CI: check dead links in docs (#114)

* CI: check dead links in docs
* fix PL logo
* azure agents

Author: Borda

.actions/helpers.py

@@ -79,7 +79,7 @@
 #
 # ### Great thanks from the entire Pytorch Lightning Team for your interest !
 #
-# ![Pytorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/docs/source/_static/images/logo.png){height="60px" width="240px"}
+# [![Pytorch Lightning](https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.png){height="60px" width="240px"}](https://pytorchlightning.ai)
 
 """
 TEMPLATE_CARD_ITEM = """

.azure-pipelines/ipynb-publish.yml

@@ -17,7 +17,7 @@ jobs:
     # - For 60 minutes on Microsoft-hosted agents with a private project or private repository
     timeoutInMinutes: 0
 
-    pool: gridai-spot-pool
+    pool: azure-gpus-persist
     # this need to have installed docker in the base image...
     container:
       # base ML image: mcr.microsoft.com/azureml/openmpi3.1.2-cuda10.2-cudnn8-ubuntu18.04

.azure-pipelines/ipynb-tests.yml

@@ -13,7 +13,7 @@ jobs:
     # how much time to give 'run always even if cancelled tasks' before stopping them
     cancelTimeoutInMinutes: 2
 
-    pool: gridai-spot-pool
+    pool: azure-gpus-spot
     # this need to have installed docker in the base image...
     container:
       # base ML image: mcr.microsoft.com/azureml/openmpi3.1.2-cuda10.2-cudnn8-ubuntu18.04

.github/workflows/ci_docs.yml

@@ -1,4 +1,4 @@
-name: test Docs
+name: validate Docs
 
 on:  # Trigger the workflow on push or pull request
   pull_request: {}
@@ -71,7 +71,7 @@ jobs:
         working-directory: ./docs
         run: |
           # First run the same pipeline as Read-The-Docs
-          make html --debug --jobs $(nproc) SPHINXOPTS="-W --keep-going"
+          make html --debug --jobs $(nproc) SPHINXOPTS="-W --keep-going" -b linkcheck
 
       - name: Upload built docs
         uses: actions/upload-artifact@v2

course_UvA-DL/01-introduction-to-pytorch/.meta.yml

@@ -1,7 +1,7 @@
 title: "Tutorial 1: Introduction to PyTorch"
 author: Phillip Lippe
 created: 2021-08-27
-updated: 2021-08-27
+updated: 2021-11-29
 license: CC BY-SA
 description: |
   This tutorial will give a short introduction to PyTorch basics, and get you setup for writing your own neural networks.

course_UvA-DL/01-introduction-to-pytorch/Introduction_to_PyTorch.py

@@ -4,7 +4,7 @@
 # The following notebook is meant to give a short introduction to PyTorch basics, and get you setup for writing your own neural networks.
 # PyTorch is an open source machine learning framework that allows you to write your own neural networks and optimize them efficiently.
 # However, PyTorch is not the only framework of its kind.
-# Alternatives to PyTorch include [TensorFlow](https://www.tensorflow.org/), [JAX](https://github.com/google/jax#quickstart-colab-in-the-cloud) and [Caffe](http://caffe.berkeleyvision.org/).
+# Alternatives to PyTorch include [TensorFlow](https://www.tensorflow.org/), [JAX](https://github.com/google/jax) and [Caffe](http://caffe.berkeleyvision.org/).
 # We choose to teach PyTorch at the University of Amsterdam because it is well established, has a huge developer community (originally developed by Facebook), is very flexible and especially used in research.
 # Many current papers publish their code in PyTorch, and thus it is good to be familiar with PyTorch as well.
 # Meanwhile, TensorFlow (developed by Google) is usually known for being a production-grade deep learning library.

course_UvA-DL/03-initialization-and-optimization/.meta.yml

@@ -1,7 +1,7 @@
 title: "Tutorial 3: Initialization and Optimization"
 author: Phillip Lippe
 created: 2021-08-27
-updated: 2021-08-27
+updated: 2021-11-29
 license: CC BY-SA
 tags:
   - Image

course_UvA-DL/03-initialization-and-optimization/Initialization_and_Optimization.py

