diff --git a/assets/hub/datvuthanh_hybridnets.ipynb b/assets/hub/datvuthanh_hybridnets.ipynb
index 95b1f0f302fc..411e0b19d841 100644
--- a/assets/hub/datvuthanh_hybridnets.ipynb
+++ b/assets/hub/datvuthanh_hybridnets.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "a03f3c27",
+   "id": "7461d39d",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c53374d0",
+   "id": "642e295f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "187d33f9",
+   "id": "51c2ea62",
    "metadata": {},
    "source": [
     "## Model Description\n",
@@ -93,7 +93,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fe220c4b",
+   "id": "bda8c68e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e86df1d3",
+   "id": "468d5dda",
    "metadata": {},
    "source": [
     "### Citation\n",
@@ -120,7 +120,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ae665090",
+   "id": "7159f81c",
    "metadata": {
     "attributes": {
      "classes": [
diff --git a/assets/hub/facebookresearch_WSL-Images_resnext.ipynb b/assets/hub/facebookresearch_WSL-Images_resnext.ipynb
index 4458d8b9fbe3..e487b7b1703f 100644
--- a/assets/hub/facebookresearch_WSL-Images_resnext.ipynb
+++ b/assets/hub/facebookresearch_WSL-Images_resnext.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7f6bc74a",
+   "id": "b80018b5",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d7c1aa68",
+   "id": "774b6486",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1be946c9",
+   "id": "047a209f",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a2cc7b85",
+   "id": "a5d58f1d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,7 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "46f1c52b",
+   "id": "dead0b79",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b597bf8a",
+   "id": "f9fac4b3",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/facebookresearch_pytorch-gan-zoo_dcgan.ipynb b/assets/hub/facebookresearch_pytorch-gan-zoo_dcgan.ipynb
index 373b9f403c34..227dfecaefb3 100644
--- a/assets/hub/facebookresearch_pytorch-gan-zoo_dcgan.ipynb
+++ b/assets/hub/facebookresearch_pytorch-gan-zoo_dcgan.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "eb6ab35a",
+   "id": "7876bcf2",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "69a21a14",
+   "id": "c13b03dd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec84b9cd",
+   "id": "15f8d6ce",
    "metadata": {},
    "source": [
     "The input to the model is a noise vector of shape `(N, 120)` where `N` is the number of images to be generated.\n",
@@ -45,7 +45,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "33e4b4ca",
+   "id": "7f0dc223",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01fe5ab4",
+   "id": "dc33614e",
    "metadata": {},
    "source": [
     "You should see an image similar to the one on the left.\n",
diff --git a/assets/hub/facebookresearch_pytorch-gan-zoo_pgan.ipynb b/assets/hub/facebookresearch_pytorch-gan-zoo_pgan.ipynb
index abece3bf99a7..3dcb30f3be38 100644
--- a/assets/hub/facebookresearch_pytorch-gan-zoo_pgan.ipynb
+++ b/assets/hub/facebookresearch_pytorch-gan-zoo_pgan.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "4f6f6eee",
+   "id": "0288a894",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "902dd745",
+   "id": "bbb7654f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "51481d60",
+   "id": "4a1157ab",
    "metadata": {},
    "source": [
     "The input to the model is a noise vector of shape `(N, 512)` where `N` is the number of images to be generated.\n",
@@ -55,7 +55,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "de9b54ff",
+   "id": "cdacbf60",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3401543",
+   "id": "1005f373",
    "metadata": {},
    "source": [
     "You should see an image similar to the one on the left.\n",
diff --git a/assets/hub/facebookresearch_pytorchvideo_resnet.ipynb b/assets/hub/facebookresearch_pytorchvideo_resnet.ipynb
index c41f48941890..6cf5c4db107f 100644
--- a/assets/hub/facebookresearch_pytorchvideo_resnet.ipynb
+++ b/assets/hub/facebookresearch_pytorchvideo_resnet.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c6e3e109",
+   "id": "00619deb",
    "metadata": {},
    "source": [
     "# 3D ResNet\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "35df0999",
+   "id": "d595788b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "565c3dd7",
+   "id": "102a8d2a",
    "metadata": {},
    "source": [
     "Import remaining functions:"
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b4a5ff33",
+   "id": "ba23ffd1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71b77d71",
+   "id": "4a60936e",
    "metadata": {},
    "source": [
     "#### Setup\n",
@@ -75,7 +75,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d61f1edb",
+   "id": "92a7855b",
    "metadata": {
     "attributes": {
      "classes": [
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3eb1ebd5",
+   "id": "44c93646",
    "metadata": {},
    "source": [
     "Download the id to label mapping for the Kinetics 400 dataset on which the torch hub models were trained. This will be used to get the category label names from the predicted class ids."
@@ -103,7 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "494f8f29",
+   "id": "e4849ccb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,7 +116,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "53f8397e",
+   "id": "b8dde08a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50b0697f",
+   "id": "ea6b33f1",
    "metadata": {},
    "source": [
     "#### Define input transform"
@@ -140,7 +140,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e59baf46",
+   "id": "71441349",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,7 +174,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a7866fd",
+   "id": "ce350472",
    "metadata": {},
    "source": [
     "#### Run Inference\n",
@@ -185,7 +185,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "342b6d09",
+   "id": "d74d9827",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c98b756",
+   "id": "2f515992",
    "metadata": {},
    "source": [
     "Load the video and transform it to the input format required by the model."
@@ -206,7 +206,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a39b1e5e",
+   "id": "a420c700",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -231,7 +231,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d17b186a",
+   "id": "fb5970b0",
    "metadata": {},
    "source": [
     "#### Get Predictions"
@@ -240,7 +240,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "df270bdb",
+   "id": "81c54fe7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -259,7 +259,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "186a7bf1",
+   "id": "563cb067",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/facebookresearch_pytorchvideo_slowfast.ipynb b/assets/hub/facebookresearch_pytorchvideo_slowfast.ipynb
index 7a4247619c46..25762f5e9571 100644
--- a/assets/hub/facebookresearch_pytorchvideo_slowfast.ipynb
+++ b/assets/hub/facebookresearch_pytorchvideo_slowfast.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e74405d0",
+   "id": "f5265f05",
    "metadata": {},
    "source": [
     "# SlowFast\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e89e8bdf",
+   "id": "84f49515",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d9086dd0",
+   "id": "6d9ce844",
    "metadata": {},
    "source": [
     "Import remaining functions:"
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f6f1f1e9",
+   "id": "e6760dcb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2aab1608",
+   "id": "70efabae",
    "metadata": {},
    "source": [
     "#### Setup\n",
@@ -76,7 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e8f2b0b8",
+   "id": "d0545729",
    "metadata": {
     "attributes": {
      "classes": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b59e82d",
+   "id": "633a6ac5",
    "metadata": {},
    "source": [
     "Download the id to label mapping for the Kinetics 400 dataset on which the torch hub models were trained. This will be used to get the category label names from the predicted class ids."
@@ -104,7 +104,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d5b7fb63",
+   "id": "11a1b79b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,7 +117,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c73e5c0c",
+   "id": "8657850f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4c57b33",
+   "id": "e8eb7c42",
    "metadata": {},
    "source": [
     "#### Define input transform"
@@ -141,7 +141,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "04975b98",
+   "id": "4f439c50",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d1c84123",
+   "id": "48608bbf",
    "metadata": {},
    "source": [
     "#### Run Inference\n",
@@ -209,7 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b9739a5d",
+   "id": "2069277e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -221,7 +221,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4119c9c6",
+   "id": "78b1fdea",
    "metadata": {},
    "source": [
     "Load the video and transform it to the input format required by the model."
@@ -230,7 +230,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8d3449ce",
+   "id": "7d188df7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -255,7 +255,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2efbee16",
+   "id": "a27b93d8",
    "metadata": {},
    "source": [
     "#### Get Predictions"
@@ -264,7 +264,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cb294087",
+   "id": "bc675197",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -283,7 +283,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd8ba8ee",
+   "id": "1b31216a",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/facebookresearch_pytorchvideo_x3d.ipynb b/assets/hub/facebookresearch_pytorchvideo_x3d.ipynb
index a98ebd735e90..8dd5bf754b6c 100644
--- a/assets/hub/facebookresearch_pytorchvideo_x3d.ipynb
+++ b/assets/hub/facebookresearch_pytorchvideo_x3d.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e19d28a0",
+   "id": "576813ae",
    "metadata": {},
    "source": [
     "# X3D\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c9fa2761",
+   "id": "259c298f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dac9e117",
+   "id": "bf673450",
    "metadata": {},
    "source": [
     "Import remaining functions:"
@@ -43,7 +43,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f6241353",
+   "id": "23051509",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e6a2fdac",
+   "id": "8f2e3a79",
    "metadata": {},
    "source": [
     "#### Setup\n",
@@ -76,7 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "afee2379",
+   "id": "78337995",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05cb6e66",
+   "id": "4f55500f",
    "metadata": {},
    "source": [
     "Download the id to label mapping for the Kinetics 400 dataset on which the torch hub models were trained. This will be used to get the category label names from the predicted class ids."
@@ -97,7 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5eae5cf3",
+   "id": "edeed9df",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +110,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cbf69d49",
+   "id": "0593cc20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25361693",
+   "id": "92bb9c5f",
    "metadata": {},
    "source": [
     "#### Define input transform"
@@ -134,7 +134,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6afab030",
+   "id": "e8031608",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "079f69ae",
+   "id": "320da8f0",
    "metadata": {},
    "source": [
     "#### Run Inference\n",
@@ -198,7 +198,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1e0e11e3",
+   "id": "f0da4568",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -210,7 +210,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e121db9",
+   "id": "792f057e",
    "metadata": {},
    "source": [
     "Load the video and transform it to the input format required by the model."
@@ -219,7 +219,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0f23b088",
+   "id": "f42cc905",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -244,7 +244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11193277",
+   "id": "e4feab1d",
    "metadata": {},
    "source": [
     "#### Get Predictions"
@@ -253,7 +253,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "decdc9ca",
+   "id": "dd707356",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -272,7 +272,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "08b07033",
+   "id": "2bb38b1b",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/facebookresearch_semi-supervised-ImageNet1K-models_resnext.ipynb b/assets/hub/facebookresearch_semi-supervised-ImageNet1K-models_resnext.ipynb
index 19e3a24dc09d..34f7d11451a1 100644
--- a/assets/hub/facebookresearch_semi-supervised-ImageNet1K-models_resnext.ipynb
+++ b/assets/hub/facebookresearch_semi-supervised-ImageNet1K-models_resnext.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2bd415fd",
+   "id": "066e7b52",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6db71247",
+   "id": "73f3121b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27049554",
+   "id": "f106ffda",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -61,7 +61,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "800e4183",
+   "id": "a3356bdb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +75,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7fc07bb7",
+   "id": "f1ced582",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a4f2406",
+   "id": "c599ed7f",
    "metadata": {},
    "source": [
     "### Model Description\n",
@@ -144,7 +144,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ede96ff1",
+   "id": "25ceb02a",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/assets/hub/huggingface_pytorch-transformers.ipynb b/assets/hub/huggingface_pytorch-transformers.ipynb
index c009b087d336..35373e65b60b 100644
--- a/assets/hub/huggingface_pytorch-transformers.ipynb
+++ b/assets/hub/huggingface_pytorch-transformers.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "feb9a1a4",
+   "id": "37965285",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -43,7 +43,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4b9306fb",
+   "id": "a9571698",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1047ea9b",
+   "id": "0e963ef8",
    "metadata": {},
    "source": [
     "# Usage\n",
@@ -86,7 +86,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "26876dcb",
+   "id": "9ab3e8e4",
    "metadata": {
     "attributes": {
      "classes": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d20e1fe7",
+   "id": "1b0b00fa",
    "metadata": {},
    "source": [
     "## Models\n",
@@ -115,7 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1c6f8a3d",
+   "id": "d99a57e2",
    "metadata": {
     "attributes": {
      "classes": [
@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6974f27",
+   "id": "0a9e83c9",
    "metadata": {},
    "source": [
     "## Models with a language modeling head\n",
@@ -149,7 +149,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "049aeb77",
+   "id": "578b75b1",
    "metadata": {
     "attributes": {
      "classes": [
@@ -172,7 +172,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0cb29c87",
+   "id": "b62b4cd8",
    "metadata": {},
    "source": [
     "## Models with a sequence classification head\n",
@@ -183,7 +183,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bcde8abd",
+   "id": "fe9fd312",
    "metadata": {
     "attributes": {
      "classes": [
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43c7d719",
+   "id": "ee1dbd19",
    "metadata": {},
    "source": [
     "## Models with a question answering head\n",
@@ -217,7 +217,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5f06db16",
+   "id": "cf5eeb36",
    "metadata": {
     "attributes": {
      "classes": [
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "986eead4",
+   "id": "a9d1f66d",
    "metadata": {},
    "source": [
     "## Configuration\n",
@@ -251,7 +251,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2a1f29c1",
+   "id": "9b2d9fc6",
    "metadata": {
     "attributes": {
      "classes": [
@@ -282,7 +282,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4cefd2c7",
+   "id": "96d26a0e",
    "metadata": {},
    "source": [
     "# Example Usage\n",
@@ -295,7 +295,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "31bd825e",
+   "id": "f0282ba1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -311,7 +311,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc2d7660",
+   "id": "ca3c2355",
    "metadata": {},
    "source": [
     "## Using `BertModel` to encode the input sentence in a sequence of last layer hidden-states"
@@ -320,7 +320,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "309c1bfb",
+   "id": "fada3e9c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -339,7 +339,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78391617",
+   "id": "2c85b954",
    "metadata": {},
    "source": [
     "## Using `modelForMaskedLM` to predict a masked token with BERT"
@@ -348,7 +348,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bcf73823",
+   "id": "369cbf00",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -370,7 +370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "856c6063",
+   "id": "6cbf8a17",
    "metadata": {},
    "source": [
     "## Using `modelForQuestionAnswering` to do question answering with BERT"
@@ -379,7 +379,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0dc9b52c",
+   "id": "cc88ba35",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -409,7 +409,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e468f5af",
+   "id": "a163d516",
    "metadata": {},
    "source": [
     "## Using `modelForSequenceClassification` to do paraphrase classification with BERT"
@@ -418,7 +418,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "708fdf05",
+   "id": "11a8db50",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/assets/hub/hustvl_yolop.ipynb b/assets/hub/hustvl_yolop.ipynb
index a055f6124c23..093c85fc96b4 100644
--- a/assets/hub/hustvl_yolop.ipynb
+++ b/assets/hub/hustvl_yolop.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "cb19e97a",
+   "id": "477755f1",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -23,7 +23,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "21a92ae1",
+   "id": "131b5528",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd23fcdb",
+   "id": "4699c8a0",
    "metadata": {},
    "source": [
     "## YOLOP: You Only Look Once for Panoptic driving Perception\n",
@@ -132,7 +132,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "42678f69",
+   "id": "e7b461bb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "728d3643",
+   "id": "cd43ef31",
    "metadata": {},
    "source": [
     "### Citation\n",
diff --git a/assets/hub/intelisl_midas_v2.ipynb b/assets/hub/intelisl_midas_v2.ipynb
index d0c8cb273ea8..7add3bce0c47 100644
--- a/assets/hub/intelisl_midas_v2.ipynb
+++ b/assets/hub/intelisl_midas_v2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c157b72c",
+   "id": "bd9ab2f2",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -32,7 +32,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2d26ee63",
+   "id": "9330a71f",
    "metadata": {
     "attributes": {
      "classes": [
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9fc975a6",
+   "id": "7e6364b8",
    "metadata": {},
    "source": [
     "### Example Usage\n",
@@ -59,7 +59,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a1e9b69c",
+   "id": "2eefb6f2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7af23620",
+   "id": "12e7f1b3",
    "metadata": {},
    "source": [
     "Load a model (see [https://github.com/intel-isl/MiDaS/#Accuracy](https://github.com/intel-isl/MiDaS/#Accuracy) for an overview)"
@@ -84,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6a7615a7",
+   "id": "1ab5a484",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e04aab33",
+   "id": "5832d501",
    "metadata": {},
    "source": [
     "Move model to GPU if available"
@@ -106,7 +106,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6ec96ca7",
+   "id": "1b885053",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f2ff58e",
+   "id": "2a5972df",
    "metadata": {},
    "source": [
     "Load transforms to resize and normalize the image for large or small model"
@@ -126,7 +126,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a373d812",
+   "id": "247f74a8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc930c88",
+   "id": "a8b2f344",
    "metadata": {},
    "source": [
     "Load image and apply transforms"
@@ -149,7 +149,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "18b14e45",
+   "id": "87257110",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -161,7 +161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45a75b2d",
+   "id": "b8043f04",
    "metadata": {},
    "source": [
     "Predict and resize to original resolution"
@@ -170,7 +170,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e8771d83",
+   "id": "a6fc05a1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -189,7 +189,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "becc084f",
+   "id": "2d3d0b97",
    "metadata": {},
    "source": [
     "Show result"
@@ -198,7 +198,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d64cde3e",
+   "id": "68d9f090",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -208,7 +208,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fa9500de",
+   "id": "e15edae4",
    "metadata": {},
    "source": [
     "### References\n",
@@ -222,7 +222,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "961fd18f",
+   "id": "28f08e21",
    "metadata": {
     "attributes": {
      "classes": [
@@ -244,7 +244,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "96344668",
+   "id": "d90e9990",
    "metadata": {
     "attributes": {
      "classes": [
diff --git a/assets/hub/mateuszbuda_brain-segmentation-pytorch_unet.ipynb b/assets/hub/mateuszbuda_brain-segmentation-pytorch_unet.ipynb
index ad0e25556013..e3357f74af93 100644
--- a/assets/hub/mateuszbuda_brain-segmentation-pytorch_unet.ipynb
+++ b/assets/hub/mateuszbuda_brain-segmentation-pytorch_unet.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "4d8401aa",
+   "id": "36854897",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e3805838",
+   "id": "adc84183",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27a6f447",
+   "id": "4f4eedad",
    "metadata": {},
    "source": [
     "Loads a U-Net model pre-trained for abnormality segmentation on a dataset of brain MRI volumes [kaggle.com/mateuszbuda/lgg-mri-segmentation](https://www.kaggle.com/mateuszbuda/lgg-mri-segmentation)\n",
@@ -57,7 +57,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "92e2721e",
+   "id": "c0b5d491",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,7 +71,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "92ec8e4f",
+   "id": "80fe56cd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "568b174e",
+   "id": "703f2552",
    "metadata": {},
    "source": [
     "### References\n",
diff --git a/assets/hub/nicolalandro_ntsnet-cub200_ntsnet.ipynb b/assets/hub/nicolalandro_ntsnet-cub200_ntsnet.ipynb
index 7a829611014d..e6b834a8bb62 100644
--- a/assets/hub/nicolalandro_ntsnet-cub200_ntsnet.ipynb
+++ b/assets/hub/nicolalandro_ntsnet-cub200_ntsnet.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7ff24817",
+   "id": "80d0fa2b",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7b480b1e",
+   "id": "254cd796",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df97dfed",
+   "id": "c8758251",
    "metadata": {},
    "source": [
     "### Example Usage"
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6fd63b9f",
+   "id": "f8a4b4c9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d25ce4ac",
+   "id": "c397c709",
    "metadata": {},
    "source": [
     "### Model Description\n",
@@ -91,7 +91,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "241f4f8f",
+   "id": "2f858e49",
    "metadata": {
     "attributes": {
      "classes": [
diff --git a/assets/hub/nvidia_deeplearningexamples_efficientnet.ipynb b/assets/hub/nvidia_deeplearningexamples_efficientnet.ipynb
index 53264f190950..a228e9765d2b 100644
--- a/assets/hub/nvidia_deeplearningexamples_efficientnet.ipynb
+++ b/assets/hub/nvidia_deeplearningexamples_efficientnet.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "06236c8b",
+   "id": "16dd12b5",
    "metadata": {},
    "source": [
     "### This notebook requires a GPU runtime to run.\n",
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "47684356",
+   "id": "579f6903",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,7 +52,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "054e88c8",
+   "id": "e54670be",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8eeca6df",
+   "id": "18ccdf10",
    "metadata": {},
    "source": [
     "Load the model pretrained on ImageNet dataset.\n",
@@ -93,7 +93,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "33d10c55",
+   "id": "97e36c99",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a0d43fe",
+   "id": "7a276050",
    "metadata": {},
    "source": [
     "Prepare sample input data."
@@ -114,7 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1a31651e",
+   "id": "4d22e75d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c333fd59",
+   "id": "cdeee9c4",
    "metadata": {},
    "source": [
     "Run inference. Use `pick_n_best(predictions=output, n=topN)` helper function to pick N most probable hypotheses according to the model."
@@ -141,7 +141,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f63e272c",
+   "id": "bdf0c853",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -153,7 +153,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f454bf4",
+   "id": "3252cf2f",
    "metadata": {},
    "source": [
     "Display the result."
@@ -162,7 +162,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7a1f1090",
+   "id": "8a69b900",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,7 +176,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ccee0adc",
+   "id": "a8426e40",
    "metadata": {},
    "source": [
     "### Details\n",
diff --git a/assets/hub/nvidia_deeplearningexamples_fastpitch.ipynb b/assets/hub/nvidia_deeplearningexamples_fastpitch.ipynb
index d657eb56bb08..6bde9566629b 100644
--- a/assets/hub/nvidia_deeplearningexamples_fastpitch.ipynb
+++ b/assets/hub/nvidia_deeplearningexamples_fastpitch.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "8335cef2",
+   "id": "35398358",
    "metadata": {},
    "source": [
     "### This notebook requires a GPU runtime to run.\n",
@@ -51,7 +51,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "99b310cd",
+   "id": "d62e722c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2d5bcfb6",
+   "id": "9ccea2b4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b331fb57",
+   "id": "6117141c",
    "metadata": {},
    "source": [
     "Download and setup FastPitch generator model."
