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<div class="section" id="torch-monitor">
<h1>torch.monitor<a class="headerlink" href="#torch-monitor" title="Permalink to this headline">¶</a></h1>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This module is a prototype release, and its interfaces and functionality may
change without warning in future PyTorch releases.</p>
</div>
<p><code class="docutils literal notranslate"><span class="pre">torch.monitor</span></code> provides an interface for logging events and counters from
PyTorch.</p>
<p>The stat interfaces are designed to be used for tracking high level metrics that
are periodically logged out to be used for monitoring system performance. Since
the stats aggregate with a specific window size you can log to them from
critical loops with minimal performance impact.</p>
<p>For more infrequent events or values such as loss, accuracy, usage tracking the
event interface can be directly used.</p>
<p>Event handlers can be registered to handle the events and pass them to an
external event sink.</p>
<div class="section" id="module-torch.monitor">
<span id="api-reference"></span><h2>API Reference<a class="headerlink" href="#module-torch.monitor" title="Permalink to this headline">¶</a></h2>
<dl class="py class">
<dt id="torch.monitor.Aggregation">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.monitor.</span></code><code class="sig-name descname"><span class="pre">Aggregation</span></code><a class="headerlink" href="#torch.monitor.Aggregation" title="Permalink to this definition">¶</a></dt>
<dd><blockquote>
<div><p>These are types of aggregations that can be used to accumulate stats.</p>
</div></blockquote>
<p>Members:</p>
<blockquote>
<div><dl class="simple">
<dt>VALUE :</dt><dd><p>VALUE returns the last value to be added.</p>
</dd>
<dt>MEAN :</dt><dd><p>MEAN computes the arithmetic mean of all the added values.</p>
</dd>
<dt>COUNT :</dt><dd><p>COUNT returns the total number of added values.</p>
</dd>
<dt>SUM :</dt><dd><p>SUM returns the sum of the added values.</p>
</dd>
<dt>MAX :</dt><dd><p>MAX returns the max of the added values.</p>
</dd>
<dt>MIN :</dt><dd><p>MIN returns the min of the added values.</p>
</dd>
</dl>
</div></blockquote>
<dl class="py method">
<dt id="torch.monitor.Aggregation.name">
<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">name</span></code><a class="headerlink" href="#torch.monitor.Aggregation.name" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt id="torch.monitor.Stat">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.monitor.</span></code><code class="sig-name descname"><span class="pre">Stat</span></code><a class="headerlink" href="#torch.monitor.Stat" title="Permalink to this definition">¶</a></dt>
<dd><p>Stat is used to compute summary statistics in a performant way over
fixed intervals. Stat logs the statistics as an Event once every
<code class="docutils literal notranslate"><span class="pre">window_size</span></code> duration. When the window closes the stats are logged
via the event handlers as a <code class="docutils literal notranslate"><span class="pre">torch.monitor.Stat</span></code> event.</p>
<p><code class="docutils literal notranslate"><span class="pre">window_size</span></code> should be set to something relatively high to avoid a
huge number of events being logged. Ex: 60s. Stat uses millisecond
precision.</p>
<p>If <code class="docutils literal notranslate"><span class="pre">max_samples</span></code> is set, the stat will cap the number of samples per
window by discarding <cite>add</cite> calls once <code class="docutils literal notranslate"><span class="pre">max_samples</span></code> adds have
occurred. If it’s not set, all <code class="docutils literal notranslate"><span class="pre">add</span></code> calls during the window will be
included. This is an optional field to make aggregations more directly
comparable across windows when the number of samples might vary.</p>
<p>When the Stat is destructed it will log any remaining data even if the
window hasn’t elapsed.</p>
<dl class="py method">
<dt id="torch.monitor.Stat.__init__">
<code class="sig-name descname"><span class="pre">__init__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch._C._monitor.Stat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregations</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">torch._C._monitor.Aggregation</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.timedelta" title="(in Python v3.10)"><span class="pre">datetime.timedelta</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><span class="pre">int</span></a></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">9223372036854775807</span></span></em><span class="sig-paren">)</span> → <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.10)"><span class="pre">None</span></a><a class="headerlink" href="#torch.monitor.Stat.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs the <code class="docutils literal notranslate"><span class="pre">Stat</span></code>.</p>
</dd></dl>
<dl class="py method">
<dt id="torch.monitor.Stat.add">
