CI: docs nightly (#152)

* fix docs links
* nightly
* timeout

Author: Borda

.github/workflows/ci_code-format.yml

@@ -12,6 +12,7 @@ on:  # Trigger the workflow on push or pull request, but only for the main branc
 jobs:
   pre-commit-check:
     runs-on: ubuntu-latest
+    timeout-minutes: 10
     steps:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v2

.github/workflows/ci_docs.yml

@@ -2,6 +2,9 @@ name: validate Docs
 
 on:  # Trigger the workflow on push or pull request
   pull_request: {}
+  schedule:
+    # At the end of every day
+    - cron: "0 0 * * *"
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref }}
@@ -13,6 +16,7 @@ jobs:
     env:
       PUB_BRANCH: publication
       PATH_DATASETS: ${{ github.workspace }}/.datasets
+    timeout-minutes: 20
     steps:
       - name: Checkout 🛎️
         uses: actions/checkout@v2

.github/workflows/ci_schema.yml

@@ -7,6 +7,7 @@ on: # Trigger the workflow on push or pull request, but only for the master bran
 jobs:
   validate-schema:
     runs-on: ubuntu-20.04
+    timeout-minutes: 5
     steps:
       - name: Checkout
         uses: actions/checkout@v2

lightning_examples/mnist-hello-world/hello-world.py

@@ -89,7 +89,7 @@ def configure_optimizers(self):
 #     - 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).
 #     - **Note this runs across all GPUs and it *is* safe to make state assignments here**
 #
-# 3. [x_dataloader()](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.core.hooks.html) ♻️
+# 3. [x_dataloader()](https://pytorch-lightning.readthedocs.io/en/stable/api_references.html#core-api) ♻️
 #     - `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()`
 
 

lightning_examples/mnist-tpu-training/.meta.yml

@@ -8,7 +8,7 @@ tags:
   - Image
 description: In this notebook, we'll train a model on TPUs. Updating one Trainer flag is all you need for that.
   The most up to documentation related to TPU training can be found
-  [here](https://pytorch-lightning.readthedocs.io/en/stable/advanced/tpu.html).
+  [here](https://pytorch-lightning.readthedocs.io/en/stable/accelerators/tpu.html).
 requirements:
   - torchvision
 accelerator: