pytorch · svekars · Jul 10, 2024 · Jun 25, 2024 · Jul 10, 2024 · Jul 10, 2024
diff --git a/_static/css/custom.css b/_static/css/custom.css
@@ -71,3 +71,23 @@
 .sd-card:hover:after {
     transform: scaleX(1);
 }
+
+.card-prerequisites:hover {
+    transition: none;
+    border: none;
+}
+
+.card-prerequisites:hover:after {
+    transition: none;
+    transform: none;
+}
+
+.card-prerequisites:after {
+    display: block;
+    content: '';
+    border-bottom: none;
+    background-color: #fff;
+    transform: none;
+    transition: none;
+    transform-origin: none;
+}
diff --git a/advanced_source/cpp_custom_ops.rst b/advanced_source/cpp_custom_ops.rst
@@ -8,14 +8,16 @@ Custom C++ and CUDA Operators
 .. grid:: 2
 
     .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn
+       :class-card: card-prerequisites
 
-      * How to integrate custom operators written in C++/CUDA with PyTorch
-      * How to test custom operators using ``torch.library.opcheck``
+       * How to integrate custom operators written in C++/CUDA with PyTorch
+       * How to test custom operators using ``torch.library.opcheck``
 
     .. grid-item-card:: :octicon:`list-unordered;1em;` Prerequisites
+       :class-card: card-prerequisites
 
-      * PyTorch 2.4 or later
-      * Basic understanding of C++ and CUDA programming
+       * PyTorch 2.4 or later
+       * Basic understanding of C++ and CUDA programming
 
 PyTorch offers a large library of operators that work on Tensors (e.g. torch.add, torch.sum, etc).
 However, you may wish to bring a new custom operator to PyTorch. This tutorial demonstrates the

diff --git a/advanced_source/python_custom_ops.py b/advanced_source/python_custom_ops.py
@@ -9,13 +9,15 @@
 .. grid:: 2
 
     .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn
+       :class-card: card-prerequisites
 
-      * How to integrate custom operators written in Python with PyTorch
-      * How to test custom operators using ``torch.library.opcheck``
+       * How to integrate custom operators written in Python with PyTorch
+       * How to test custom operators using ``torch.library.opcheck``
 
     .. grid-item-card:: :octicon:`list-unordered;1em;` Prerequisites
+       :class-card: card-prerequisites
 
-      * PyTorch 2.4 or later
+       * PyTorch 2.4 or later
 
 PyTorch offers a large library of operators that work on Tensors (e.g.
 ``torch.add``, ``torch.sum``, etc). However, you might wish to use a new customized

diff --git a/beginner_source/ddp_series_fault_tolerance.rst b/beginner_source/ddp_series_fault_tolerance.rst
@@ -14,11 +14,12 @@ Authors: `Suraj Subramanian <https://github.com/suraj813>`__
 .. grid:: 2
 
    .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn
+      :class-card: card-prerequisites
       :margin: 0
 
-      -  Launching multi-GPU training jobs with ``torchrun``
-      -  Saving and loading snapshots of your training job
-      -  Structuring your training script for graceful restarts
+      - Launching multi-GPU training jobs with ``torchrun``
+      - Saving and loading snapshots of your training job
+      - Structuring your training script for graceful restarts
 
       .. grid:: 1
 
@@ -27,6 +28,7 @@ Authors: `Suraj Subramanian <https://github.com/suraj813>`__
             :octicon:`code-square;1.0em;` View the code used in this tutorial on `GitHub <https://github.com/pytorch/examples/blob/main/distributed/ddp-tutorial-series/multigpu_torchrun.py>`__
 
    .. grid-item-card:: :octicon:`list-unordered;1em;` Prerequisites
+      :class-card: card-prerequisites
       :margin: 0
 
       * High-level `overview <ddp_series_theory.html>`__ of DDP

diff --git a/beginner_source/ddp_series_multigpu.rst b/beginner_source/ddp_series_multigpu.rst
@@ -14,6 +14,7 @@ Authors: `Suraj Subramanian <https://github.com/suraj813>`__
 .. grid:: 2
 
    .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn
+      :class-card: card-prerequisites
 
       -  How to migrate a single-GPU training script to multi-GPU via DDP
       -  Setting up the distributed process group
@@ -26,6 +27,7 @@ Authors: `Suraj Subramanian <https://github.com/suraj813>`__
             :octicon:`code-square;1.0em;` View the code used in this tutorial on `GitHub <https://github.com/pytorch/examples/blob/main/distributed/ddp-tutorial-series/multigpu.py>`__
 
    .. grid-item-card:: :octicon:`list-unordered;1em;` Prerequisites
+      :class-card: card-prerequisites
 
