Automated tutorials push

Author: pytorchbot

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1452bf59",
+   "id": "d0a7147b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a321387",
+   "id": "7f58a882",
    "metadata": {},
    "source": [
     "\n",

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6e012bbf",
+   "id": "53a67736",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a937745f",
+   "id": "20324c00",
    "metadata": {},
    "source": [
     "\n",

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2cd7970b",
+   "id": "e70bac24",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5cd3c6f",
+   "id": "5652b2b3",
    "metadata": {},
    "source": [
     "\n",

_downloads/74b90fda9cec339519e5ef764956100f/profiler_recipe.py

@@ -163,18 +163,20 @@
 # Note the occurrence of ``aten::convolution`` twice with different input shapes.
 
 ######################################################################
-# Profiler can also be used to analyze performance of models executed on GPUs and XPUs:
+# Profiler can also be used to analyze performance of models executed on GPUs:
 # Users could switch between cpu, cuda and xpu
+activities = [ProfilerActivity.CPU]
 if torch.cuda.is_available():
     device = 'cuda'
+    activities += [ProfilerActivity.CUDA]
 elif torch.xpu.is_available():
     device = 'xpu'
+    activities += [ProfilerActivity.XPU]
 else:
     print('Neither CUDA nor XPU devices are available to demonstrate profiling on acceleration devices')
     import sys
     sys.exit(0)
 
-activities = [ProfilerActivity.CPU, ProfilerActivity.CUDA, ProfilerActivity.XPU]
 sort_by_keyword = device + "_time_total"
 
 model = models.resnet18().to(device)
@@ -308,9 +310,17 @@
 # Profiling results can be outputted as a ``.json`` trace file:
 # Tracing CUDA or XPU kernels
 # Users could switch between cpu, cuda and xpu
-device = 'cuda'
-
-activities = [ProfilerActivity.CPU, ProfilerActivity.CUDA, ProfilerActivity.XPU]
+activities = [ProfilerActivity.CPU]
+if torch.cuda.is_available():
+    device = 'cuda'
+    activities += [ProfilerActivity.CUDA]
+elif torch.xpu.is_available():
+    device = 'xpu'
+    activities += [ProfilerActivity.XPU]
+else:
+    print('Neither CUDA nor XPU devices are available to demonstrate profiling on acceleration devices')
+    import sys
+    sys.exit(0)
 
 model = models.resnet18().to(device)
 inputs = torch.randn(5, 3, 224, 224).to(device)

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "aa26c931",
+   "id": "2188aef8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d5bd28d2",
+   "id": "648d5416",
    "metadata": {},
    "source": [
     "\n",

_downloads/b2c9c15033f17c2bdf31c864f9d39c76/profiler_recipe.ipynb

@@ -270,7 +270,7 @@
    "metadata": {},
    "source": [
     "Profiler can also be used to analyze performance of models executed on\n",
-    "GPUs and XPUs: Users could switch between cpu, cuda and xpu\n"
+    "GPUs: Users could switch between cpu, cuda and xpu\n"
    ]
   },
   {
@@ -281,16 +281,18 @@
    },
    "outputs": [],
    "source": [
+    "activities = [ProfilerActivity.CPU]\n",
     "if torch.cuda.is_available():\n",
     "    device = 'cuda'\n",
+    "    activities += [ProfilerActivity.CUDA]\n",
     "elif torch.xpu.is_available():\n",
     "    device = 'xpu'\n",
+    "    activities += [ProfilerActivity.XPU]\n",
     "else:\n",
     "    print('Neither CUDA nor XPU devices are available to demonstrate profiling on acceleration devices')\n",
     "    import sys\n",
     "    sys.exit(0)\n",
     "\n",
-    "activities = [ProfilerActivity.CPU, ProfilerActivity.CUDA, ProfilerActivity.XPU]\n",
     "sort_by_keyword = device + \"_time_total\"\n",
     "\n",
     "model = models.resnet18().to(device)\n",
@@ -473,9 +475,17 @@
    },
    "outputs": [],
    "source": [
-    "device = 'cuda'\n",
-    "\n",
-    "activities = [ProfilerActivity.CPU, ProfilerActivity.CUDA, ProfilerActivity.XPU]\n",
+    "activities = [ProfilerActivity.CPU]\n",
+    "if torch.cuda.is_available():\n",
+    "    device = 'cuda'\n",
+    "    activities += [ProfilerActivity.CUDA]\n",
+    "elif torch.xpu.is_available():\n",
+    "    device = 'xpu'\n",
+    "    activities += [ProfilerActivity.XPU]\n",
+    "else:\n",
+    "    print('Neither CUDA nor XPU devices are available to demonstrate profiling on acceleration devices')\n",
+    "    import sys\n",
+    "    sys.exit(0)\n",
     "\n",
     "model = models.resnet18().to(device)\n",
     "inputs = torch.randn(5, 3, 224, 224).to(device)\n",

_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fc24ebb9",
+   "id": "4ac4e0be",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba61b37d",
+   "id": "641de0c3",
    "metadata": {},
    "source": [
     "\n",

_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fff599ca",
+   "id": "04c41588",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e75bfb3",
+   "id": "3743fb51",
    "metadata": {},
    "source": [
     "\n",

_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8dfc30fd",
+   "id": "875759f4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39a1a7a5",
+   "id": "cb03fcb6",
    "metadata": {},
    "source": [
     "\n",

_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2f61dc95",
+   "id": "2aaffb92",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b85e8134",
+   "id": "c4828e1c",
    "metadata": {},
    "source": [
     "\n",