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<h1>Source code for distributed.deploy.adaptive</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span><span class="p">,</span> <span class="n">absolute_import</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">from</span> <span class="nn">tornado</span> <span class="k">import</span> <span class="n">gen</span>
<span class="kn">from</span> <span class="nn">..utils</span> <span class="k">import</span> <span class="n">log_errors</span><span class="p">,</span> <span class="n">PeriodicCallback</span>
<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
<div class="viewcode-block" id="Adaptive"><a class="viewcode-back" href="../../../api.html#distributed.deploy.Adaptive">[docs]</a><span class="k">class</span> <span class="nc">Adaptive</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">'''</span>
<span class="sd"> Adaptively allocate workers based on scheduler load. A superclass.</span>
<span class="sd"> Contains logic to dynamically resize a Dask cluster based on current use.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> scheduler: distributed.Scheduler</span>
<span class="sd"> cluster: object</span>
<span class="sd"> Must have scale_up and scale_down methods/coroutines</span>
<span class="sd"> startup_cost : int, default 1</span>
<span class="sd"> Factor representing how costly it is to start an additional worker.</span>
<span class="sd"> Affects quickly to adapt to high tasks per worker loads</span>
<span class="sd"> scale_factor : int, default 2</span>
<span class="sd"> Factor to scale by when it's determined additional workers are needed</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> class MyCluster(object):</span>
<span class="sd"> ... def scale_up(self, n):</span>
<span class="sd"> ... """ Bring worker count up to n """</span>
<span class="sd"> ... def scale_down(self, workers):</span>
<span class="sd"> ... """ Remove worker addresses from cluster """</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Subclasses can override :meth:`Adaptive.should_scale_up` and</span>
<span class="sd"> :meth:`Adaptive.should_scale_down` to control when the cluster should be</span>
<span class="sd"> resized. The default implementation checks if there are too many tasks</span>
<span class="sd"> per worker or too little memory available (see :meth:`Adaptive.needs_cpu`</span>
<span class="sd"> and :meth:`Adaptive.needs_memory`).</span>
<span class="sd"> :meth:`Adaptive.get_scale_up_kwargs` method controls the arguments passed to</span>
<span class="sd"> the cluster's ``scale_up`` method.</span>
<span class="sd"> '''</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scheduler</span><span class="p">,</span> <span class="n">cluster</span><span class="p">,</span> <span class="n">interval</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">startup_cost</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">scale_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span> <span class="o">=</span> <span class="n">scheduler</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cluster</span> <span class="o">=</span> <span class="n">cluster</span>
<span class="bp">self</span><span class="o">.</span><span class="n">startup_cost</span> <span class="o">=</span> <span class="n">startup_cost</span>
<span class="bp">self</span><span class="o">.</span><span class="n">scale_factor</span> <span class="o">=</span> <span class="n">scale_factor</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_adapt_callback</span> <span class="o">=</span> <span class="n">PeriodicCallback</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_adapt</span><span class="p">,</span> <span class="n">interval</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">loop</span><span class="o">.</span><span class="n">add_callback</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_adapt_callback</span><span class="o">.</span><span class="n">start</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_adapting</span> <span class="o">=</span> <span class="kc">False</span>
<div class="viewcode-block" id="Adaptive.needs_cpu"><a class="viewcode-back" href="../../../api.html#distributed.deploy.Adaptive.needs_cpu">[docs]</a> <span class="k">def</span> <span class="nf">needs_cpu</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Check if the cluster is CPU constrained (too many tasks per core)</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Returns ``True`` if the occupancy per core is some factor larger</span>
<span class="sd"> than ``startup_cost``.</span>
<span class="sd"> """</span>
<span class="n">total_occupancy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">total_occupancy</span>
<span class="n">total_cores</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">ncores</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
<span class="k">if</span> <span class="n">total_occupancy</span> <span class="o">/</span> <span class="p">(</span><span class="n">total_cores</span> <span class="o">+</span> <span class="mf">1e-9</span><span class="p">)</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">startup_cost</span> <span class="o">*</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"CPU limit exceeded [</span><span class="si">%d</span><span class="s2"> occupancy / </span><span class="si">%d</span><span class="s2"> cores]"</span><span class="p">,</span>
<span class="n">total_occupancy</span><span class="p">,</span> <span class="n">total_cores</span><span class="p">)</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">False</span></div>
<div class="viewcode-block" id="Adaptive.needs_memory"><a class="viewcode-back" href="../../../api.html#distributed.deploy.Adaptive.needs_memory">[docs]</a> <span class="k">def</span> <span class="nf">needs_memory</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Check if the cluster is RAM constrained</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Returns ``True`` if the required bytes in distributed memory is some</span>
<span class="sd"> factor larger than the actual distributed memory available.</span>
<span class="sd"> """</span>
<span class="n">limit_bytes</span> <span class="o">=</span> <span class="p">{</span><span class="n">w</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">worker_info</span><span class="p">[</span><span class="n">w</span><span class="p">][</span><span class="s1">'memory_limit'</span><span class="p">]</span>
<span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">worker_info</span><span class="p">}</span>
<span class="n">worker_bytes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">worker_bytes</span>
<span class="n">limit</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">limit_bytes</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
<span class="n">total</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">worker_bytes</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
<span class="k">if</span> <span class="n">total</span> <span class="o">></span> <span class="mf">0.6</span> <span class="o">*</span> <span class="n">limit</span><span class="p">:</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Ram limit exceeded [</span><span class="si">%d</span><span class="s2">/</span><span class="si">%d</span><span class="s2">]"</span><span class="p">,</span> <span class="n">limit</span><span class="p">,</span> <span class="n">total</span><span class="p">)</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">False</span></div>
