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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="module-torch.distributed.elastic.agent">
<span id="elastic-agent"></span><h1>Elastic Agent<a class="headerlink" href="#module-torch.distributed.elastic.agent" title="Permalink to this headline">¶</a></h1>
<div class="section" id="module-torch.distributed.elastic.agent.server">
<span id="server"></span><h2>Server<a class="headerlink" href="#module-torch.distributed.elastic.agent.server" title="Permalink to this headline">¶</a></h2>
<p>The elastic agent is the control plane of torchelastic. It is a process
that launches and manages underlying worker processes. The agent is
responsible for:</p>
<ol class="arabic simple">
<li><p>Working with distributed torch: the workers are started with all the
necessary information to successfully and trivially call
<code class="docutils literal notranslate"><span class="pre">torch.distributed.init_process_group()</span></code>.</p></li>
<li><p>Fault tolerance: monitors workers and upon detecting worker failures
or unhealthiness, tears down all workers and restarts everyone.</p></li>
<li><p>Elasticity: Reacts to membership changes and restarts workers with the new
members.</p></li>
</ol>
<p>The simplest agents are deployed per node and works with local processes.
A more advanced agent can launch and manage workers remotely. Agents can
be completely decentralized, making decisions based on the workers it manages.
Or can be coordinated, communicating to other agents (that manage workers
in the same job) to make a collective decision.</p>
<p>Below is a diagram of an agent that manages a local group of workers.</p>
<img alt="../_images/agent_diagram.jpg" src="../_images/agent_diagram.jpg" />
</div>
<div class="section" id="concepts">
<h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this headline">¶</a></h2>
<p>This section describes the high-level classes and concepts that
are relevant to understanding the role of the <code class="docutils literal notranslate"><span class="pre">agent</span></code> in torchelastic.</p>
<dl class="py class">
<dt id="torch.distributed.elastic.agent.server.ElasticAgent">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></code><code class="sig-name descname"><span class="pre">ElasticAgent</span></code><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#ElasticAgent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.ElasticAgent" title="Permalink to this definition">¶</a></dt>
<dd><p>Agent process responsible for managing one or more worker processes.
The worker processes are assumed to be regular distributed PyTorch scripts.
When the worker process is created by the agent, the agent provides the
necessary information for the worker processes to properly initialize
a torch process group.</p>
<p>The exact deployment topology and ratio of agent-to-worker is dependent
on the specific implementation of the agent and the user’s job placement
preferences. For instance, to run a distributed training job on GPU with
8 trainers (one per GPU) one can:</p>
<ol class="arabic simple">
<li><p>Use 8 x single GPU instances, place an agent per instance, managing
1 worker per agent.</p></li>
<li><p>Use 4 x double GPU instances, place an agent per instance, managing
2 workers per agent.</p></li>
<li><p>Use 2 x quad GPU instances, place an agent per instance, managing
4 workers per agent.</p></li>
<li><p>Use 1 x 8 GPU instance, place an agent per instance, managing
8 workers per agent.</p></li>
</ol>
<p>Usage</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">group_result</span> <span class="o">=</span> <span class="n">agent</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
<span class="k">if</span> <span class="n">group_result</span><span class="o">.</span><span class="n">is_failed</span><span class="p">():</span>
<span class="c1"># workers failed</span>
<span class="n">failure</span> <span class="o">=</span> <span class="n">group_result</span><span class="o">.</span><span class="n">failures</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">log</span><span class="o">.</span><span class="n">exception</span><span class="p">(</span><span class="sa">f</span><span class="s2">"worker 0 failed with exit code : </span><span class="si">{</span><span class="n">failure</span><span class="o">.</span><span class="n">exit_code</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">group_result</span><span class="o">.</span><span class="n">return_values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># return rank 0's results</span>
</pre></div>
</div>
<dl class="py method">
<dt id="torch.distributed.elastic.agent.server.ElasticAgent.get_worker_group">
<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">get_worker_group</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">role</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#ElasticAgent.get_worker_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.ElasticAgent.get_worker_group" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>The <code class="docutils literal notranslate"><span class="pre">WorkerGroup</span></code> for the given <code class="docutils literal notranslate"><span class="pre">role</span></code>.
Note that the worker group is a mutable object and hence in a
multi-threaded/process environment it may change state.
