</span></code></a> instance on each distinct RRef owner, and
updates parameters accordingly when running <code class="docutils literal notranslate"><span class="pre">step()</span></code>. When you have
distributed forward and backward passes, parameters and gradients will be
scattered across multiple workers, and hence it requires an optimizer on each
of the involved workers. Distributed Optimizer wraps all those local
optimizers into one, and provides a concise constructor and <code class="docutils literal notranslate"><span class="pre">step()</span></code> API.</p></li>
</ol>
</div>
<div class="section" id="rpc">
<span id="id2"></span><h2>RPC<a class="headerlink" href="#rpc" title="Permalink to this heading">¶</a></h2>
<p>Before using RPC and distributed autograd primitives, initialization must take
place. To initialize the RPC framework we need to use
<a class="reference internal" href="#torch.distributed.rpc.init_rpc" title="torch.distributed.rpc.init_rpc"><code class="xref py py-meth docutils literal notranslate"><span class="pre">init_rpc()</span></code></a> which would initialize the RPC
framework, RRef framework and distributed autograd.</p>
<span class="target" id="module-torch.distributed.rpc"></span><dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.rpc.init_rpc">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">init_rpc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <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">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">world_size</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">rpc_backend_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/rpc.html#init_rpc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.init_rpc" title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes RPC primitives such as the local RPC agent
and distributed autograd, which immediately makes the current
process ready to send and receive RPCs.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>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>) – a globally unique name of this node. (e.g.,
<code class="docutils literal notranslate"><span class="pre">Trainer3</span></code>, <code class="docutils literal notranslate"><span class="pre">ParameterServer2</span></code>, <code class="docutils literal notranslate"><span class="pre">Master</span></code>, <code class="docutils literal notranslate"><span class="pre">Worker1</span></code>)
Name can only contain number, alphabet, underscore, colon,
and/or dash, and must be shorter than 128 characters.</p></li>
<li><p><strong>backend</strong> (<a class="reference internal" href="#torch.distributed.rpc.BackendType" title="torch.distributed.rpc.BackendType"><em>BackendType</em></a><em>, </em><em>optional</em>) – The type of RPC backend
implementation. Supported values is
<code class="docutils literal notranslate"><span class="pre">BackendType.TENSORPIPE</span></code> (the default).
See <a class="reference internal" href="#rpc-backends"><span class="std std-ref">Backends</span></a> for more information.</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>) – a globally unique id/rank of this node.</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>) – The number of workers in the group.</p></li>
<li><p><strong>rpc_backend_options</strong> (<a class="reference internal" href="#torch.distributed.rpc.RpcBackendOptions" title="torch.distributed.rpc.RpcBackendOptions"><em>RpcBackendOptions</em></a><em>, </em><em>optional</em>) – The options
passed to the RpcAgent constructor. It must be an agent-specific
subclass of <a class="reference internal" href="#torch.distributed.rpc.RpcBackendOptions" title="torch.distributed.rpc.RpcBackendOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">RpcBackendOptions</span></code></a>
and contains agent-specific initialization configurations. By
default, for all agents, it sets the default timeout to 60
seconds and performs the rendezvous with an underlying process
group initialized using <code class="docutils literal notranslate"><span class="pre">init_method</span> <span class="pre">=</span> <span class="pre">"env://"</span></code>,
meaning that environment variables <code class="docutils literal notranslate"><span class="pre">MASTER_ADDR</span></code> and
<code class="docutils literal notranslate"><span class="pre">MASTER_PORT</span></code> need to be set properly. See
<a class="reference internal" href="#rpc-backends"><span class="std std-ref">Backends</span></a> for more information and find which options
are available.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<p>The following APIs allow users to remotely execute functions as well as create
references (RRefs) to remote data objects. In these APIs, when passing a
<code class="docutils literal notranslate"><span class="pre">Tensor</span></code> as an argument or a return value, the destination worker will try to
create a <code class="docutils literal notranslate"><span class="pre">Tensor</span></code> with the same meta (i.e., shape, stride, etc.). We
intentionally disallow transmitting CUDA tensors because it might crash if the
device lists on source and destination workers do not match. In such cases,
applications can always explicitly move the input tensors to CPU on the caller
and move it to the desired devices on the callee if necessary.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>TorchScript support in RPC is a prototype feature and subject to change. Since
v1.5.0, <code class="docutils literal notranslate"><span class="pre">torch.distributed.rpc</span></code> supports calling TorchScript functions as
RPC target functions, and this will help improve parallelism on the callee
side as executing TorchScript functions does not require GIL.</p>
