Automated tutorials push

Author: pytorchbot

_downloads/256861ec2cab5f0dc50c523f520dfefd/dynamic_quantization_tutorial.py

@@ -134,10 +134,18 @@ def tokenize(self, path):
 # -----------------------------
 #
 # This is a tutorial on dynamic quantization, a quantization technique
-# that is applied after a model has been trained. Therefore, we'll simply load some
-# pretrained weights into this model architecture; these weights were obtained
-# by training for five epochs using the default settings in the word language model
-# example.
+# that is applied after a model has been trained. Therefore, we'll simply
+# load some pretrained weights into this model architecture; these
+# weights were obtained by training for five epochs using the default
+# settings in the word language model example.
+#
+# Before running this tutorial, download the required pre-trained model:
+#
+# .. code-block:: bash
+#
+#     wget https://s3.amazonaws.com/pytorch-tutorial-assets/word_language_model_quantize.pth
+#
+# Place the downloaded file in the data directory or update the model_data_filepath accordingly.
 
 ntokens = len(corpus.dictionary)
 

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0b136fac",
+   "id": "c89cdaa8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14b2c9e0",
+   "id": "79064ab7",
    "metadata": {},
    "source": [
     "\n",

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "28016577",
+   "id": "3eef1456",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d66e1a4a",
+   "id": "94777eff",
    "metadata": {},
    "source": [
     "\n",

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "669e83ce",
+   "id": "0c5ea646",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "148443b9",
+   "id": "261289fd",
    "metadata": {},
    "source": [
     "\n",

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7ee055c3",
+   "id": "bf2bba0d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9ee9d44",
+   "id": "efa41e29",
    "metadata": {},
    "source": [
     "\n",

_downloads/9a0e851f5cb70c78bfde07b9bd268569/dynamic_quantization_tutorial.ipynb

@@ -195,7 +195,16 @@
     "that is applied after a model has been trained. Therefore, we\\'ll simply\n",
     "load some pretrained weights into this model architecture; these weights\n",
     "were obtained by training for five epochs using the default settings in\n",
-    "the word language model example.\n"
+    "the word language model example.\n",
+    "\n",
+    "Before running this tutorial, download the required pre-trained model:\n",
+    "\n",
+    "``` {.bash}\n",
+    "wget https://s3.amazonaws.com/pytorch-tutorial-assets/word_language_model_quantize.pth\n",
+    "```\n",
+    "\n",
+    "Place the downloaded file in the data directory or update the\n",
+    "model\\_data\\_filepath accordingly.\n"
    ]
   },
   {

_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2c40fc4a",
+   "id": "8fc086ce",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13114492",
+   "id": "f40d97f4",
    "metadata": {},
    "source": [
     "\n",

_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bf979900",
+   "id": "9ae2705b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0da394b8",
+   "id": "f8abb154",
    "metadata": {},
    "source": [
     "\n",

_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "991f1c4d",
+   "id": "bc7f6850",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aae0db0a",
+   "id": "4e400474",
    "metadata": {},
    "source": [
     "\n",

_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8a35018c",
+   "id": "416cb5eb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d51d810",
+   "id": "78da22be",
    "metadata": {},
    "source": [
     "\n",