<code class="sig-name descname"><span class="pre">add</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch._C._monitor.Stat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><span class="pre">float</span></a></span></em><span class="sig-paren">)</span> → <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.10)"><span class="pre">None</span></a><a class="headerlink" href="#torch.monitor.Stat.add" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds a value to the stat to be aggregated according to the
configured stat type and aggregations.</p>
</dd></dl>
<dl class="py method">
<dt id="torch.monitor.Stat.count">
<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">count</span></code><a class="headerlink" href="#torch.monitor.Stat.count" title="Permalink to this definition">¶</a></dt>
<dd><p>Number of data points that have currently been collected. Resets
once the event has been logged.</p>
</dd></dl>
<dl class="py method">
<dt id="torch.monitor.Stat.get">
<code class="sig-name descname"><span class="pre">get</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch._C._monitor.Stat</span></span></em><span class="sig-paren">)</span> → <span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">torch._C._monitor.Aggregation</span><span class="p"><span class="pre">,</span> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#torch.monitor.Stat.get" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the current value of the stat, primarily for testing
purposes. If the stat has logged and no additional values have been
added this will be zero.</p>
</dd></dl>
<dl class="py method">
<dt id="torch.monitor.Stat.name">
<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">name</span></code><a class="headerlink" href="#torch.monitor.Stat.name" title="Permalink to this definition">¶</a></dt>
<dd><p>The name of the stat that was set during creation.</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="torch.monitor.data_value_t">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.monitor.</span></code><code class="sig-name descname"><span class="pre">data_value_t</span></code><a class="headerlink" href="#torch.monitor.data_value_t" title="Permalink to this definition">¶</a></dt>
<dd><p>data_value_t is one of <code class="docutils literal notranslate"><span class="pre">str</span></code>, <code class="docutils literal notranslate"><span class="pre">float</span></code>, <code class="docutils literal notranslate"><span class="pre">int</span></code>, <code class="docutils literal notranslate"><span class="pre">bool</span></code>.</p>
</dd></dl>
<dl class="py class">
<dt id="torch.monitor.Event">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.monitor.</span></code><code class="sig-name descname"><span class="pre">Event</span></code><a class="headerlink" href="#torch.monitor.Event" title="Permalink to this definition">¶</a></dt>
<dd><p>Event represents a specific typed event to be logged. This can represent
high-level data points such as loss or accuracy per epoch or more
low-level aggregations such as through the Stats provided through this
library.</p>
<p>All Events of the same type should have the same name so downstream
handlers can correctly process them.</p>
<dl class="py method">
<dt id="torch.monitor.Event.__init__">
<code class="sig-name descname"><span class="pre">__init__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch._C._monitor.Event</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">timestamp</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.10)"><span class="pre">datetime.datetime</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="#torch.monitor.data_value_t" title="torch.monitor.data_value_t"><span class="pre">data_value_t</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> → <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.10)"><span class="pre">None</span></a><a class="headerlink" href="#torch.monitor.Event.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs the <code class="docutils literal notranslate"><span class="pre">Event</span></code>.</p>
</dd></dl>
<dl class="py method">
<dt id="torch.monitor.Event.data">
<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">data</span></code><a class="headerlink" href="#torch.monitor.Event.data" title="Permalink to this definition">¶</a></dt>
<dd><p>The structured data contained within the <code class="docutils literal notranslate"><span class="pre">Event</span></code>.</p>
</dd></dl>
<dl class="py method">
<dt id="torch.monitor.Event.name">
<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">name</span></code><a class="headerlink" href="#torch.monitor.Event.name" title="Permalink to this definition">¶</a></dt>
<dd><p>The name of the <code class="docutils literal notranslate"><span class="pre">Event</span></code>.</p>
</dd></dl>
<dl class="py method">
<dt id="torch.monitor.Event.timestamp">
<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">timestamp</span></code><a class="headerlink" href="#torch.monitor.Event.timestamp" title="Permalink to this definition">¶</a></dt>
<dd><p>The timestamp when the <code class="docutils literal notranslate"><span class="pre">Event</span></code> happened.</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="torch.monitor.EventHandlerHandle">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.monitor.</span></code><code class="sig-name descname"><span class="pre">EventHandlerHandle</span></code><a class="headerlink" href="#torch.monitor.EventHandlerHandle" title="Permalink to this definition">¶</a></dt>
