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<li class="toctree-l1"><a class="reference internal" href="../notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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<div class="section" id="module-torch.distributed.elastic.multiprocessing">
<span id="multiprocessing"></span><h1>Multiprocessing<a class="headerlink" href="#module-torch.distributed.elastic.multiprocessing" title="Permalink to this headline">¶</a></h1>
<p>Library that launches and manages <code class="docutils literal notranslate"><span class="pre">n</span></code> copies of worker subprocesses
either specified by a function or a binary.</p>
<p>For functions, it uses <code class="docutils literal notranslate"><span class="pre">torch.multiprocessing</span></code> (and therefore python
<code class="docutils literal notranslate"><span class="pre">multiprocessing</span></code>) to spawn/fork worker processes. For binaries it uses python
<code class="docutils literal notranslate"><span class="pre">subprocessing.Popen</span></code> to create worker processes.</p>
<p>Usage 1: Launching two trainers as a function</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.distributed.elastic.multiprocessing</span> <span class="kn">import</span> <span class="n">Std</span><span class="p">,</span> <span class="n">start_processes</span>
<span class="k">def</span> <span class="nf">trainer</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
<span class="k">pass</span> <span class="c1"># train</span>
<span class="c1"># runs two trainers</span>
<span class="c1"># LOCAL_RANK=0 trainer(1,2,3)</span>
<span class="c1"># LOCAL_RANK=1 trainer(4,5,6)</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="n">start_processes</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s2">"trainer"</span><span class="p">,</span>
<span class="n">entrypoint</span><span class="o">=</span><span class="n">trainer</span><span class="p">,</span>
<span class="n">args</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:</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="mi">3</span><span class="p">),</span> <span class="mi">1</span><span class="p">:</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">)},</span>
<span class="n">envs</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">{</span><span class="s2">"LOCAL_RANK"</span><span class="p">:</span> <span class="mi">0</span><span class="p">},</span> <span class="mi">1</span><span class="p">:</span> <span class="p">{</span><span class="s2">"LOCAL_RANK"</span><span class="p">:</span> <span class="mi">1</span><span class="p">}},</span>
<span class="n">log_dir</span><span class="o">=</span><span class="s2">"/tmp/foobar"</span><span class="p">,</span>
<span class="n">redirects</span><span class="o">=</span><span class="n">Std</span><span class="o">.</span><span class="n">ALL</span><span class="p">,</span> <span class="c1"># write all worker stdout/stderr to a log file</span>
<span class="n">tee</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">Std</span><span class="o">.</span><span class="n">ERR</span><span class="p">},</span> <span class="c1"># tee only local rank 0's stderr to console</span>
<span class="p">)</span>
<span class="c1"># waits for all copies of trainer to finish</span>
<span class="n">ctx</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span>
</pre></div>
</div>
<p>Usage 2: Launching 2 echo workers as a binary</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># same as invoking</span>
<span class="c1"># echo hello</span>
<span class="c1"># echo world > stdout.log</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="n">start_processes</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s2">"echo"</span>
<span class="n">entrypoint</span><span class="o">=</span><span class="s2">"echo"</span><span class="p">,</span>
<span class="n">log_dir</span><span class="o">=</span><span class="s2">"/tmp/foobar"</span><span class="p">,</span>
<span class="n">args</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="s2">"hello"</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span> <span class="s2">"world"</span><span class="p">},</span>
<span class="n">redirects</span><span class="o">=</span><span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="n">Std</span><span class="p">:</span><span class="n">OUT</span><span class="p">},</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Just like <code class="docutils literal notranslate"><span class="pre">torch.multiprocessing</span></code>, the return value of the function
<a class="reference internal" href="#torch.distributed.elastic.multiprocessing.start_processes" title="torch.distributed.elastic.multiprocessing.start_processes"><code class="xref py py-func docutils literal notranslate"><span class="pre">start_processes()</span></code></a> is a process context (<a class="reference internal" href="#torch.distributed.elastic.multiprocessing.api.PContext" title="torch.distributed.elastic.multiprocessing.api.PContext"><code class="xref py py-class docutils literal notranslate"><span class="pre">api.PContext</span></code></a>). If a function
