Merge branch 'main' into docathon-3877-modernize-neural-networks

Author: sekyondaMeta

beginner_source/examples_tensor/polynomial_tensor.py

@@ -19,15 +19,13 @@
 """
 
 import torch
-import math
-
 
 dtype = torch.float
 device = torch.device("cpu")
 # device = torch.device("cuda:0") # Uncomment this to run on GPU
 
 # Create random input and output data
-x = torch.linspace(-math.pi, math.pi, 2000, device=device, dtype=dtype)
+x = torch.linspace(-torch.pi, torch.pi, 2000, device=device, dtype=dtype)
 y = torch.sin(x)
 
 # Randomly initialize weights

beginner_source/introyt/captumyt.py

@@ -106,16 +106,7 @@
 -  Matplotlib version 3.3.4, since Captum currently uses a Matplotlib
    function whose arguments have been renamed in later versions
 
-To install Captum in an Anaconda or pip virtual environment, use the
-appropriate command for your environment below:
-
-With ``conda``:
-
-.. code-block:: sh
-
-    conda install pytorch torchvision captum flask-compress matplotlib=3.3.4 -c pytorch
-
-With ``pip``:
+To install Captum in a virtual environment, use:
 
 .. code-block:: sh
 

beginner_source/introyt/tensorboardyt_tutorial.py

@@ -24,13 +24,6 @@
 To run this tutorial, you’ll need to install PyTorch, TorchVision,
 Matplotlib, and TensorBoard.
 
-With ``conda``:
-
-.. code-block:: sh
-
-    conda install pytorch torchvision -c pytorch
-    conda install matplotlib tensorboard
-
 With ``pip``:
 
 .. code-block:: sh

index.rst

@@ -103,6 +103,13 @@ Welcome to PyTorch Tutorials
    :link: intermediate/pinmem_nonblock.html
    :tags: Getting-Started
 
+.. customcarditem::
+   :header: Data Loading Optimization in PyTorch
+   :card_description: Optimize DataLoader configuration with num_workers, pin_memory, persistent_workers for maximum training throughput.
+   :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
+   :link: intermediate/intermediate_data_loading_tutorial.html
+   :tags: Getting-Started,Best-Practice
+
 .. customcarditem::
    :header: Understanding requires_grad, retain_grad, Leaf, and Non-leaf Tensors
    :card_description: Learn the subtleties of requires_grad, retain_grad, leaf, and non-leaf tensors