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William Falcon
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* added outline of all features * updated common use cases doc * updated common use cases doc * updated common use cases doc * updated common use cases doc * updated common use cases doc * updated common use cases doc * updated common use cases doc * updated common use cases doc * updated common use cases doc * updated common use cases doc * updated common use cases doc * updated common use cases doc * updated common use cases doc * updated docs
80 lines
2.5 KiB
ReStructuredText
80 lines
2.5 KiB
ReStructuredText
Checkpointing
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==============
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.. _model-saving:
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Model saving
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-------------------
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To save a LightningModule, provide a :meth:`pytorch_lightning.callbacks.ModelCheckpoint` callback.
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The Lightning checkpoint also saves the hparams (hyperparams) passed into the LightningModule init.
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.. note:: hparams is a `Namespace <https://docs.python.org/2/library/argparse.html#argparse.Namespace>`_ or dictionary.
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.. code-block:: python
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:emphasize-lines: 8
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from argparse import Namespace
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# usually these come from command line args
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args = Namespace(**{'learning_rate':0.001})
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# define you module to have hparams as the first arg
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# this means your checkpoint will have everything that went into making
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# this model (in this case, learning rate)
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class MyLightningModule(pl.LightningModule):
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def __init__(self, hparams, ...):
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self.hparams = hparams
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my_model = MyLightningModule(args)
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# auto-saves checkpoint
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checkpoint_callback = ModelCheckpoint(filepath='my_path')
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Trainer(checkpoint_callback=checkpoint_callback)
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Model loading
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-----------------------------------
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To load a model, use :meth:`pytorch_lightning.core.LightningModule.load_from_checkpoint`
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.. note:: If lightning created your checkpoint, your model will receive all the hyperparameters used
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to create the checkpoint. (See: :ref:`model-saving`).
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.. code-block:: python
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# load weights without mapping
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MyLightningModule.load_from_checkpoint('path/to/checkpoint.ckpt')
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# load weights mapping all weights from GPU 1 to GPU 0
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map_location = {'cuda:1':'cuda:0'}
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MyLightningModule.load_from_checkpoint('path/to/checkpoint.ckpt', map_location=map_location)
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Restoring training session
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-----------------------------------
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If you want to pick up training from where you left off, you have a few options.
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1. Pass in a logger with the same experiment version to continue training.
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.. code-block:: python
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# train the first time and set the version number
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logger = TensorboardLogger(version=10)
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trainer = Trainer(logger=logger)
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trainer.fit(model)
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# when you init another logger with that same version, the model
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# will continue where it left off
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logger = TensorboardLogger(version=10)
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trainer = Trainer(logger=logger)
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trainer.fit(model)
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2. A second option is to pass in a path to a checkpoint (see: :ref:`pytorch_lightning.trainer`).
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.. code-block:: python
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# train the first time and set the version number
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trainer = Trainer(resume_from_checkpoint='some/path/to/my_checkpoint.ckpt')
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trainer.fit(model) |