Add a deprecation notice to the Profile Tensorboard tutorial

beginner_source/hyperparameter_tuning_tutorial.py

@@ -10,7 +10,7 @@
 Fortunately, there are tools that help with finding the best combination of parameters.
 `Ray Tune <https://docs.ray.io/en/latest/tune.html>`_ is an industry standard tool for
 distributed hyperparameter tuning. Ray Tune includes the latest hyperparameter search
-algorithms, integrates with TensorBoard and other analysis libraries, and natively
+algorithms, integrates with various analysis libraries, and natively
 supports distributed training through `Ray's distributed machine learning engine
 <https://ray.io/>`_.
 

intermediate_source/tensorboard_profiler_tutorial.py

@@ -4,6 +4,14 @@
 This tutorial demonstrates how to use TensorBoard plugin with PyTorch Profiler
 to detect performance bottlenecks of the model.
 
+.. warning::
+   The TensorBoard integration with the PyTorch profiler is now
+   deprecated. Instead, use Perfetto or the Chrome trace to
+   view ``trace.json`` files. After
+   `generating a trace <https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html#using-tracing-functionality>`__,
+   simply drag the ``trace.json`` into `Perfetto UI <https://ui.perfetto.dev/>`__
+   or ``chrome://tracing`` to visualize your profile.
+
 Introduction
 ------------
 PyTorch 1.8 includes an updated profiler API capable of

recipes_source/recipes/profiler_recipe.py

@@ -460,6 +460,4 @@ def trace_handler(p):
 # Take a look at the following recipes/tutorials to continue your learning:
 #
 # -  `PyTorch Benchmark <https://pytorch.org/tutorials/recipes/recipes/benchmark.html>`_
-# -  `PyTorch Profiler with TensorBoard <https://pytorch.org/tutorials/intermediate/tensorboard_profiler_tutorial.html>`_ tutorial
-# -  `Visualizing models, data, and training with TensorBoard <https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html>`_ tutorial
 #