</div>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.rpc.rpc_sync">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">rpc_sync</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</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">kwargs</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">-1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#rpc_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.rpc_sync" title="Permalink to this definition">¶</a></dt>
<dd><p>Make a blocking RPC call to run function <code class="docutils literal notranslate"><span class="pre">func</span></code> on worker <code class="docutils literal notranslate"><span class="pre">to</span></code>. RPC
messages are sent and received in parallel to execution of Python code. This
method is thread-safe.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>to</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.rpc.WorkerInfo" title="torch.distributed.rpc.WorkerInfo"><em>WorkerInfo</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – name/rank/<code class="docutils literal notranslate"><span class="pre">WorkerInfo</span></code> of the destination worker.</p></li>
<li><p><strong>func</strong> (<em>Callable</em>) – a callable function, such as Python callables, builtin
operators (e.g. <a class="reference internal" href="generated/torch.add.html#torch.add" title="torch.add"><code class="xref py py-meth docutils literal notranslate"><span class="pre">add()</span></code></a>) and annotated
TorchScript functions.</p></li>
<li><p><strong>args</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a>) – the argument tuple for the <code class="docutils literal notranslate"><span class="pre">func</span></code> invocation.</p></li>
<li><p><strong>kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – is a dictionary of keyword arguments for the <code class="docutils literal notranslate"><span class="pre">func</span></code>
invocation.</p></li>
<li><p><strong>timeout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – timeout in seconds to use for this RPC. If
the RPC does not complete in this amount of
time, an exception indicating it has
timed out will be raised. A value of 0
indicates an infinite timeout, i.e. a timeout
error will never be raised. If not provided,
the default value set during initialization
or with <code class="docutils literal notranslate"><span class="pre">_set_rpc_timeout</span></code> is used.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Returns the result of running <code class="docutils literal notranslate"><span class="pre">func</span></code> with <code class="docutils literal notranslate"><span class="pre">args</span></code> and <code class="docutils literal notranslate"><span class="pre">kwargs</span></code>.</p>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><p>Make sure that <code class="docutils literal notranslate"><span class="pre">MASTER_ADDR</span></code> and <code class="docutils literal notranslate"><span class="pre">MASTER_PORT</span></code> are set properly
on both workers. Refer to <a class="reference internal" href="distributed.html#torch.distributed.init_process_group" title="torch.distributed.init_process_group"><code class="xref py py-meth docutils literal notranslate"><span class="pre">init_process_group()</span></code></a>
API for more details. For example,</p>
<p>export MASTER_ADDR=localhost
export MASTER_PORT=5678</p>
<p>Then run the following code in two different processes:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># On worker 0:</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="s2">"worker0"</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">ret</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">rpc_sync</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># On worker 1:</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
</pre></div>
</div>
<p>Below is an example of running a TorchScript function using RPC.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># On both workers:</span>
<span class="gp">>>> </span><span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">my_script_add</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># On worker 0:</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="s2">"worker0"</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">ret</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">rpc_sync</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="n">my_script_add</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># On worker 1:</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</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.rpc.rpc_async">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">rpc_async</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</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">kwargs</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">-1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#rpc_async"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.rpc_async" title="Permalink to this definition">¶</a></dt>
<dd><p>Make a non-blocking RPC call to run function <code class="docutils literal notranslate"><span class="pre">func</span></code> on worker <code class="docutils literal notranslate"><span class="pre">to</span></code>. RPC
messages are sent and received in parallel to execution of Python code. This
method is thread-safe. This method will immediately return a
<a class="reference internal" href="futures.html#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> that can be awaited on.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>to</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.rpc.WorkerInfo" title="torch.distributed.rpc.WorkerInfo"><em>WorkerInfo</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – name/rank/<code class="docutils literal notranslate"><span class="pre">WorkerInfo</span></code> of the destination worker.</p></li>
<li><p><strong>func</strong> (<em>Callable</em>) – a callable function, such as Python callables, builtin
