FIX: Redirect old Loading data in PyTorch to newer Datasets & DataLoaders

.jenkins/validate_tutorials_built.py

@@ -37,7 +37,6 @@
     "prototype_source/nestedtensor",
     "recipes_source/recipes/saving_and_loading_models_for_inference",
     "recipes_source/recipes/saving_multiple_models_in_one_file",
-    "recipes_source/recipes/loading_data_recipe",
     "recipes_source/recipes/tensorboard_with_pytorch",
     "recipes_source/recipes/what_is_state_dict",
     "recipes_source/recipes/profiler_recipe",

recipes_source/loading_data_recipe.rst

@@ -0,0 +1,8 @@
+Loading data in PyTorch
+=======================
+
+The content is deprecated. See `Datasets & DataLoaders <https://pytorch.org/tutorials/beginner/basics/data_tutorial.html>`__ instead.
+
+.. raw:: html
+
+   <meta http-equiv="Refresh" content="1; url='https://pytorch.org/tutorials/beginner/basics/data_tutorial.html'" />

recipes_source/recipes/README.txt

@@ -1,62 +1,58 @@
 PyTorch Recipes
 ---------------------------------------------
-1. loading_data_recipe.py
-	 Loading Data in PyTorch
-         https://pytorch.org/tutorials/recipes/recipes/loading_data_recipe.html
-
-2. defining_a_neural_network.py
+1. defining_a_neural_network.py
 	 Defining a Neural Network in PyTorch
          https://pytorch.org/tutorials/recipes/recipes/defining_a_neural_network.html
 
-3. what_is_state_dict.py
+2. what_is_state_dict.py
 	 What is a state_dict in PyTorch
          https://pytorch.org/tutorials/recipes/recipes/what_is_state_dict.html
 
-4. saving_and_loading_models_for_inference.py
+3. saving_and_loading_models_for_inference.py
 	 Saving and loading models for inference in PyTorch
          https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_models_for_inference.html
 
-5. custom_dataset_transforms_loader.py 
+4. custom_dataset_transforms_loader.py
 	 Developing Custom PyTorch Dataloaders
          https://pytorch.org/tutorials/recipes/recipes/custom_dataset_transforms_loader.html
 
 
-6. Captum_Recipe.py 
+5. Captum_Recipe.py
 	 Model Interpretability using Captum
          https://pytorch.org/tutorials/recipes/recipes/Captum_Recipe.html
 
-7. dynamic_quantization.py
+6. dynamic_quantization.py
          Dynamic Quantization
          https://pytorch.org/tutorials/recipes/recipes/dynamic_quantization.html
 
-8. save_load_across_devices.py
+7. save_load_across_devices.py
          Saving and loading models across devices in PyTorch
          https://pytorch.org/tutorials/recipes/recipes/save_load_across_devices.html
 
-9. saving_and_loading_a_general_checkpoint.py
+8. saving_and_loading_a_general_checkpoint.py
          Saving and loading a general checkpoint in PyTorch
          https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_a_general_checkpoint.html
 
-10. saving_and_loading_models_for_inference.py
+9. saving_and_loading_models_for_inference.py
          Saving and loading models for inference in PyTorch
          https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_models_for_inference.html
 
-11. saving_multiple_models_in_one_file.py
+10. saving_multiple_models_in_one_file.py
          Saving and loading multiple models in one file using PyTorch
          https://pytorch.org/tutorials/recipes/recipes/saving_multiple_models_in_one_file.html
 
-12. warmstarting_model_using_parameters_from_a_different_model.py 
+11. warmstarting_model_using_parameters_from_a_different_model.py
          Warmstarting models using parameters from different model
          https://pytorch.org/tutorials/recipes/recipes/warmstarting_model_using_parameters_from_a_different_model.html
 
-13. zeroing_out_gradients.py
+12. zeroing_out_gradients.py
          Zeroing out gradients
          https://pytorch.org/tutorials/recipes/recipes/zeroing_out_gradients.html
 
-14. mobile_perf.py
+13. mobile_perf.py
          PyTorch Mobile Performance Recipes
          https://pytorch.org/tutorials/recipes/mobile_perf.html
 