<dd><p>EventHandlerHandle is a wrapper type returned by
<code class="docutils literal notranslate"><span class="pre">register_event_handler</span></code> used to unregister the handler via
<code class="docutils literal notranslate"><span class="pre">unregister_event_handler</span></code>. This cannot be directly initialized.</p>
</dd></dl>
<dl class="py function">
<dt id="torch.monitor.log_event">
<code class="sig-prename descclassname"><span class="pre">torch.monitor.</span></code><code class="sig-name descname"><span class="pre">log_event</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch._C._monitor.Event</span></span></em><span class="sig-paren">)</span> → <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.10)"><span class="pre">None</span></a><a class="headerlink" href="#torch.monitor.log_event" title="Permalink to this definition">¶</a></dt>
<dd><p>log_event logs the specified event to all of the registered event
handlers. It’s up to the event handlers to log the event out to the
corresponding event sink.</p>
<p>If there are no event handlers registered this method is a no-op.</p>
</dd></dl>
<dl class="py function">
<dt id="torch.monitor.register_event_handler">
<code class="sig-prename descclassname"><span class="pre">torch.monitor.</span></code><code class="sig-name descname"><span class="pre">register_event_handler</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch._C._monitor.Event</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.10)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> → <span class="pre">torch._C._monitor.EventHandlerHandle</span><a class="headerlink" href="#torch.monitor.register_event_handler" title="Permalink to this definition">¶</a></dt>
<dd><p>register_event_handler registers a callback to be called whenever an
event is logged via <code class="docutils literal notranslate"><span class="pre">log_event</span></code>. These handlers should avoid blocking
the main thread since that may interfere with training as they run
during the <code class="docutils literal notranslate"><span class="pre">log_event</span></code> call.</p>
</dd></dl>
<dl class="py function">
<dt id="torch.monitor.unregister_event_handler">
<code class="sig-prename descclassname"><span class="pre">torch.monitor.</span></code><code class="sig-name descname"><span class="pre">unregister_event_handler</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">handler</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch._C._monitor.EventHandlerHandle</span></span></em><span class="sig-paren">)</span> → <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.10)"><span class="pre">None</span></a><a class="headerlink" href="#torch.monitor.unregister_event_handler" title="Permalink to this definition">¶</a></dt>
<dd><p>unregister_event_handler unregisters the <code class="docutils literal notranslate"><span class="pre">EventHandlerHandle</span></code> returned
after calling <code class="docutils literal notranslate"><span class="pre">register_event_handler</span></code>. After this returns the event
handler will no longer receive events.</p>
</dd></dl>
<dl class="py class">
<dt id="torch.monitor.TensorboardEventHandler">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.monitor.</span></code><code class="sig-name descname"><span class="pre">TensorboardEventHandler</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">writer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/monitor.html#TensorboardEventHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.monitor.TensorboardEventHandler" title="Permalink to this definition">¶</a></dt>
<dd><p>TensorboardEventHandler is an event handler that will write known events to
the provided SummaryWriter.</p>
<p>This currently only supports <code class="docutils literal notranslate"><span class="pre">torch.monitor.Stat</span></code> events which are logged
as scalars.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.monitor</span> <span class="kn">import</span> <span class="n">TensorboardEventHandler</span><span class="p">,</span> <span class="n">register_event_handler</span>
<span class="gp">>>> </span><span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">(</span><span class="s2">"log_dir"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">register_event_handler</span><span class="p">(</span><span class="n">TensorboardEventHandler</span><span class="p">(</span><span class="n">writer</span><span class="p">))</span>
</pre></div>
</div>
<dl class="py method">
<dt id="torch.monitor.TensorboardEventHandler.__init__">
<code class="sig-name descname"><span class="pre">__init__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">writer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/monitor.html#TensorboardEventHandler.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.monitor.TensorboardEventHandler.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs the <code class="docutils literal notranslate"><span class="pre">TensorboardEventHandler</span></code>.</p>
</dd></dl>
</dd></dl>
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