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<a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/SLURM Managed Cluster.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
<h1>SLURM Managed Cluster</h1>
<p>Lightning supports model training on a cluster managed by SLURM in the following cases: </p>
<ol>
<li>Training on single or multi-cpus only.</li>
<li>Training on single or multi-gpus on the same node.</li>
<li>Coming SOON: Training across multiple nodes.</li>
</ol>
<hr />
<h4 id="running-grid-search-on-a-cluster">Running grid search on a cluster</h4>
<p>To use lightning to run a hyperparameter search (grid-search or random-search) on a cluster do 4 things: </p>
<p>(1). Define the parameters for the grid search </p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
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<span class="c1"># subclass of argparse</span>
<span class="n">parser</span> <span class="o">=</span> <span class="n">HyperOptArgumentParser</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s1">&#39;random_search&#39;</span><span class="p">)</span>
<span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--learning_rate&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mf">0.002</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s1">&#39;the learning rate&#39;</span><span class="p">)</span>
<span class="c1"># let&#39;s enable optimizing over the number of layers in the network</span>
<span class="n">parser</span><span class="o">.</span><span class="n">opt_list</span><span class="p">(</span><span class="s1">&#39;--nb_layers&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">tunable</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span>
<span class="n">hparams</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
</pre></div>
</td></tr></table>
<p>(2). Define the cluster options in the <a href="https://williamfalcon.github.io/test-tube/hpc/SlurmCluster/">SlurmCluster object</a> (over 5 nodes and 8 gpus) </p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
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26</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">test_tube.hpc</span> <span class="kn">import</span> <span class="n">SlurmCluster</span>
<span class="c1"># hyperparameters is a test-tube hyper params object</span>
<span class="c1"># see https://williamfalcon.github.io/test-tube/hyperparameter_optimization/HyperOptArgumentParser/</span>
<span class="n">hyperparams</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">parse</span><span class="p">()</span>
<span class="c1"># init cluster</span>
<span class="n">cluster</span> <span class="o">=</span> <span class="n">SlurmCluster</span><span class="p">(</span>
<span class="n">hyperparam_optimizer</span><span class="o">=</span><span class="n">hyperparams</span><span class="p">,</span>
<span class="n">log_path</span><span class="o">=</span><span class="s1">&#39;/path/to/log/results/to&#39;</span><span class="p">,</span>
<span class="n">python_cmd</span><span class="o">=</span><span class="s1">&#39;python3&#39;</span>
<span class="p">)</span>
<span class="c1"># let the cluster know where to email for a change in job status (ie: complete, fail, etc...)</span>
<span class="n">cluster</span><span class="o">.</span><span class="n">notify_job_status</span><span class="p">(</span><span class="n">email</span><span class="o">=</span><span class="s1">&#39;some@email.com&#39;</span><span class="p">,</span> <span class="n">on_done</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">on_fail</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="c1"># set the job options. In this instance, we&#39;ll run 20 different models</span>
<span class="c1"># each with its own set of hyperparameters giving each one 1 GPU (ie: taking up 20 GPUs)</span>
<span class="n">cluster</span><span class="o">.</span><span class="n">per_experiment_nb_gpus</span> <span class="o">=</span> <span class="mi">8</span>
<span class="n">cluster</span><span class="o">.</span><span class="n">per_experiment_nb_nodes</span> <span class="o">=</span> <span class="mi">5</span>
<span class="c1"># we&#39;ll request 10GB of memory per node</span>
<span class="n">cluster</span><span class="o">.</span><span class="n">memory_mb_per_node</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="c1"># set a walltime of 10 minues</span>
<span class="n">cluster</span><span class="o">.</span><span class="n">job_time</span> <span class="o">=</span> <span class="s1">&#39;10:00&#39;</span>
</pre></div>
</td></tr></table>
<p>(3). Give trainer the cluster_manager in your main function: </p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
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10</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">pytorch_lightning</span> <span class="kn">import</span> <span class="n">Trainer</span>
<span class="k">def</span> <span class="nf">train_fx</span><span class="p">(</span><span class="n">trial_hparams</span><span class="p">,</span> <span class="n">cluster_manager</span><span class="p">,</span> <span class="n">_</span><span class="p">):</span>
<span class="c1"># hparams has a specific set of hyperparams</span>
<span class="n">my_model</span> <span class="o">=</span> <span class="n">MyLightningModel</span><span class="p">()</span>
<span class="c1"># give the trainer the cluster object</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">cluster</span><span class="o">=</span><span class="n">cluster_manager</span><span class="p">)</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">my_model</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<p>(4). Start the grid search </p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
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6</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># run the models on the cluster</span>
<span class="n">cluster</span><span class="o">.</span><span class="n">optimize_parallel_cluster_gpu</span><span class="p">(</span>
<span class="n">train_fx</span><span class="p">,</span>
<span class="n">nb_trials</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
<span class="n">job_name</span><span class="o">=</span><span class="s1">&#39;my_grid_search_exp_name&#39;</span><span class="p">,</span>
<span class="n">job_display_name</span><span class="o">=</span><span class="s1">&#39;my_exp&#39;</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<p>That's it! The SlurmCluster object will automatically checkpoint the lightning model and resubmit if it runs into the walltime!</p>
<hr />
<h4 id="walltime-auto-resubmit">Walltime auto-resubmit</h4>
<p>Lightning automatically resubmits jobs when they reach the walltime. You get this behavior for free if you give lightning
a slurm cluster object.</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">def</span> <span class="nf">my_main_fx</span><span class="p">(</span><span class="n">hparams</span><span class="p">,</span> <span class="n">slurm_manager</span><span class="p">,</span> <span class="n">_</span><span class="p">):</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">cluster</span><span class="o">=</span><span class="n">slurm_manager</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<p>(See the grid search example above for cluster configuration).
With this feature lightning will: </p>
<ol>
<li>automatically checkpoint the model</li>
<li>checkpoint the trainer session</li>
<li>resubmit a continuation job.</li>
<li>load the checkpoint and trainer session in the new model</li>
</ol>
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