Remove devcontainer

beginner_source/quickstart/autograd_tutorial.py

@@ -240,3 +240,4 @@
 # Further Reading
 # ~~~~~~~~~~~~~~~~~
 # - `Autograd Mechanics <https://pytorch.org/docs/stable/notes/autograd.html>`_
+

beginner_source/quickstart/buildmodel_tutorial.py

@@ -140,6 +140,7 @@ def forward(self, x):
 print(f"After ReLU: {hidden1}")
 
 
+
 #################################################
 # nn.Sequential
 # ^^^^^^^^^^^^^^^^^^^^^^
@@ -179,6 +180,7 @@ def forward(self, x):
 # In this example, we iterate over each parameter, and print its size and a preview of its values. 
 #
 
+
 print("Model structure: ", model, "\n\n")
 
 for name, param in model.named_parameters():
@@ -194,3 +196,4 @@ def forward(self, x):
 # - `torch.nn API <https://pytorch.org/docs/stable/nn.html>`_
 
 
+

beginner_source/quickstart/quickstart_tutorial.py

@@ -28,6 +28,7 @@
 
 `This tutorial assumes a basic familiarity with Python and Deep Learning concepts.`
 
+
 Running the Tutorial Code
 ------------------
 You can run this tutorial in a couple of ways:
@@ -168,6 +169,7 @@ def forward(self, x):
 # --------------
 #
 
+
 #####################################################################
 # Optimizing the Model Parameters
 # ----------------------------------------
@@ -177,6 +179,7 @@ def forward(self, x):
 loss_fn = nn.CrossEntropyLoss()
 optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
 
+
 ####################################################################### 
 # In a single training loop, the model makes predictions on the training dataset (fed to it in batches), and 
 # backpropagates the prediction error to adjust the model's parameters. 
@@ -245,6 +248,7 @@ def test(dataloader, model):
 print("Saved PyTorch Model State to model.pth")
 
 
+
 ######################################################################
 # Loading Models
 # ----------------------------
@@ -277,8 +281,10 @@ def test(dataloader, model):
     pred = model(x)
     predicted, actual = classes[pred[0].argmax(0)], classes[y]
     print(f'Predicted: "{predicted}", Actual: "{actual}"')
+
 
 ######################################################################
 # Read more about `Saving & Loading your model <saveloadrun_tutorial.html>`_.
 #
 
+

beginner_source/quickstart/tensor_tutorial.py

@@ -136,6 +136,7 @@
 t1 = torch.cat([tensor, tensor, tensor], dim=1)
 print(t1)
 
+
 ######################################################################
 # **Arithmetic operations**
 

index.rst

@@ -8,12 +8,11 @@ Welcome to PyTorch Tutorials
 
 .. Add callout items below this line
 
-
 .. customcalloutitem::
-   :description: Familiarize yourself with PyTorch concepts and modules. Learn how to load data, build deep neural networks, train and save your models in this quickstart guide. 
-   :header: Learn the Basics
-   :button_link:  beginner/quickstart/quickstart_tutorial.html
-   :button_text: Get started with PyTorch
+   :description: The 60 min blitz is the most common starting point and provides a broad view on how to use PyTorch. It covers the basics all the way to constructing deep neural networks.
+   :header: New to PyTorch?
+   :button_link: beginner/deep_learning_60min_blitz.html
+   :button_text: Start 60-min blitz
 
 .. customcalloutitem::
    :description: Bite-size, ready-to-deploy PyTorch code examples.
@@ -52,10 +51,10 @@ Welcome to PyTorch Tutorials
 .. Learning PyTorch
 
 .. customcarditem::
-   :header: Learn the Basics
-   :card_description: A step-by-step guide to building a complete ML workflow with PyTorch.
+   :header: Deep Learning with PyTorch: A 60 Minute Blitz
+   :card_description: Understand PyTorch’s Tensor library and neural networks at a high level.
    :image: _static/img/thumbnails/cropped/60-min-blitz.png
-   :link: beginner/quickstart/quickstart_tutorial.html
+   :link: beginner/deep_learning_60min_blitz.html
    :tags: Getting-Started
 
 .. customcarditem::
@@ -166,7 +165,7 @@ Welcome to PyTorch Tutorials
    :header: Language Translation with Torchtext
    :card_description: Use torchtext to reprocess data from a well-known datasets containing both English and German. Then use it to train a sequence-to-sequence model.
    :image: _static/img/thumbnails/cropped/Language-Translation-with-TorchText.png
-   :link: beginner/torchtext_translation_tutorial.html
+   :link: beginner/torchtext_translation.html
    :tags: Text
 
 .. Reinforcement Learning
@@ -446,7 +445,6 @@ Additional Resources
    :includehidden:
    :caption: Learning PyTorch
 
-   beginner/quickstart/quickstart_tutorial
    beginner/deep_learning_60min_blitz
    beginner/pytorch_with_examples
    beginner/nn_tutorial
@@ -484,7 +482,7 @@ Additional Resources
    intermediate/char_rnn_generation_tutorial
    intermediate/seq2seq_translation_tutorial
    beginner/text_sentiment_ngrams_tutorial
-   beginner/torchtext_translation_tutorial
+   beginner/torchtext_translation
 
 
 .. toctree::
@@ -551,4 +549,4 @@ Additional Resources
    intermediate/rpc_param_server_tutorial
    intermediate/dist_pipeline_parallel_tutorial
    intermediate/rpc_async_execution
-   advanced/rpc_ddp_tutorial
+   advanced/rpc_ddp_tutorial