fix docs links (#6057)

Author: Borda

.github/stale.yml

@@ -29,7 +29,7 @@ pulls:
     markComment: >
       This pull request has been automatically marked as stale because it has not had recent activity.
       It will be closed in 7 days if no further activity occurs. If you need further help see our docs:
-      https://pytorch-lightning.readthedocs.io/en/latest/CONTRIBUTING.html#pull-request
+      https://pytorch-lightning.readthedocs.io/en/latest/generated/CONTRIBUTING.html#pull-request
       or ask the assistance of a core contributor here or on Slack.
       Thank you for your contributions.
     # Comment to post when closing a stale issue. Set to `false` to disable

README.md

@@ -63,7 +63,7 @@ Lightning forces the following structure to your code which makes it reusable an
 
 Once you do this, you can train on multiple-GPUs, TPUs, CPUs and even in 16-bit precision without changing your code!
 
-Get started with our [2 step guide](https://pytorch-lightning.readthedocs.io/en/stable/new-project.html)
+Get started with our [2 step guide](https://pytorch-lightning.readthedocs.io/en/latest/starter/new-project.html)
 
 ---
 
@@ -219,7 +219,7 @@ trainer.fit(autoencoder, DataLoader(train), DataLoader(val))
 ```
 
 ## Advanced features
-Lightning has over [40+ advanced features](https://pytorch-lightning.readthedocs.io/en/stable/trainer.html#trainer-flags) designed for professional AI research at scale.
+Lightning has over [40+ advanced features](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags) designed for professional AI research at scale.
 
 Here are some examples:
 
@@ -379,7 +379,7 @@ class LitAutoEncoder(pl.LightningModule):
 ## Community
 
 The lightning community is maintained by
-- [16 core contributors](https://pytorch-lightning.readthedocs.io/en/latest/governance.html) who are all a mix of professional engineers, Research Scientists, and Ph.D. students from top AI labs.
+- [10+ core contributors](https://pytorch-lightning.readthedocs.io/en/latest/governance.html) who are all a mix of professional engineers, Research Scientists, and Ph.D. students from top AI labs.
 - 400+ community contributors.
 
 Lightning is also part of the [PyTorch ecosystem](https://pytorch.org/ecosystem/) which requires projects to have solid testing, documentation and support.

notebooks/01-mnist-hello-world.ipynb

@@ -176,13 +176,13 @@
     "    - This is where we can download the dataset. We point to our desired dataset and ask torchvision's `MNIST` dataset class to download if the dataset isn't found there.\n",
     "    - **Note we do not make any state assignments in this function** (i.e. `self.something = ...`)\n",
     "\n",
-    "2. [setup(stage)](https://pytorch-lightning.readthedocs.io/en/latest/lightning-module.html#setup) ⚙️\n",
+    "2. [setup(stage)](https://pytorch-lightning.readthedocs.io/en/latest/common/lightning-module.html#setup) ⚙️\n",
     "    - Loads in data from file and prepares PyTorch tensor datasets for each split (train, val, test). \n",
     "    - Setup expects a 'stage' arg which is used to separate logic for 'fit' and 'test'.\n",
     "    - If you don't mind loading all your datasets at once, you can set up a condition to allow for both 'fit' related setup and 'test' related setup to run whenever `None` is passed to `stage` (or ignore it altogether and exclude any conditionals).\n",
     "    - **Note this runs across all GPUs and it *is* safe to make state assignments here**\n",
     "\n",
-    "3. [x_dataloader()](https://pytorch-lightning.readthedocs.io/en/latest/lightning-module.html#data-hooks) ♻️\n",
+    "3. [x_dataloader()](https://pytorch-lightning.readthedocs.io/en/latest/common/lightning-module.html#data-hooks) ♻️\n",
     "    - `train_dataloader()`, `val_dataloader()`, and `test_dataloader()` all return PyTorch `DataLoader` instances that are created by wrapping their respective datasets that we prepared in `setup()`"
    ]
   },

notebooks/02-datamodules.ipynb

@@ -23,7 +23,7 @@
     "\n",
     "This notebook will walk you through how to start using Datamodules.\n",
     "\n",
-    "The most up to date documentation on datamodules can be found [here](https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html).\n",
+    "The most up to date documentation on datamodules can be found [here](https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.html).\n",
     "\n",
     "---\n",
     "\n",

notebooks/03-basic-gan.ipynb

@@ -91,7 +91,7 @@
    "source": [
     "### MNIST DataModule\n",
     "\n",
-    "Below, we define a DataModule for the MNIST Dataset. To learn more about DataModules, check out our tutorial on them or see the [latest docs](https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html)."
+    "Below, we define a DataModule for the MNIST Dataset. To learn more about DataModules, check out our tutorial on them or see the [latest docs](https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.html)."
    ]
   },
   {

notebooks/06-mnist-tpu-training.ipynb

@@ -33,7 +33,7 @@
         "\n",
         "In this notebook, we'll train a model on TPUs. Changing one line of code is all you need to that.\n",
         "\n",
-        "The most up to documentation related to TPU training can be found [here](https://pytorch-lightning.readthedocs.io/en/latest/tpu.html).\n",
+        "The most up to documentation related to TPU training can be found [here](https://pytorch-lightning.readthedocs.io/en/latest/advanced/tpu.html).\n",
         "\n",
         "---\n",
         "\n",
@@ -114,7 +114,7 @@
       "source": [
         "### Defining The `MNISTDataModule`\n",
         "\n",
-        "Below we define `MNISTDataModule`. You can learn more about datamodules in [docs](https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html) and [datamodule notebook](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/notebooks/02-datamodules.ipynb)."
+        "Below we define `MNISTDataModule`. You can learn more about datamodules in [docs](https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.html) and [datamodule notebook](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/notebooks/02-datamodules.ipynb)."
       ]
     },
     {

notebooks/07-cifar10-baseline.ipynb

@@ -185,7 +185,7 @@
       },
       "source": [
         "### Lightning Module\n",
-        "Check out the [`configure_optimizers`](https://pytorch-lightning.readthedocs.io/en/stable/lightning_module.html#configure-optimizers) method to use custom Learning Rate schedulers. The OneCycleLR with SGD will get you to around 92-93% accuracy in 20-30 epochs and 93-94% accuracy in 40-50 epochs. Feel free to experiment with different LR schedules from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate"
+        "Check out the [`configure_optimizers`](https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html#configure-optimizers) method to use custom Learning Rate schedulers. The OneCycleLR with SGD will get you to around 92-93% accuracy in 20-30 epochs and 93-94% accuracy in 40-50 epochs. Feel free to experiment with different LR schedules from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate"
       ]
     },
     {