operators (e.g. <a class="reference internal" href="generated/torch.add.html#torch.add" title="torch.add"><code class="xref py py-meth docutils literal notranslate"><span class="pre">add()</span></code></a>) and annotated
TorchScript functions.</p></li>
<li><p><strong>args</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a>) – the argument tuple for the <code class="docutils literal notranslate"><span class="pre">func</span></code> invocation.</p></li>
<li><p><strong>kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – is a dictionary of keyword arguments for the <code class="docutils literal notranslate"><span class="pre">func</span></code>
invocation.</p></li>
<li><p><strong>timeout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – timeout in seconds to use for this RPC. If
the RPC does not complete in this amount of
time, an exception indicating it has
timed out will be raised. A value of 0
indicates an infinite timeout, i.e. a timeout
error will never be raised. If not provided,
the default value set during initialization
or with <code class="docutils literal notranslate"><span class="pre">_set_rpc_timeout</span></code> is used.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Returns a <a class="reference internal" href="futures.html#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> object that can be waited
on. When completed, the return value of <code class="docutils literal notranslate"><span class="pre">func</span></code> on <code class="docutils literal notranslate"><span class="pre">args</span></code> and
<code class="docutils literal notranslate"><span class="pre">kwargs</span></code> can be retrieved from the <a class="reference internal" href="futures.html#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a>
object.</p>
</dd>
</dl>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Using GPU tensors as arguments or return values of <code class="docutils literal notranslate"><span class="pre">func</span></code> is not
supported since we don’t support sending GPU tensors over the wire. You
need to explicitly copy GPU tensors to CPU before using them as
arguments or return values of <code class="docutils literal notranslate"><span class="pre">func</span></code>.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The <code class="docutils literal notranslate"><span class="pre">rpc_async</span></code> API does not copy storages of argument tensors until
sending them over the wire, which could be done by a different thread
depending on the RPC backend type. The caller should make sure that the
contents of those tensors stay intact until the returned
<a class="reference internal" href="futures.html#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> completes.</p>
</div>
<dl>
<dt>Example::</dt><dd><p>Make sure that <code class="docutils literal notranslate"><span class="pre">MASTER_ADDR</span></code> and <code class="docutils literal notranslate"><span class="pre">MASTER_PORT</span></code> are set properly
on both workers. Refer to <a class="reference internal" href="distributed.html#torch.distributed.init_process_group" title="torch.distributed.init_process_group"><code class="xref py py-meth docutils literal notranslate"><span class="pre">init_process_group()</span></code></a>
API for more details. For example,</p>
<p>export MASTER_ADDR=localhost
export MASTER_PORT=5678</p>
<p>Then run the following code in two different processes:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># On worker 0:</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="s2">"worker0"</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">fut1</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">rpc_async</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">fut2</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">rpc_async</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="nb">min</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">result</span> <span class="o">=</span> <span class="n">fut1</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span> <span class="o">+</span> <span class="n">fut2</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># On worker 1:</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
</pre></div>
</div>
<p>Below is an example of running a TorchScript function using RPC.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># On both workers:</span>
<span class="gp">>>> </span><span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">my_script_add</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># On worker 0:</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="s2">"worker0"</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">fut</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">rpc_async</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="n">my_script_add</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">ret</span> <span class="o">=</span> <span class="n">fut</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># On worker 1:</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</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.rpc.remote">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">remote</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</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">kwargs</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">-1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#remote"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.remote" title="Permalink to this definition">¶</a></dt>
<dd><p>Make a remote call to run <code class="docutils literal notranslate"><span class="pre">func</span></code> on worker <code class="docutils literal notranslate"><span class="pre">to</span></code> and return an
<code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code> to the result value immediately.
