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<div class="section" id="module-torch.distributed.elastic.metrics">
<span id="metrics"></span><span id="metrics-api"></span><h1>Metrics<a class="headerlink" href="#module-torch.distributed.elastic.metrics" title="Permalink to this headline">¶</a></h1>
<p>Metrics API</p>
<p><strong>Overview</strong>:</p>
<p>The metrics API in torchelastic is used to publish telemetry metrics.
It is designed to be used by torchelastic’s internal modules to
publish metrics for the end user with the goal of increasing visibility
and helping with debugging. However you may use the same API in your
jobs to publish metrics to the same metrics <code class="docutils literal notranslate"><span class="pre">sink</span></code>.</p>
<p>A <code class="docutils literal notranslate"><span class="pre">metric</span></code> can be thought of as timeseries data
and is uniquely identified by the string-valued tuple
<code class="docutils literal notranslate"><span class="pre">(metric_group,</span> <span class="pre">metric_name)</span></code>.</p>
<p>torchelastic makes no assumptions about what a <code class="docutils literal notranslate"><span class="pre">metric_group</span></code> is
and what relationship it has with <code class="docutils literal notranslate"><span class="pre">metric_name</span></code>. It is totally up
to the user to use these two fields to uniquely identify a metric.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The metric group <code class="docutils literal notranslate"><span class="pre">torchelastic</span></code> is reserved by torchelastic for
platform level metrics that it produces.
For instance torchelastic may output the latency (in milliseconds)
of a re-rendezvous operation from the agent as
<code class="docutils literal notranslate"><span class="pre">(torchelastic,</span> <span class="pre">agent.rendezvous.duration.ms)</span></code></p>
</div>
<p>A sensible way to use metric groups is to map them to a stage or module
in your job. You may also encode certain high level properties
the job such as the region or stage (dev vs prod).</p>
<p><strong>Publish Metrics</strong>:</p>
<p>Using torchelastic’s metrics API is similar to using python’s logging
framework. You first have to configure a metrics handler before
trying to add metric data.</p>
<p>The example below measures the latency for the <code class="docutils literal notranslate"><span class="pre">calculate()</span></code> function.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">torch.distributed.elastic.metrics</span> <span class="k">as</span> <span class="nn">metrics</span>
<span class="c1"># makes all metrics other than the one from "my_module" to go /dev/null</span>
<span class="n">metrics</span><span class="o">.</span><span class="n">configure</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">NullMetricsHandler</span><span class="p">())</span>
<span class="n">metrics</span><span class="o">.</span><span class="n">configure</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">ConsoleMetricsHandler</span><span class="p">(),</span> <span class="s2">"my_module"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">my_method</span><span class="p">():</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">calculate</span><span class="p">()</span>
<span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">metrics</span><span class="o">.</span><span class="n">put_metric</span><span class="p">(</span><span class="s2">"calculate_latency"</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">end</span><span class="o">-</span><span class="n">start</span><span class="p">),</span> <span class="s2">"my_module"</span><span class="p">)</span>
</pre></div>
</div>
<p>You may also use the torch.distributed.elastic.metrics.prof` decorator
to conveniently and succinctly profile functions</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># -- in module examples.foobar --</span>
<span class="kn">import</span> <span class="nn">torch.distributed.elastic.metrics</span> <span class="k">as</span> <span class="nn">metrics</span>
<span class="n">metrics</span><span class="o">.</span><span class="n">configure</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">ConsoleMetricsHandler</span><span class="p">(),</span> <span class="s2">"foobar"</span><span class="p">)</span>
<span class="n">metrics</span><span class="o">.</span><span class="n">configure</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">ConsoleMetricsHandler</span><span class="p">(),</span> <span class="s2">"Bar"</span><span class="p">)</span>
<span class="nd">@metrics</span><span class="o">.</span><span class="n">prof</span>
<span class="k">def</span> <span class="nf">foo</span><span class="p">():</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">Bar</span><span class="p">():</span>
<span class="nd">@metrics</span><span class="o">.</span><span class="n">prof</span>
<span class="k">def</span> <span class="nf">baz</span><span class="p">():</span>
<span class="k">pass</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">@metrics.prof</span></code> will publish the following metrics</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o"><</span><span class="n">leaf_module</span> <span class="ow">or</span> <span class="n">classname</span><span class="o">>.</span><span class="n">success</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">the</span> <span class="n">function</span> <span class="n">finished</span> <span class="n">successfully</span>
<span class="o"><</span><span class="n">leaf_module</span> <span class="ow">or</span> <span class="n">classname</span><span class="o">>.</span><span class="n">failure</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">the</span> <span class="n">function</span> <span class="n">threw</span> <span class="n">an</span> <span class="n">exception</span>
<span class="o"><</span><span class="n">leaf_module</span> <span class="ow">or</span> <span class="n">classname</span><span class="o">>.</span><span class="n">duration</span><span class="o">.</span><span class="n">ms</span> <span class="o">-</span> <span class="n">function</span> <span class="n">duration</span> <span class="ow">in</span> <span class="n">milliseconds</span>
</pre></div>
</div>
<p><strong>Configuring Metrics Handler</strong>:</p>
<p><cite>torch.distributed.elastic.metrics.MetricHandler</cite> is responsible for emitting
the added metric values to a particular destination. Metric groups can be
configured with different metric handlers.</p>
<p>By default torchelastic emits all metrics to <code class="docutils literal notranslate"><span class="pre">/dev/null</span></code>.