       * High-level overview of `how DDP works  <ddp_series_theory.html>`__
       * A machine with multiple GPUs (this tutorial uses an AWS p3.8xlarge instance)

diff --git a/beginner_source/ddp_series_theory.rst b/beginner_source/ddp_series_theory.rst
@@ -12,13 +12,15 @@ Authors: `Suraj Subramanian <https://github.com/suraj813>`__
 .. grid:: 2
 
    .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn
+      :class-card: card-prerequisites
 
       *  How DDP works under the hood
       *  What is ``DistributedSampler``
       *  How gradients are synchronized across GPUs
 
 
    .. grid-item-card:: :octicon:`list-unordered;1em;` Prerequisites
+      :class-card: card-prerequisites
 
       * Familiarity with `basic non-distributed training  <https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html>`__ in PyTorch
 

diff --git a/beginner_source/template_tutorial.py b/beginner_source/template_tutorial.py
@@ -9,16 +9,18 @@
 .. grid:: 2
 
     .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn
+       :class-card: card-prerequisites
 
-      * Item 1
-      * Item 2
-      * Item 3
+       * Item 1
+       * Item 2
+       * Item 3
 
     .. grid-item-card:: :octicon:`list-unordered;1em;` Prerequisites
+       :class-card: card-prerequisites
 
-      * PyTorch v2.0.0
-      * GPU ???
-      * Other items 3
+       * PyTorch v2.0.0
+       * GPU ???
+       * Other items 3
 
 If you have a video, add it here like this:
 

diff --git a/intermediate_source/ddp_series_minGPT.rst b/intermediate_source/ddp_series_minGPT.rst
@@ -11,6 +11,7 @@ Authors: `Suraj Subramanian <https://github.com/suraj813>`__
 .. grid:: 2
 
    .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn
+      :class-card: card-prerequisites
 
       -  Best practices when writing a distributed training script
       -  Increased flexibility with saving/loading artifacts in the cloud
@@ -23,6 +24,7 @@ Authors: `Suraj Subramanian <https://github.com/suraj813>`__
             :octicon:`code-square;1.0em;` View the code used in this tutorial on `GitHub <https://github.com/pytorch/examples/tree/main/distributed/minGPT-ddp>`__
 
    .. grid-item-card:: :octicon:`list-unordered;1em;` Prerequisites
+      :class-card: card-prerequisites
 
       - Familiarity with `multi-GPU training <../beginner/ddp_series_multigpu.html>`__ and `torchrun <../beginner/ddp_series_fault_tolerance.html>`__ 
       - [Optional] Familiarity with `multinode training <ddp_series_multinode.html>`__

diff --git a/intermediate_source/ddp_series_multinode.rst b/intermediate_source/ddp_series_multinode.rst
@@ -11,6 +11,7 @@ Authors: `Suraj Subramanian <https://github.com/suraj813>`__
 .. grid:: 2
 
    .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn
+      :class-card: card-prerequisites
 
       -  Launching multinode training jobs with ``torchrun``
       -  Code changes (and things to keep in mind) when moving from single-node to multinode training.
@@ -22,6 +23,7 @@ Authors: `Suraj Subramanian <https://github.com/suraj813>`__
             :octicon:`code-square;1.0em;` View the code used in this tutorial on `GitHub <https://github.com/pytorch/examples/blob/main/distributed/ddp-tutorial-series/multinode.py>`__
 
    .. grid-item-card:: :octicon:`list-unordered;1em;` Prerequisites
+      :class-card: card-prerequisites
 
       -  Familiarity with `multi-GPU training <../beginner/ddp_series_multigpu.html>`__ and `torchrun <../beginner/ddp_series_fault_tolerance.html>`__ 
       -  2 or more TCP-reachable GPU machines (this tutorial uses AWS p3.2xlarge instances)

diff --git a/intermediate_source/dqn_with_rnn_tutorial.py b/intermediate_source/dqn_with_rnn_tutorial.py
@@ -9,15 +9,17 @@
 .. grid:: 2
 
     .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn
+       :class-card: card-prerequisites
 
-      * How to incorporating an RNN in an actor in TorchRL
-      * How to use that memory-based policy with a replay buffer and a loss module
+       * How to incorporating an RNN in an actor in TorchRL
+       * How to use that memory-based policy with a replay buffer and a loss module
 
     .. grid-item-card:: :octicon:`list-unordered;1em;` Prerequisites
+       :class-card: card-prerequisites
 
-      * PyTorch v2.0.0
-      * gym[mujoco]
-      * tqdm
+       * PyTorch v2.0.0
+       * gym[mujoco]
+       * tqdm
 """
 
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