Implementors are encouraged (but not required) to return
a defensive read-only copy.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.elastic.agent.server.ElasticAgent.run">
<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">run</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">role</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#ElasticAgent.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.ElasticAgent.run" title="Permalink to this definition">¶</a></dt>
<dd><p>Runs the agent, retrying the worker group on failures up to
<code class="docutils literal notranslate"><span class="pre">max_restarts</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>The result of the execution, containing the return values or
failure details for each worker mapped by the worker’s global rank.</p>
</dd>
<dt class="field-even">Raises</dt>
<dd class="field-even"><p><strong>Exception - any other failures NOT related to worker process</strong> – </p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.elastic.agent.server.WorkerSpec">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></code><code class="sig-name descname"><span class="pre">WorkerSpec</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">role</span></em>, <em class="sig-param"><span class="pre">local_world_size</span></em>, <em class="sig-param"><span class="pre">rdzv_handler</span></em>, <em class="sig-param"><span class="pre">fn=None</span></em>, <em class="sig-param"><span class="pre">entrypoint=None</span></em>, <em class="sig-param"><span class="pre">args=()</span></em>, <em class="sig-param"><span class="pre">max_restarts=3</span></em>, <em class="sig-param"><span class="pre">monitor_interval=30.0</span></em>, <em class="sig-param"><span class="pre">master_port=None</span></em>, <em class="sig-param"><span class="pre">master_addr=None</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/agent/server/api.html#WorkerSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerSpec" title="Permalink to this definition">¶</a></dt>
<dd><p>Contains blueprint information about a particular type of worker.
For a given role, there must only exist a single worker spec.
Worker spec is expected to be homogenous across all nodes (machine),
that is each node runs the same number of workers for a particular spec.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>role</strong> – user-defined role for the workers with this spec</p></li>
<li><p><strong>local_world_size</strong> – number local workers to run</p></li>
<li><p><strong>fn</strong> – (deprecated use entrypoint instead)</p></li>
<li><p><strong>entrypoint</strong> – worker function or command</p></li>
<li><p><strong>args</strong> – arguments to pass to <code class="docutils literal notranslate"><span class="pre">entrypoint</span></code></p></li>
<li><p><strong>rdzv_handler</strong> – handles rdzv for this set of workers</p></li>
<li><p><strong>max_restarts</strong> – number of max retries for the workers</p></li>
<li><p><strong>monitor_interval</strong> – monitor status of workers every <code class="docutils literal notranslate"><span class="pre">n</span></code> seconds</p></li>
<li><p><strong>master_port</strong> – fixed port to run the c10d store on rank 0
if not specified then will chose a random free port</p></li>
<li><p><strong>master_addr</strong> – fixed master_addr to run the c10d store on rank 0
if not specified then will chose hostname on agent rank 0</p></li>
<li><p><strong>redirects</strong> – redirect std streams to a file,
selectively redirect for a particular
local rank by passing a map</p></li>
<li><p><strong>tee</strong> – tees the specified std stream(s) to console + file,
selectively tee for a particular local rank by passing a map,
takes precedence over <code class="docutils literal notranslate"><span class="pre">redirects</span></code> settings.</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="torch.distributed.elastic.agent.server.WorkerSpec.get_entrypoint_name">
<code class="sig-name descname"><span class="pre">get_entrypoint_name</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerSpec.get_entrypoint_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerSpec.get_entrypoint_name" title="Permalink to this definition">¶</a></dt>
<dd><p>If the entrypoint is a function (e.g. <code class="docutils literal notranslate"><span class="pre">Callable</span></code>) returns its <code class="docutils literal notranslate"><span class="pre">__qualname__</span></code>,
else if the entrypoint is a binary (e.g. <code class="docutils literal notranslate"><span class="pre">str</span></code>), returns the binary name.</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.elastic.agent.server.WorkerState">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></code><code class="sig-name descname"><span class="pre">WorkerState</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerState"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerState" title="Permalink to this definition">¶</a></dt>
<dd><p>State of the <code class="docutils literal notranslate"><span class="pre">WorkerGroup</span></code>. Workers in a worker group change state as a unit.