@@ -91,7 +91,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f0f9a48c",
+   "id": "1178aeaa",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2fedf1d3",
+   "id": "fed393a2",
    "metadata": {},
    "source": [
     "Download and setup vocoder and denoiser models."
@@ -109,7 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "774f291c",
+   "id": "10853661",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86ac124c",
+   "id": "9a21ef3f",
    "metadata": {},
    "source": [
     "Verify that generator and vocoder models agree on input parameters."
@@ -127,7 +127,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2601ca60",
+   "id": "7cc0a869",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6298ddfb",
+   "id": "52e11f95",
    "metadata": {},
    "source": [
     "Put all models on available device."
@@ -156,7 +156,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2352292b",
+   "id": "a69596a4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63676a62",
+   "id": "be021ea2",
    "metadata": {},
    "source": [
     "Load text processor."
@@ -176,7 +176,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b794808d",
+   "id": "98fef381",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -185,7 +185,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c671847",
+   "id": "136c21df",
    "metadata": {},
    "source": [
     "Set the text to be synthetized, prepare input and set additional generation parameters."
@@ -194,7 +194,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9ecd2f4d",
+   "id": "61f8192d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -204,7 +204,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c12420f6",
+   "id": "c17d1989",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,7 +214,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fa51b9ba",
+   "id": "8253d94e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -228,7 +228,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9415c6b1",
+   "id": "4d4a1a71",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -242,7 +242,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e6d7312",
+   "id": "e6f32706",
    "metadata": {},
    "source": [
     "Plot the intermediate spectorgram."
@@ -251,7 +251,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "970d4994",
+   "id": "a889b6b3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,7 +265,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad54977d",
+   "id": "5edfccfa",
    "metadata": {},
    "source": [
     "Syntesize audio."
@@ -274,7 +274,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1b9e64fe",
+   "id": "8c8e1fda",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,7 +284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4a3cfd4",
+   "id": "cb917a83",
    "metadata": {},
    "source": [
     "Write audio to wav file."
@@ -293,7 +293,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7189d7a5",
+   "id": "a20af121",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -303,7 +303,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7723b2eb",
+   "id": "35e1c107",
    "metadata": {},
    "source": [
     "### Details\n",
diff --git a/assets/hub/nvidia_deeplearningexamples_gpunet.ipynb b/assets/hub/nvidia_deeplearningexamples_gpunet.ipynb
index 6126b1125513..63603a3d08df 100644
--- a/assets/hub/nvidia_deeplearningexamples_gpunet.ipynb
+++ b/assets/hub/nvidia_deeplearningexamples_gpunet.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "51f776e6",
+   "id": "94c28869",
    "metadata": {},
    "source": [
     "### This notebook requires a GPU runtime to run.\n",
@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b6265e7d",
+   "id": "6a56c86d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,7 +45,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f58a929c",
+   "id": "7e5cd61c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05bafba9",
+   "id": "82d85a57",
    "metadata": {},
    "source": [
     "### Load Pretrained model\n",
@@ -97,7 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bec84ee3",
+   "id": "dbadc515",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd7c8a91",
+   "id": "de80ac7e",
    "metadata": {},
    "source": [
     "### Prepare inference data\n",
@@ -123,7 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ecc48106",
+   "id": "1d61f625",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,7 +146,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1252a354",
+   "id": "6b536cfa",
    "metadata": {},
    "source": [
     "### Run inference\n",
@@ -156,7 +156,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5972ad6f",
+   "id": "c31a78ac",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,7 +168,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "687f85fa",
+   "id": "6741336f",
    "metadata": {},
    "source": [
     "### Display result"
@@ -177,7 +177,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "716238dd",
+   "id": "e405aa9f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "76a61b49",
+   "id": "4b5c39bb",
    "metadata": {},
    "source": [
     "### Details\n",
diff --git a/assets/hub/nvidia_deeplearningexamples_hifigan.ipynb b/assets/hub/nvidia_deeplearningexamples_hifigan.ipynb
index ea51692dedcb..c383d5a6fe8f 100644
--- a/assets/hub/nvidia_deeplearningexamples_hifigan.ipynb
+++ b/assets/hub/nvidia_deeplearningexamples_hifigan.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "4a175da6",
+   "id": "b7e24248",
    "metadata": {},
    "source": [
     "### This notebook requires a GPU runtime to run.\n",
@@ -44,7 +44,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a1e63852",
+   "id": "fe720bfd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +59,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ac17f8e1",
+   "id": "bd68badf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c58ed30",
+   "id": "69c9817c",
    "metadata": {},
    "source": [
     "Download and setup FastPitch generator model."
@@ -84,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "25ef3004",
+   "id": "b42fa7c0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b2a5481",
+   "id": "343d174e",
    "metadata": {},
    "source": [
     "Download and setup vocoder and denoiser models."
@@ -102,7 +102,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a6ae5a69",
+   "id": "749745de",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "228da38c",
+   "id": "c5f94c55",
    "metadata": {},
    "source": [
     "Verify that generator and vocoder models agree on input parameters."
@@ -120,7 +120,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "11c7a53a",
+   "id": "26c363bd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "652177b7",
+   "id": "d3185f09",
    "metadata": {},
    "source": [
     "Put all models on available device."
@@ -149,7 +149,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e5bb7ebc",
+   "id": "9d0af800",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c420d0ee",
+   "id": "95edfb46",
    "metadata": {},
    "source": [
     "Load text processor."
@@ -169,7 +169,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "085aed06",
+   "id": "3f161fbe",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cbcc85d2",
+   "id": "94372288",
    "metadata": {},
    "source": [
     "Set the text to be synthetized, prepare input and set additional generation parameters."
@@ -187,7 +187,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a781eeed",
+   "id": "2b920e08",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,7 +197,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "95570589",
+   "id": "a6329134",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -207,7 +207,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bff1d381",
+   "id": "27d3e565",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -221,7 +221,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8b40325a",
+   "id": "fe1f0bb5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,7 +235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d3b756e",
+   "id": "5e6e9027",
    "metadata": {},
    "source": [
     "Plot the intermediate spectorgram."
@@ -244,7 +244,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4a3888b8",
+   "id": "e7afd2e8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb74472a",
+   "id": "056a517a",
    "metadata": {},
    "source": [
     "Syntesize audio."
@@ -267,7 +267,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "825b2587",
+   "id": "0084610f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -277,7 +277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bcbad8d7",
+   "id": "bb4df622",
    "metadata": {},
    "source": [
     "Write audio to wav file."
@@ -286,7 +286,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3402ccbc",
+   "id": "d4f72102",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -296,7 +296,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca8d169d",
+   "id": "37b219e7",
    "metadata": {},
    "source": [
     "### Details\n",
diff --git a/assets/hub/nvidia_deeplearningexamples_resnet50.ipynb b/assets/hub/nvidia_deeplearningexamples_resnet50.ipynb
index 0c7d42833924..fd7208324fd5 100644
--- a/assets/hub/nvidia_deeplearningexamples_resnet50.ipynb
+++ b/assets/hub/nvidia_deeplearningexamples_resnet50.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2904159f",
+   "id": "dc10fa2d",
    "metadata": {},
    "source": [
     "### This notebook requires a GPU runtime to run.\n",
@@ -44,7 +44,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "db2d989b",
+   "id": "4b7e4c74",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +54,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "749ad4c2",
+   "id": "0a38e856",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "76ceaf8b",
+   "id": "dcb057cf",
    "metadata": {},
    "source": [
     "Load the model pretrained on ImageNet dataset."
@@ -84,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5714c3c1",
+   "id": "3ba613fa",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7ad98d14",
+   "id": "64d06fce",
    "metadata": {},
    "source": [
     "Prepare sample input data."
@@ -105,7 +105,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1d5f30bf",
+   "id": "06f24989",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23547f43",
+   "id": "99c597ac",
    "metadata": {},
    "source": [
     "Run inference. Use `pick_n_best(predictions=output, n=topN)` helper function to pick N most probably hypothesis according to the model."
@@ -132,7 +132,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "09eee7e5",
+   "id": "0dc8a1a9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,7 +144,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8a9b75d",
+   "id": "5965f6ed",
    "metadata": {},
    "source": [
     "Display the result."
@@ -153,7 +153,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8641346b",
+   "id": "b1dea786",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1aab4df2",
+   "id": "5b83f14b",
    "metadata": {},
    "source": [
     "### Details\n",
diff --git a/assets/hub/nvidia_deeplearningexamples_resnext.ipynb b/assets/hub/nvidia_deeplearningexamples_resnext.ipynb
index 461fbf53aa01..c4b1f0bec70c 100644
--- a/assets/hub/nvidia_deeplearningexamples_resnext.ipynb
+++ b/assets/hub/nvidia_deeplearningexamples_resnext.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "1ededb6c",
+   "id": "e72c9bda",
    "metadata": {},
    "source": [
     "### This notebook requires a GPU runtime to run.\n",
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "51aea367",
+   "id": "a7510258",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a7a6068a",
+   "id": "17f9db6f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,7 +84,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "214e06ed",
+   "id": "4926bab0",
    "metadata": {},
    "source": [
     "Load the model pretrained on ImageNet dataset."
@@ -93,7 +93,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "015395b9",
+   "id": "6a3b38bd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d51738a3",
+   "id": "b6ef4884",
    "metadata": {},
    "source": [
     "Prepare sample input data."
@@ -114,7 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "337fa0bc",
+   "id": "7de2ce79",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "108e2242",
+   "id": "9dab7a61",
    "metadata": {},
    "source": [
     "Run inference. Use `pick_n_best(predictions=output, n=topN)` helper function to pick N most probably hypothesis according to the model."
@@ -142,7 +142,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8acc61bf",
+   "id": "ed2224e0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6cabcb50",
+   "id": "14ca87c6",
    "metadata": {},
    "source": [
     "Display the result."
@@ -163,7 +163,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9177ac0f",
+   "id": "c3a2823b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "425a843e",
+   "id": "e45813fe",
    "metadata": {},
    "source": [
     "### Details\n",
diff --git a/assets/hub/nvidia_deeplearningexamples_se-resnext.ipynb b/assets/hub/nvidia_deeplearningexamples_se-resnext.ipynb
index 4d9ea90377c2..0736fca96a8c 100644
--- a/assets/hub/nvidia_deeplearningexamples_se-resnext.ipynb
+++ b/assets/hub/nvidia_deeplearningexamples_se-resnext.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7bdab1c5",
+   "id": "aef46189",
    "metadata": {},
    "source": [
     "### This notebook requires a GPU runtime to run.\n",
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "85b54891",
+   "id": "7b4d66fc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dd058a88",
+   "id": "ad33b836",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,7 +84,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abcfcc55",
+   "id": "84aad8fc",
    "metadata": {},
    "source": [
     "Load the model pretrained on ImageNet dataset."
@@ -93,7 +93,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f6841514",
+   "id": "2fdb6af5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0f23ebe",
+   "id": "cdb34ad0",
    "metadata": {},
    "source": [
     "Prepare sample input data."
@@ -114,7 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7669db84",
+   "id": "da929c1d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1dded722",
+   "id": "082f27a7",
    "metadata": {},
    "source": [
     "Run inference. Use `pick_n_best(predictions=output, n=topN)` helper function to pick N most probable hypotheses according to the model."
