Automated tutorials push

Author: pytorchbot

_downloads/0aec568a42e89122e5ca293c86289287/tensorboard_profiler_tutorial.ipynb

@@ -23,6 +23,11 @@
     "This tutorial demonstrates how to use TensorBoard plugin with PyTorch\n",
     "Profiler to detect performance bottlenecks of the model.\n",
     "\n",
+    "<div style=\"background-color: #e94f3b; color: #fff; font-weight: 700; padding-left: 10px; padding-top: 5px; padding-bottom: 5px\"><strong>WARNING:</strong></div>\n",
+    "<div style=\"background-color: #f3f4f7; padding-left: 10px; padding-top: 10px; padding-bottom: 10px; padding-right: 10px\">\n",
+    "<p>The TensorBoard integration with the PyTorch profiler is nowdeprecated. Instead, use Perfetto or the Chrome trace toview <code>trace.json</code> files. After<a href=\"https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html#using-tracing-functionality\">generating a trace</a>,simply drag the <code>trace.json</code> into <a href=\"https://ui.perfetto.dev/\">Perfetto UI</a>or <code>chrome://tracing</code> to visualize your profile.</p>\n",
+    "</div>\n",
+    "\n",
     "Introduction\n",
     "------------\n",
     "\n",

_downloads/30bcc2970bf630097b13789b5cdcea48/hyperparameter_tuning_tutorial.ipynb

@@ -29,9 +29,8 @@
     "of parameters. [Ray Tune](https://docs.ray.io/en/latest/tune.html) is an\n",
     "industry standard tool for distributed hyperparameter tuning. Ray Tune\n",
     "includes the latest hyperparameter search algorithms, integrates with\n",
-    "TensorBoard and other analysis libraries, and natively supports\n",
-    "distributed training through [Ray\\'s distributed machine learning\n",
-    "engine](https://ray.io/).\n",
+    "various analysis libraries, and natively supports distributed training\n",
+    "through [Ray\\'s distributed machine learning engine](https://ray.io/).\n",
     "\n",
     "In this tutorial, we will show you how to integrate Ray Tune into your\n",
     "PyTorch training workflow. We will extend [this tutorial from the\n",

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "12331fc4",
+   "id": "ed405304",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f9e5fcc6",
+   "id": "77d13a80",
    "metadata": {},
    "source": [
     "\n",

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "82871af4",
+   "id": "fbe67e7d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39a0e3f0",
+   "id": "97f1179f",
    "metadata": {},
    "source": [
     "\n",

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ae60a0fc",
+   "id": "87ff77a3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d078d59",
+   "id": "af669fe5",
    "metadata": {},
    "source": [
     "\n",

_downloads/67e47b6d6793c700666471b688068f72/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

_downloads/74b90fda9cec339519e5ef764956100f/profiler_recipe.py

@@ -459,7 +459,6 @@ 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
+# - `PyTorch Benchmark <https://pytorch.org/tutorials/recipes/recipes/benchmark.html>`_
+# - `Visualizing models, data, and training with TensorBoard <https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html>`_ tutorial
 #

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "eea127d7",
+   "id": "1fc019a2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61b46529",
+   "id": "dce60215",
    "metadata": {},
    "source": [
     "\n",

_downloads/b2c9c15033f17c2bdf31c864f9d39c76/profiler_recipe.ipynb

@@ -731,9 +731,6 @@
     "\n",
     "-   [PyTorch\n",
     "    Benchmark](https://pytorch.org/tutorials/recipes/recipes/benchmark.html)\n",
-    "-   [PyTorch Profiler with\n",
-    "    TensorBoard](https://pytorch.org/tutorials/intermediate/tensorboard_profiler_tutorial.html)\n",
-    "    tutorial\n",
     "-   [Visualizing models, data, and training with\n",
     "    TensorBoard](https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html)\n",
     "    tutorial\n"

_downloads/b2e3bdbf14ea1e9b3a80770f0a498037/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/>`_.