Brianjo torchrec update (#1838)

* Update torchrec_tutorial.rst

Some formatting fixes.

* Update index.rst

* Add files via upload

adds torchrec thumbnail.

* Add files via upload

* Add files via upload

* Update index.rst

* Delete 60-min-blitz.png

* Update torchrec_tutorial.rst

* Update torchrec_tutorial.rst

Author: brianjo

_static/img/thumbnails/torchrec.png

@@ -575,6 +575,15 @@ Welcome to PyTorch Tutorials
    :link: beginner/deeplabv3_on_android.html
    :tags: Mobile
 
+.. Recommendation Systems
+
+.. customcarditem::
+   :header: Introduction to TorchRec
+   :card_description: TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems.
+   :image: _static/img/thumbnails/torchrec.png
+   :link: intermediate/torchrec_tutorial.html
+   :tags: TorchRec,Recommender
+
 .. End of tutorial card section
 
 .. raw:: html
@@ -832,3 +841,11 @@ Additional Resources
 
    beginner/deeplabv3_on_ios
    beginner/deeplabv3_on_android
+   
+.. toctree::
+   :maxdepth: 2
+   :includehidden:
+   :hidden:
+   :caption: Recommendation Systems
+
+   intermediate/torchrec_tutorial

index.rst

@@ -1,5 +1,5 @@
-Introduction to Torchrec
-====================================================
+Introduction to TorchRec
+========================
 
 .. tip::
    To get the most of this tutorial, we suggest using this
@@ -12,34 +12,34 @@ AI’s `Deep learning recommendation
 model <https://arxiv.org/abs/1906.00091>`__, or DLRM. As the number of
 entities grow, the size of the embedding tables can exceed a single
 GPU’s memory. A common practice is to shard the embedding table across
-devices, a type of model parallelism. To that end, **torchRec introduces
+devices, a type of model parallelism. To that end, TorchRec introduces
 its primary API
-called** |DistributedModelParallel|_ **,
-or DMP. Like pytorch’s DistributedDataParallel, DMP wraps a model to
-enable distributed training.**
+called |DistributedModelParallel|_ ,
+or DMP. Like PyTorch’s DistributedDataParallel, DMP wraps a model to
+enable distributed training.
 
-**Installation**
---------------------
+Installation
+------------
 
 Requirements:
 - python >= 3.7
 
-We highly recommend CUDA when using torchRec. If using CUDA:
+We highly recommend CUDA when using TorchRec. If using CUDA:
 - cuda >= 11.0
 
 
 .. code:: shell
 
     # install pytorch with cudatoolkit 11.3
     conda install pytorch cudatoolkit=11.3 -c pytorch-nightly -y
-    # install torchrec
+    # install TorchTec
     pip3 install torchrec-nightly
 
 
-**Overview**
-------------
+Overview
+--------
 
-This tutorial will cover three pieces of torchRec - the ``nn.module`` |EmbeddingBagCollection|_, the |DistributedModelParallel|_ API, and
+This tutorial will cover three pieces of TorchRec - the ``nn.module`` |EmbeddingBagCollection|_, the |DistributedModelParallel|_ API, and
 the datastructure |KeyedJaggedTensor|_.
 
 
@@ -75,7 +75,7 @@ GPU.
 From EmbeddingBag to EmbeddingBagCollection
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Pytorch represents embeddings through |torch.nn.Embedding|_ and |torch.nn.EmbeddingBag|_.
+PyTorch represents embeddings through |torch.nn.Embedding|_ and |torch.nn.EmbeddingBag|_.
 EmbeddingBag is a pooled version of Embedding.
 
 TorchRec extends these modules by creating collections of embeddings. We
@@ -121,7 +121,7 @@ Now, we’re ready to wrap our model with |DistributedModelParallel|_ (DMP). Ins
    embedding table on the appropriate device(s).
 
 In this toy example, since we have two EmbeddingTables and one GPU,
-torchRec will place both on the single GPU.
+TorchRec will place both on the single GPU.
 
 .. code:: python
 
@@ -161,7 +161,7 @@ Representing minibatches with KeyedJaggedTensor
 
 We need an efficient representation of multiple examples of an arbitrary
 number of entity IDs per feature per example. In order to enable this
-“jagged” representation, we use the torchRec datastructure
+“jagged” representation, we use the TorchRec datastructure
 |KeyedJaggedTensor|_ (KJT).
 
 Let’s take a look at **how to lookup a collection of two embedding