@@ -323,7 +323,7 @@ def visualize_activations(model, color="C0", print_variance=False):
 # %% [markdown]
 # ## Initialization
 #
-# Before starting our discussion about initialization, it should be noted that there exist many very good blog posts about the topic of neural network initialization (for example [deeplearning.ai](https://www.deeplearning.ai/ai-notes/initialization/), or a more [math-focused blog post](https://pouannes.github.io/blog/initialization/#mjx-eqn-eqfwd_K)).
+# Before starting our discussion about initialization, it should be noted that there exist many very good blog posts about the topic of neural network initialization (for example [deeplearning.ai](https://www.deeplearning.ai/ai-notes/initialization/), or a more [math-focused blog post](https://pouannes.github.io/blog/initialization)).
 # In case something remains unclear after this tutorial, we recommend skimming through these blog posts as well.
 #
 # When initializing a neural network, there are a few properties we would like to have.
@@ -457,7 +457,7 @@ def equal_var_init(model):
 # Besides the variance of the activations, another variance we would like to stabilize is the one of the gradients.
 # This ensures a stable optimization for deep networks.
 # It turns out that we can do the same calculation as above starting from $\Delta x=W\Delta y$, and come to the conclusion that we should initialize our layers with $1/d_y$ where $d_y$ is the number of output neurons.
-# You can do the calculation as a practice, or check a thorough explanation in [this blog post](https://pouannes.github.io/blog/initialization/#mjx-eqn-eqfwd_K).
+# You can do the calculation as a practice, or check a thorough explanation in [this blog post](https://pouannes.github.io/blog/initialization).
 # As a compromise between both constraints, [Glorot and Bengio (2010)](http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf?hc_location=ufi) proposed to use the harmonic mean of both values.
 # This leads us to the well-known Xavier initialization:
 #

course_UvA-DL/04-inception-resnet-densenet/.meta.yaml

@@ -1,7 +1,7 @@
 title: "Tutorial 4: Inception, ResNet and DenseNet"
 author: Phillip Lippe
 created: 2021-08-27
-updated: 2021-08-27
+updated: 2021-11-29
 license: CC BY-SA
 tags:
   - Image

course_UvA-DL/04-inception-resnet-densenet/Inception_ResNet_DenseNet.py

@@ -208,7 +208,7 @@
 # 5. Test loop (`test_step`) which is the same as validation, only on a test set.
 #
 # Therefore, we don't abstract the PyTorch code, but rather organize it and define some default operations that are commonly used.
-# If you need to change something else in your training/validation/test loop, there are many possible functions you can overwrite (see the [docs](https://pytorch-lightning.readthedocs.io/en/stable/lightning_module.html) for details).
+# If you need to change something else in your training/validation/test loop, there are many possible functions you can overwrite (see the [docs](https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html) for details).
 #
 # Now we can look at an example of how a Lightning Module for training a CNN looks like:
 
@@ -322,7 +322,7 @@ def create_model(model_name, model_hparams):
 # Besides the Lightning module, the second most important module in PyTorch Lightning is the `Trainer`.
 # The trainer is responsible to execute the training steps defined in the Lightning module and completes the framework.
 # Similar to the Lightning module, you can override any key part that you don't want to be automated, but the default settings are often the best practice to do.
-# For a full overview, see the [documentation](https://pytorch-lightning.readthedocs.io/en/stable/trainer.html).
+# For a full overview, see the [documentation](https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html).
 # The most important functions we use below are:
 #
 # * `trainer.fit`: Takes as input a lightning module, a training dataset, and an (optional) validation dataset.
@@ -764,7 +764,7 @@ def forward(self, x):
 #
 # The three groups operate on the resolutions $32\times32$, $16\times16$ and $8\times8$ respectively.
 # The blocks in orange denote ResNet blocks with downsampling.
-# The same notation is used by many other implementations such as in the [torchvision library](https://pytorch.org/docs/stable/_modules/torchvision/models/resnet.html#resnet18) from PyTorch.
+# The same notation is used by many other implementations such as in the [torchvision library](https://pytorch.org/vision/0.11/models.html#torchvision.models.resnet18) from PyTorch.
 # Thus, our code looks as follows: