Merge branch 'main' into mlazos/log-recipe

Author: svekars

.pyspelling.yml

@@ -100,7 +100,7 @@ matrix:
         content: '''''*'
         close: '$'
       # Ignore reStructuredText block directives
-      - open: '\.\. (code-block|math)::.*$\n*'
+      - open: '\.\. (code-block|math|table)::.*$\n*'
         content: '(?P<first>(^(?P<indent>[ ]+).*$\n))(?P<other>(^([ \t]+.*|[ \t]*)$\n)*)'
         close: '(^(?![ \t]+.*$))'
       - open: '\.\. (raw)::.*$\n*'

README.md

@@ -0,0 +1,19 @@
+/* Survey banner .css */
+
+.survey-banner {
+  margin-top: 10px;
+  background-color: #f3f4f7;
+  padding-top: 15px;
+  padding-left: 10px;
+  padding-bottom: 1px;
+}
+
+@media screen and (max-width: 600px) {
+  .survey-banner {
+    padding-top: 5px;
+    padding-left: 5px;
+    padding-bottom: -1px;
+    font-size: 12px;
+    margin-bottom: 5px;
+  }
+}

_static/css/custom2.css

@@ -1,6 +1,5 @@
 {% extends "!layout.html" %}
 
-
 {%- block content %}
 {{ super() }}
 <script>

_static/img/hta/comm_across_ranks.png

@@ -439,7 +439,7 @@ def evaluate(eval_model, data_source):
 
 ######################################################################
 # Evaluate the model with the test dataset
-# -------------------------------------
+# ----------------------------------------
 #
 # Apply the best model to check the result with the test dataset.
 

_static/img/hta/counts_diff.png

@@ -157,7 +157,7 @@
 
 ######################################################################
 # Use USB to Train ``SoftMatch`` with specific imbalanced algorithm on imbalanced CIFAR-10
-# ------------------------------------------------------------------------------------
+# ----------------------------------------------------------------------------------------
 # 
 # Now let's say we have imbalanced labeled set and unlabeled set of CIFAR-10,
 # and we want to train a ``SoftMatch`` model on it.

_static/img/hta/cuda_kernel_launch.png

@@ -10,7 +10,7 @@
 `Save & Load Model <saveloadrun_tutorial.html>`_
 
 Automatic Differentiation with ``torch.autograd``
-=======================================
+=================================================
 
 When training neural networks, the most frequently used algorithm is
 **back propagation**. In this algorithm, parameters (model weights) are
@@ -170,7 +170,7 @@
 
 ######################################################################
 # Optional Reading: Tensor Gradients and Jacobian Products
-# --------------------------------------
+# --------------------------------------------------------
 #
 # In many cases, we have a scalar loss function, and we need to compute
 # the gradient with respect to some parameters. However, there are cases

_static/img/hta/cuda_kernel_launch_stats.png

@@ -10,7 +10,7 @@
 `Save & Load Model <saveloadrun_tutorial.html>`_
 
 Build the Neural Network
-===================
+========================
 
 Neural networks comprise of layers/modules that perform operations on data.
 The `torch.nn <https://pytorch.org/docs/stable/nn.html>`_ namespace provides all the building blocks you need to
@@ -197,5 +197,5 @@ def forward(self, x):
 
 #################################################################
 # Further Reading
-# --------------
+# -----------------
 # - `torch.nn API <https://pytorch.org/docs/stable/nn.html>`_

_static/img/hta/duration_diff.png

@@ -10,7 +10,7 @@
 `Save & Load Model <saveloadrun_tutorial.html>`_
 
 Datasets & DataLoaders
-===================
+======================
 
 """
 
@@ -69,7 +69,7 @@
 
 #################################################################
 # Iterating and Visualizing the Dataset
-# -----------------
+# -------------------------------------
 #
 # We can index ``Datasets`` manually like a list: ``training_data[index]``.
 # We use ``matplotlib`` to visualize some samples in our training data.
@@ -144,7 +144,7 @@ def __getitem__(self, idx):
 