-15. amp_recipe.py
+14. amp_recipe.py
          Automatic Mixed Precision
          https://pytorch.org/tutorials/recipes/amp_recipe.html

recipes_source/recipes/zeroing_out_gradients.py

@@ -44,23 +44,23 @@
 ######################################################################
 # Steps
 # -----
-# 
+#
 # Steps 1 through 4 set up our data and neural network for training. The
 # process of zeroing out the gradients happens in step 5. If you already
 # have your data and neural network built, skip to 5.
-# 
+#
 # 1. Import all necessary libraries for loading our data
 # 2. Load and normalize the dataset
 # 3. Build the neural network
 # 4. Define the loss function
 # 5. Zero the gradients while training the network
-# 
+#
 # 1. Import necessary libraries for loading our data
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-# 
+#
 # For this recipe, we will just be using ``torch`` and ``torchvision`` to
 # access the dataset.
-# 
+#
 
 import torch
 
@@ -76,10 +76,10 @@
 ######################################################################
 # 2. Load and normalize the dataset
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-# 
+#
 # PyTorch features various built-in datasets (see the Loading Data recipe
 # for more information).
-# 
+#
 
 transform = transforms.Compose(
     [transforms.ToTensor(),
@@ -102,10 +102,10 @@
 ######################################################################
 # 3. Build the neural network
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-# 
+#
 # We will use a convolutional neural network. To learn more see the
 # Defining a Neural Network recipe.
-# 
+#
 
 class Net(nn.Module):
     def __init__(self):
@@ -130,9 +130,9 @@ def forward(self, x):
 ######################################################################
 # 4. Define a Loss function and optimizer
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-# 
+#
 # Let’s use a Classification Cross-Entropy loss and SGD with momentum.
-# 
+#
 
 net = Net()
 criterion = nn.CrossEntropyLoss()
@@ -142,14 +142,14 @@ def forward(self, x):
 ######################################################################
 # 5. Zero the gradients while training the network
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-# 
+#
 # This is when things start to get interesting. We simply have to loop
 # over our data iterator, and feed the inputs to the network and optimize.
-# 
+#
 # Notice that for each entity of data, we zero out the gradients. This is
 # to ensure that we aren’t tracking any unnecessary information when we
 # train our neural network.
-# 
+#
 
 for epoch in range(2):  # loop over the dataset multiple times
 
@@ -181,13 +181,13 @@ def forward(self, x):
 # You can also use ``model.zero_grad()``. This is the same as using
 # ``optimizer.zero_grad()`` as long as all your model parameters are in
 # that optimizer. Use your best judgment to decide which one to use.
-# 
+#
 # Congratulations! You have successfully zeroed out gradients PyTorch.
-# 
+#
 # Learn More
 # ----------
-# 
+#
 # Take a look at these other recipes to continue your learning:
-# 
-# - `Loading data in PyTorch <https://pytorch.org/tutorials/recipes/recipes/loading_data_recipe.html>`__
+#
+# - `Loading data in PyTorch <https://pytorch.org/tutorials/beginner/basics/data_tutorial.html>`__
 # - `Saving and loading models across devices in PyTorch <https://pytorch.org/tutorials/recipes/recipes/save_load_across_devices.html>`__

recipes_source/recipes_index.rst

@@ -30,14 +30,6 @@ Recipes are bite-sized, actionable examples of how to use specific PyTorch featu
 
 .. Basics
 
-.. customcarditem::
-   :header: Loading data in PyTorch
-   :card_description: Learn how to use PyTorch packages to prepare and load common datasets for your model.
-   :image: ../_static/img/thumbnails/cropped/loading-data.PNG
-   :link: ../recipes/recipes/loading_data_recipe.html
-   :tags: Basics
-
-
 .. customcarditem::
    :header: Defining a Neural Network
    :card_description: Learn how to use PyTorch's torch.nn package to create and define a neural network for the MNIST dataset.
@@ -407,7 +399,6 @@ Recipes are bite-sized, actionable examples of how to use specific PyTorch featu
 .. toctree::
    :hidden:
 
-   /recipes/recipes/loading_data_recipe
    /recipes/recipes/defining_a_neural_network
    /recipes/torch_logs
    /recipes/recipes/what_is_state_dict