@@ -142,7 +142,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5bf3fb72",
+   "id": "0bb62c4c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff01b742",
+   "id": "b60137fc",
    "metadata": {},
    "source": [
     "Display the result."
@@ -163,7 +163,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "045b1371",
+   "id": "d02b1810",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40629204",
+   "id": "741f6dd4",
    "metadata": {},
    "source": [
     "### Details\n",
diff --git a/assets/hub/nvidia_deeplearningexamples_ssd.ipynb b/assets/hub/nvidia_deeplearningexamples_ssd.ipynb
index 669a05963e08..cd5c71ff6687 100644
--- a/assets/hub/nvidia_deeplearningexamples_ssd.ipynb
+++ b/assets/hub/nvidia_deeplearningexamples_ssd.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "00fc29ac",
+   "id": "a7fd2953",
    "metadata": {},
    "source": [
     "### This notebook requires a GPU runtime to run.\n",
@@ -56,7 +56,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "216fd60c",
+   "id": "310e0f77",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3598938",
+   "id": "393309f2",
    "metadata": {},
    "source": [
     "Load an SSD model pretrained on COCO dataset, as well as a set of utility methods for convenient and comprehensive formatting of input and output of the model."
@@ -75,7 +75,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5d01faeb",
+   "id": "411f2e80",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d4590bb",
+   "id": "d3d2f691",
    "metadata": {},
    "source": [
     "Now, prepare the loaded model for inference"
@@ -95,7 +95,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f3cc878d",
+   "id": "b28ad4a9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e211633",
+   "id": "0e9187a6",
    "metadata": {},
    "source": [
     "Prepare input images for object detection.\n",
@@ -115,7 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b5520438",
+   "id": "722223e7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "952427fe",
+   "id": "d8d0c93f",
    "metadata": {},
    "source": [
     "Format the images to comply with the network input and convert them to tensor."
@@ -137,7 +137,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "11a99b99",
+   "id": "00f422fb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3d89308",
+   "id": "dfe4b433",
    "metadata": {},
    "source": [
     "Run the SSD network to perform object detection."
@@ -156,7 +156,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9489953f",
+   "id": "5e358933",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "62d5b47b",
+   "id": "e4c6c76b",
    "metadata": {},
    "source": [
     "By default, raw output from SSD network per input image contains\n",
@@ -177,7 +177,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "897ec8f1",
+   "id": "6f2d74a6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5649c506",
+   "id": "c5af6c78",
    "metadata": {},
    "source": [
     "The model was trained on COCO dataset, which we need to access in order to translate class IDs into object names.\n",
@@ -197,7 +197,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "33096f43",
+   "id": "05aba236",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "324c5a71",
+   "id": "85c5a626",
    "metadata": {},
    "source": [
     "Finally, let's visualize our detections"
@@ -215,7 +215,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5b12929b",
+   "id": "de00fb67",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "96276363",
+   "id": "358e1ae2",
    "metadata": {},
    "source": [
     "### Details\n",
diff --git a/assets/hub/nvidia_deeplearningexamples_tacotron2.ipynb b/assets/hub/nvidia_deeplearningexamples_tacotron2.ipynb
index b922dbc7a436..4fffc9591ab0 100644
--- a/assets/hub/nvidia_deeplearningexamples_tacotron2.ipynb
+++ b/assets/hub/nvidia_deeplearningexamples_tacotron2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "1d2a5fd6",
+   "id": "e0e3d3c4",
    "metadata": {},
    "source": [
     "### This notebook requires a GPU runtime to run.\n",
@@ -41,7 +41,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c8e99b9c",
+   "id": "f041e30b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "767c188d",
+   "id": "9c3811b8",
    "metadata": {},
    "source": [
     "Load the Tacotron2 model pre-trained on [LJ Speech dataset](https://keithito.com/LJ-Speech-Dataset/) and prepare it for inference:"
@@ -62,7 +62,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "13d5b887",
+   "id": "1882fd02",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b58c0c88",
+   "id": "b983374e",
    "metadata": {},
    "source": [
     "Load pretrained WaveGlow model"
@@ -83,7 +83,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a5451a54",
+   "id": "1f2d1c60",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7eba6377",
+   "id": "7cbddd69",
    "metadata": {},
    "source": [
     "Now, let's make the model say:"
@@ -104,7 +104,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e4b76dc5",
+   "id": "0aff3bf8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c6bed8a",
+   "id": "fc8b5e40",
    "metadata": {},
    "source": [
     "Format the input using utility methods"
@@ -122,7 +122,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e838d14d",
+   "id": "2473c538",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63801402",
+   "id": "f7e694c7",
    "metadata": {},
    "source": [
     "Run the chained models:"
@@ -141,7 +141,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "19b611fa",
+   "id": "874ecd1e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77262942",
+   "id": "f80ca3ec",
    "metadata": {},
    "source": [
     "You can write it to a file and listen to it"
@@ -163,7 +163,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3eb8e532",
+   "id": "b6cfedfb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2592a3b",
+   "id": "4e0c3d14",
    "metadata": {},
    "source": [
     "Alternatively, play it right away in a notebook with IPython widgets"
@@ -182,7 +182,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b22c7a44",
+   "id": "cac99f65",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,7 +192,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fea6073",
+   "id": "e4e7d656",
    "metadata": {},
    "source": [
     "### Details\n",
diff --git a/assets/hub/nvidia_deeplearningexamples_waveglow.ipynb b/assets/hub/nvidia_deeplearningexamples_waveglow.ipynb
index 11533bee3866..bafefc93a500 100644
--- a/assets/hub/nvidia_deeplearningexamples_waveglow.ipynb
+++ b/assets/hub/nvidia_deeplearningexamples_waveglow.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c2e68cca",
+   "id": "4737738d",
    "metadata": {},
    "source": [
     "### This notebook requires a GPU runtime to run.\n",
@@ -39,7 +39,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "762e0c43",
+   "id": "16144d7f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c38795ea",
+   "id": "9058df91",
    "metadata": {},
    "source": [
     "Load the WaveGlow model pre-trained on [LJ Speech dataset](https://keithito.com/LJ-Speech-Dataset/)"
@@ -60,7 +60,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d860aa0e",
+   "id": "cfb19ffe",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "83d10fab",
+   "id": "4161c0ad",
    "metadata": {},
    "source": [
     "Prepare the WaveGlow model for inference"
@@ -79,7 +79,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "20b47dd6",
+   "id": "7bc84f49",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a091647e",
+   "id": "4d8154f0",
    "metadata": {},
    "source": [
     "Load a pretrained Tacotron2 model"
@@ -99,7 +99,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a342ba38",
+   "id": "45b294a9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98c82f9d",
+   "id": "0e97546e",
    "metadata": {},
    "source": [
     "Now, let's make the model say:"
@@ -119,7 +119,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "589d76ba",
+   "id": "e72c24e6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db5e0026",
+   "id": "bd2667fd",
    "metadata": {},
    "source": [
     "Format the input using utility methods"
@@ -137,7 +137,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1f74382e",
+   "id": "b1b8d5ad",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f5b4896",
+   "id": "303943c1",
    "metadata": {},
    "source": [
     "Run the chained models"
@@ -156,7 +156,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7be3aca9",
+   "id": "2615435e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad8869e1",
+   "id": "c270bfc3",
    "metadata": {},
    "source": [
     "You can write it to a file and listen to it"
@@ -178,7 +178,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "15a0c6d0",
+   "id": "26825978",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,7 +188,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1771622",
+   "id": "14484a89",
    "metadata": {},
    "source": [
     "Alternatively, play it right away in a notebook with IPython widgets"
@@ -197,7 +197,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "51e1dcd7",
+   "id": "4cc5f8f8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -207,7 +207,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4cfb5ba4",
+   "id": "427cf6bf",
    "metadata": {},
    "source": [
     "### Details\n",
diff --git a/assets/hub/pytorch_fairseq_roberta.ipynb b/assets/hub/pytorch_fairseq_roberta.ipynb
index 196068448cee..7ae7b041e31e 100644
--- a/assets/hub/pytorch_fairseq_roberta.ipynb
+++ b/assets/hub/pytorch_fairseq_roberta.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "31ad7aa7",
+   "id": "94a60a92",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -43,7 +43,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "121498f6",
+   "id": "862010ad",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d425fe97",
+   "id": "3aee90ba",
    "metadata": {},
    "source": [
     "### Example\n",
@@ -64,7 +64,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "454e72ca",
+   "id": "d17bf5e4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "360318ab",
+   "id": "b3dd17f8",
    "metadata": {},
    "source": [
     "##### Apply Byte-Pair Encoding (BPE) to input text"
@@ -84,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5d179e52",
+   "id": "21c10e8a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6b8994a9",
+   "id": "75dd8336",
    "metadata": {},
    "source": [
     "##### Extract features from RoBERTa"
@@ -104,7 +104,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d57b6500",
+   "id": "53830c5b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85168502",
+   "id": "22d492d5",
    "metadata": {},
    "source": [
     "##### Use RoBERTa for sentence-pair classification tasks"
@@ -129,7 +129,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9a220ccb",
+   "id": "bccc692e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6f34c54",
+   "id": "3fc1a321",
    "metadata": {},
    "source": [
     "##### Register a new (randomly initialized) classification head"
@@ -160,7 +160,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fda20e6e",
+   "id": "338f0cce",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4190649b",
+   "id": "e2d53b85",
    "metadata": {},
    "source": [
     "### References\n",
diff --git a/assets/hub/pytorch_fairseq_translation.ipynb b/assets/hub/pytorch_fairseq_translation.ipynb
index 62a04e2d49df..12fc64602aaf 100644
--- a/assets/hub/pytorch_fairseq_translation.ipynb
+++ b/assets/hub/pytorch_fairseq_translation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "03a70c24",
+   "id": "a0ef562f",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "20bcbe45",
+   "id": "11769181",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10a36215",
+   "id": "d8d3f273",
    "metadata": {},
    "source": [
     "### English-to-French Translation\n",
@@ -59,7 +59,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ce42e2ad",
+   "id": "8c417e7d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8409f70a",
+   "id": "8479dcc8",
    "metadata": {},
    "source": [
     "### English-to-German Translation\n",
@@ -123,7 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "70c5ad1a",
+   "id": "9da22e93",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f625ff49",
+   "id": "36b4666a",
    "metadata": {},
    "source": [
     "We can also do a round-trip translation to create a paraphrase:"
@@ -151,7 +151,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "49c2eb96",
+   "id": "bf89c380",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -172,7 +172,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a85bc1a5",
+   "id": "bcb08298",
    "metadata": {},
    "source": [
     "### References\n",
diff --git a/assets/hub/pytorch_vision_alexnet.ipynb b/assets/hub/pytorch_vision_alexnet.ipynb
index 36f035736f7e..f4c622962c42 100644
--- a/assets/hub/pytorch_vision_alexnet.ipynb
+++ b/assets/hub/pytorch_vision_alexnet.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "16626dc1",
+   "id": "3c885dd5",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "95d8ebe4",
+   "id": "36e46ac5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2953f18",
+   "id": "abf97661",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -49,7 +49,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "308fc9cb",
+   "id": "65d9054a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "682c4582",
+   "id": "e624f28d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "163b43f5",
+   "id": "e2a34643",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d5883fd0",
+   "id": "617e0700",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "449f01a3",
+   "id": "c21ae5c4",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_deeplabv3_resnet101.ipynb b/assets/hub/pytorch_vision_deeplabv3_resnet101.ipynb
index 633e2c71cd2b..91144ad0f438 100644
--- a/assets/hub/pytorch_vision_deeplabv3_resnet101.ipynb
+++ b/assets/hub/pytorch_vision_deeplabv3_resnet101.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "acd9bbb8",
+   "id": "091cbcb7",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "74540f95",
+   "id": "ce0f1a10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3faa89a5",
+   "id": "64e97cbc",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -54,7 +54,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fc820f6a",
+   "id": "4401a5ee",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +68,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d5a87afa",
+   "id": "92a05765",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab84a767",
+   "id": "35151109",
    "metadata": {},
    "source": [
     "The output here is of shape `(21, H, W)`, and at each location, there are unnormalized probabilities corresponding to the prediction of each class.\n",
@@ -109,7 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c59aae4b",
+   "id": "83d14a8a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +129,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13df020f",
+   "id": "a60195d1",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_densenet.ipynb b/assets/hub/pytorch_vision_densenet.ipynb