By adding the following configuration metrics,
<code class="docutils literal notranslate"><span class="pre">torchelastic</span></code> and <code class="docutils literal notranslate"><span class="pre">my_app</span></code> metric groups will be printed out to
console.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.distributed.elastic.metrics</span> <span class="k">as</span> <span class="nn">metrics</span>
<span class="n">metrics</span><span class="o">.</span><span class="n">configure</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">ConsoleMetricHandler</span><span class="p">(),</span> <span class="n">group</span> <span class="o">=</span> <span class="s2">"torchelastic"</span><span class="p">)</span>
<span class="n">metrics</span><span class="o">.</span><span class="n">configure</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">ConsoleMetricHandler</span><span class="p">(),</span> <span class="n">group</span> <span class="o">=</span> <span class="s2">"my_app"</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>Writing a Custom Metric Handler</strong>:</p>
<p>If you want your metrics to be emitted to a custom location, implement
the <cite>torch.distributed.elastic.metrics.MetricHandler</cite> interface
and configure your job to use your custom metric handler.</p>
<p>Below is a toy example that prints the metrics to <code class="docutils literal notranslate"><span class="pre">stdout</span></code></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.distributed.elastic.metrics</span> <span class="k">as</span> <span class="nn">metrics</span>
<span class="k">class</span> <span class="nc">StdoutMetricHandler</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">MetricHandler</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">emit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">metric_data</span><span class="p">):</span>
<span class="n">ts</span> <span class="o">=</span> <span class="n">metric_data</span><span class="o">.</span><span class="n">timestamp</span>
<span class="n">group</span> <span class="o">=</span> <span class="n">metric_data</span><span class="o">.</span><span class="n">group_name</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">metric_data</span><span class="o">.</span><span class="n">name</span>
<span class="n">value</span> <span class="o">=</span> <span class="n">metric_data</span><span class="o">.</span><span class="n">value</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"[</span><span class="si">{</span><span class="n">ts</span><span class="si">}</span><span class="s2">][</span><span class="si">{</span><span class="n">group</span><span class="si">}</span><span class="s2">]: </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">=</span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">metrics</span><span class="o">.</span><span class="n">configure</span><span class="p">(</span><span class="n">StdoutMetricHandler</span><span class="p">(),</span> <span class="n">group</span><span class="o">=</span><span class="s2">"my_app"</span><span class="p">)</span>
</pre></div>
</div>
<p>Now all metrics in the group <code class="docutils literal notranslate"><span class="pre">my_app</span></code> will be printed to stdout as:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mf">1574213883.4182858</span><span class="p">][</span><span class="n">my_app</span><span class="p">]:</span> <span class="n">my_metric</span><span class="o">=<</span><span class="n">value</span><span class="o">></span>
<span class="p">[</span><span class="mf">1574213940.5237644</span><span class="p">][</span><span class="n">my_app</span><span class="p">]:</span> <span class="n">my_metric</span><span class="o">=<</span><span class="n">value</span><span class="o">></span>
</pre></div>
</div>
<div class="section" id="metric-handlers">
<h2>Metric Handlers<a class="headerlink" href="#metric-handlers" title="Permalink to this headline">¶</a></h2>
<p>Below are the metric handlers that come included with torchelastic.</p>
<dl class="py class">
<dt id="torch.distributed.elastic.metrics.api.MetricHandler">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.api.</span></code><code class="sig-name descname"><span class="pre">MetricHandler</span></code><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#MetricHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.api.MetricHandler" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="py class">
<dt id="torch.distributed.elastic.metrics.api.ConsoleMetricHandler">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.api.</span></code><code class="sig-name descname"><span class="pre">ConsoleMetricHandler</span></code><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#ConsoleMetricHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.api.ConsoleMetricHandler" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="py class">
<dt id="torch.distributed.elastic.metrics.api.NullMetricHandler">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.api.</span></code><code class="sig-name descname"><span class="pre">NullMetricHandler</span></code><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#NullMetricHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.api.NullMetricHandler" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</div>
<div class="section" id="methods">
<h2>Methods<a class="headerlink" href="#methods" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="torch.distributed.elastic.metrics.configure">
<code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.</span></code><code class="sig-name descname"><span class="pre">configure</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">handler</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</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/elastic/metrics/api.html#configure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.configure" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="py function">
<dt id="torch.distributed.elastic.metrics.prof">
<code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.</span></code><code class="sig-name descname"><span class="pre">prof</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'torchelastic'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#prof"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.prof" title="Permalink to this definition">¶</a></dt>
<dd><p>@profile decorator publishes duration.ms, count, success, failure
metrics for the function that it decorates. The metric name defaults
to the qualified name (<code class="docutils literal notranslate"><span class="pre">class_name.def_name</span></code>) of the function.
If the function does not belong to a class, it uses the leaf module name
instead.</p>
<p>Usage</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@metrics</span><span class="o">.</span><span class="n">prof</span>
<span class="k">def</span> <span class="nf">x</span><span class="p">():</span>
<span class="k">pass</span>
<span class="nd">@metrics</span><span class="o">.</span><span class="n">prof</span><span class="p">(</span><span class="n">group</span><span class="o">=</span><span class="s2">"agent"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">y</span><span class="p">():</span>
<span class="k">pass</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt id="torch.distributed.elastic.metrics.put_metric">
<code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.</span></code><code class="sig-name descname"><span class="pre">put_metric</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metric_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'torchelastic'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#put_metric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.put_metric" title="Permalink to this definition">¶</a></dt>
<dd><p>Publishes a metric data point.</p>
<p>Usage</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">put_metric</span><span class="p">(</span><span class="s2">"metric_name"</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">put_metric</span><span class="p">(</span><span class="s2">"metric_name"</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"metric_group_name"</span><span class="p">)</span>
</pre></div>
</div>
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