Merge branch 'main' into pytorch-perf-guide-doca

Author: svekars

.jenkins/validate_tutorials_built.py

@@ -26,7 +26,6 @@
     "beginner_source/text_sentiment_ngrams_tutorial", # not building with 2.3 RC, might be able to turn on with GA
     "beginner_source/t5_tutorial", # re-enable after this is fixed: https://github.com/pytorch/text/issues/1756
     "intermediate_source/mnist_train_nas",  # used by ax_multiobjective_nas_tutorial.py
-    "intermediate_source/torchvision_tutorial", # disable due to RuntimeError: DataLoader worker (pid(s) 20092) exited unexpectedly
     "intermediate_source/fx_conv_bn_fuser",
     "intermediate_source/_torch_export_nightly_tutorial",  # does not work on release
     "advanced_source/super_resolution_with_onnxruntime",

CONTRIBUTING.md

@@ -326,9 +326,9 @@ example](https://github.com/pytorch/tutorials/blob/main/_static/img/thumbnails/c
 
 The following command builds an HTML version of the tutorial website.
 
-    ```
-    make html-noplot
-    ```
+```
+make html-noplot
+```
 
 This command does not run your tutorial code. To build the tutorial in a
 way that executes the code, use `make docs`. However, unless you have a

beginner_source/transformer_tutorial.rst

@@ -0,0 +1,7 @@
+Language Modeling with ``nn.Transformer`` and torchtext
+===========================================================
+
+The content is deprecated.
+
+.. raw:: html
+   <meta http-equiv="refresh" content="0; url=https://pytorch.org/tutorials/">

intermediate_source/torchvision_tutorial.py

@@ -382,14 +382,12 @@ def get_transform(train):
 # expects during training and inference time on sample data.
 import utils
 
-
 model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="DEFAULT")
 dataset = PennFudanDataset('data/PennFudanPed', get_transform(train=True))
 data_loader = torch.utils.data.DataLoader(
     dataset,
     batch_size=2,
     shuffle=True,
-    num_workers=4,
     collate_fn=utils.collate_fn
 )
 
@@ -433,15 +431,13 @@ def get_transform(train):
     dataset,
     batch_size=2,
     shuffle=True,
-    num_workers=4,
     collate_fn=utils.collate_fn
 )
 
 data_loader_test = torch.utils.data.DataLoader(
     dataset_test,
     batch_size=1,
     shuffle=False,
-    num_workers=4,
     collate_fn=utils.collate_fn
 )