index 818a9867b715..48c3160bd82e 100644
--- a/assets/hub/pytorch_vision_densenet.ipynb
+++ b/assets/hub/pytorch_vision_densenet.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "0f8e983f",
+   "id": "76708fd0",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ac57fe2c",
+   "id": "cf3fe541",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a13e61a",
+   "id": "dcc580e7",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7796387d",
+   "id": "8512c10f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,7 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5c75871f",
+   "id": "2ba264fe",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +101,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6ccdd13c",
+   "id": "2df44bc5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5ced19d3",
+   "id": "8e45515e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "225e1c26",
+   "id": "06de3acb",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_fcn_resnet101.ipynb b/assets/hub/pytorch_vision_fcn_resnet101.ipynb
index 23cd72afec3a..019df15255b9 100644
--- a/assets/hub/pytorch_vision_fcn_resnet101.ipynb
+++ b/assets/hub/pytorch_vision_fcn_resnet101.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "65c8d9c9",
+   "id": "ef2032c1",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3ba547ac",
+   "id": "6b86c3e5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b7fde87",
+   "id": "0a5de13c",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1853b36e",
+   "id": "3e479d46",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,7 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4b80c169",
+   "id": "d7e777d9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7436a26c",
+   "id": "901192f4",
    "metadata": {},
    "source": [
     "The output here is of shape `(21, H, W)`, and at each location, there are unnormalized probabilities corresponding to the prediction of each class.\n",
@@ -108,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7cd0b22c",
+   "id": "5773dc62",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e77c06f",
+   "id": "c7a9fb19",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_ghostnet.ipynb b/assets/hub/pytorch_vision_ghostnet.ipynb
index 9acaf46345e3..d43d92ce3b10 100644
--- a/assets/hub/pytorch_vision_ghostnet.ipynb
+++ b/assets/hub/pytorch_vision_ghostnet.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "161def7a",
+   "id": "a295a3ca",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "17689f73",
+   "id": "8b08a9ba",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee748fa7",
+   "id": "090c64d8",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -47,7 +47,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ec8611d2",
+   "id": "dc642178",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +61,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2f1d5893",
+   "id": "581701ff",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bb1dba4d",
+   "id": "3cb0ee89",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,7 +106,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ebf31050",
+   "id": "ef7e4a50",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c29b10c6",
+   "id": "fb9a2922",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_googlenet.ipynb b/assets/hub/pytorch_vision_googlenet.ipynb
index 8b23f90001ca..f2624747e375 100644
--- a/assets/hub/pytorch_vision_googlenet.ipynb
+++ b/assets/hub/pytorch_vision_googlenet.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f1a7b60a",
+   "id": "265124d8",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "02901d68",
+   "id": "0814b75a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "efb92e8a",
+   "id": "1800f10c",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -49,7 +49,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c620d5b5",
+   "id": "a64461f1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "918dd743",
+   "id": "730ca989",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5b376b6c",
+   "id": "18e3d059",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7fad7b71",
+   "id": "182cbdbc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f2ef850",
+   "id": "c383b4bf",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_hardnet.ipynb b/assets/hub/pytorch_vision_hardnet.ipynb
index 6424d5154b15..6cd8a709f1e7 100644
--- a/assets/hub/pytorch_vision_hardnet.ipynb
+++ b/assets/hub/pytorch_vision_hardnet.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "6a281ce0",
+   "id": "38cc7d97",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "907b9af6",
+   "id": "9cc02677",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9af0f110",
+   "id": "479bc5da",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3fa2cf0e",
+   "id": "b636068c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,7 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "44b7eeb5",
+   "id": "2d962bfc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +101,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3799c18e",
+   "id": "bf29afeb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e5f4b725",
+   "id": "d117ce33",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abf47544",
+   "id": "906f320d",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_ibnnet.ipynb b/assets/hub/pytorch_vision_ibnnet.ipynb
index 382524a87a4d..8b5d86fa25f8 100644
--- a/assets/hub/pytorch_vision_ibnnet.ipynb
+++ b/assets/hub/pytorch_vision_ibnnet.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "4f13635a",
+   "id": "60422909",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "56a4dcb3",
+   "id": "066eedcc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c398f0ba",
+   "id": "a57b711a",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -47,7 +47,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fd070cbd",
+   "id": "e8b72a02",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +61,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2d54e05b",
+   "id": "98283199",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cd7ea50b",
+   "id": "cb6ff6a8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,7 +106,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "75345899",
+   "id": "897b2490",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "326578b9",
+   "id": "84cd3475",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_inception_v3.ipynb b/assets/hub/pytorch_vision_inception_v3.ipynb
index 92c99f2af61f..715ee584fb8e 100644
--- a/assets/hub/pytorch_vision_inception_v3.ipynb
+++ b/assets/hub/pytorch_vision_inception_v3.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f2c74b7c",
+   "id": "dc51d9d5",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "84dbf1bd",
+   "id": "4df2a343",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6388089",
+   "id": "85dc5701",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -47,7 +47,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "69feb11a",
+   "id": "9bca7a4a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +61,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2c57f09c",
+   "id": "1be3ccb3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9877fa26",
+   "id": "c084cb06",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,7 +106,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "435fd085",
+   "id": "df58bd8b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6a360d3",
+   "id": "925cc221",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_meal_v2.ipynb b/assets/hub/pytorch_vision_meal_v2.ipynb
index b6ced128e15e..dc69aa6646ac 100644
--- a/assets/hub/pytorch_vision_meal_v2.ipynb
+++ b/assets/hub/pytorch_vision_meal_v2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "78518d9d",
+   "id": "69e7c335",
    "metadata": {},
    "source": [
     "### This notebook requires a GPU runtime to run.\n",
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "db36ecf4",
+   "id": "478cec56",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4647ee28",
+   "id": "144635e0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "667b27b2",
+   "id": "379d9d7f",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -65,7 +65,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "58111d27",
+   "id": "331e41b6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +79,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "edb71391",
+   "id": "8434e911",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +113,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "af62952b",
+   "id": "726b8b7b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -124,7 +124,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f5eeb7f8",
+   "id": "dc36f79f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,7 +139,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05b99954",
+   "id": "f2a96be3",
    "metadata": {},
    "source": [
     "### Model Description\n",
@@ -167,7 +167,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9b70eb90",
+   "id": "58b0bd08",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -181,7 +181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99f86567",
+   "id": "85824670",
    "metadata": {},
    "source": [
     "@inproceedings{shen2019MEAL,\n",
diff --git a/assets/hub/pytorch_vision_mobilenet_v2.ipynb b/assets/hub/pytorch_vision_mobilenet_v2.ipynb
index 802303469a2a..1ea6f0f67d17 100644
--- a/assets/hub/pytorch_vision_mobilenet_v2.ipynb
+++ b/assets/hub/pytorch_vision_mobilenet_v2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7953a328",
+   "id": "26aa9f97",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "317ec13f",
+   "id": "4423fb1f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef53f37a",
+   "id": "d951fc23",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -49,7 +49,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4588a23a",
+   "id": "d6a078f9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "380ccc04",
+   "id": "66d7f82b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "60e2d2cd",
+   "id": "5cdc2653",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b1be69f6",
+   "id": "6d95262c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61eef719",
+   "id": "8a13bc6b",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_once_for_all.ipynb b/assets/hub/pytorch_vision_once_for_all.ipynb
index 3b8a36b12698..cb468dcf8164 100644
--- a/assets/hub/pytorch_vision_once_for_all.ipynb
+++ b/assets/hub/pytorch_vision_once_for_all.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "9daeccba",
+   "id": "39d74515",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -29,7 +29,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e2920106",
+   "id": "915406d2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fa6bf8c3",
+   "id": "a6b4baf5",
    "metadata": {},
    "source": [
     "| OFA Network         | Design Space | Resolution | Width Multiplier |  Depth |  Expand Ratio |  kernel Size | \n",
@@ -62,7 +62,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "44318a1a",
+   "id": "1fbb128a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab3fae3c",
+   "id": "fa1c85fa",
    "metadata": {},
    "source": [
     "### Get Specialized Architecture"
@@ -86,7 +86,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "97fe0aac",
+   "id": "141ce42e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c941f15",
+   "id": "62131db8",
    "metadata": {},
    "source": [
     "More models and configurations can be found in [once-for-all/model-zoo](https://github.com/mit-han-lab/once-for-all#evaluate-1)\n",
@@ -111,7 +111,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2d577955",
+   "id": "3e2ce7a1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4490eab",
+   "id": "4c775b82",
    "metadata": {},
    "source": [
     "The model's prediction can be evalutaed by"
@@ -131,7 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "db41275c",
+   "id": "66ce5d71",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95dbd248",
+   "id": "e01481b9",
    "metadata": {},
    "source": [
     "### Model Description\n",
@@ -189,7 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a46493e4",
+   "id": "2ba18e84",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/assets/hub/pytorch_vision_proxylessnas.ipynb b/assets/hub/pytorch_vision_proxylessnas.ipynb
index 5fb18284a6bf..fb3148f49baa 100644
--- a/assets/hub/pytorch_vision_proxylessnas.ipynb
+++ b/assets/hub/pytorch_vision_proxylessnas.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "5373c917",
+   "id": "a391a344",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4e082662",
+   "id": "99601745",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3c04d33",
+   "id": "2325f09c",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -49,7 +49,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9e5fc0f1",
+   "id": "4dae43ad",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7de307ea",
+   "id": "5ccf8eaf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9815a194",
+   "id": "e1ea05a3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4444854e",
+   "id": "17f6fe00",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "88f51718",
+   "id": "646e2bfb",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_resnest.ipynb b/assets/hub/pytorch_vision_resnest.ipynb
index 194fc5c96d4a..26f8ecc7eca5 100644
--- a/assets/hub/pytorch_vision_resnest.ipynb
+++ b/assets/hub/pytorch_vision_resnest.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "abca1a5a",
+   "id": "f6ad132d",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a2e95264",
+   "id": "5941a9cc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "befe843f",
+   "id": "8463e678",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -50,7 +50,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4dcfce53",
+   "id": "cc3c3c8c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +64,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5dab18e5",
+   "id": "2f979882",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,7 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "63b96316",
+   "id": "95be068b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,7 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "eae10c0e",
+   "id": "5ac52421",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -124,7 +124,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a3e8db4",
+   "id": "e9ac388e",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_resnet.ipynb b/assets/hub/pytorch_vision_resnet.ipynb
index 4a19f442667e..36629aad573c 100644
--- a/assets/hub/pytorch_vision_resnet.ipynb
+++ b/assets/hub/pytorch_vision_resnet.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "d8087c15",
+   "id": "1c0aaf86",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "41a2a36a",
+   "id": "bc98d2fd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92097d39",
+   "id": "80a2cf64",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -52,7 +52,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c14501e7",
+   "id": "79ced132",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9028552b",
