Add labels to sphinx docs (#2964)

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* Update fast_training.rst

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* Update performance.rst

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* Update sequences.rst

* Update single_gpu.rst

* Update slurm.rst

* Update test_set.rst

* Update tpu.rst

* Update trainer.rst

* Update training_tricks.rst

* Update transfer_learning.rst

* Update weights_loading.rst

* Update governance.rst

* Update hooks.rst

* Update bolts.rst

* Update child_modules.rst

* Update hyperparameters.rst

* Update transfer_learning.rst

docs/source/apex.rst

@@ -2,6 +2,7 @@
 
     from pytorch_lightning.trainer.trainer import Trainer
 
+.. _16-bit:
 
 16-bit training
 =================

docs/source/child_modules.rst

@@ -16,7 +16,6 @@
         def val_dataloader():
             pass
 
-
 Child Modules
 -------------
 Research projects tend to test different approaches to the same dataset.

docs/source/datamodules.rst

@@ -1,3 +1,5 @@
+.. _data-modules:
+
 LightningDataModule
 ===================
 A datamodule is a shareable, reusable class that encapsulates all the steps needed to process data:

docs/source/debugging.rst

@@ -1,6 +1,8 @@
 .. testsetup:: *
 
     from pytorch_lightning.trainer.trainer import Trainer
+    
+ .. _debugging:
 
 Debugging
 =========
@@ -129,4 +131,4 @@ argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
 .. testcode::
 
     # DEFAULT
-    trainer = Trainer(num_sanity_val_steps=2)
+    trainer = Trainer(num_sanity_val_steps=2)

docs/source/early_stopping.rst

@@ -3,6 +3,7 @@
     from pytorch_lightning.trainer.trainer import Trainer
     from pytorch_lightning.callbacks.early_stopping import EarlyStopping
 
+.. _early-stopping:
 
 Early stopping
 ==============

docs/source/experiment_logging.rst

@@ -3,6 +3,7 @@
     from pytorch_lightning.trainer.trainer import Trainer
     from pytorch_lightning.core.lightning import LightningModule
 
+.. _experiment-logging:
 
 Experiment Logging
 ==================

docs/source/experiment_reporting.rst

@@ -2,6 +2,7 @@
 
     from pytorch_lightning.trainer.trainer import Trainer
 
+.. _experiment-reporting:
 
 Experiment Reporting
 =====================

docs/source/fast_training.rst

@@ -2,6 +2,7 @@
 
     from pytorch_lightning.trainer.trainer import Trainer
 
+.. _fast-training:
 
 Fast Training
 =============

docs/source/governance.rst

@@ -1,3 +1,5 @@
+.. _governance:
+
 Pytorch Lightning Governance | Persons of interest
 ==================================================
 

docs/source/hooks.rst

@@ -1,3 +1,5 @@
+.. _hooks:
+
 Model Hooks
 ===========
 
@@ -88,4 +90,4 @@ General hooks
 -------------
 
 .. automodule:: pytorch_lightning.core.hooks
-    :noindex:
+    :noindex:

docs/source/hyperparameters.rst

@@ -7,7 +7,6 @@
     import sys
     sys.argv = ['foo']
 
-
 Hyperparameters
 ---------------
 Lightning has utilities to interact seamlessly with the command line ArgumentParser

docs/source/introduction_guide.rst

@@ -3,6 +3,7 @@
     from pytorch_lightning.core.lightning import LightningModule
     from pytorch_lightning.trainer.trainer import Trainer
 
+.. _introduction-guide:
 
 Step-by-step walk-through
 =========================

docs/source/lightning-module.rst

@@ -1,5 +1,6 @@
 .. role:: hidden
     :class: hidden-section
+.. _lightning-module:
 
 LightningModule
 ===============

docs/source/loggers.rst

@@ -6,6 +6,8 @@
 
 .. role:: hidden
     :class: hidden-section
+    
+.. _loggers:
 
 Loggers
 ===========

docs/source/lr_finder.rst

@@ -2,6 +2,8 @@
 
     from pytorch_lightning.trainer.trainer import Trainer
     from pytorch_lightning.core.lightning import LightningModule
+    
+.. _lr_finder:
 
