Merge branch 'master' into DQN_revise_training

Author: vmoens

.devcontainer/requirements.txt

@@ -24,7 +24,7 @@ ipython
 # to run examples
 pandas
 scikit-image
-pillow==9.0.1
+pillow==9.3.0
 wget
 
 # for codespaces env

.jenkins/validate_tutorials_built.py

@@ -39,6 +39,7 @@
     "recipes/profiler_recipe",
     "recipes/save_load_across_devices",
     "recipes/warmstarting_model_using_parameters_from_a_different_model",
+    "torch_compile_tutorial_",
     "recipes/dynamic_quantization",
     "recipes/saving_and_loading_a_general_checkpoint",
     "recipes/benchmark",
@@ -51,6 +52,7 @@
     "hyperparameter_tuning_tutorial",
     "flask_rest_api_tutorial",
     "text_to_speech_with_torchaudio",
+    "ax_multiobjective_nas_tutorial"
 ]
 
 

beginner_source/basics/buildmodel_tutorial.py

@@ -77,7 +77,7 @@ def forward(self, x):
 # along with some `background operations <https://github.com/pytorch/pytorch/blob/270111b7b611d174967ed204776985cefca9c144/torch/nn/modules/module.py#L866>`_.
 # Do not call ``model.forward()`` directly!
 #
-# Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to each output of 10 raw predicted values for each class, and dim=1 corresponding to the individual values of each output.  .
+# Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to each output of 10 raw predicted values for each class, and dim=1 corresponding to the individual values of each output.
 # We get the prediction probabilities by passing it through an instance of the ``nn.Softmax`` module.
 
 X = torch.rand(1, 28, 28, device=device)

beginner_source/dist_overview.rst

@@ -126,7 +126,7 @@ DDP materials are listed below:
    described in the
    `Single-Machine Model Parallel Best Practices <../intermediate/model_parallel_tutorial.html>`__
    tutorial.
-3. The `Launching and configuring distributed data parallel applications <https://github.com/pytorch/examples/blob/stable/distributed/ddp/README.md>`__
+3. The `Launching and configuring distributed data parallel applications <https://github.com/pytorch/examples/blob/main/distributed/ddp/README.md>`__
    document shows how to use the DDP launching script.
 4. The `Shard Optimizer States With ZeroRedundancyOptimizer <../recipes/zero_redundancy_optimizer.html>`__
    recipe demonstrates how `ZeroRedundancyOptimizer <https://pytorch.org/docs/stable/distributed.optim.html>`__

beginner_source/translation_transformer.py

@@ -266,7 +266,7 @@ def tensor_transform(token_ids: List[int]):
                                                tensor_transform) # Add BOS/EOS and create tensor
 
 
-# function to collate data samples into batch tesors
+# function to collate data samples into batch tensors
 def collate_fn(batch):
     src_batch, tgt_batch = [], []
     for src_sample, tgt_sample in batch:

index.rst

@@ -3,6 +3,7 @@ Welcome to PyTorch Tutorials
 
 What's new in PyTorch tutorials?
 
+* `Survey: Let us know how the PyTorch Tutorials are working for you! <https://docs.google.com/forms/d/e/1FAIpQLSfM-n7fF-K8AqkOcdSlzS_0g-xd6ith7U_W36xG-C_6Q2B6Mg/viewform>`__
 * `PyTorch Distributed Series <https://pytorch.org/tutorials/beginner/ddp_series_intro.html?utm_source=whats_new_tutorials&utm_medium=ddp_series_intro>`__
 * `Fast Transformer Inference with Better Transformer <https://pytorch.org/tutorials/beginner/bettertransformer_tutorial.html?utm_source=whats_new_tutorials&utm_medium=bettertransformer>`__
 * `Advanced model training with Fully Sharded Data Parallel (FSDP) <https://pytorch.org/tutorials/intermediate/FSDP_adavnced_tutorial.html?utm_source=whats_new_tutorials&utm_medium=FSDP_advanced>`__
@@ -517,6 +518,13 @@ What's new in PyTorch tutorials?
    :link: intermediate/ax_multiobjective_nas_tutorial.html
    :tags: Model-Optimization,Best-Practice,Ax,TorchX
 
+.. customcarditem::
+   :header: torch.compile Tutorial
+   :card_description: Speed up your models with minimal code changes using torch.compile, the latest PyTorch compiler solution.
+   :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
+   :link: intermediate/torch_compile_tutorial.html
+   :tags: Model-Optimization
+
 .. Parallel-and-Distributed-Training
 
 
@@ -901,6 +909,7 @@ Additional Resources
    intermediate/torchserve_with_ipex_2
    intermediate/nvfuser_intro_tutorial
    intermediate/ax_multiobjective_nas_tutorial
+   intermediate/torch_compile_tutorial
 
 .. toctree::
    :maxdepth: 2