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|
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|
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|
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<p>A useful debugging trick is to make your model overfit a tiny fraction of the data.</p>
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<h1 id="trainer">Trainer</h1>
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<p>[<a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/models/trainer.py">Github Code</a>]</p>
|
||||
<p>The lightning trainer abstracts best practices for running a training, val, test routine. It calls parts of your model when it wants to hand over full control and otherwise makes training assumptions which are now standard practice in AI research.</p>
|
||||
<p>This is the basic use of the trainer:</p>
|
||||
<pre><code class="python">from pytorch_lightning import Trainer
|
||||
|
||||
model = LightningTemplate()
|
||||
|
||||
trainer = Trainer()
|
||||
trainer.fit(model)
|
||||
</code></pre>
|
||||
|
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<p>But of course the fun is in all the advanced things it can do:</p>
|
||||
<p><strong>Training loop</strong> </p>
|
||||
<ul>
|
||||
<li><a href="Training%20Loop/#accumulated-gradients">Accumulate gradients</a></li>
|
||||
<li><a href="Training%20Loop/#anneal-learning-rate">Anneal Learning rate</a></li>
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<li><a href="Training%20Loop/#Check-gpu-usage">Check GPU usage</a></li>
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<li><a href="Training%20Loop/#check-which-gradients-are-nan">Check which gradients are nan</a></li>
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<li><a href="Training%20Loop/#display-the-parameter-count-by-layer">Display the parameter count by layer</a></li>
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<li><a href="Training%20Loop/#force-training-for-min-or-max-epochs">Force training for min or max epochs</a></li>
|
||||
<li><a href="Training%20Loop/#inspect-gradient-norms">Inspect gradient norms</a></li>
|
||||
<li><a href="Training%20Loop/#make-model-overfit-on-subset-of-data">Make model overfit on subset of data</a></li>
|
||||
<li><a href="../Pytorch-lightning/LightningModule/#configure_optimizers">Use multiple optimizers (like GANs)</a></li>
|
||||
<li><a href="Training%20Loop/#set-how-much-of-the-training-set-to-check">Set how much of the training set to check (1-100%)</a></li>
|
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</ul>
|
||||
<p><strong>Validation loop</strong> </p>
|
||||
<ul>
|
||||
<li>Display metrics in progress bar</li>
|
||||
<li>Set how much of the validation set to check (1-100%)</li>
|
||||
<li>Set validation check frequency within 1 training epoch (1-100%)</li>
|
||||
<li>validation_step function</li>
|
||||
<li>Why does validation run first for 5 steps?</li>
|
||||
</ul>
|
||||
<p><strong>Distributed training</strong> </p>
|
||||
<ul>
|
||||
<li>Single-gpu </li>
|
||||
<li>Multi-gpu </li>
|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
<li>Model saving</li>
|
||||
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|
||||
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|
||||
<p><strong>Computing cluster (SLURM)</strong> </p>
|
||||
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|
||||
<li>Automatic checkpointing </li>
|
||||
<li>Automatic saving, loading </li>
|
||||
<li>Running grid search on a cluster </li>
|
||||
<li>Walltime auto-resubmit </li>
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