fix links to Docs (#744)

* update log

* fix links

* formatting

* fixing docs path

* formatting

README.md

@@ -84,12 +84,12 @@ Lightning sets up all the boilerplate state-of-the-art training for you so you c
 ---
 
 ## How do I do use it?   
-Think about Lightning as refactoring your research code instead of using a new framework. The research code goes into a [LightningModule](https://pytorch-lightning.rtfd.io/en/latest/LightningModule/RequiredTrainerInterface/) which you fit using a Trainer.
+Think about Lightning as refactoring your research code instead of using a new framework. The research code goes into a [LightningModule](https://pytorch-lightning.rtfd.io/en/latest/lightning-module.html) which you fit using a Trainer.
 
 The LightningModule defines a *system* such as seq-2-seq, GAN, etc... It can ALSO define a simple classifier such as the example below.     
 
 To use lightning do 2 things:  
-1. [Define a LightningModule](https://pytorch-lightning.rtfd.io/en/latest/LightningModule/RequiredTrainerInterface/)
+1. [Define a LightningModule](https://pytorch-lightning.rtfd.io/en/latest/lightning-module.html)
 **WARNING:** This syntax is for version 0.5.0+ where abbreviations were removed.
     ```python
     import os
@@ -165,7 +165,7 @@ To use lightning do 2 things:
             # OPTIONAL
             return DataLoader(MNIST(os.getcwd(), train=False, download=True, transform=transforms.ToTensor()), batch_size=32)
     ```
-2. Fit with a [trainer](https://pytorch-lightning.rtfd.io/en/latest/Trainer/)    
+2. Fit with a [trainer](https://pytorch-lightning.rtfd.io/en/latest/pytorch_lightning.trainer.html)    
     ```python
     from pytorch_lightning import Trainer
     

docs/source/conf.py

@@ -318,7 +318,7 @@ def linkcode_resolve(domain, info):
             obj = getattr(obj, part)
         fname = inspect.getsourcefile(obj)
         # https://github.com/rtfd/readthedocs.org/issues/5735
-        if any([s in fname for s in ('readthedocs', 'checkouts')]):
+        if any([s in fname for s in ('readthedocs', 'rtfd', 'checkouts')]):
             # /home/docs/checkouts/readthedocs.org/user_builds/pytorch_lightning/checkouts/
             #  devel/pytorch_lightning/utilities/cls_experiment.py#L26-L176
             path_top = os.path.abspath(os.path.join('..', '..', '..'))
@@ -338,6 +338,10 @@ def linkcode_resolve(domain, info):
     # import subprocess
     # tag = subprocess.Popen(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE,
     #                        universal_newlines=True).communicate()[0][:-1]
+    branch = filename.split('/')[0]
+    # do mapping from latest tags to master
+    branch = {'latest': 'master', 'stable': 'master'}.get(branch, branch)
+    filename = '/'.join([branch] + filename.split('/')[1:])
     return "https://github.com/%s/%s/blob/%s" \
            % (github_user, github_repo, filename)
 

docs/source/examples.rst

@@ -1,33 +1,33 @@
 GAN
-====
+===
 .. toctree::
    :maxdepth: 3
 
    pl_examples.domain_templates.gan
 
 MNIST
-====
+=====
 .. toctree::
    :maxdepth: 3
 
    pl_examples.basic_examples.lightning_module_template
 
 Multi-node (ddp) MNIST
-====
+======================
 .. toctree::
    :maxdepth: 3
 
    pl_examples.multi_node_examples.multi_node_ddp_demo
 
 Multi-node (ddp2) MNIST
-====
+=======================
 .. toctree::
    :maxdepth: 3
 
    pl_examples.multi_node_examples.multi_node_ddp2_demo
 
 Imagenet
-====
+========
 .. toctree::
    :maxdepth: 3
 

pytorch_lightning/callbacks/pt_callbacks.py

@@ -1,6 +1,7 @@
 """
 Callbacks
-====================================
+=========
+
 Callbacks supported by Lightning
 """
 

pytorch_lightning/core/__init__.py

@@ -85,6 +85,7 @@ Most methods are optional. Here's a minimal example.
 Once you've defined the LightningModule, fit  it using a trainer.
 
 .. code-block:: python
+
    trainer = pl.Trainer()
    model = CoolModel()
 

pytorch_lightning/logging/__init__.py

@@ -3,6 +3,7 @@ Lightning supports most popular logging frameworks (Tensorboard, comet, weights
 To use a logger, simply pass it into the trainer.
 
