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<div class="section" id="elastic-launch">
<span id="launcher-api"></span><h1>Elastic Launch<a class="headerlink" href="#elastic-launch" title="Permalink to this headline">¶</a></h1>
<div class="section" id="module-torch.distributed.run">
<span id="torch-distributed-run"></span><h2>torch.distributed.run<a class="headerlink" href="#module-torch.distributed.run" title="Permalink to this headline">¶</a></h2>
<p>This module provides similar functionality as <code class="docutils literal notranslate"><span class="pre">torch.distributed.launch</span></code> with the following
additional functionalities:</p>
<ol class="arabic simple">
<li><p>Worker failures are handled gracefully by restarting all workers.</p></li>
<li><p>Worker <code class="docutils literal notranslate"><span class="pre">RANK</span></code> and <code class="docutils literal notranslate"><span class="pre">WORLD_SIZE</span></code> are assigned automatically.</p></li>
<li><p>Number of nodes is allowed to change between minimum and maximum sizes (elasticity).</p></li>
</ol>
<p><strong>Usage:</strong></p>
<ol class="arabic simple">
<li><p>Single-node multi-worker</p></li>
</ol>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">run</span>
<span class="go"> --standalone</span>
<span class="go"> --nnodes=1</span>
<span class="go"> --nproc_per_node=$NUM_TRAINERS</span>
<span class="go"> YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)</span>
</pre></div>
</div>
<ol class="arabic simple" start="2">
<li><p>Fault tolerant (fixed sized number of workers, no elasticity):</p></li>
</ol>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">run</span>
<span class="go"> --nnodes=$NUM_NODES</span>
<span class="go"> --nproc_per_node=$NUM_TRAINERS</span>
<span class="go"> --rdzv_id=$JOB_ID</span>
<span class="go"> --rdzv_backend=c10d</span>
<span class="go"> --rdzv_endpoint=$HOST_NODE_ADDR</span>
<span class="go"> YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">HOST_NODE_ADDR</span></code>, in form <host>[:<port>] (e.g. node1.example.com:29400), specifies the node and
the port on which the C10d rendezvous backend should be instantiated and hosted. It can be any
node in your training cluster, but ideally you should pick a node that has a high bandwidth.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If no port number is specified <code class="docutils literal notranslate"><span class="pre">HOST_NODE_ADDR</span></code> defaults to 29400.</p>
</div>
<ol class="arabic simple" start="3">
<li><p>Elastic (<code class="docutils literal notranslate"><span class="pre">min=1</span></code>, <code class="docutils literal notranslate"><span class="pre">max=4</span></code>):</p></li>
</ol>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">run</span>
<span class="go"> --nnodes=1:4</span>
<span class="go"> --nproc_per_node=$NUM_TRAINERS</span>
<span class="go"> --rdzv_id=$JOB_ID</span>
<span class="go"> --rdzv_backend=c10d</span>
<span class="go"> --rdzv_endpoint=$HOST_NODE_ADDR</span>
<span class="go"> YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">HOST_NODE_ADDR</span></code>, in form <host>[:<port>] (e.g. node1.example.com:29400), specifies the node and
the port on which the C10d rendezvous backend should be instantiated and hosted. It can be any
node in your training cluster, but ideally you should pick a node that has a high bandwidth.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If no port number is specified <code class="docutils literal notranslate"><span class="pre">HOST_NODE_ADDR</span></code> defaults to 29400.</p>
</div>
<p><strong>Note on rendezvous backend</strong>:</p>
<p>For multi-node training you need to specify:</p>
<ol class="arabic simple">
<li><p><code class="docutils literal notranslate"><span class="pre">--rdzv_id</span></code>: A unique job id (shared by all nodes participating in the job)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--rdzv_backend</span></code>: An implementation of
<a class="reference internal" href="rendezvous.html#torch.distributed.elastic.rendezvous.RendezvousHandler" title="torch.distributed.elastic.rendezvous.RendezvousHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.distributed.elastic.rendezvous.RendezvousHandler</span></code></a></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--rdzv_endpoint</span></code>: The endpoint where the rendezvous backend is running; usually in form
<code class="docutils literal notranslate"><span class="pre">host:port</span></code>.</p></li>
</ol>
<p>Currently <code class="docutils literal notranslate"><span class="pre">c10d</span></code> (recommended), <code class="docutils literal notranslate"><span class="pre">etcd-v2</span></code>, and <code class="docutils literal notranslate"><span class="pre">etcd</span></code> (legacy) rendezvous backends are
supported out of the box. To use <code class="docutils literal notranslate"><span class="pre">etcd-v2</span></code> or <code class="docutils literal notranslate"><span class="pre">etcd</span></code>, setup an etcd server with the <code class="docutils literal notranslate"><span class="pre">v2</span></code> api
enabled (e.g. <code class="docutils literal notranslate"><span class="pre">--enable-v2</span></code>).</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><code class="docutils literal notranslate"><span class="pre">etcd-v2</span></code> and <code class="docutils literal notranslate"><span class="pre">etcd</span></code> rendezvous use etcd API v2. You MUST enable the v2 API on the etcd