If a single worker in a worker group fails the entire set is considered
failed:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>UNKNOWN - agent lost track of worker group state, unrecoverable
INIT - worker group object created not yet started
HEALTHY - workers running and healthy
UNHEALTHY - workers running and unhealthy
STOPPED - workers stopped (interruped) by the agent
SUCCEEDED - workers finished running (exit 0)
FAILED - workers failed to successfully finish (exit !0)
</pre></div>
</div>
<p>A worker group starts from an initial <code class="docutils literal notranslate"><span class="pre">INIT</span></code> state,
then progresses to <code class="docutils literal notranslate"><span class="pre">HEALTHY</span></code> or <code class="docutils literal notranslate"><span class="pre">UNHEALTHY</span></code> states,
and finally reaches a terminal <code class="docutils literal notranslate"><span class="pre">SUCCEEDED</span></code> or <code class="docutils literal notranslate"><span class="pre">FAILED</span></code> state.</p>
<p>Worker groups can be interrupted and temporarily put into <code class="docutils literal notranslate"><span class="pre">STOPPED</span></code> state
by the agent. Workers in <code class="docutils literal notranslate"><span class="pre">STOPPED</span></code> state are scheduled to be restarted
in the near future by the agent. Some examples of workers being put into
<code class="docutils literal notranslate"><span class="pre">STOPPED</span></code> state are:</p>
<ol class="arabic simple">
<li><p>Worker group failure|unhealthy observed</p></li>
<li><p>Membership change detected</p></li>
</ol>
<p>When actions (start, stop, rdzv, retry, etc) on worker group fails
and results in the action being partially applied to the worker group
the state will be <code class="docutils literal notranslate"><span class="pre">UNKNOWN</span></code>. Typically this happens on uncaught/unhandled
exceptions during state change events on the agent. The agent is not
expected to recover worker groups in <code class="docutils literal notranslate"><span class="pre">UNKNOWN</span></code> state and is better off
self terminating and allowing the job manager to retry the node.</p>
<dl class="py method">
<dt id="torch.distributed.elastic.agent.server.WorkerState.is_running">
<em class="property"><span class="pre">static</span> </em><code class="sig-name descname"><span class="pre">is_running</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerState.is_running"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerState.is_running" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>True if the worker state represents workers still running
(e.g. that the process exists but not necessarily healthy).</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.elastic.agent.server.Worker">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></code><code class="sig-name descname"><span class="pre">Worker</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">role_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <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">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">role_world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#Worker"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.Worker" title="Permalink to this definition">¶</a></dt>
<dd><p>Represents a worker instance. Contrast this with <code class="docutils literal notranslate"><span class="pre">WorkerSpec</span></code> that
represents the specifications of a worker. A <code class="docutils literal notranslate"><span class="pre">Worker</span></code> is created from
a <code class="docutils literal notranslate"><span class="pre">WorkerSpec</span></code>. A <code class="docutils literal notranslate"><span class="pre">Worker</span></code> is to a <code class="docutils literal notranslate"><span class="pre">WorkerSpec</span></code> as an object is to
a class.</p>
<p>The <code class="docutils literal notranslate"><span class="pre">id</span></code> of the worker is interpreted
by the specific implementation of <code class="docutils literal notranslate"><span class="pre">ElasticAgent</span></code>. For a local
agent, it could be the <code class="docutils literal notranslate"><span class="pre">pid</span> <span class="pre">(int)</span></code> of the worker, for a remote
agent it could be encoded as <code class="docutils literal notranslate"><span class="pre">host:port</span> <span class="pre">(string)</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>id</strong> (<em>Any</em>) – uniquely identifies a worker (interpreted by the agent)</p></li>
<li><p><strong>local_rank</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – local rank of the worker</p></li>
<li><p><strong>global_rank</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – global rank of the worker</p></li>
<li><p><strong>role_rank</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – rank of the worker across all workers that have the same role</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 v3.10)"><em>int</em></a>) – number of workers (globally)</p></li>
<li><p><strong>role_world_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – number of workers that have the same role</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.elastic.agent.server.WorkerGroup">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></code><code class="sig-name descname"><span class="pre">WorkerGroup</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerGroup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerGroup" title="Permalink to this definition">¶</a></dt>
<dd><p>Represents the set of <code class="docutils literal notranslate"><span class="pre">Worker</span></code> instances for the given <code class="docutils literal notranslate"><span class="pre">WorkerSpec</span></code>
managed by <code class="docutils literal notranslate"><span class="pre">ElasticAgent</span></code>. Whether the worker group contains cross
instance workers or not depends on the implementation of the agent.</p>
</dd></dl>
</div>
<div class="section" id="implementations">
<h2>Implementations<a class="headerlink" href="#implementations" title="Permalink to this headline">¶</a></h2>
<p>Below are the agent implementations provided by torchelastic.</p>
<dl class="py class">
<dt id="torch.distributed.elastic.agent.server.local_elastic_agent.LocalElasticAgent">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.local_elastic_agent.</span></code><code class="sig-name descname"><span class="pre">LocalElasticAgent</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spawn'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exit_barrier_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_dir</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/agent/server/local_elastic_agent.html#LocalElasticAgent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.local_elastic_agent.LocalElasticAgent" title="Permalink to this definition">¶</a></dt>
<dd><p>An implementation of <code class="xref py py-class docutils literal notranslate"><span class="pre">torchelastic.agent.server.ElasticAgent</span></code>
that handles host-local workers.
