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@@ -350,6 +350,13 @@
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</li>
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<li class="md-nav__item">
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<a href="#restoring-training-session" title="Restoring training session" class="md-nav__link">
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Restoring training session
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</a>
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</li>
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@@ -511,6 +518,13 @@
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</li>
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<li class="md-nav__item">
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<a href="#restoring-training-session" title="Restoring training session" class="md-nav__link">
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Restoring training session
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</a>
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</li>
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@@ -548,6 +562,21 @@ checkpoint_callback = ModelCheckpoint(
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trainer = Trainer(checkpoint_callback=checkpoint_callback)
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</code></pre>
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<hr />
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<h3 id="restoring-training-session">Restoring training session</h3>
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<p>You might want to not only load a model but also continue training it. Use this method to
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restore the trainer state as well. This will continue from the epoch and global step you last left off.<br />
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However, the dataloaders will start from the first batch again (if you shuffled it shouldn't matter). </p>
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<p>Lightning will restore the session if you pass an experiment with the same version and there's a saved checkpoint. </p>
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<pre><code class="python">from test_tube import Experiment
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exp = Experiment(version=a_previous_version_with_a_saved_checkpoint)
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Trainer(experiment=exp)
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trainer = Trainer(checkpoint_callback=checkpoint_callback)
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# the trainer is now restored
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</code></pre>
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@@ -698,7 +698,7 @@ cluster.add_command('export NCCL_SOCKET_IFNAME=^docker0,lo')
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cluster.add_command('export NCCL_DEBUG=INFO')
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# setting a master port here is a good idea.
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cluster.add_command(f'export MASTER_PORT={PORT}')
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cluster.add_command('export MASTER_PORT=%r' % PORT)
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# good to load the latest NCCL version
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cluster.load_modules(['NCCL/2.4.7-1-cuda.10.0'])
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@@ -508,6 +508,7 @@ trainer.fit(model)
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<ul>
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<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving">Model saving</a></li>
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<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics">Model loading</a> </li>
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<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#restoring-training-session">Restoring training session</a></li>
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</ul>
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<p><strong>Computing cluster (SLURM)</strong> </p>
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<ul>
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@@ -687,6 +687,7 @@ one could be a seq-2-seq model, both (optionally) ran by the same trainer file.<
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<ul>
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<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving">Model saving</a></li>
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<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics">Model loading</a> </li>
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<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#restoring-training-session">Restoring training session</a></li>
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</ul>
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<h6 id="computing-cluster-slurm">Computing cluster (SLURM)</h6>
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<ul>
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