Merge branch 'main' into seemethere/use_index_in_requirements

Author: Svetlana Karslioglu

beginner_source/colab.rst

@@ -1,5 +1,30 @@
+Running Tutorials in Google Colab
+=================================
+
+When you run a tutorial in Google Colab, there might be additional
+requirements and dependencies that you need to meet in order
+for the tutorial to work properly. This section contains notes on how to
+configure various settings in order to successfully
+run PyTorch tutorials in Google Colab.
+
+PyTorch Version in Google Colab
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+When you are running a tutorial that requires a version of PyTorch that has
+just been released, that version might not be yet available in Google Colab.
+To check that you have the required ``torch`` and compatible domain libraries
+installed, run ``!pip list``.
+
+If the installed version of PyTorch is lower than required,
+uninstall it and reinstall again by running the following commands:
+
+.. code-block:: python
+
+   !pip3 uninstall --yes torch torchaudio torchvision torchtext torchdata
+   !pip3 install torch torchaudio torchvision torchtext torchdata
+
 Using Tutorial Data from Google Drive in Colab
-==============================================
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 We've added a new feature to tutorials that allows users to open the
 notebook associated with a tutorial in Google Colab. You may need to

beginner_source/new-release-colab.rst

@@ -18,8 +18,9 @@
 # 3. Read in the CNNDM, IMDB, and Multi30k datasets and pre-process their texts in preparation for the model
 # 4. Perform text summarization, sentiment classification, and translation
 #
+# .. note::
+#    This tutorial requires PyTorch 2.0.0 or later.
 #
-
 #######################################################################
 # Data Transformation
 # -------------------
@@ -34,8 +35,7 @@
 #
 # T5 uses a SentencePiece model for text tokenization. Below, we use a pre-trained SentencePiece model to build
 # the text pre-processing pipeline using torchtext's T5Transform. Note that the transform supports both
-# batched and non-batched text input (for example, one can either pass a single sentence or a list of sentences), however
-# the T5 model expects the input to be batched.
+# batched and non-batched text input (for example, one can either pass a single sentence or a list of sentences), however the T5 model expects the input to be batched.
 #
 
 from torchtext.models import T5Transform

beginner_source/t5_tutorial.py

@@ -91,7 +91,10 @@
     'gallery_dirs': ['beginner', 'intermediate', 'advanced', 'recipes', 'prototype'],
     'filename_pattern': re.compile(SPHINX_SHOULD_RUN),
     'promote_jupyter_magic': True,
-    'backreferences_dir': None
+    'backreferences_dir': None,
+    'first_notebook_cell': ("# For tips on running notebooks in Google Colab, see\n"
+                            "# https://pytorch.org/tutorials/beginner/colab\n"
+                            "%matplotlib inline")
 }
 
 if os.getenv('GALLERY_PATTERN'):

conf.py

@@ -17,8 +17,9 @@
 
 Let's demonstrate how to do this using an ensemble of simple MLPs.
 
-.. include:: ../beginner_source/new-release-colab.rst
+.. note::
 
+   This tutorial requires PyTorch 2.0.0 or later.
 """
 
 import torch

intermediate_source/ensembling.py

@@ -12,7 +12,9 @@
 provides ways of computing various higher-order autodiff quantities
 efficiently.
 
-.. include:: ../beginner_source/new-release-colab.rst
+.. note::
+
+   This tutorial requires PyTorch 2.0.0 or later.
 
 Computing the Jacobian
 ----------------------

intermediate_source/jacobians_hessians.py

@@ -11,7 +11,9 @@
 demonstrates how to easily compute this quantity using ``torch.func``,
 composable function transforms for PyTorch.
 
-.. include:: ../beginner_source/new-release-colab.rst
+.. note::
+
+   This tutorial requires PyTorch 2.0.0 or later.
 
 Setup
 -----

intermediate_source/neural_tangent_kernels.py

@@ -10,7 +10,9 @@
 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
+.. note::
+
+   This tutorial requires PyTorch 2.0.0 or later.
 
 """
 

intermediate_source/per_sample_grads.py

@@ -35,8 +35,10 @@
 # * `Memory-Efficient Attention <https://github.com/facebookresearch/xformers>`__
 # * A PyTorch implementation defined in C++
 #
-# .. literalinclude:: ../beginner_source/new-release-colab.rst
-#    :language: rst
+# .. note::
+#
+#   This tutorial requires PyTorch 2.0.0 or later.
+#
 
 import torch
 import torch.nn as nn

intermediate_source/scaled_dot_product_attention_tutorial.py

@@ -36,8 +36,6 @@
 #
 # Note: a modern NVIDIA GPU (Volta or Ampere) is recommended for this tutorial.
 #
-# .. include:: ../beginner_source/new-release-colab.rst
-#
 
 ######################################################################
 # Basic Usage

intermediate_source/torch_compile_tutorial.py

@@ -7,6 +7,9 @@
 Typically, to do this you might have used if-statements and ``cuda()`` calls
 to do this:
 
+.. note::
+   This recipe requires PyTorch 2.0.0 or later.
+
 """
 import torch