 
 #################################################################
-# __init__
+# ``__init__``
 # ^^^^^^^^^^^^^^^^^^^^
 #
 # The __init__ function is run once when instantiating the Dataset object. We initialize
@@ -167,7 +167,7 @@ def __init__(self, annotations_file, img_dir, transform=None, target_transform=N
 
 
 #################################################################
-# __len__
+# ``__len__``
 # ^^^^^^^^^^^^^^^^^^^^
 #
 # The __len__ function returns the number of samples in our dataset.
@@ -180,7 +180,7 @@ def __len__(self):
 
 
 #################################################################
-# __getitem__
+# ``__getitem__``
 # ^^^^^^^^^^^^^^^^^^^^
 #
 # The __getitem__ function loads and returns a sample from the dataset at the given index ``idx``.
@@ -220,7 +220,7 @@ def __getitem__(self, idx):
 
 ###########################
 # Iterate through the DataLoader
-# --------------------------
+# -------------------------------
 #
 # We have loaded that dataset into the ``DataLoader`` and can iterate through the dataset as needed.
 # Each iteration below returns a batch of ``train_features`` and ``train_labels`` (containing ``batch_size=64`` features and labels respectively).
@@ -243,5 +243,5 @@ def __getitem__(self, idx):
 
 #################################################################
 # Further Reading
-# --------------
+# ----------------
 # - `torch.utils.data API <https://pytorch.org/docs/stable/data.html>`_

_static/img/hta/idle_time.png

@@ -31,15 +31,15 @@
 
 
 Running the Tutorial Code
-------------------
+-------------------------
 You can run this tutorial in a couple of ways:
 
 - **In the cloud**: This is the easiest way to get started! Each section has a "Run in Microsoft Learn" and "Run in Google Colab" link at the top, which opens an integrated notebook in Microsoft Learn or Google Colab, respectively, with the code in a fully-hosted environment.
 - **Locally**: This option requires you to setup PyTorch and TorchVision first on your local machine (`installation instructions <https://pytorch.org/get-started/locally/>`_). Download the notebook or copy the code into your favorite IDE.
 
 
 How to Use this Guide
------------------
+---------------------
 If you're familiar with other deep learning frameworks, check out the `0. Quickstart <quickstart_tutorial.html>`_ first
 to quickly familiarize yourself with PyTorch's API.
 

_static/img/hta/idle_time_breakdown_percentage.png

@@ -80,7 +80,7 @@
 
 ######################################################################
 # Attributes of a Tensor
-# ~~~~~~~~~~~~~~~~~
+# ~~~~~~~~~~~~~~~~~~~~~~
 #
 # Tensor attributes describe their shape, datatype, and the device on which they are stored.
 
@@ -97,7 +97,7 @@
 
 ######################################################################
 # Operations on Tensors
-# ~~~~~~~~~~~~~~~~~
+# ~~~~~~~~~~~~~~~~~~~~~~~
 #
 # Over 100 tensor operations, including arithmetic, linear algebra, matrix manipulation (transposing,
 # indexing, slicing), sampling and more are

_static/img/hta/idle_time_summary.png

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 """
 A Gentle Introduction to ``torch.autograd``
----------------------------------
+===========================================
 
 ``torch.autograd`` is PyTorch’s automatic differentiation engine that powers
 neural network training. In this section, you will get a conceptual
@@ -149,7 +149,7 @@
 
 ######################################################################
 # Optional Reading - Vector Calculus using ``autograd``
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 #
 # Mathematically, if you have a vector valued function
 # :math:`\vec{y}=f(\vec{x})`, then the gradient of :math:`\vec{y}` with

_static/img/hta/kernel_metrics_df.png

@@ -115,7 +115,7 @@ def imshow(img):
 
 ########################################################################
 # 2. Define a Convolutional Neural Network
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 # Copy the neural network from the Neural Networks section before and modify it to
 # take 3-channel images (instead of 1-channel images as it was defined).
 

_static/img/hta/kernel_type_breakdown.png

@@ -1,6 +1,6 @@
 """
 Tensors
---------------------------------------------
+========
 
 Tensors are a specialized data structure that are very similar to arrays
 and matrices. In PyTorch, we use tensors to encode the inputs and