Merge branch 'master' into jingxu10

Author: brianjo

_static/images/microsoft-logo.svg

@@ -3,6 +3,43 @@
 {% block footer %}
 {{ super() }}
 <script>
+
+  
+//add microsoft link
+
+if(window.location.href.indexOf("/beginner/basics/")!= -1)
+{
+  var url="https://docs.microsoft.com/learn/paths/pytorch-fundamentals/?wt.mc_id=aiml-7486-cxa";
+  switch(window.location.pathname.split("/").pop().replace('.html',''))
+  {
+    case"quickstart_tutorial":
+      url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/9-quickstart?WT.mc_id=aiml-7486-cxa";
+      break;
+    case"tensorqs_tutorial":
+      url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/2-tensors?WT.mc_id=aiml-7486-cxa";
+      break;
+    case"data_tutorial":
+      url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/3-data?WT.mc_id=aiml-7486-cxa";
+      break;
+    case"transforms_tutorial":
+      url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/4-transforms?WT.mc_id=aiml-7486-cxa";
+      break;
+    case"buildmodel_tutorial":
+      url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/5-model?WT.mc_id=aiml-7486-cxa";
+      break;
+    case"autogradqs_tutorial":
+      url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/6-autograd?WT.mc_id=aiml-7486-cxa";
+      break;
+    case"optimization_tutorial":
+      url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/7-optimization?WT.mc_id=aiml-7486-cxa";
+      break;
+    case"saveloadrun_tutorial":
+      url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/8-inference?WT.mc_id=aiml-7486-cxa";
+    }
+    
+    $(".pytorch-call-to-action-links").children().first().before("<a href="+url+' data-behavior="call-to-action-event" data-response="Run in Microsoft Learn" target="_blank"><div id="microsoft-learn-link" style="padding-bottom: 0.625rem;border-bottom: 1px solid #f3f4f7;padding-right: 2.5rem;display: -webkit-box;  display: -ms-flexbox; isplay: flex; -webkit-box-align: center;-ms-flex-align: center;align-items: center;"><img class="call-to-action-img" src="../../_static/images/microsoft-logo.svg"/><div class="call-to-action-desktop-view">Run in Microsoft Learn</div><div class="call-to-action-mobile-view">Learn</div></div></a>')
+  }
+
   !function(f,b,e,v,n,t,s)
   {if(f.fbq)return;n=f.fbq=function(){n.callMethod?
   n.callMethod.apply(n,arguments):n.queue.push(arguments)};
@@ -46,6 +83,8 @@
      $(".rating-container").hide();
      $(".hr-bottom").hide();
    }
+
+
 </script>
 
 <noscript>

_templates/layout.html

@@ -35,7 +35,7 @@
 # -------------------
 #
 # Here is an example of how to load the `Fashion-MNIST <https://research.zalando.com/project/fashion_mnist/fashion_mnist/>`_ dataset from TorchVision.
-# Fashion-MNIST is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples.
+# Fashion-MNIST is a dataset of Zalando’s article images consisting of 60,000 training examples and 10,000 test examples.
 # Each example comprises a 28×28 grayscale image and an associated label from one of 10 classes.
 #
 # We load the `FashionMNIST Dataset <https://pytorch.org/vision/stable/datasets.html#fashion-mnist>`_ with the following parameters:
@@ -140,8 +140,7 @@ def __getitem__(self, idx):
             image = self.transform(image)
         if self.target_transform:
             label = self.target_transform(label)
-        sample = {"image": image, "label": label}
-        return sample
+        return image, label
 
 
 #################################################################
@@ -187,7 +186,7 @@ def __len__(self):
 # The __getitem__ function loads and returns a sample from the dataset at the given index ``idx``. 
 # Based on the index, it identifies the image's location on disk, converts that to a tensor using ``read_image``, retrieves the 
 # corresponding label from the csv data in ``self.img_labels``, calls the transform functions on them (if applicable), and returns the 
-# tensor image and corresponding label in a Python dict.
+# tensor image and corresponding label in a tuple.
 
 def __getitem__(self, idx):
     img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
@@ -197,8 +196,7 @@ def __getitem__(self, idx):
         image = self.transform(image)
     if self.target_transform:
         label = self.target_transform(label)
-    sample = {"image": image, "label": label}
-    return sample
+    return image, label
 
 
 ######################################################################

beginner_source/basics/data_tutorial.py

@@ -34,7 +34,7 @@
 ------------------
 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 Colab link at the top, which opens a notebook with the code in a fully-hosted environment. Pro tip: Use Colab with a GPU runtime to speed up operations *Runtime > Change runtime type > GPU*
+- **In the cloud**: This is the easiest way to get started! Each section has a "Run in Microsoft Learn" link at the top, which opens an integrated notebook in Microsoft Learn 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.
 
 

beginner_source/basics/intro.py

@@ -1207,7 +1207,7 @@ def evaluate(encoder, decoder, searcher, voc, sentence, max_length=MAX_LENGTH):
     input_batch = torch.LongTensor(indexes_batch).transpose(0, 1)
     # Use appropriate device
     input_batch = input_batch.to(device)
-    lengths = lengths.to(device)
+    lengths = lengths.to("cpu")
     # Decode sentence with searcher
     tokens, scores = searcher(input_batch, lengths, max_length)
     # indexes -> words

beginner_source/chatbot_tutorial.py

@@ -1,10 +1,5 @@
 Welcome to PyTorch Tutorials
 ============================
-.. raw:: html
-
-    <p>Share your thoughts. Take our short <a href=https://forms.gle/ekTZY4uSJaw5Vr849 target="_blank">PyTorch Tutorials reader survey</a>. </p>
-
-
 .. raw:: html
 
     <div class="tutorials-callout-container">

index.rst

@@ -207,7 +207,7 @@ to obtain the sum of all tensors at all processes, we can use the
 
     """ All-Reduce example."""
     def run(rank, size):
-        """ Simple point-to-point communication. """
+        """ Simple collective communication. """
         group = dist.new_group([0, 1])
         tensor = torch.ones(1)
         dist.all_reduce(tensor, op=dist.ReduceOp.SUM, group=group)

intermediate_source/dist_tuto.rst

@@ -139,7 +139,7 @@
 # the networks later. To keep track of all this we will use a helper class
 # called ``Lang`` which has word → index (``word2index``) and index → word
 # (``index2word``) dictionaries, as well as a count of each word
-# ``word2count`` to use to later replace rare words.
+# ``word2count`` which will be used to replace rare words later.
 #
 
 SOS_token = 0