+   "id": "94b15881",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,7 +100,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "67497b46",
+   "id": "fbecef47",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,7 +111,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "29fa3c75",
+   "id": "f0271a30",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "49085582",
+   "id": "3647fd75",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_resnext.ipynb b/assets/hub/pytorch_vision_resnext.ipynb
index 7987db9b398a..bbed5e9bc0c5 100644
--- a/assets/hub/pytorch_vision_resnext.ipynb
+++ b/assets/hub/pytorch_vision_resnext.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "35e27b7b",
+   "id": "24d91fcc",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1a2a41b3",
+   "id": "d0214938",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01946ced",
+   "id": "628cd867",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -49,7 +49,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cac096b4",
+   "id": "d3d0cc3c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "40d0f659",
+   "id": "d693cdf1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "55a9a1d8",
+   "id": "4060cee4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f3866e62",
+   "id": "27a812c9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "040ccd09",
+   "id": "18460454",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_shufflenet_v2.ipynb b/assets/hub/pytorch_vision_shufflenet_v2.ipynb
index 2d2beca20add..5e25f34a6b66 100644
--- a/assets/hub/pytorch_vision_shufflenet_v2.ipynb
+++ b/assets/hub/pytorch_vision_shufflenet_v2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "95ff55c3",
+   "id": "2a7af770",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fc271b95",
+   "id": "b0c9c160",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "22e21ca4",
+   "id": "670d3296",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -49,7 +49,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fbce3848",
+   "id": "acb2705f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "59c8d3fb",
+   "id": "dfb380fe",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7c543e92",
+   "id": "da43c9ac",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7e173469",
+   "id": "bef76571",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5e5ccceb",
+   "id": "3ebbabcd",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_snnmlp.ipynb b/assets/hub/pytorch_vision_snnmlp.ipynb
index 9601cc96206a..2e98cdfb5f81 100644
--- a/assets/hub/pytorch_vision_snnmlp.ipynb
+++ b/assets/hub/pytorch_vision_snnmlp.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "87c35091",
+   "id": "26001b9c",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a9dc31a1",
+   "id": "8aefcce7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33554d77",
+   "id": "6e3587dc",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -51,7 +51,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ddc30dfe",
+   "id": "05c10bb6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,7 +65,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c51f09db",
+   "id": "c548b9d3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10f333eb",
+   "id": "61afa7bd",
    "metadata": {},
    "source": [
     "### Model Description\n",
@@ -121,7 +121,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "288c4bed",
+   "id": "b070a0d3",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/assets/hub/pytorch_vision_squeezenet.ipynb b/assets/hub/pytorch_vision_squeezenet.ipynb
index 1f3178a8d53b..8eeee053cea2 100644
--- a/assets/hub/pytorch_vision_squeezenet.ipynb
+++ b/assets/hub/pytorch_vision_squeezenet.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "990b17b6",
+   "id": "e3cf10c0",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6eec995d",
+   "id": "6b0f04f5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef7589d3",
+   "id": "14301e0d",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -49,7 +49,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d455b779",
+   "id": "bd13e568",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d382dc58",
+   "id": "18675fde",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1172d23b",
+   "id": "d4329710",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a19b4f86",
+   "id": "4755ebbf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cdab898c",
+   "id": "7b7d6c59",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_vgg.ipynb b/assets/hub/pytorch_vision_vgg.ipynb
index 1f7567aa42a5..94082489f2d8 100644
--- a/assets/hub/pytorch_vision_vgg.ipynb
+++ b/assets/hub/pytorch_vision_vgg.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b7bf779d",
+   "id": "24dbd7fd",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cf95fa3b",
+   "id": "0f90b7a9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a09fce9d",
+   "id": "94461774",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -55,7 +55,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e4cc1824",
+   "id": "cb9c4d28",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +69,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6d1cf177",
+   "id": "624987be",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,7 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "497e676e",
+   "id": "3b8897e4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,7 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "14d11348",
+   "id": "5d69095a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +129,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e61007b6",
+   "id": "03f74824",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/pytorch_vision_wide_resnet.ipynb b/assets/hub/pytorch_vision_wide_resnet.ipynb
index 542a219382ab..cf161c963d87 100644
--- a/assets/hub/pytorch_vision_wide_resnet.ipynb
+++ b/assets/hub/pytorch_vision_wide_resnet.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2d21a3e8",
+   "id": "d366fb24",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ad9c5b1e",
+   "id": "d1a86f6e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "051c5f23",
+   "id": "62fc0b0b",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -50,7 +50,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7c5780de",
+   "id": "5edc1040",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +64,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e42019aa",
+   "id": "a86c792c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,7 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ff656268",
+   "id": "f09c00c9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,7 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d739c287",
+   "id": "4f257dba",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -124,7 +124,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c073a20",
+   "id": "52ade214",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/sigsep_open-unmix-pytorch_umx.ipynb b/assets/hub/sigsep_open-unmix-pytorch_umx.ipynb
index 110765165d8d..8b4ceede5f16 100644
--- a/assets/hub/sigsep_open-unmix-pytorch_umx.ipynb
+++ b/assets/hub/sigsep_open-unmix-pytorch_umx.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b82b10f0",
+   "id": "6a1a65cd",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "44aa00e6",
+   "id": "f9790a9b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7649dafc",
+   "id": "47fe7b1f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12bb0cfc",
+   "id": "1982e29b",
    "metadata": {},
    "source": [
     "### Model Description\n",
@@ -94,7 +94,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2ce82f72",
+   "id": "fb5c980f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "138c8662",
+   "id": "55c65701",
    "metadata": {},
    "source": [
     "### References\n",
diff --git a/assets/hub/simplenet.ipynb b/assets/hub/simplenet.ipynb
index 3fef62c5553f..892b473529f1 100644
--- a/assets/hub/simplenet.ipynb
+++ b/assets/hub/simplenet.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "40c42022",
+   "id": "b2a471c2",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "99af1e5b",
+   "id": "ad86a684",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fef7ea73",
+   "id": "d7daf7cb",
    "metadata": {},
    "source": [
     "All pre-trained models expect input images normalized in the same way,\n",
@@ -55,7 +55,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d8df08b5",
+   "id": "cc0ab575",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +69,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "efd8576d",
+   "id": "a6a16cfb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,7 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "88d04e45",
+   "id": "ee1319ea",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,7 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fd2932fd",
+   "id": "daebb731",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +129,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10c955ff",
+   "id": "c79fc11b",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/snakers4_silero-models_stt.ipynb b/assets/hub/snakers4_silero-models_stt.ipynb
index c04b82edd7ec..c93ee85be423 100644
--- a/assets/hub/snakers4_silero-models_stt.ipynb
+++ b/assets/hub/snakers4_silero-models_stt.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "860cb313",
+   "id": "eb575053",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ed8ffa2a",
+   "id": "3a9d262f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ed535a3f",
+   "id": "8dd1d901",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b82b835c",
+   "id": "50caba0b",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/snakers4_silero-models_tts.ipynb b/assets/hub/snakers4_silero-models_tts.ipynb
index 685de7507119..5a7178430ac3 100644
--- a/assets/hub/snakers4_silero-models_tts.ipynb
+++ b/assets/hub/snakers4_silero-models_tts.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f0a6d820",
+   "id": "0db6016b",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -20,7 +20,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "223e020b",
+   "id": "e643ea69",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1ca8e914",
+   "id": "1b0fffba",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "037a5256",
+   "id": "d1a169dc",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/snakers4_silero-vad_vad.ipynb b/assets/hub/snakers4_silero-vad_vad.ipynb
index db0eeb3b2fa5..83ec083b978d 100644
--- a/assets/hub/snakers4_silero-vad_vad.ipynb
+++ b/assets/hub/snakers4_silero-vad_vad.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "bb0fd16b",
+   "id": "a4268e07",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "212d3342",
+   "id": "e073be9d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "27ef74aa",
+   "id": "8adf2b00",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "965c01c1",
+   "id": "6963bff7",
    "metadata": {},
    "source": [
     "### Model Description\n",
diff --git a/assets/hub/ultralytics_yolov5.ipynb b/assets/hub/ultralytics_yolov5.ipynb
index 873a64ac9276..fa39fb4d897c 100644
--- a/assets/hub/ultralytics_yolov5.ipynb
+++ b/assets/hub/ultralytics_yolov5.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "a0180524",
+   "id": "1c32e755",
    "metadata": {},
    "source": [
     "### This notebook is optionally accelerated with a GPU runtime.\n",
@@ -29,7 +29,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "99c50335",
+   "id": "1ef68660",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3209a0a5",
+   "id": "953b653d",
    "metadata": {},
    "source": [
     "## Model Description\n",
@@ -82,7 +82,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2c778684",
+   "id": "c3dd5584",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc6c1715",
+   "id": "d328eadf",
    "metadata": {},
    "source": [
     "## Citation\n",
@@ -125,7 +125,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a4d290d8",
+   "id": "14ad32c4",
    "metadata": {
     "attributes": {
      "classes": [
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2845b328",
+   "id": "9329814d",
    "metadata": {},
    "source": [
     "## Contact\n",
diff --git a/case_studies/amazon-ads.html b/case_studies/amazon-ads.html
index afb1691e229c..19fe8366a880 100644
--- a/case_studies/amazon-ads.html
+++ b/case_studies/amazon-ads.html
@@ -310,7 +310,7 @@
 
     <div class="jumbotron jumbotron-fluid blog-detail-jumbotron">
         <div class="container blog-detail-container">
-            <p class="featured-post">November 07, 2024</p>
+            <p class="featured-post">November 08, 2024</p>
             <h1>
                 <a class="blog-title">Amazon Ads</a>
             </h1>
diff --git a/case_studies/salesforce.html b/case_studies/salesforce.html
index 69fad66d84a4..a05a59689bc1 100644
--- a/case_studies/salesforce.html
+++ b/case_studies/salesforce.html
@@ -310,7 +310,7 @@
 
     <div class="jumbotron jumbotron-fluid blog-detail-jumbotron">
         <div class="container blog-detail-container">
-            <p class="featured-post">November 07, 2024</p>
+            <p class="featured-post">November 08, 2024</p>
             <h1>
                 <a class="blog-title">Salesforce</a>
             </h1>
diff --git a/case_studies/stanford-university.html b/case_studies/stanford-university.html
index 91290664ac78..d45558504037 100644
--- a/case_studies/stanford-university.html
+++ b/case_studies/stanford-university.html
@@ -310,7 +310,7 @@
 
     <div class="jumbotron jumbotron-fluid blog-detail-jumbotron">
         <div class="container blog-detail-container">
-            <p class="featured-post">November 07, 2024</p>
+            <p class="featured-post">November 08, 2024</p>
             <h1>
                 <a class="blog-title">Stanford University</a>
             </h1>
diff --git a/ecosystem/index.html b/ecosystem/index.html
index 159e9edc1121..427989c5b161 100644
--- a/ecosystem/index.html
+++ b/ecosystem/index.html
@@ -373,13 +373,13 @@ <h1>
         <div class="list row ecosystem-cards-wrapper cards-right">
           
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="8/6/20" data-id="pfnet/pfrl" data-title="PFRL">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="pyg-team/pytorch_geometric" data-title="PyTorch Geometric">
               <div class="card ecosystem-card">
-                <a href="https://github.com/pfnet/pfrl" target="_blank">
+                <a href="https://github.com/pyg-team/pytorch_geometric/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">PFRL</li>
+                        <li class="card-title">PyTorch Geometric</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -388,19 +388,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">PFRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using PyTorch.</p>
+                    <p class="card-summary">PyTorch Geometric is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/24/24" data-id="hpcaitech/ColossalAI" data-title="Colossal-LLaMA-2">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/10/21" data-id="lyft/l5kit" data-title="L5Kit">
               <div class="card ecosystem-card">
-                <a href="https://github.com/hpcaitech/ColossalAI/tree/main/applications/Colossal-LLaMA-2" target="_blank">