This agent is deployed per host and is configured to spawn <code class="docutils literal notranslate"><span class="pre">n</span></code> workers.
When using GPUs, <code class="docutils literal notranslate"><span class="pre">n</span></code> maps to the number of GPUs available on the host.</p>
<p>The local agent does not communicate to other local agents deployed on
other hosts, even if the workers may communicate inter-host. The worker id
is interpreted to be a local process. The agent starts and stops all worker
processes as a single unit.</p>
<p>The worker function and argument passed to the worker function must be
python multiprocessing compatible. To pass multiprocessing data structures
to the workers you may create the data structure in the same multiprocessing
context as the specified <code class="docutils literal notranslate"><span class="pre">start_method</span></code> and pass it as a function argument.</p>
<p>The <code class="docutils literal notranslate"><span class="pre">exit_barrier_timeout</span></code> specifies the amount of time (in seconds) to wait
for other agents to finish. This acts as a safety net to handle cases where
workers finish at different times, to prevent agents from viewing workers
that finished early as a scale-down event. It is strongly advised that the
user code deal with ensuring that workers are terminated in a synchronous
manner rather than relying on the exit_barrier_timeout.</p>
<p>Example launching function</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">trainer</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">-></span> <span class="nb">str</span><span class="p">:</span>
<span class="k">return</span> <span class="s2">"do train"</span>
<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="n">start_method</span><span class="o">=</span><span class="s2">"spawn"</span>
<span class="n">shared_queue</span><span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">get_context</span><span class="p">(</span><span class="n">start_method</span><span class="p">)</span><span class="o">.</span><span class="n">Queue</span><span class="p">()</span>
<span class="n">spec</span> <span class="o">=</span> <span class="n">WorkerSpec</span><span class="p">(</span>
<span class="n">role</span><span class="o">=</span><span class="s2">"trainer"</span><span class="p">,</span>
<span class="n">local_world_size</span><span class="o">=</span><span class="n">nproc_per_process</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="s2">"foobar"</span><span class="p">,),</span>
<span class="o">...<</span><span class="n">OTHER_PARAMS</span><span class="o">...></span><span class="p">)</span>
<span class="n">agent</span> <span class="o">=</span> <span class="n">LocalElasticAgent</span><span class="p">(</span><span class="n">spec</span><span class="p">,</span> <span class="n">start_method</span><span class="p">)</span>
<span class="n">results</span> <span class="o">=</span> <span class="n">agent</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
<span class="k">if</span> <span class="n">results</span><span class="o">.</span><span class="n">is_failed</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"trainer failed"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"rank 0 return value: </span><span class="si">{</span><span class="n">results</span><span class="o">.</span><span class="n">return_values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># prints -> rank 0 return value: do train</span>
</pre></div>
</div>
<p>Example launching binary</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="n">spec</span> <span class="o">=</span> <span class="n">WorkerSpec</span><span class="p">(</span>
<span class="n">role</span><span class="o">=</span><span class="s2">"trainer"</span><span class="p">,</span>
<span class="n">local_world_size</span><span class="o">=</span><span class="n">nproc_per_process</span><span class="p">,</span>
<span class="n">entrypoint</span><span class="o">=</span><span class="s2">"/usr/local/bin/trainer"</span><span class="p">,</span>
<span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="s2">"--trainer_args"</span><span class="p">,</span> <span class="s2">"foobar"</span><span class="p">),</span>
<span class="o">...<</span><span class="n">OTHER_PARAMS</span><span class="o">...></span><span class="p">)</span>
<span class="n">agent</span> <span class="o">=</span> <span class="n">LocalElasticAgent</span><span class="p">(</span><span class="n">spec</span><span class="p">)</span>
<span class="n">results</span> <span class="o">=</span> <span class="n">agent</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">results</span><span class="o">.</span><span class="n">is_failed</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"binary launches do not have return values"</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="extending-the-agent">
<h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="Permalink to this headline">¶</a></h2>
<p>To extend the agent you can implement <code class="docutils literal notranslate"><span class="pre">`ElasticAgent</span></code> directly, however