 Learning Rate Finder
 --------------------

docs/source/metrics.rst

@@ -5,6 +5,8 @@
     from pytorch_lightning.core.lightning import LightningModule
     from pytorch_lightning.metrics import TensorMetric, NumpyMetric
 
+.. _metrics:
+
 Metrics
 =======
 This is a general package for PyTorch Metrics. These can also be used with regular non-lightning PyTorch code.

docs/source/multiple_loaders.rst

@@ -2,6 +2,8 @@
 
     from pytorch_lightning.core.lightning import LightningModule
 
+.. _multiple_loaders:
+
 Multiple Datasets
 =================
 Lightning supports multiple dataloaders in a few ways.

docs/source/new-project.rst

@@ -7,6 +7,7 @@
     from torch.nn import functional as F
     from torch.utils.data import DataLoader
 
+.. _quick-start:
 
 Quick Start
 ===========

docs/source/optimizers.rst

@@ -1,3 +1,5 @@
+.. _optimizers:
+
 Optimization
 ===============
 

docs/source/performance.rst

@@ -1,3 +1,5 @@
+.. _performance:
+
 Fast Performance
 ================
 Here are some best practices to increase your performance.

docs/source/production_inference.rst

@@ -1,3 +1,5 @@
+.. _production-inference:
+
 Inference in Production
 =======================
 PyTorch Lightning eases the process of deploying models into production.

docs/source/profiler.rst

@@ -1,6 +1,7 @@
 .. role:: hidden
     :class: hidden-section
 
+.. _profiler:
 
 Performance and Bottleneck Profiler
 ===================================

docs/source/results.rst

@@ -1,3 +1,5 @@
+.. _result:
+
 Result
 ======
 Lightning has two results objects `TrainResult` and `EvalResult`.

docs/source/sequences.rst

@@ -2,6 +2,8 @@
 
     from torch.utils.data import IterableDataset
     from pytorch_lightning.trainer.trainer import Trainer
+    
+.. _sequences:
 
 Sequential Data
 ================

docs/source/single_gpu.rst

@@ -2,6 +2,8 @@
 
     from pytorch_lightning.trainer.trainer import Trainer
 
+.. _single-gpu:
+
 Single GPU Training
 ===================
 Make sure you are running on a machine that has at least one GPU. Lightning handles all the NVIDIA flags for you,
@@ -11,4 +13,4 @@ there's no need to set them yourself.
     :skipif: torch.cuda.device_count() < 1
 
     # train on 1 GPU (using dp mode)
-    trainer = Trainer(gpus=1)
+    trainer = Trainer(gpus=1)

docs/source/slurm.rst

@@ -1,6 +1,8 @@
 .. testsetup:: *
 
     from pytorch_lightning.trainer.trainer import Trainer
+    
+.. _slurm:
 
 Computing cluster (SLURM)
 =========================

docs/source/test_set.rst

@@ -1,3 +1,5 @@
+.. _test-set:
+
 Test set
 ========
 Lightning forces the user to run the test set separately to make sure it isn't evaluated by mistake.

docs/source/tpu.rst

@@ -1,3 +1,5 @@
+.. _tpu:
+
 TPU support
 ===========
 

docs/source/trainer.rst

@@ -1,6 +1,8 @@
 .. role:: hidden
     :class: hidden-section
 
+.. _trainer:
+
 Trainer
 =======
 .. automodule:: pytorch_lightning.trainer

docs/source/training_tricks.rst

@@ -2,6 +2,7 @@
 
     from pytorch_lightning.trainer.trainer import Trainer
 
+.. _training-tricks:
 
 Training Tricks
 ================

docs/source/transfer_learning.rst

@@ -1,7 +1,7 @@
 .. testsetup:: *
 
     from pytorch_lightning.core.lightning import LightningModule
-
+    
 Transfer Learning
 -----------------
 
@@ -115,4 +115,4 @@ Here's a model that uses `Huggingface transformers <https://github.com/huggingfa
 
             h_cls = h[:, 0]
             logits = self.W(h_cls)
-            return logits, attn
+            return logits, attn

docs/source/weights_loading.rst

@@ -4,6 +4,7 @@
     from pytorch_lightning.trainer.trainer import Trainer
     from pytorch_lightning.core.lightning import LightningModule
 
+.. _weights-loading:
 
 Saving and loading weights
 ==========================