server. Our tests use etcd v3.4.3.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>For etcd-based rendezvous we recommend using <code class="docutils literal notranslate"><span class="pre">etcd-v2</span></code> over <code class="docutils literal notranslate"><span class="pre">etcd</span></code> which is functionally
equivalent, but uses a revised implementation. <code class="docutils literal notranslate"><span class="pre">etcd</span></code> is in maintenance mode and will be
removed in a future version.</p>
</div>
<p><strong>Definitions:</strong></p>
<ol class="arabic simple">
<li><p><code class="docutils literal notranslate"><span class="pre">Node</span></code> - A physical instance or a container; maps to the unit that the job manager works with.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">Worker</span></code> - A worker in the context of distributed training.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">WorkerGroup</span></code> - The set of workers that execute the same function (e.g. trainers).</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">LocalWorkerGroup</span></code> - A subset of the workers in the worker group running on the same node.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">RANK</span></code> - The rank of the worker within a worker group.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">WORLD_SIZE</span></code> - The total number of workers in a worker group.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">LOCAL_RANK</span></code> - The rank of the worker within a local worker group.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">LOCAL_WORLD_SIZE</span></code> - The size of the local worker group.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">rdzv_id</span></code> - A user-defined id that uniquely identifies the worker group for a job. This id is
used by each node to join as a member of a particular worker group.</p></li>
</ol>
<ol class="arabic simple" start="9">
<li><p><code class="docutils literal notranslate"><span class="pre">rdzv_backend</span></code> - The backend of the rendezvous (e.g. <code class="docutils literal notranslate"><span class="pre">c10d</span></code>). This is typically a strongly
consistent key-value store.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">rdzv_endpoint</span></code> - The rendezvous backend endpoint; usually in form <code class="docutils literal notranslate"><span class="pre"><host>:<port></span></code>.</p></li>
</ol>
<p>A <code class="docutils literal notranslate"><span class="pre">Node</span></code> runs <code class="docutils literal notranslate"><span class="pre">LOCAL_WORLD_SIZE</span></code> workers which comprise a <code class="docutils literal notranslate"><span class="pre">LocalWorkerGroup</span></code>. The union of
all <code class="docutils literal notranslate"><span class="pre">LocalWorkerGroups</span></code> in the nodes in the job comprise the <code class="docutils literal notranslate"><span class="pre">WorkerGroup</span></code>.</p>
<p><strong>Environment Variables:</strong></p>
<p>The following environment variables are made available to you in your script:</p>
<ol class="arabic simple">
<li><p><code class="docutils literal notranslate"><span class="pre">LOCAL_RANK</span></code> - The local rank.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">RANK</span></code> - The global rank.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">GROUP_RANK</span></code> - The rank of the worker group. A number between 0 and <code class="docutils literal notranslate"><span class="pre">max_nnodes</span></code>. When
running a single worker group per node, this is the rank of the node.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">ROLE_RANK</span></code> - The rank of the worker across all the workers that have the same role. The role
of the worker is specified in the <code class="docutils literal notranslate"><span class="pre">WorkerSpec</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">LOCAL_WORLD_SIZE</span></code> - The local world size (e.g. number of workers running locally); equals to
<code class="docutils literal notranslate"><span class="pre">--nproc_per_node</span></code> specified on <code class="docutils literal notranslate"><span class="pre">torch.distributed.run</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">WORLD_SIZE</span></code> - The world size (total number of workers in the job).</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">ROLE_WORLD_SIZE</span></code> - The total number of workers that was launched with the same role specified
in <code class="docutils literal notranslate"><span class="pre">WorkerSpec</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">MASTER_ADDR</span></code> - The FQDN of the host that is running worker with rank 0; used to initialize
the Torch Distributed backend.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">MASTER_PORT</span></code> - The port on the <code class="docutils literal notranslate"><span class="pre">MASTER_ADDR</span></code> that can be used to host the C10d TCP store.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">TORCHELASTIC_RESTART_COUNT</span></code> - The number of worker group restarts so far.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">TORCHELASTIC_MAX_RESTARTS</span></code> - The configured maximum number of restarts.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">TORCHELASTIC_RUN_ID</span></code> - Equal to the rendezvous <code class="docutils literal notranslate"><span class="pre">run_id</span></code> (e.g. unique job id).</p></li>