we recommend you extend <code class="docutils literal notranslate"><span class="pre">SimpleElasticAgent</span></code> instead, which provides
most of the scaffolding and leaves you with a few specific abstract methods
to implement.</p>
<dl class="py class">
<dt id="torch.distributed.elastic.agent.server.SimpleElasticAgent">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></code><code class="sig-name descname"><span class="pre">SimpleElasticAgent</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exit_barrier_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent" title="Permalink to this definition">¶</a></dt>
<dd><p>An <code class="docutils literal notranslate"><span class="pre">ElasticAgent</span></code> that manages workers (<code class="docutils literal notranslate"><span class="pre">WorkerGroup</span></code>)
for a single <code class="docutils literal notranslate"><span class="pre">WorkerSpec</span></code> (e.g. one particular type of worker role).</p>
<dl class="py method">
<dt id="torch.distributed.elastic.agent.server.SimpleElasticAgent._assign_worker_ranks">
<code class="sig-name descname"><span class="pre">_assign_worker_ranks</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_world_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._assign_worker_ranks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._assign_worker_ranks" title="Permalink to this definition">¶</a></dt>
<dd><p>Determines proper ranks for worker processes. The rank assignment
is done according to the following algorithm:</p>
<ol class="arabic simple">
<li><p>Each agent writes its configuration(group_rank, group_world_size
, num_workers) to the common store.</p></li>
<li><p>Each agent retrieves configuration for all agents
and performs two level sort using role and rank.</p></li>
<li><p>Determine the global rank: the global rank of the workers for the current
agent is the offset of the infos array up to group_rank of the agent.
The offset is computed as a sum of local_world_size of all agents that
have rank less than the group_rank. The workers would have the ranks:
[offset, offset+local_world_size)</p></li>
<li><p>Determine the role rank: The role rank is determined using the algorithms
in the point 3 with the exception that the offset is done from the first
agent that has the same role as current one and has the minimum group rank.</p></li>
</ol>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.elastic.agent.server.SimpleElasticAgent._exit_barrier">
<code class="sig-name descname"><span class="pre">_exit_barrier</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._exit_barrier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._exit_barrier" title="Permalink to this definition">¶</a></dt>
<dd><p>Wait for <code class="docutils literal notranslate"><span class="pre">exit_barrier_timeout</span></code> seconds for all agents to finish
executing their local workers (either successfully or not). This
acts as a safety guard against user scripts that terminate at different
times. This barrier keeps the agent process alive until all workers finish.</p>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.elastic.agent.server.SimpleElasticAgent._initialize_workers">
<code class="sig-name descname"><span class="pre">_initialize_workers</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._initialize_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._initialize_workers" title="Permalink to this definition">¶</a></dt>
<dd><p>Starts a fresh set of workers for the worker_group.
Essentially a rendezvous followed by a start_workers.</p>
<p>The caller should first call <code class="docutils literal notranslate"><span class="pre">_stop_workers()</span></code> to stop running workers
prior to calling this method.</p>
<p>Optimistically sets the state of the worker group that
just started as <code class="docutils literal notranslate"><span class="pre">HEALTHY</span></code> and delegates the actual monitoring
of state to <code class="docutils literal notranslate"><span class="pre">_monitor_workers()</span></code> method</p>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.elastic.agent.server.SimpleElasticAgent._monitor_workers">
<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">_monitor_workers</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._monitor_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._monitor_workers" title="Permalink to this definition">¶</a></dt>
<dd><p>Checks on the workers for the <code class="docutils literal notranslate"><span class="pre">worker_group</span></code> and returns
the new state of the worker group.</p>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.elastic.agent.server.SimpleElasticAgent._rendezvous">
<code class="sig-name descname"><span class="pre">_rendezvous</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._rendezvous"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._rendezvous" title="Permalink to this definition">¶</a></dt>
<dd><p>Runs rendezvous for the workers specified by worker spec.