 .. code-block:: python
+
     from pytorch_lightning import logging
 
     # lightning uses tensorboard by default

pytorch_lightning/logging/test_tube.py

@@ -46,7 +46,7 @@ class TestTubeLogger(LightningLoggerBase):
     `os.path.join(save_dir, name, version)`
 
     Example
-    --------
+    -------
 
     .. code-block:: python
 

pytorch_lightning/trainer/trainer.py

@@ -96,6 +96,7 @@ class Trainer(TrainerIOMixin,
         Args:
             logger (:class:`.Logger`): Logger for experiment tracking.
                 Example::
+
                     from pytorch_lightning.logging import TensorBoardLogger
 
                     # default logger used by trainer
@@ -106,8 +107,10 @@ class Trainer(TrainerIOMixin,
                     )
 
                     Trainer(logger=logger)
+
             checkpoint_callback (:class:`CheckpointCallback`): Callback for checkpointing.
                 Example::
+
                     from pytorch_lightning.callbacks import ModelCheckpoint
 
                     # default used by the Trainer
@@ -121,6 +124,7 @@ class Trainer(TrainerIOMixin,
                     )
 
                     trainer = Trainer(checkpoint_callback=checkpoint_callback)
+
             early_stop_callback (:class:`.EarlyStopping`): Callback for early stopping. If
                 set to ``True``, then the default callback monitoring ``'val_loss'`` is created.
                 Will raise an error if ``'val_loss'`` is not found.
@@ -129,6 +133,7 @@ class Trainer(TrainerIOMixin,
                 If ``'val_loss'`` is not found will work as if early stopping is disabled.
                 Default: ``None``.
                 Example::
+
                     from pytorch_lightning.callbacks import EarlyStopping
 
                     # default used by the Trainer
@@ -141,25 +146,32 @@ class Trainer(TrainerIOMixin,
                     )
 
                     trainer = Trainer(early_stop_callback=early_stop_callback)
+
             default_save_path (str): Default path for logs and weights when no logger/ckpt_callback passed
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(default_save_path=os.getcwd())
+
             gradient_clip_val (float): 0 means don't clip.
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(gradient_clip_val=0.0)
+
             gradient_clip (int):
                 .. deprecated:: 0.5.0
                     Use `gradient_clip_val` instead. Will remove 0.8.0.
 
             process_position (int): orders the tqdm bar when running multiple models on same machine.
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(process_position=0)
 
             num_nodes (int): number of GPU nodes for distributed training.
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(num_nodes=1)
 
@@ -172,6 +184,7 @@ class Trainer(TrainerIOMixin,
 
             gpus (list|str|int): Which GPUs to train on.
                 Example::
+
                     # default used by the Trainer (ie: train on CPU)
                     trainer = Trainer(gpus=None)
 
@@ -192,6 +205,7 @@ class Trainer(TrainerIOMixin,
             log_gpu_memory (str): None, 'min_max', 'all'. Might slow performance
                 because it uses the output of nvidia-smi.
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(log_gpu_memory=None)
 
@@ -203,11 +217,13 @@ class Trainer(TrainerIOMixin,
 
             show_progress_bar (bool): If true shows tqdm progress bar
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(show_progress_bar=True)
 
             overfit_pct (float): uses this much data of all datasets.
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(overfit_pct=0.0)
 
@@ -216,14 +232,16 @@ class Trainer(TrainerIOMixin,
 
             track_grad_norm (int): -1 no tracking. Otherwise tracks that norm
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(track_grad_norm=-1)
 
                     # track the 2-norm
                     trainer = Trainer(track_grad_norm=2)
 
-            check_val_every_n_epoch (int): check val every n train epochs
+            check_val_every_n_epoch (int): Check val every n train epochs.
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(check_val_every_n_epoch=1)
 
@@ -232,6 +250,7 @@ class Trainer(TrainerIOMixin,
 
             fast_dev_run (bool): runs 1 batch of train, test  and val to find any bugs (ie: a sort of unit test).
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(fast_dev_run=False)
 
@@ -240,6 +259,7 @@ class Trainer(TrainerIOMixin,
 
             accumulate_grad_batches (int|dict): Accumulates grads every k batches or as set up in the dict.
                 Example::
+
                     # default used by the Trainer (no accumulation)
                     trainer = Trainer(accumulate_grad_batches=1)
 
@@ -249,8 +269,9 @@ class Trainer(TrainerIOMixin,
                     # no accumulation for epochs 1-4. accumulate 3 for epochs 5-10. accumulate 20 after that
                     trainer = Trainer(accumulate_grad_batches={5: 3, 10: 20})
 
-            max_epochs (int): Stop training once this number of epochs is reached
+            max_epochs (int): Stop training once this number of epochs is reached.
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(max_epochs=1000)
 
@@ -260,6 +281,7 @@ class Trainer(TrainerIOMixin,
 
             min_epochs (int): Force training for at least these many epochs
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(min_epochs=1)
 