</ol>
<p><strong>Deployment:</strong></p>
<ol class="arabic simple">
<li><p>(Not needed for the C10d backend) Start the rendezvous backend server and get the endpoint (to be
passed as <code class="docutils literal notranslate"><span class="pre">--rdzv_endpoint</span></code> to the launcher script)</p></li>
<li><p>Single-node multi-worker: Start the launcher on the host to start the agent process which
creates and monitors a local worker group.</p></li>
<li><p>Multi-node multi-worker: Start the launcher with the same arguments on all the nodes
participating in training.</p></li>
</ol>
<p>When using a job/cluster manager the entry point command to the multi-node job should be this
launcher.</p>
<p><strong>Failure Modes:</strong></p>
<ol class="arabic simple">
<li><p>Worker failure: For a training job with <code class="docutils literal notranslate"><span class="pre">n</span></code> workers, if <code class="docutils literal notranslate"><span class="pre">k<=n</span></code> workers fail all workers
are stopped and restarted up to <code class="docutils literal notranslate"><span class="pre">max_restarts</span></code>.</p></li>
<li><p>Agent failure: An agent failure results in a local worker group failure. It is up to the job
manager to fail the entire job (gang semantics) or attempt to replace the node. Both behaviors
are supported by the agent.</p></li>
<li><p>Node failure: Same as agent failure.</p></li>
</ol>
<p><strong>Membership Changes:</strong></p>
<ol class="arabic simple">
<li><p>Node departure (scale-down): The agent is notified of the departure, all existing workers are
stopped, a new <code class="docutils literal notranslate"><span class="pre">WorkerGroup</span></code> is formed, and all workers are started with a new <code class="docutils literal notranslate"><span class="pre">RANK</span></code> and
<code class="docutils literal notranslate"><span class="pre">WORLD_SIZE</span></code>.</p></li>
<li><p>Node arrival (scale-up): The new node is admitted to the job, all existing workers are stopped,
a new <code class="docutils literal notranslate"><span class="pre">WorkerGroup</span></code> is formed, and all workers are started with a new <code class="docutils literal notranslate"><span class="pre">RANK</span></code> and
<code class="docutils literal notranslate"><span class="pre">WORLD_SIZE</span></code>.</p></li>
</ol>
<p><strong>Important Notices:</strong></p>
<ol class="arabic simple">
<li><p>All the items in the important notices section of <code class="docutils literal notranslate"><span class="pre">torch.distributed.launch</span></code> apply to this
module as well.</p></li>
<li><p>The environment variables necessary to initialize a Torch process group are provided to you by
this module, no need for you to pass <code class="docutils literal notranslate"><span class="pre">RANK</span></code> manually. To initialize a process group in your
training script, simply run:</p></li>
</ol>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
<span class="gp">>>> </span><span class="n">dist</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span><span class="n">backend</span><span class="o">=</span><span class="s2">"gloo|nccl"</span><span class="p">)</span>
</pre></div>
</div>
<ol class="arabic simple" start="3">
<li><p>On failures or membership changes ALL surviving workers are killed immediately. Make sure to
checkpoint your progress. The frequency of checkpoints should depend on your job’s tolerance
for lost work.</p></li>
<li><p>This module only supports homogeneous <code class="docutils literal notranslate"><span class="pre">LOCAL_WORLD_SIZE</span></code>. That is, it is assumed that all
nodes run the same number of local workers (per role).</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">RANK</span></code> is NOT stable. Between restarts, the local workers on a node can be assgined a
different range of ranks than before. NEVER hard code any assumptions about the stable-ness of
ranks or some correlation between <code class="docutils literal notranslate"><span class="pre">RANK</span></code> and <code class="docutils literal notranslate"><span class="pre">LOCAL_RANK</span></code>.</p></li>
<li><p>When using elasticity (<code class="docutils literal notranslate"><span class="pre">min_size!=max_size</span></code>) DO NOT hard code assumptions about
<code class="docutils literal notranslate"><span class="pre">WORLD_SIZE</span></code> as the world size can change as nodes are allowed to leave and join.</p></li>
<li><p>It is recommended for your script to have the following structure:</p></li>
</ol>
<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">load_checkpoint</span><span class="p">(</span><span class="n">checkpoint_path</span><span class="p">)</span>
<span class="n">initialize</span><span class="p">()</span>
<span class="n">train</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">train</span><span class="p">():</span>
<span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">iter</span><span class="p">(</span><span class="n">dataset</span><span class="p">):</span>
<span class="n">train_step</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>
<span class="k">if</span> <span class="n">should_checkpoint</span><span class="p">:</span>
<span class="n">save_checkpoint</span><span class="p">(</span><span class="n">checkpoint_path</span><span class="p">)</span>
</pre></div>
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