Assigns workers a new global rank and world size.
Updates the rendezvous store for the worker group.</p>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.elastic.agent.server.SimpleElasticAgent._restart_workers">
<code class="sig-name descname"><span class="pre">_restart_workers</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._restart_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._restart_workers" title="Permalink to this definition">¶</a></dt>
<dd><p>Restarts (stops, rendezvous, starts) all local workers in the group.</p>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.elastic.agent.server.SimpleElasticAgent._shutdown">
<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">_shutdown</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">death_sig=<Signals.SIGTERM:</span> <span class="pre">15></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._shutdown"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._shutdown" title="Permalink to this definition">¶</a></dt>
<dd><p>Cleans up any resources that were allocated during the agent’s work.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>death_sig</strong> – Signal to send to the child process, SIGTERM is default</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.elastic.agent.server.SimpleElasticAgent._start_workers">
<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">_start_workers</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._start_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._start_workers" title="Permalink to this definition">¶</a></dt>
<dd><p>Starts <code class="docutils literal notranslate"><span class="pre">worker_group.spec.local_world_size</span></code> number of workers
according to worker spec for the worker group .</p>
<p>Returns a map of <code class="docutils literal notranslate"><span class="pre">local_rank</span></code> to worker <code class="docutils literal notranslate"><span class="pre">id</span></code>.</p>
</dd></dl>
<dl class="py method">
<dt id="torch.distributed.elastic.agent.server.SimpleElasticAgent._stop_workers">
<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">_stop_workers</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._stop_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._stop_workers" title="Permalink to this definition">¶</a></dt>
<dd><p>Stops all workers in the given worker group. Implementors
must deal with workers in all states defined by <code class="docutils literal notranslate"><span class="pre">WorkerState</span></code>.
That is, it must gracefully handle stopping non-existent workers,
unhealthy (stuck) workers, etc.</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="torch.distributed.elastic.agent.server.api.RunResult">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.api.</span></code><code class="sig-name descname"><span class="pre">RunResult</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">state</span></em>, <em class="sig-param"><span class="pre">return_values=<factory></span></em>, <em class="sig-param"><span class="pre">failures=<factory></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#RunResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.api.RunResult" title="Permalink to this definition">¶</a></dt>
<dd><p>Results returned by the worker executions. Run results follow an “all-or-nothing” policy
where the run is successful if and only if ALL local workers managed by this agent
complete successfully.</p>
<p>If the result is successful (e.g. <code class="docutils literal notranslate"><span class="pre">is_failed()</span> <span class="pre">=</span> <span class="pre">False</span></code>) then the <code class="docutils literal notranslate"><span class="pre">return_values</span></code>
field contains the outputs (return values) of the workers managed by THIS agent mapped
by their GLOBAL ranks. That is <code class="docutils literal notranslate"><span class="pre">result.return_values[0]</span></code> is the return value of
global rank 0.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code class="docutils literal notranslate"><span class="pre">return_values</span></code> are only meaningful for when the worker entrypoint
is a function. Workers specified as a binary entrypoint do not canonically
have a return value and the <code class="docutils literal notranslate"><span class="pre">return_values</span></code> field is meaningless and
may be empty.</p>
</div>
<p>If <code class="docutils literal notranslate"><span class="pre">is_failed()</span></code> returns <code class="docutils literal notranslate"><span class="pre">True</span></code> then the <code class="docutils literal notranslate"><span class="pre">failures</span></code> field contains the
failure information, again, mapped by the GLOBAL rank of the worker that failed.</p>
<p>The keys in <code class="docutils literal notranslate"><span class="pre">return_values</span></code> and <code class="docutils literal notranslate"><span class="pre">failures</span></code> are mutually exclusive, that is,
a worker’s final state can only be one of: succeeded, failed. Workers intentionally
terminated by the agent according to the agent’s restart policy, are not represented
in either <code class="docutils literal notranslate"><span class="pre">return_values</span></code> nor <code class="docutils literal notranslate"><span class="pre">failures</span></code>.</p>
</dd></dl>
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