Automated tutorials push

Author: pytorchbot

_downloads/1ac8a049de0513cb49a0e834e4c27a20/regional_compilation.py

@@ -3,6 +3,7 @@
 ============================================================================
 
 **Author:** `Animesh Jain <https://github.com/anijain2305>`_
+
 As deep learning models get larger, the compilation time of these models also
 increases. This extended compilation time can result in a large startup time in
 inference services or wasted resources in large-scale training. This recipe
@@ -23,7 +24,7 @@
 
    pip install torch
 
-.. note:: 
+.. note::
    This feature is available starting with the 2.5 release. If you are using version 2.4,
    you can enable the configuration flag ``torch._dynamo.config.inline_inbuilt_nn_modules=True``
    to prevent recompilations during regional compilation. In version 2.5, this flag is enabled by default.

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3f405de1",
+   "id": "4dac0422",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b704caf",
+   "id": "d9d3c3d4",
    "metadata": {},
    "source": [
     "\n",

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "65e65c44",
+   "id": "335f8dc2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fca3a90",
+   "id": "88467d9a",
    "metadata": {},
    "source": [
     "\n",

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b02f0231",
+   "id": "dafe94d5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cea2f250",
+   "id": "88fe8d2c",
    "metadata": {},
    "source": [
     "\n",

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0adb0193",
+   "id": "1df017eb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8aa5630b",
+   "id": "578bf3ae",
    "metadata": {},
    "source": [
     "\n",

_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "95c3b59d",
+   "id": "e7fbf414",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35fe9124",
+   "id": "3d96f2c9",
    "metadata": {},
    "source": [
     "\n",

_downloads/cbd5804c4553cb4a23dc24137bde6077/regional_compilation.ipynb

@@ -20,13 +20,14 @@
     "Reducing torch.compile cold start compilation time with regional compilation\n",
     "============================================================================\n",
     "\n",
-    "**Author:** [Animesh Jain](https://github.com/anijain2305) As deep\n",
-    "learning models get larger, the compilation time of these models also\n",
-    "increases. This extended compilation time can result in a large startup\n",
-    "time in inference services or wasted resources in large-scale training.\n",
-    "This recipe shows an example of how to reduce the cold start compilation\n",
-    "time by choosing to compile a repeated region of the model instead of\n",
-    "the entire model.\n",
+    "**Author:** [Animesh Jain](https://github.com/anijain2305)\n",
+    "\n",
+    "As deep learning models get larger, the compilation time of these models\n",
+    "also increases. This extended compilation time can result in a large\n",
+    "startup time in inference services or wasted resources in large-scale\n",
+    "training. This recipe shows an example of how to reduce the cold start\n",
+    "compilation time by choosing to compile a repeated region of the model\n",
+    "instead of the entire model.\n",
     "\n",
     "Prerequisites\n",
     "-------------\n",

_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0742f714",
+   "id": "cf3c8e39",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a69d50b",
+   "id": "a26c0941",
    "metadata": {},
    "source": [
     "\n",

_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c33aeaa9",
+   "id": "6a4d7c89",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a5833a8",
+   "id": "99be565a",
    "metadata": {},
    "source": [
     "\n",

_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1dc9d54e",
+   "id": "3d8b7f84",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e52c29f2",
+   "id": "3c95a1fd",
    "metadata": {},
    "source": [
     "\n",