Automated tutorials push

Author: pytorchbot

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f4a6940a",
+   "id": "fac5891a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4d8cdb2",
+   "id": "98badc00",
    "metadata": {},
    "source": [
     "\n",

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
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    "execution_count": null,
-   "id": "34f9f994",
+   "id": "e7dbf541",
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    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47699cd0",
+   "id": "ce09721d",
    "metadata": {},
    "source": [
     "\n",

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8b754446",
+   "id": "46e0447c",
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    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7931765",
+   "id": "10ad5c81",
    "metadata": {},
    "source": [
     "\n",

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0bcf5f15",
+   "id": "e8a82bab",
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    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c9e1a7b9",
+   "id": "215cb1e2",
    "metadata": {},
    "source": [
     "\n",

_downloads/8d83c5b10438a4bcc94963daaddeaeec/torchrec_intro_tutorial.ipynb

@@ -1121,13 +1121,14 @@
     "very sensitive to **performance and size of the model**. Running just\n",
     "the trained model in a Python environment is incredibly inefficient.\n",
     "There are two key differences between inference and training\n",
-    "environments: \\* **Quantization**: Inference models are typically\n",
-    "quantized, where model parameters lose precision for lower latency in\n",
-    "predictions and reduced model size. For example FP32 (4 bytes) in\n",
-    "trained model to INT8 (1 byte) for each embedding weight. This is also\n",
-    "necessary given the vast scale of embedding tables, as we want to use as\n",
-    "few devices as possible for inference to minimize latency.\n",
-    "\n",
+    "environments:\n",
+    "\n",
+    "-   **Quantization**: Inference models are typically quantized, where\n",
+    "    model parameters lose precision for lower latency in predictions and\n",
+    "    reduced model size. For example FP32 (4 bytes) in trained model to\n",
+    "    INT8 (1 byte) for each embedding weight. This is also necessary\n",
+    "    given the vast scale of embedding tables, as we want to use as few\n",
+    "    devices as possible for inference to minimize latency.\n",
     "-   **C++ environment**: Inference latency is very important, so in\n",
     "    order to ensure ample performance, the model is typically ran in a\n",
     "    C++ environment, along with the situations where we don\\'t have a\n",

_downloads/b07b6a647a3bf9e6882df8ca2cc20e8b/torchrec_intro_tutorial.py

@@ -919,6 +919,7 @@ def _wait_impl(self) -> torch.Tensor:
 # the trained model in a Python environment is incredibly inefficient.
 # There are two key differences between inference and training
 # environments:
+# 
 # * **Quantization**: Inference models are typically
 #   quantized, where model parameters lose precision for lower latency in
 #   predictions and reduced model size. For example FP32 (4 bytes) in

_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

@@ -37,7 +37,7 @@
   {
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    "execution_count": null,
-   "id": "d69f961a",
+   "id": "cd23ede1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d7c91f6",
+   "id": "8e244018",
    "metadata": {},
    "source": [
     "\n",

_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a9715460",
+   "id": "135cf2dd",
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    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d16add6",
+   "id": "c30dda37",
    "metadata": {},
    "source": [
     "\n",

_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
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    "execution_count": null,
-   "id": "9ae8ad8e",
+   "id": "493e73f9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aa178334",
+   "id": "5d4f5407",
    "metadata": {},
    "source": [
     "\n",

_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "51625671",
+   "id": "a111e54d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7095dd8b",
+   "id": "f89240d0",
    "metadata": {},
    "source": [
     "\n",