Worker <code class="docutils literal notranslate"><span class="pre">to</span></code> will be the owner of the returned
<code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code>, and the worker calling <code class="docutils literal notranslate"><span class="pre">remote</span></code> is
a user. The owner manages the global reference count of its
<code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code>, and the owner
<code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code> is only destructed when globally there
are no living references to it.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>to</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.rpc.WorkerInfo" title="torch.distributed.rpc.WorkerInfo"><em>WorkerInfo</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – name/rank/<code class="docutils literal notranslate"><span class="pre">WorkerInfo</span></code> of the destination worker.</p></li>
<li><p><strong>func</strong> (<em>Callable</em>) – a callable function, such as Python callables, builtin
operators (e.g. <a class="reference internal" href="generated/torch.add.html#torch.add" title="torch.add"><code class="xref py py-meth docutils literal notranslate"><span class="pre">add()</span></code></a>) and annotated
TorchScript functions.</p></li>
<li><p><strong>args</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a>) – the argument tuple for the <code class="docutils literal notranslate"><span class="pre">func</span></code> invocation.</p></li>
<li><p><strong>kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – is a dictionary of keyword arguments for the <code class="docutils literal notranslate"><span class="pre">func</span></code>
invocation.</p></li>
<li><p><strong>timeout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – timeout in seconds for this remote call. If the
creation of this
<code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code> on worker
<code class="docutils literal notranslate"><span class="pre">to</span></code> is not successfully processed on this
worker within this timeout, then the next time
there is an attempt to use the RRef (such as
<code class="docutils literal notranslate"><span class="pre">to_here()</span></code>), a timeout will be raised
indicating this failure. A value of 0 indicates
an infinite timeout, i.e. a timeout error will
never be raised. If not provided, the default
value set during initialization or with
<code class="docutils literal notranslate"><span class="pre">_set_rpc_timeout</span></code> is used.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A user <code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code> instance to the result
value. Use the blocking API <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.distributed.rpc.RRef.to_here()</span></code>
to retrieve the result value locally.</p>
</dd>
</dl>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The <code class="docutils literal notranslate"><span class="pre">remote</span></code> API does not copy storages of argument tensors until
sending them over the wire, which could be done by a different thread
depending on the RPC backend type. The caller should make sure that the
contents of those tensors stay intact until the returned RRef is
confirmed by the owner, which can be checked using the
<code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.distributed.rpc.RRef.confirmed_by_owner()</span></code> API.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Errors such as timeouts for the <code class="docutils literal notranslate"><span class="pre">remote</span></code> API are handled on a
best-effort basis. This means that when remote calls initiated by
<code class="docutils literal notranslate"><span class="pre">remote</span></code> fail, such as with a timeout error, we take a best-effort
approach to error handling. This means that errors are handled and set
on the resulting RRef on an asynchronous basis. If the RRef has not been
used by the application before this handling (such as <code class="docutils literal notranslate"><span class="pre">to_here</span></code> or
fork call), then future uses of the <code class="docutils literal notranslate"><span class="pre">RRef</span></code> will appropriately raise
errors. However, it is possible that the user application will use the
<code class="docutils literal notranslate"><span class="pre">RRef</span></code> before the errors are handled. In this case, errors may not be
raised as they have not yet been handled.</p>
</div>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>Make sure that ``MASTER_ADDR`` and ``MASTER_PORT`` are set properly
on both workers. Refer to :meth:`~torch.distributed.init_process_group`
API for more details. For example,
export MASTER_ADDR=localhost
export MASTER_PORT=5678
Then run the following code in two different processes:
>>> # On worker 0:
>>> import torch
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker0", rank=0, world_size=2)
>>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
>>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
>>> x = rref1.to_here() + rref2.to_here()
>>> rpc.shutdown()
>>> # On worker 1:
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker1", rank=1, world_size=2)
>>> rpc.shutdown()
Below is an example of running a TorchScript function using RPC.
>>> # On both workers:
>>> @torch.jit.script
>>> def my_script_add(t1, t2):
>>> return torch.add(t1, t2)
>>> # On worker 0:
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker0", rank=0, world_size=2)
>>> rref = rpc.remote("worker1", my_script_add, args=(torch.ones(2), 3))
>>> rref.to_here()
>>> rpc.shutdown()
>>> # On worker 1:
>>> import torch.distributed.rpc as rpc
>>> rpc.init_rpc("worker1", rank=1, world_size=2)
>>> rpc.shutdown()
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.rpc.get_worker_info">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">get_worker_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_name</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/rpc/api.html#get_worker_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.get_worker_info" title="Permalink to this definition">¶</a></dt>
<dd><p>Get <a class="reference internal" href="#torch.distributed.rpc.WorkerInfo" title="torch.distributed.rpc.WorkerInfo"><code class="xref py py-class docutils literal notranslate"><span class="pre">WorkerInfo</span></code></a> of a given worker name.