was launched, a <a class="reference internal" href="#torch.distributed.elastic.multiprocessing.api.MultiprocessContext" title="torch.distributed.elastic.multiprocessing.api.MultiprocessContext"><code class="xref py py-class docutils literal notranslate"><span class="pre">api.MultiprocessContext</span></code></a> is returned and if a binary
was launched a <a class="reference internal" href="#torch.distributed.elastic.multiprocessing.api.SubprocessContext" title="torch.distributed.elastic.multiprocessing.api.SubprocessContext"><code class="xref py py-class docutils literal notranslate"><span class="pre">api.SubprocessContext</span></code></a> is returned. Both are specific
implementations of the parent <a class="reference internal" href="#torch.distributed.elastic.multiprocessing.api.PContext" title="torch.distributed.elastic.multiprocessing.api.PContext"><code class="xref py py-class docutils literal notranslate"><span class="pre">api.PContext</span></code></a> class.</p>
<div class="section" id="starting-multiple-workers">
<h2>Starting Multiple Workers<a class="headerlink" href="#starting-multiple-workers" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="torch.distributed.elastic.multiprocessing.start_processes">
<code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.</span></code><code class="sig-name descname"><span class="pre">start_processes</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">name</span></em>, <em class="sig-param"><span class="pre">entrypoint</span></em>, <em class="sig-param"><span class="pre">args</span></em>, <em class="sig-param"><span class="pre">envs</span></em>, <em class="sig-param"><span class="pre">log_dir</span></em>, <em class="sig-param"><span class="pre">start_method='spawn'</span></em>, <em class="sig-param"><span class="pre">redirects=<Std.NONE:</span> <span class="pre">0></span></em>, <em class="sig-param"><span class="pre">tee=<Std.NONE:</span> <span class="pre">0></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing.html#start_processes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.start_processes" title="Permalink to this definition">¶</a></dt>
<dd><p>Starts <code class="docutils literal notranslate"><span class="pre">n</span></code> copies of <code class="docutils literal notranslate"><span class="pre">entrypoint</span></code> processes with the provided options.
<code class="docutils literal notranslate"><span class="pre">entrypoint</span></code> is either a <code class="docutils literal notranslate"><span class="pre">Callable</span></code> (function) or a <code class="docutils literal notranslate"><span class="pre">str</span></code> (binary).
The number of copies is determined by the number of entries for <code class="docutils literal notranslate"><span class="pre">args</span></code> and
<code class="docutils literal notranslate"><span class="pre">envs</span></code> arguments, which need to have the same key set.</p>
<p><code class="docutils literal notranslate"><span class="pre">args</span></code> and <code class="docutils literal notranslate"><span class="pre">env</span></code> parameters are the arguments and environment variables
to pass down to the entrypoint mapped by the replica index (local rank).
All local ranks must be accounted for.
That is, the keyset should be <code class="docutils literal notranslate"><span class="pre">{0,1,...,(nprocs-1)}</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When the <code class="docutils literal notranslate"><span class="pre">entrypoint</span></code> is a binary (<code class="docutils literal notranslate"><span class="pre">str</span></code>), <code class="docutils literal notranslate"><span class="pre">args</span></code> can only be strings.
If any other type is given, then it is casted to a string representation
(e.g. <code class="docutils literal notranslate"><span class="pre">str(arg1)</span></code>). Furthermore, a binary failure will only write
an <code class="docutils literal notranslate"><span class="pre">error.json</span></code> error file if the main function is annotated with
<code class="docutils literal notranslate"><span class="pre">torch.distributed.elastic.multiprocessing.errors.record</span></code>. For function launches,
this is done by default and there is no need to manually annotate
with the <code class="docutils literal notranslate"><span class="pre">@record</span></code> annotation.</p>
</div>
<p><code class="docutils literal notranslate"><span class="pre">redirects</span></code> and <code class="docutils literal notranslate"><span class="pre">tees</span></code> are bitmasks specifying which std stream(s) to redirect
to a log file in the <code class="docutils literal notranslate"><span class="pre">log_dir</span></code>. Valid mask values are defined in <code class="docutils literal notranslate"><span class="pre">Std</span></code>.
To redirect/tee only certain local ranks, pass <code class="docutils literal notranslate"><span class="pre">redirects</span></code> as a map with the key as
the local rank to specify the redirect behavior for.
