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

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docs/source/checkpointing.rst

@@ -1,80 +0,0 @@
-Checkpointing
-==============
-
-.. _model-saving:
-
-Model saving
--------------------
-To save a LightningModule, provide a :meth:`pytorch_lightning.callbacks.ModelCheckpoint` callback.
-
-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>`_ or dictionary.
-
-.. 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
-
-   my_model = MyLightningModule(args)
-
-   # auto-saves checkpoint
-   checkpoint_callback = ModelCheckpoint(filepath='my_path')
-   Trainer(checkpoint_callback=checkpoint_callback)
-
-
-Model loading
------------------------------------
-
-To load a model, use :meth:`pytorch_lightning.core.LightningModule.load_from_checkpoint`
-
-.. note:: If lightning created your checkpoint, your model will receive all the hyperparameters used
-   to create the checkpoint. (See: :ref:`model-saving`).
-
-.. code-block:: python
-
-    # load weights without mapping
-    MyLightningModule.load_from_checkpoint('path/to/checkpoint.ckpt')
-
-    # load weights mapping all weights from GPU 1 to GPU 0
-    map_location = {'cuda:1':'cuda:0'}
-    MyLightningModule.load_from_checkpoint('path/to/checkpoint.ckpt', map_location=map_location)
-
-Restoring training session
------------------------------------
-
-If you want to pick up training from where you left off, you have a few options.
-
-1. Pass in a logger with the same experiment version to continue training.
-
-.. code-block:: python
-
-   # train the first time and set the version number
-   logger = TensorboardLogger(version=10)
-   trainer = Trainer(logger=logger)
-   trainer.fit(model)
-
-   # when you init another logger with that same version, the model
-   # will continue where it left off
-   logger = TensorboardLogger(version=10)
-   trainer = Trainer(logger=logger)
-   trainer.fit(model)
-
-2. A second option is to pass in a path to a checkpoint (see: :ref:`pytorch_lightning.trainer.trainer.Trainer`).
-
-.. code-block:: python
-
-   # train the first time and set the version number
-   trainer = Trainer(resume_from_checkpoint='some/path/to/my_checkpoint.ckpt')
-   trainer.fit(model)

docs/source/index.rst

@@ -45,7 +45,6 @@ PyTorch-Lightning Documentation
    :caption: Common Use Cases
 
    apex
-   checkpointing
    slurm
    debugging
    experiment_logging
@@ -54,6 +53,7 @@ PyTorch-Lightning Documentation
    fast_training
    hooks
    multi_gpu
+   weights_loading
    single_gpu
    sequences
    training_tricks

docs/source/weights_loading.rst

@@ -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.