Use this <a class="reference internal" href="#torch.distributed.rpc.WorkerInfo" title="torch.distributed.rpc.WorkerInfo"><code class="xref py py-class docutils literal notranslate"><span class="pre">WorkerInfo</span></code></a> to avoid passing an
expensive string on every invocation.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>worker_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>) – the string name of a worker. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, return the
the id of the current worker. (default <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><a class="reference internal" href="#torch.distributed.rpc.WorkerInfo" title="torch.distributed.rpc.WorkerInfo"><code class="xref py py-class docutils literal notranslate"><span class="pre">WorkerInfo</span></code></a> instance for the given
<code class="docutils literal notranslate"><span class="pre">worker_name</span></code> or <a class="reference internal" href="#torch.distributed.rpc.WorkerInfo" title="torch.distributed.rpc.WorkerInfo"><code class="xref py py-class docutils literal notranslate"><span class="pre">WorkerInfo</span></code></a> of the
current worker if <code class="docutils literal notranslate"><span class="pre">worker_name</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.rpc.shutdown">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">shutdown</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graceful</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</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">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#shutdown"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.shutdown" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform a shutdown of the RPC agent, and then destroy the RPC agent. This
stops the local agent from accepting outstanding requests, and shuts
down the RPC framework by terminating all RPC threads. If <code class="docutils literal notranslate"><span class="pre">graceful=True</span></code>,
this will block until all local and remote RPC processes reach this method
and wait for all outstanding work to complete. Otherwise, if
<code class="docutils literal notranslate"><span class="pre">graceful=False</span></code>, this is a local shutdown, and it does not wait for other
RPC processes to reach this method.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>For <a class="reference internal" href="futures.html#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> objects returned by
<a class="reference internal" href="#torch.distributed.rpc.rpc_async" title="torch.distributed.rpc.rpc_async"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rpc_async()</span></code></a>, <code class="docutils literal notranslate"><span class="pre">future.wait()</span></code> should not
be called after <code class="docutils literal notranslate"><span class="pre">shutdown()</span></code>.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>graceful</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to do a graceful shutdown or not. If True,
this will 1) wait until there is no pending system
messages for <code class="docutils literal notranslate"><span class="pre">UserRRefs</span></code> and delete them; 2) block
until all local and remote RPC processes have reached
this method and wait for all outstanding work to
complete.</p>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><p>Make sure that <code class="docutils literal notranslate"><span class="pre">MASTER_ADDR</span></code> and <code class="docutils literal notranslate"><span class="pre">MASTER_PORT</span></code> are set properly
on both workers. Refer to <a class="reference internal" href="distributed.html#torch.distributed.init_process_group" title="torch.distributed.init_process_group"><code class="xref py py-meth docutils literal notranslate"><span class="pre">init_process_group()</span></code></a>
API for more details. For example,</p>
<p>export MASTER_ADDR=localhost
export MASTER_PORT=5678</p>
<p>Then run the following code in two different processes:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># On worker 0:</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="s2">"worker0"</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># do some work</span>
<span class="gp">>>> </span><span class="n">result</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">rpc_sync</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
<span class="gp">>>> </span><span class="c1"># ready to shutdown</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># On worker 1:</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># wait for worker 0 to finish work, and then shutdown.</span>
<span class="gp">>>> </span><span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.rpc.WorkerInfo">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">WorkerInfo</span></span><a class="headerlink" href="#torch.distributed.rpc.WorkerInfo" title="Permalink to this definition">¶</a></dt>
<dd><p>A structure that encapsulates information of a worker in the system.