+                <a href="https://github.com/lyft/l5kit" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Colossal-LLaMA-2</li>
+                        <li class="card-title">L5Kit</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -409,19 +409,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">A complete and open-sourced solution for injecting domain-specific knowledge into pre-trained LLM.</p>
+                    <p class="card-summary">ML Prediction, Planning and Simulation for Self-Driving built on PyTorch.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="8/20/20" data-id="ray-project/ray" data-title="Ray">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="metaopt/TorchOpt" data-title="TorchOpt">
               <div class="card ecosystem-card">
-                <a href="https://github.com/ray-project/ray" target="_blank">
+                <a href="https://torchopt.readthedocs.io/en/latest/#" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Ray</li>
+                        <li class="card-title">TorchOpt</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -430,19 +430,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Ray is a fast and simple framework for building and running distributed applications.</p>
+                    <p class="card-summary">TorchOpt is a PyTorch-based library for efficient differentiable optimization.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/11/20" data-id="facebookresearch/mmf" data-title="MMF">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="9/8/20" data-id="determined-ai/determined" data-title="Determined">
               <div class="card ecosystem-card">
-                <a href="https://mmf.sh/" target="_blank">
+                <a href="https://github.com/determined-ai/determined" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">MMF</li>
+                        <li class="card-title">Determined</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -451,19 +451,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">A modular framework for vision & language multimodal research from Facebook AI Research (FAIR).</p>
+                    <p class="card-summary">Determined is a platform that helps deep learning teams train models more quickly, easily share GPU resources, and effectively collaborate.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/24/24" data-id="thuml/depyf" data-title="depyf">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/24/24" data-id="hpcaitech/ColossalAI" data-title="Colossal-LLaMA-2">
               <div class="card ecosystem-card">
-                <a href="https://github.com/thuml/depyf" target="_blank">
+                <a href="https://github.com/hpcaitech/ColossalAI/tree/main/applications/Colossal-LLaMA-2" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">depyf</li>
+                        <li class="card-title">Colossal-LLaMA-2</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -472,19 +472,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">depyf is a tool to help users understand and adapt to PyTorch compiler torch.compile.</p>
+                    <p class="card-summary">A complete and open-sourced solution for injecting domain-specific knowledge into pre-trained LLM.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="03/28/22" data-id="intel/neural-compressor" data-title="neural-compressor">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/14/19" data-id="allenai/allennlp" data-title="AllenNLP">
               <div class="card ecosystem-card">
-                <a href="https://intel.github.io/neural-compressor/" target="_blank">
+                <a href="https://allennlp.org/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">neural-compressor</li>
+                        <li class="card-title">AllenNLP</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -493,7 +493,7 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Intel® Neural Compressor provides unified APIs for network compression technologies for faster inference</p>
+                    <p class="card-summary">AllenNLP is an open-source research library built on PyTorch for designing and evaluating deep learning models for NLP.</p>
                   </div>
                 </a>
               </div>
@@ -520,13 +520,13 @@ <h1>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/16/20" data-id="NVIDIA/NeMo" data-title="NeMo">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="01/05/22" data-id="adap/flower" data-title="Flower">
               <div class="card ecosystem-card">
-                <a href="https://github.com/NVIDIA/NeMo" target="_blank">
+                <a href="https://flower.dev" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">NeMo</li>
+                        <li class="card-title">Flower</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -535,19 +535,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">NeMo: a toolkit for conversational AI.</p>
+                    <p class="card-summary">Flower - A Friendly Federated Learning Framework</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="5/20/20" data-id="nicolas-chaulet/torch-points3d" data-title="PyTorch-Points3d">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="08/15/21" data-id="facebookresearch/pytorchvideo" data-title="PyTorchVideo">
               <div class="card ecosystem-card">
-                <a href="https://torch-points3d.readthedocs.io/en/latest/" target="_blank">
+                <a href="https://pytorchvideo.org/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">PyTorch-Points3d</li>
+                        <li class="card-title">PyTorchVideo</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -556,19 +556,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">A PyTorch framework for deep learning on point clouds.</p>
+                    <p class="card-summary">A deep learning library for video understanding research. Hosts various video-focused models, datasets, training pipelines and more.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/19/20" data-id="ElementAI/baal" data-title="baal">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="08/23/21" data-id="lightly-ai/lightly" data-title="Lightly">
               <div class="card ecosystem-card">
-                <a href="https://baal.readthedocs.io/en/latest/" target="_blank">
+                <a href="https://github.com/lightly-ai/lightly" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">baal</li>
+                        <li class="card-title">Lightly</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -577,19 +577,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">baal (bayesian active learning) aims to implement active learning using metrics of uncertainty derived from approximations of bayesian posteriors in neural networks.</p>
+                    <p class="card-summary">Lightly is a computer vision framework for self-supervised learning.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="9/8/20" data-id="determined-ai/determined" data-title="Determined">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="10/29/19" data-id="kornia/kornia" data-title="Kornia">
               <div class="card ecosystem-card">
-                <a href="https://github.com/determined-ai/determined" target="_blank">
+                <a href="https://kornia.github.io/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Determined</li>
+                        <li class="card-title">Kornia</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -598,19 +598,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Determined is a platform that helps deep learning teams train models more quickly, easily share GPU resources, and effectively collaborate.</p>
+                    <p class="card-summary">Kornia is a differentiable computer vision library that consists of a set of routines and differentiable modules to solve generic CV problems.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="5/1/20" data-id="Project-MONAI/MONAI" data-title="MONAI">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="PennyLaneAI/pennylane" data-title="PennyLane">
               <div class="card ecosystem-card">
-                <a href="https://monai.io" target="_blank">
+                <a href="https://pennylane.ai/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">MONAI</li>
+                        <li class="card-title">PennyLane</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -619,19 +619,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">MONAI provides domain-optimized foundational capabilities for developing healthcare imaging training workflows.</p>
+                    <p class="card-summary">PennyLane is a library for quantum ML, automatic differentiation, and optimization of hybrid quantum-classical computations.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/20/21" data-id="KevinMusgrave/pytorch-metric-learning" data-title="PyTorch Metric Learning">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="12/06/20" data-id="fepegar/torchio" data-title="TorchIO">
               <div class="card ecosystem-card">
-                <a href="https://github.com/KevinMusgrave/pytorch-metric-learning" target="_blank">
+                <a href="https://github.com/fepegar/torchio" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">PyTorch Metric Learning</li>
+                        <li class="card-title">TorchIO</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -640,19 +640,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">The easiest way to use deep metric learning in your application. Modular, flexible, and extensible.</p>
+                    <p class="card-summary">TorchIO is a set of tools to efficiently read, preprocess, sample, augment, and write 3D medical images in deep learning applications written in PyTorch.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/14/19" data-id="allenai/allennlp" data-title="AllenNLP">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/27/20" data-id="facebookresearch/detectron2" data-title="Detectron2">
               <div class="card ecosystem-card">
-                <a href="https://allennlp.org/" target="_blank">
+                <a href="https://github.com/facebookresearch/detectron2" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">AllenNLP</li>
+                        <li class="card-title">Detectron2</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -661,19 +661,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">AllenNLP is an open-source research library built on PyTorch for designing and evaluating deep learning models for NLP.</p>
+                    <p class="card-summary">Detectron2 is FAIR's next-generation platform for object detection and segmentation.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="01/18/21" data-id="huggingface/transformers" data-title="Transformers">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="5/1/20" data-id="Project-MONAI/MONAI" data-title="MONAI">
               <div class="card ecosystem-card">
-                <a href="https://github.com/huggingface/transformers" target="_blank">
+                <a href="https://monai.io" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Transformers</li>
+                        <li class="card-title">MONAI</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -682,19 +682,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">State-of-the-art Natural Language Processing for PyTorch.</p>
+                    <p class="card-summary">MONAI provides domain-optimized foundational capabilities for developing healthcare imaging training workflows.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/14/19" data-id="pytorch/botorch" data-title="BoTorch">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="05/20/22" data-id="ludwig-ai/ludwig" data-title="ludwig">
               <div class="card ecosystem-card">
-                <a href="https://botorch.org/" target="_blank">
+                <a href="http://ludwig.ai" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">BoTorch</li>
+                        <li class="card-title">ludwig</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -703,19 +703,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">BoTorch is a library for Bayesian Optimization. It provides a modular, extensible interface for composing Bayesian optimization primitives.</p>
+                    <p class="card-summary">Data-centric declarative deep learning framework</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="02/16/22" data-id="intel/intel-extension-for-pytorch" data-title="intel-extension-for-pytorch">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/28/23" data-id="substra" data-title="Substra">
               <div class="card ecosystem-card">
-                <a href="https://intel.github.io/intel-extension-for-pytorch/" target="_blank">
+                <a href="https://github.com/Substra" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">intel-extension-for-pytorch</li>
+                        <li class="card-title">Substra</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -724,19 +724,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">A Python package for improving PyTorch performance on Intel platforms</p>
+                    <p class="card-summary">Substra is a federated learning Python library to run federated learning experiments at scale on real distributed data.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="11/13/20" data-id="microsoft/DeepSpeed" data-title="DeepSpeed">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="01/04/23" data-id="hpcaitech/ColossalAI" data-title="ColossalAI">
               <div class="card ecosystem-card">
-                <a href="https://www.deepspeed.ai/" target="_blank">
+                <a href="https://www.colossalai.org/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">DeepSpeed</li>
+                        <li class="card-title">ColossalAI</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -745,19 +745,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.</p>
+                    <p class="card-summary">Colossal-AI is a Unified Deep Learning System for Big Model Era</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="08/23/21" data-id="lightly-ai/lightly" data-title="Lightly">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="awslabs/renate" data-title="Renate">
               <div class="card ecosystem-card">
-                <a href="https://github.com/lightly-ai/lightly" target="_blank">
+                <a href="https://renate.readthedocs.io/en/latest/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Lightly</li>
+                        <li class="card-title">Renate</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -766,19 +766,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Lightly is a computer vision framework for self-supervised learning.</p>
