Add a note on running in colab

beginner_source/new-release-colab.rst

@@ -0,0 +1,16 @@
+.. _new-release_colab::
+
+Notes for Running in Colab
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. note::
+   This tutorial requires PyTorch 2.0.0 or later. If you are running this
+   in Google Colab, verify that you have the required ``torch`` and
+   compatible domain libraties installed by running ``!pip list``.
+   If the installed version of PyTorch is lower than required,
+   unistall it and reinstall again by running the following commands:
+
+   .. code-block:: python
+
+      !pip3 uninstall --yes torch torchaudio torchvideo torchdata
+      !pip3 install torch torchaudio torchvideo torchdata

intermediate_source/ensembling.py

@@ -16,6 +16,9 @@
 for-loops and speeding them up through vectorization.
 
 Let's demonstrate how to do this using an ensemble of simple MLPs.
+
+.. include:: ../beginner_source/new-release-colab.rst
+
 """
 
 import torch

intermediate_source/jacobians_hessians.py

@@ -12,6 +12,8 @@
 provides ways of computing various higher-order autodiff quantities
 efficiently.
 
+.. include:: ../beginner_source/new-release-colab.rst
+
 Computing the Jacobian
 ----------------------
 """

intermediate_source/neural_tangent_kernels.py

@@ -11,6 +11,8 @@
 demonstrates how to easily compute this quantity using ``torch.func``,
 composable function transforms for PyTorch.
 
+.. include:: ../beginner_source/new-release-colab.rst
+
 Setup
 -----
 

intermediate_source/per_sample_grads.py

@@ -9,6 +9,9 @@
 Per-sample-gradient computation is computing the gradient for each and every
 sample in a batch of data. It is a useful quantity in differential privacy,
 meta-learning, and optimization research.
+
+.. include:: ../beginner_source/new-release-colab.rst
+
 """
 
 import torch

intermediate_source/torch_compile_tutorial.py

@@ -35,6 +35,9 @@
 # - ``tabulate``
 #
 # Note: a modern NVIDIA GPU (Volta or Ampere) is recommended for this tutorial.
+#
+# .. include:: ../beginner_source/new-release-colab.rst
+#
 
 ######################################################################
 # Basic Usage