@@ -270,6 +292,7 @@ class Trainer(TrainerIOMixin,
             train_percent_check (int): How much of training dataset to check.
                 Useful when debugging or testing something that happens at the end of an epoch.
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(train_percent_check=1.0)
 
@@ -279,6 +302,7 @@ class Trainer(TrainerIOMixin,
             val_percent_check (int): How much of validation dataset to check.
                 Useful when debugging or testing something that happens at the end of an epoch.
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(val_percent_check=1.0)
 
@@ -288,6 +312,7 @@ class Trainer(TrainerIOMixin,
             test_percent_check (int): How much of test dataset to check.
                 Useful when debugging or testing something that happens at the end of an epoch.
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(test_percent_check=1.0)
 
@@ -297,6 +322,7 @@ class Trainer(TrainerIOMixin,
             val_check_interval (float|int): How often within one training epoch to check the validation set
                 If float, % of tng epoch. If int, check every n batch
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(val_check_interval=1.0)
 
@@ -310,11 +336,13 @@ class Trainer(TrainerIOMixin,
 
             log_save_interval (int): Writes logs to disk this often
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(log_save_interval=100)
 
             row_log_interval (int): How often to add logging rows (does not write to disk)
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(row_log_interval=10)
 
@@ -325,6 +353,7 @@ class Trainer(TrainerIOMixin,
             distributed_backend (str): The distributed backend to use.
                 Options: 'dp', 'ddp', 'ddp2'.
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(distributed_backend=None)
 
@@ -344,17 +373,20 @@ class Trainer(TrainerIOMixin,
 
             use_amp (bool): If true uses apex for 16bit precision
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(use_amp=False)
 
             print_nan_grads (bool): Prints gradients with nan values
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(print_nan_grads=False)
 
             weights_summary (str): Prints a summary of the weights when training begins.
                 Options: 'full', 'top', None.
                 Example::
+
                     # default used by the Trainer (ie: print all weights)
                     trainer = Trainer(weights_summary='full')
 
@@ -366,6 +398,7 @@ class Trainer(TrainerIOMixin,
 
             weights_save_path (str): Where to save weights if specified.
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(weights_save_path=os.getcwd())
 
@@ -383,6 +416,7 @@ class Trainer(TrainerIOMixin,
             amp_level (str): The optimization level to use (O1, O2, etc...).
                 Check nvidia docs for level (https://nvidia.github.io/apex/amp.html#opt-levels)
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(amp_level='O1')
 
@@ -390,6 +424,7 @@ class Trainer(TrainerIOMixin,
                 This catches any bugs in your validation without having to wait for the first validation check.
                 The Trainer uses 5 steps by default. Turn it off or modify it here.
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(num_sanity_val_steps=5)
 
@@ -407,6 +442,7 @@ class Trainer(TrainerIOMixin,
                 recurrent network trajectories."
                 <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.7941&rep=rep1&type=pdf>`_)
                 Example::
+
                     # default used by the Trainer (ie: disabled)
                     trainer = Trainer(truncated_bptt_steps=None)
 
@@ -415,15 +451,18 @@ class Trainer(TrainerIOMixin,
 
             resume_from_checkpoint (str): To resume training from a specific checkpoint pass in the path here.k
                 Example::
+
                     # default used by the Trainer
                     trainer = Trainer(resume_from_checkpoint=None)
 
                     # resume from a specific checkpoint
                     trainer = Trainer(resume_from_checkpoint='some/path/to/my_checkpoint.ckpt')
+
+        .. warning:: Following arguments become deprecated and they will be removed in v0.8.0:
+
+            - `nb_sanity_val_steps`
+
         """
-        #
-        # .. warning:: Following arguments become deprecated and they will be removed in v0.8.0:
-        #     - `nb_sanity_val_steps`
 
         # Transfer params
         # Backward compatibility
@@ -660,8 +699,10 @@ class Trainer(TrainerIOMixin,
     def tng_tqdm_dic(self):
         """Read-only for tqdm metrics.
 
-        .. warning:: Deprecated in v0.5.0. use training_tqdm_dict instead.
-        :return:
+        :return: dictionary
+
+        .. deprecated:: 0.5.0
+                    Use `training_tqdm_dict` instead. Will remove 0.8.0.
         """
         warnings.warn("`tng_tqdm_dic` has renamed to `training_tqdm_dict` since v0.5.0"
                       " and will be removed in v0.8.0", DeprecationWarning)
@@ -718,7 +759,6 @@ class Trainer(TrainerIOMixin,
         return 1
 
     def init_optimizers(self, optimizers):
-
         # single optimizer
         if isinstance(optimizers, Optimizer):
             return [optimizers], []