Any missing local ranks will default to <code class="docutils literal notranslate"><span class="pre">Std.NONE</span></code>.</p>
<p><code class="docutils literal notranslate"><span class="pre">tee</span></code> acts like the unix “tee” command in that it redirects + prints to console.
To avoid worker stdout/stderr from printing to console, use the <code class="docutils literal notranslate"><span class="pre">redirects</span></code> parameter.</p>
<p>For each process, the <code class="docutils literal notranslate"><span class="pre">log_dir</span></code> will contain:</p>
<ol class="arabic simple">
<li><p><code class="docutils literal notranslate"><span class="pre">{local_rank}/error.json</span></code>: if the process failed, a file with the error info</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">{local_rank}/stdout.json</span></code>: if <code class="docutils literal notranslate"><span class="pre">redirect</span> <span class="pre">&</span> <span class="pre">STDOUT</span> <span class="pre">==</span> <span class="pre">STDOUT</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">{local_rank}/stderr.json</span></code>: if <code class="docutils literal notranslate"><span class="pre">redirect</span> <span class="pre">&</span> <span class="pre">STDERR</span> <span class="pre">==</span> <span class="pre">STDERR</span></code></p></li>
</ol>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>It is expected that the <code class="docutils literal notranslate"><span class="pre">log_dir</span></code> exists, is empty, and is a directory.</p>
</div>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">log_dir</span> <span class="o">=</span> <span class="s2">"/tmp/test"</span>
<span class="c1"># ok; two copies of foo: foo("bar0"), foo("bar1")</span>
<span class="n">start_processes</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s2">"trainer"</span><span class="p">,</span>
<span class="n">entrypoint</span><span class="o">=</span><span class="n">foo</span><span class="p">,</span>
<span class="n">args</span><span class="p">:{</span><span class="mi">0</span><span class="p">:(</span><span class="s2">"bar0"</span><span class="p">,),</span> <span class="mi">1</span><span class="p">:(</span><span class="s2">"bar1"</span><span class="p">,),</span>
<span class="n">envs</span><span class="p">:{</span><span class="mi">0</span><span class="p">:{},</span> <span class="mi">1</span><span class="p">:{}},</span>
<span class="n">log_dir</span><span class="o">=</span><span class="n">log_dir</span>
<span class="p">)</span>
<span class="c1"># invalid; envs missing for local rank 1</span>
<span class="n">start_processes</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s2">"trainer"</span><span class="p">,</span>
<span class="n">entrypoint</span><span class="o">=</span><span class="n">foo</span><span class="p">,</span>
<span class="n">args</span><span class="p">:{</span><span class="mi">0</span><span class="p">:(</span><span class="s2">"bar0"</span><span class="p">,),</span> <span class="mi">1</span><span class="p">:(</span><span class="s2">"bar1"</span><span class="p">,),</span>
<span class="n">envs</span><span class="p">:{</span><span class="mi">0</span><span class="p">:{}},</span>
<span class="n">log_dir</span><span class="o">=</span><span class="n">log_dir</span>
<span class="p">)</span>
<span class="c1"># ok; two copies of /usr/bin/touch: touch file1, touch file2</span>
<span class="n">start_processes</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s2">"trainer"</span><span class="p">,</span>
<span class="n">entrypoint</span><span class="o">=</span><span class="s2">"/usr/bin/touch"</span><span class="p">,</span>
<span class="n">args</span><span class="p">:{</span><span class="mi">0</span><span class="p">:(</span><span class="s2">"file1"</span><span class="p">,),</span> <span class="mi">1</span><span class="p">:(</span><span class="s2">"file2"</span><span class="p">,),</span>
<span class="n">envs</span><span class="p">:{</span><span class="mi">0</span><span class="p">:{},</span> <span class="mi">1</span><span class="p">:{}},</span>
<span class="n">log_dir</span><span class="o">=</span><span class="n">log_dir</span>
<span class="p">)</span>
<span class="c1"># caution; arguments casted to string, runs:</span>
<span class="c1"># echo "1" "2" "3" and echo "[1, 2, 3]"</span>
<span class="n">start_processes</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s2">"trainer"</span><span class="p">,</span>
<span class="n">entrypoint</span><span class="o">=</span><span class="s2">"/usr/bin/echo"</span><span class="p">,</span>
<span class="n">args</span><span class="p">:{</span><span class="mi">0</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="mi">3</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="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],),</span>
<span class="n">envs</span><span class="p">:{</span><span class="mi">0</span><span class="p">:{},</span> <span class="mi">1</span><span class="p">:{}},</span>
<span class="n">log_dir</span><span class="o">=</span><span class="n">log_dir</span>
<span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>name</strong> – a human readable short name that describes what the processes are
(used as header when tee’ing stdout/stderr outputs)</p></li>
<li><p><strong>entrypoint</strong> – either a <code class="docutils literal notranslate"><span class="pre">Callable</span></code> (function) or <code class="docutils literal notranslate"><span class="pre">cmd</span></code> (binary)</p></li>
