Merge branch 'master' into jingxu10/itt

Author: Svetlana Karslioglu

beginner_source/blitz/cifar10_tutorial.py

@@ -105,7 +105,7 @@ def imshow(img):
 
 # get some random training images
 dataiter = iter(trainloader)
-images, labels = dataiter.next()
+images, labels = next(dataiter)
 
 # show images
 imshow(torchvision.utils.make_grid(images))
@@ -210,7 +210,7 @@ def forward(self, x):
 # Okay, first step. Let us display an image from the test set to get familiar.
 
 dataiter = iter(testloader)
-images, labels = dataiter.next()
+images, labels = next(dataiter)
 
 # print images
 imshow(torchvision.utils.make_grid(images))

beginner_source/introyt/introyt1_tutorial.py

@@ -369,7 +369,7 @@ def imshow(img):
 
 # get some random training images
 dataiter = iter(trainloader)
-images, labels = dataiter.next()
+images, labels = next(dataiter)
 
 # show images
 imshow(torchvision.utils.make_grid(images))
@@ -446,7 +446,7 @@ def imshow(img):
 
 # get some random training images
 dataiter = iter(trainloader)
-images, labels = dataiter.next()
+images, labels = next(dataiter)
 
 # show images
 imshow(torchvision.utils.make_grid(images))

beginner_source/introyt/tensorboardyt_tutorial.py

@@ -115,7 +115,7 @@ def matplotlib_imshow(img, one_channel=False):
 
 # Extract a batch of 4 images
 dataiter = iter(training_loader)
-images, labels = dataiter.next()
+images, labels = next(dataiter)
 
 # Create a grid from the images and show them
 img_grid = torchvision.utils.make_grid(images)
@@ -242,7 +242,7 @@ def forward(self, x):
 
 # Again, grab a single mini-batch of images
 dataiter = iter(training_loader)
-images, labels = dataiter.next()
+images, labels = next(dataiter)
 
 # add_graph() will trace the sample input through your model,
 # and render it as a graph.

beginner_source/introyt/trainingyt.py

@@ -112,7 +112,7 @@ def matplotlib_imshow(img, one_channel=False):
         plt.imshow(np.transpose(npimg, (1, 2, 0)))
 
 dataiter = iter(training_loader)
-images, labels = dataiter.next()
+images, labels = next(dataiter)
 
 # Create a grid from the images and show them
 img_grid = torchvision.utils.make_grid(images)

index.rst

@@ -889,6 +889,7 @@ Additional Resources
    intermediate/quantized_transfer_learning_tutorial
    advanced/static_quantization_tutorial
    intermediate/torchserve_with_ipex
+   intermediate/torchserve_with_ipex_2
    intermediate/nvfuser_intro_tutorial
    intermediate/ax_multiobjective_nas_tutorial
 

intermediate_source/nvfuser_intro_tutorial.py

@@ -557,9 +557,16 @@ def primitive_definition_for_memory_efficient_fusion(
 #
 # .. figure:: /_static/img/nvfuser_intro/nvfuser_tutorial_5.png
 #
-# .. note:: FuncTorch’s memory efficient pass is still actively in development
-#           and future versions are expected to achieve performance closer
-#           to that of TorchScript with the composite definition.
+# .. note:: FuncTorch’s memory efficient pass is experimental and still
+#           actively in development.
+#           Future versions of the API are expected to achieve performance
+#           closer to that of TorchScript with the composite definition.
+#
+# .. note:: FuncTorch’s memory efficient pass specializes on the shapes of
+#           the inputs to the function. If new inputs are provided with
+#           different shapes, then you need to construct a new function
+#           using `memory_efficient_fusion` and apply it to the new inputs.
+
 
 ######################################################################
 # Transformer Block With a Novel Normalization

intermediate_source/tensorboard_tutorial.rst

@@ -144,7 +144,7 @@ using `make_grid <https://pytorch.org/vision/stable/utils.html#torchvision.utils
 
     # get some random training images
     dataiter = iter(trainloader)
-    images, labels = dataiter.next()
+    images, labels = next(dataiter)
 
     # create grid of images
     img_grid = torchvision.utils.make_grid(images)

recipes_source/recipes/saving_multiple_models_in_one_file.py

@@ -54,7 +54,7 @@
 
 
 ######################################################################
-# 2. Define and intialize the neural network
+# 2. Define and initialize the neural network
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 # 
 # For sake of example, we will create a neural network for training

requirements.txt

@@ -29,6 +29,10 @@ ax-platform
 nbformat>=4.2.0
 deep_phonemizer==0.0.17
 
+# the following is necessary due to https://github.com/python/importlib_metadata/issues/411
+importlib-metadata < 5.0; python_version <= "3.7"
+importlib-metadata; python_version > "3.7"
+
 # PyTorch Theme
 -e git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme