No auto load weights (#985)

* remove autoload * remove autoload * added weights loading docs * checkpoint loading saving docs * checkpoint loading saving docs * checkpoint loading saving docs * docs (#1010) * remove autoload * remove autoload * added weights loading docs * checkpoint loading saving docs * checkpoint loading saving docs * checkpoint loading saving docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs * docs
2026-09-09 11:32:07 +08:00 · 2020-03-02 17:12:22 -05:00
parent e15c0419c0
commit 2a04be0386
10 changed files with 112 additions and 325 deletions
@@ -0,0 +1,76 @@
+Saving and loading weights
+==========================
+
+Lightning can automate saving and loading checkpoints.
+
+Checkpoint saving
+-----------------
+
+Checkpointing is enabled by default to the current working directory.
+To change the checkpoint path pass in:
+
+.. code-block:: python
+
+    Trainer(default_save_path='/your/path/to/save/checkpoints')
+
+To modify the behavior of checkpointing pass in your own callback.
+
+.. code-block:: python
+
+    from pytorch_lightning.callbacks import ModelCheckpoint
+
+    # DEFAULTS used by the Trainer
+    checkpoint_callback = ModelCheckpoint(
+        filepath=os.getcwd(),
+        save_best_only=True,
+        verbose=True,
+        monitor='val_loss',
+        mode='min',
+        prefix=''
+    )
+
+    trainer = Trainer(checkpoint_callback=checkpoint_callback)
+
+
+Or disable it by passing
+
+.. code-block:: python
+
+        trainer = Trainer(checkpoint_callback=False)
+
+
+The Lightning checkpoint also saves the hparams (hyperparams) passed into the LightningModule init.
+
+.. note:: hparams is a `Namespace <https://docs.python.org/2/library/argparse.html#argparse.Namespace>`_.
+
+.. code-block:: python
+   :emphasize-lines: 8
+
+   from argparse import Namespace
+
+   # usually these come from command line args
+   args = Namespace(**{'learning_rate':0.001})
+
+   # define you module to have hparams as the first arg
+   # this means your checkpoint will have everything that went into making
+   # this model (in this case, learning rate)
+   class MyLightningModule(pl.LightningModule):
+
+       def __init__(self, hparams, ...):
+           self.hparams = hparams
+
+Checkpoint Loading
+------------------
+
+You might want to not only load a model but also continue training it. Use this method to
+restore the trainer state as well. This will continue from the epoch and global step you last left off.
+However, the dataloaders will start from the first batch again (if you shuffled it shouldn't matter).
+
+.. code-block:: python
+
+    model = MyLightingModule.load_from_checkpoint(PATH)
+    model.eval()
+    y_hat = model(x)
+
+A LightningModule is no different than a nn.Module. This means you can load it and use it for
+predictions as you would a nn.Module.