<li><p><strong>args</strong> – arguments to each replica</p></li>
<li><p><strong>envs</strong> – env vars to each replica</p></li>
<li><p><strong>log_dir</strong> – directory used to write log files</p></li>
<li><p><strong>nprocs</strong> – number of copies to create (one on each process)</p></li>
<li><p><strong>start_method</strong> – multiprocessing start method (spawn, fork, forkserver)
ignored for binaries</p></li>
<li><p><strong>redirects</strong> – which std streams to redirect to a log file</p></li>
<li><p><strong>tees</strong> – which std streams to redirect + print to console</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="process-context">
<h2>Process Context<a class="headerlink" href="#process-context" title="Permalink to this headline">¶</a></h2>
<dl class="py class">
<dt id="torch.distributed.elastic.multiprocessing.api.PContext">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></code><code class="sig-name descname"><span class="pre">PContext</span></code><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">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stdouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderrs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee_stdouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee_stderrs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_files</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#PContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.PContext" title="Permalink to this definition">¶</a></dt>
<dd><p>The base class that standardizes operations over a set of processes
that are launched via different mechanisms. The name <code class="docutils literal notranslate"><span class="pre">PContext</span></code>
is intentional to disambiguate with <code class="docutils literal notranslate"><span class="pre">torch.multiprocessing.ProcessContext</span></code>.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>stdouts and stderrs should ALWAYS be a superset of
tee_stdouts and tee_stderrs (respectively) this is b/c
tee is implemented as a redirect + tail -f <stdout/stderr.log></p>
</div>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.elastic.multiprocessing.api.MultiprocessContext">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></code><code class="sig-name descname"><span class="pre">MultiprocessContext</span></code><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">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stdouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderrs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee_stdouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee_stderrs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_files</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#MultiprocessContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.MultiprocessContext" title="Permalink to this definition">¶</a></dt>
<dd><p><code class="docutils literal notranslate"><span class="pre">PContext</span></code> holding worker processes invoked as a function.</p>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.elastic.multiprocessing.api.SubprocessContext">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></code><code class="sig-name descname"><span class="pre">SubprocessContext</span></code><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">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stdouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderrs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee_stdouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee_stderrs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_files</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#SubprocessContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.SubprocessContext" title="Permalink to this definition">¶</a></dt>
<dd><p><code class="docutils literal notranslate"><span class="pre">PContext</span></code> holding worker processes invoked as a binary.</p>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.elastic.multiprocessing.api.RunProcsResult">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></code><code class="sig-name descname"><span class="pre">RunProcsResult</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">return_values=<factory></span></em>, <em class="sig-param"><span class="pre">failures=<factory></span></em>, <em class="sig-param"><span class="pre">stdouts=<factory></span></em>, <em class="sig-param"><span class="pre">stderrs=<factory></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#RunProcsResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.RunProcsResult" title="Permalink to this definition">¶</a></dt>
<dd><p>Results of a completed run of processes started with <code class="docutils literal notranslate"><span class="pre">start_processes()</span></code>.
Returned by <code class="docutils literal notranslate"><span class="pre">PContext</span></code>.</p>
<p>Note the following:</p>
<ol class="arabic simple">
<li><p>All fields are mapped by local rank</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">return_values</span></code> - only populated for functions (not the binaries).</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">stdouts</span></code> - path to stdout.log (empty string if no redirect)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">stderrs</span></code> - path to stderr.log (empty string if no redirect)</p></li>
</ol>
</dd></dl>
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