Contains the name and ID of the worker. This class is not meant to
be constructed directly, rather, an instance can be retrieved
through <a class="reference internal" href="#torch.distributed.rpc.get_worker_info" title="torch.distributed.rpc.get_worker_info"><code class="xref py py-meth docutils literal notranslate"><span class="pre">get_worker_info()</span></code></a> and the
result can be passed in to functions such as
<a class="reference internal" href="#torch.distributed.rpc.rpc_sync" title="torch.distributed.rpc.rpc_sync"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rpc_sync()</span></code></a>, <a class="reference internal" href="#torch.distributed.rpc.rpc_async" title="torch.distributed.rpc.rpc_async"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rpc_async()</span></code></a>,
<a class="reference internal" href="#torch.distributed.rpc.remote" title="torch.distributed.rpc.remote"><code class="xref py py-meth docutils literal notranslate"><span class="pre">remote()</span></code></a> to avoid copying a string on
every invocation.</p>
<dl class="py property">
<dt class="sig sig-object py" id="torch.distributed.rpc.WorkerInfo.id">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">id</span></span><a class="headerlink" href="#torch.distributed.rpc.WorkerInfo.id" title="Permalink to this definition">¶</a></dt>
<dd><p>Globally unique id to identify the worker.</p>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="torch.distributed.rpc.WorkerInfo.name">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">name</span></span><a class="headerlink" href="#torch.distributed.rpc.WorkerInfo.name" title="Permalink to this definition">¶</a></dt>
<dd><p>The name of the worker.</p>
</dd></dl>
</dd></dl>
<p>The RPC package also provides decorators which allow applications to specify
how a given function should be treated on the callee side.</p>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.rpc.functions.async_execution">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.functions.</span></span><span class="sig-name descname"><span class="pre">async_execution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/functions.html#async_execution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.functions.async_execution" title="Permalink to this definition">¶</a></dt>
<dd><p>A decorator for a function indicating that the return value of the function
is guaranteed to be a <a class="reference internal" href="futures.html#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> object and this
function can run asynchronously on the RPC callee. More specifically, the
callee extracts the <a class="reference internal" href="futures.html#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> returned by the wrapped
function and installs subsequent processing steps as a callback to that
<a class="reference internal" href="futures.html#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a>. The installed callback will read the value
from the <a class="reference internal" href="futures.html#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> when completed and send the
value back as the RPC response. That also means the returned
<a class="reference internal" href="futures.html#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> only exists on the callee side and is never
sent through RPC. This decorator is useful when the wrapped function’s
(<code class="docutils literal notranslate"><span class="pre">fn</span></code>) execution needs to pause and resume due to, e.g., containing
<a class="reference internal" href="#torch.distributed.rpc.rpc_async" title="torch.distributed.rpc.rpc_async"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rpc_async()</span></code></a> or waiting for other signals.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>To enable asynchronous execution, applications must pass the
function object returned by this decorator to RPC APIs. If RPC detected
attributes installed by this decorator, it knows that this function
returns a <code class="docutils literal notranslate"><span class="pre">Future</span></code> object and will handle that accordingly.
However, this does not mean this decorator has to be outmost one when
defining a function. For example, when combined with <code class="docutils literal notranslate"><span class="pre">@staticmethod</span></code>
or <code class="docutils literal notranslate"><span class="pre">@classmethod</span></code>, <code class="docutils literal notranslate"><span class="pre">@rpc.functions.async_execution</span></code> needs to be the
inner decorator to allow the target function be recognized as a static
or class function. This target function can still execute asynchronously
because, when accessed, the static or class method preserves attributes
installed by <code class="docutils literal notranslate"><span class="pre">@rpc.functions.async_execution</span></code>.</p>
</div>
<dl>
<dt>Example::</dt><dd><p>The returned <a class="reference internal" href="futures.html#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> object can come from
<a class="reference internal" href="#torch.distributed.rpc.rpc_async" title="torch.distributed.rpc.rpc_async"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rpc_async()</span></code></a>,
<a class="reference internal" href="futures.html#torch.futures.Future.then" title="torch.futures.Future.then"><code class="xref py py-meth docutils literal notranslate"><span class="pre">then()</span></code></a>, or <a class="reference internal" href="futures.html#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a>
constructor. The example below shows directly using the