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Jirka BorovecandWilliam Falcon
e586ed4767
* hparams as dict * hparams as dict * fixing * fixing * fixing * fixing * typing * typing * chnagelog * update set hparams * use setter * simplify * chnagelog * imports * pylint * typing * Update training_io.py * Update training_io.py * Update lightning.py * Update test_trainer.py * Update __init__.py * Update base.py * Update utils.py * Update test_trainer.py * Update training_io.py * Update test_trainer.py * Update test_trainer.py * Update test_trainer.py * Update test_trainer.py * Update callback_config.py * Update callback_config.py * Update test_trainer.py Co-authored-by: William Falcon <waf2107@columbia.edu>
98 lines
2.6 KiB
ReStructuredText
98 lines
2.6 KiB
ReStructuredText
Saving and loading weights
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==========================
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Lightning can automate saving and loading checkpoints.
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Checkpoint saving
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-----------------
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A Lightning checkpoint has everything needed to restore a training session including:
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- 16-bit scaling factor (apex)
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- Current epoch
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- Global step
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- Model state_dict
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- State of all optimizers
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- State of all learningRate schedulers
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- State of all callbacks
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- The hyperparameters used for that model if passed in as hparams (Argparse.Namespace)
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Automatic saving
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^^^^^^^^^^^^^^^^
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Checkpointing is enabled by default to the current working directory.
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To change the checkpoint path pass in:
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.. code-block:: python
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Trainer(default_save_path='/your/path/to/save/checkpoints')
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To modify the behavior of checkpointing pass in your own callback.
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.. code-block:: python
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from pytorch_lightning.callbacks import ModelCheckpoint
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# DEFAULTS used by the Trainer
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checkpoint_callback = ModelCheckpoint(
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filepath=os.getcwd(),
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save_best_only=True,
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verbose=True,
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monitor='val_loss',
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mode='min',
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prefix=''
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)
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trainer = Trainer(checkpoint_callback=checkpoint_callback)
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Or disable it by passing
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.. code-block:: python
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trainer = Trainer(checkpoint_callback=False)
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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>`_.
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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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Manual saving
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^^^^^^^^^^^^^
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To save your own checkpoint call:
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.. code-block:: python
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model.save_checkpoint(PATH)
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Checkpoint Loading
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------------------
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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.
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However, the dataloaders will start from the first batch again (if you shuffled it shouldn't matter).
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.. code-block:: python
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model = MyLightingModule.load_from_checkpoint(PATH)
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model.eval()
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y_hat = model(x)
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A LightningModule is no different than a nn.Module. This means you can load it and use it for
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predictions as you would a nn.Module. |