<div class="viewcode-block" id="Adaptive.should_scale_up"><a class="viewcode-back" href="../../../api.html#distributed.deploy.Adaptive.should_scale_up">[docs]</a> <span class="k">def</span> <span class="nf">should_scale_up</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Determine whether additional workers should be added to the cluster</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> scale_up : bool</span>
<span class="sd"> Notes</span>
<span class="sd"> ----</span>
<span class="sd"> Additional workers are added whenever</span>
<span class="sd"> 1. There are unrunnable tasks and no workers</span>
<span class="sd"> 2. The cluster is CPU constrained</span>
<span class="sd"> 3. The cluster is RAM constrained</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> needs_cpu</span>
<span class="sd"> needs_memory</span>
<span class="sd"> """</span>
<span class="k">with</span> <span class="n">log_errors</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">unrunnable</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">ncores</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="n">needs_cpu</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">needs_cpu</span><span class="p">()</span>
<span class="n">needs_memory</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">needs_memory</span><span class="p">()</span>
<span class="k">if</span> <span class="n">needs_cpu</span> <span class="ow">or</span> <span class="n">needs_memory</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="k">return</span> <span class="kc">False</span></div>
<div class="viewcode-block" id="Adaptive.should_scale_down"><a class="viewcode-back" href="../../../api.html#distributed.deploy.Adaptive.should_scale_down">[docs]</a> <span class="k">def</span> <span class="nf">should_scale_down</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Determine whether any workers should potentially be removed from</span>
<span class="sd"> the cluster.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> scale_down : bool</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> ``Adaptive.should_scale_down`` always returns True, so we will always</span>
<span class="sd"> attempt to remove workers as determined by</span>
<span class="sd"> ``Scheduler.workers_to_close``.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> Scheduler.workers_to_close</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">workers_to_close</span><span class="p">())</span> <span class="o">></span> <span class="mi">0</span></div>
<span class="nd">@gen</span><span class="o">.</span><span class="n">coroutine</span>
<span class="k">def</span> <span class="nf">_retire_workers</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">with</span> <span class="n">log_errors</span><span class="p">():</span>
<span class="n">workers</span> <span class="o">=</span> <span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">retire_workers</span><span class="p">(</span><span class="n">remove</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">close_workers</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">if</span> <span class="n">workers</span><span class="p">:</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Retiring workers </span><span class="si">%s</span><span class="s2">"</span><span class="p">,</span> <span class="n">workers</span><span class="p">)</span>
<span class="n">f</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cluster</span><span class="o">.</span><span class="n">scale_down</span><span class="p">(</span><span class="n">workers</span><span class="p">)</span>
<span class="k">if</span> <span class="n">gen</span><span class="o">.</span><span class="n">is_future</span><span class="p">(</span><span class="n">f</span><span class="p">):</span>
<span class="k">yield</span> <span class="n">f</span>
<div class="viewcode-block" id="Adaptive.get_scale_up_kwargs"><a class="viewcode-back" href="../../../api.html#distributed.deploy.Adaptive.get_scale_up_kwargs">[docs]</a> <span class="k">def</span> <span class="nf">get_scale_up_kwargs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Get the arguments to be passed to ``self.cluster.scale_up``.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> By default the desired number of total workers is returned (``n``).</span>
<span class="sd"> Subclasses should ensure that the return dictionary includes a key-</span>
<span class="sd"> value pair for ``n``, either by implementing it or by calling the</span>
<span class="sd"> parent's ``get_scale_up_kwargs``.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> LocalCluster.scale_up</span>
<span class="sd"> """</span>
<span class="n">instances</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">ncores</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_factor</span><span class="p">)</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Scaling up to </span><span class="si">%d</span><span class="s2"> workers"</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span>
<span class="k">return</span> <span class="p">{</span><span class="s1">'n'</span><span class="p">:</span> <span class="n">instances</span><span class="p">}</span></div>
<span class="nd">@gen</span><span class="o">.</span><span class="n">coroutine</span>
<span class="k">def</span> <span class="nf">_adapt</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adapting</span><span class="p">:</span> <span class="c1"># Semaphore to avoid overlapping adapt calls</span>
<span class="k">return</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_adapting</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">should_scale_up</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">should_scale_up</span><span class="p">()</span>
<span class="n">should_scale_down</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">should_scale_down</span><span class="p">()</span>
<span class="k">if</span> <span class="n">should_scale_up</span> <span class="ow">and</span> <span class="n">should_scale_down</span><span class="p">:</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Attempting to scale up and scale down simultaneously."</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">if</span> <span class="n">should_scale_up</span><span class="p">:</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_scale_up_kwargs</span><span class="p">()</span>
<span class="n">f</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cluster</span><span class="o">.</span><span class="n">scale_up</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">if</span> <span class="n">gen</span><span class="o">.</span><span class="n">is_future</span><span class="p">(</span><span class="n">f</span><span class="p">):</span>
<span class="k">yield</span> <span class="n">f</span>
<span class="k">if</span> <span class="n">should_scale_down</span><span class="p">:</span>
<span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">_retire_workers</span><span class="p">()</span>
<span class="k">finally</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_adapting</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">def</span> <span class="nf">adapt</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">loop</span><span class="o">.</span><span class="n">add_callback</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_adapt</span><span class="p">)</span></div>
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