+                    <p class="card-summary">Renate is a library providing tools for re-training pytorch models over time as new data becomes available.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="09/08/21" data-id="fidelity/stoke" data-title="stoke">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/19/20" data-id="ElementAI/baal" data-title="baal">
               <div class="card ecosystem-card">
-                <a href="https://fidelity.github.io/stoke/" target="_blank">
+                <a href="https://baal.readthedocs.io/en/latest/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">stoke</li>
+                        <li class="card-title">baal</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -787,19 +787,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">A lightweight declarative PyTorch wrapper for context switching between devices, distributed modes, mixed-precision, and PyTorch extensions.</p>
+                    <p class="card-summary">baal (bayesian active learning) aims to implement active learning using metrics of uncertainty derived from approximations of bayesian posteriors in neural networks.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="09/08/21" data-id="pytorchfi/pytorchfi" data-title="pytorchfi">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/27/20" data-id="pytorch/glow" data-title="Glow">
               <div class="card ecosystem-card">
-                <a href="https://github.com/pytorchfi/pytorchfi" target="_blank">
-                  <div class="card-body  ">
+                <a href="https://github.com/pytorch/glow" target="_blank">
+                  <div class="card-body tool ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">pytorchfi</li>
+                        <li class="card-title">Glow</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -808,19 +808,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">A runtime fault injection tool for PyTorch.</p>
+                    <p class="card-summary">Glow is a ML compiler that accelerates the performance of deep learning frameworks on different hardware platforms.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="8/14/19" data-id="skorch-dev/skorch" data-title="skorch">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/17/20" data-id="allegroai/clearml" data-title="Clear ML">
               <div class="card ecosystem-card">
-                <a href="https://github.com/skorch-dev/skorch" target="_blank">
+                <a href="https://github.com/allegroai/trains/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">skorch</li>
+                        <li class="card-title">Clear ML</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -829,19 +829,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">skorch is a high-level library for PyTorch that provides full scikit-learn compatibility.</p>
+                    <p class="card-summary">ClearML is a full system ML / DL experiment manager, versioning and ML-Ops solution.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/23/21" data-id="graphcore/poptorch" data-title="PopTorch">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="03/28/22" data-id="NVIDIA/Torch-TensorRT" data-title="Torch-TensorRT">
               <div class="card ecosystem-card">
-                <a href="https://docs.graphcore.ai/projects/poptorch-user-guide/en/latest/" target="_blank">
+                <a href="https://pytorch.org/TensorRT/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">PopTorch</li>
+                        <li class="card-title">Torch-TensorRT</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -850,19 +850,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">The PopTorch interface library is a simple wrapper for running PyTorch programs directly on Graphcore IPUs.</p>
+                    <p class="card-summary">PyTorch/TorchScript compiler for NVIDIA GPUs using TensorRT</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="5/21/20" data-id="facebookresearch/higher" data-title="higher">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/22/21" data-id="facebookresearch/fairscale" data-title="FairScale">
               <div class="card ecosystem-card">
-                <a href="https://github.com/facebookresearch/higher" target="_blank">
+                <a href="https://github.com/facebookresearch/fairscale" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">higher</li>
+                        <li class="card-title">FairScale</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -871,19 +871,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">higher is a library which facilitates the implementation of arbitrarily complex gradient-based meta-learning algorithms and nested optimisation loops with near-vanilla PyTorch.</p>
+                    <p class="card-summary">FairScale is a PyTorch extension library for high performance and large scale training on one or multiple machines/nodes.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="03/28/22" data-id="mosaicml/composer" data-title="composer">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="05/07/21" data-id="mlcommons/GaNDLF" data-title="GaNDLF">
               <div class="card ecosystem-card">
-                <a href="https://github.com/mosaicml/composer" target="_blank">
+                <a href="https://mlcommons.github.io/GaNDLF/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">composer</li>
+                        <li class="card-title">GaNDLF</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -892,19 +892,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">library of algorithms to speed up neural network training</p>
+                    <p class="card-summary">A generalizable application framework for segmentation, regression, and classification using PyTorch</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="pytorch/ignite" data-title="Ignite">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="facebookresearch/ParlAI" data-title="ParlAI">
               <div class="card ecosystem-card">
-                <a href="https://github.com/pytorch/ignite" target="_blank">
+                <a href="http://parl.ai/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Ignite</li>
+                        <li class="card-title">ParlAI</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -913,19 +913,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Ignite is a high-level library for training neural networks in PyTorch. It helps with writing compact, but full-featured training loops.</p>
+                    <p class="card-summary">ParlAI is a unified platform for sharing, training, and evaluating dialog models across many tasks.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="PennyLaneAI/pennylane" data-title="PennyLane">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="10/28/19" data-id="albumentations-team/albumentations" data-title="Albumentations">
               <div class="card ecosystem-card">
-                <a href="https://pennylane.ai/" target="_blank">
+                <a href="https://github.com/albu/albumentations" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">PennyLane</li>
+                        <li class="card-title">Albumentations</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -934,19 +934,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">PennyLane is a library for quantum ML, automatic differentiation, and optimization of hybrid quantum-classical computations.</p>
+                    <p class="card-summary">Fast and extensible image augmentation library for different CV tasks like classification, segmentation, object detection and pose estimation.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="10/18/19" data-id="facebookresearch/CrypTen" data-title="CrypTen">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="11/13/20" data-id="microsoft/DeepSpeed" data-title="DeepSpeed">
               <div class="card ecosystem-card">
-                <a href="https://github.com/facebookresearch/CrypTen" target="_blank">
+                <a href="https://www.deepspeed.ai/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">CrypTen</li>
+                        <li class="card-title">DeepSpeed</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -955,19 +955,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">CrypTen is a framework for Privacy Preserving ML. Its goal is to make secure computing techniques accessible to ML practitioners.</p>
+                    <p class="card-summary">DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/17/20" data-id="allegroai/clearml" data-title="Clear ML">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="10/18/19" data-id="facebookresearch/CrypTen" data-title="CrypTen">
               <div class="card ecosystem-card">
-                <a href="https://github.com/allegroai/trains/" target="_blank">
+                <a href="https://github.com/facebookresearch/CrypTen" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Clear ML</li>
+                        <li class="card-title">CrypTen</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -976,19 +976,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">ClearML is a full system ML / DL experiment manager, versioning and ML-Ops solution.</p>
+                    <p class="card-summary">CrypTen is a framework for Privacy Preserving ML. Its goal is to make secure computing techniques accessible to ML practitioners.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="05/07/21" data-id="azavea/raster-vision" data-title="raster-vision">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/3/20" data-id="dmlc/dgl" data-title="DGL">
               <div class="card ecosystem-card">
-                <a href="https://docs.rastervision.io" target="_blank">
+                <a href="https://www.dgl.ai" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">raster-vision</li>
+                        <li class="card-title">DGL</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -997,19 +997,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">An open source framework for deep learning on satellite and aerial imagery.</p>
+                    <p class="card-summary">Deep Graph Library (DGL) is a Python package built for easy implementation of graph neural network model family, on top of PyTorch and other frameworks.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/24/24" data-id="IBM/simulai" data-title="SimulAI">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="2/1/21" data-id="facebookresearch/vissl" data-title="VISSL">
               <div class="card ecosystem-card">
-                <a href="https://github.com/IBM/simulai" target="_blank">
+                <a href="https://vissl.ai/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">SimulAI</li>
+                        <li class="card-title">VISSL</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1018,19 +1018,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">SimulAI is basically a toolkit with pipelines for physics-informed machine learning.</p>
+                    <p class="card-summary">A library for state-of-the-art self-supervised learning</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/27/20" data-id="facebookresearch/detectron2" data-title="Detectron2">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="9/21/23" data-id="" data-title="RoMa">
               <div class="card ecosystem-card">
-                <a href="https://github.com/facebookresearch/detectron2" target="_blank">
+                <a href="https://github.com/naver/roma" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Detectron2</li>
+                        <li class="card-title">RoMa</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1039,19 +1039,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Detectron2 is FAIR's next-generation platform for object detection and segmentation.</p>
+                    <p class="card-summary">RoMa is a standalone library to handle rotation representations with PyTorch (rotation matrices, quaternions, rotation vectors, etc). It aims for robustness, ease-of-use, and efficiency.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/27/20" data-id="pytorch/glow" data-title="Glow">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="5/21/20" data-id="facebookresearch/higher" data-title="higher">
               <div class="card ecosystem-card">
-                <a href="https://github.com/pytorch/glow" target="_blank">
-                  <div class="card-body tool ">
+                <a href="https://github.com/facebookresearch/higher" target="_blank">
+                  <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Glow</li>
+                        <li class="card-title">higher</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1060,19 +1060,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Glow is a ML compiler that accelerates the performance of deep learning frameworks on different hardware platforms.</p>
+                    <p class="card-summary">higher is a library which facilitates the implementation of arbitrarily complex gradient-based meta-learning algorithms and nested optimisation loops with near-vanilla PyTorch.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="10/29/20" data-id="pytorch/opacus" data-title="Opacus">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="2/13/20" data-id="GRAAL-Research/poutyne" data-title="Poutyne">
               <div class="card ecosystem-card">
-                <a href="https://opacus.ai/" target="_blank">
+                <a href="https://poutyne.org/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Opacus</li>
+                        <li class="card-title">Poutyne</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1081,19 +1081,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Train PyTorch models with Differential Privacy</p>
+                    <p class="card-summary">Poutyne is a Keras-like framework for PyTorch and handles much of the boilerplating code needed to train neural networks.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/6/20" data-id="facebookresearch/hydra" data-title="Hydra">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="2/1/21" data-id="microsoft/onnxruntime" data-title="ONNX Runtime">
               <div class="card ecosystem-card">
-                <a href="https://hydra.cc/" target="_blank">
+                <a href="https://github.com/microsoft/onnxruntime" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Hydra</li>
+                        <li class="card-title">ONNX Runtime</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1102,19 +1102,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">A framework for elegantly configuring complex applications.</p>
+                    <p class="card-summary">ONNX Runtime is a cross-platform inferencing and training accelerator.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/10/21" data-id="lyft/l5kit" data-title="L5Kit">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/31/21" data-id="TorchDrift/TorchDrift" data-title="TorchDrift">
               <div class="card ecosystem-card">
-                <a href="https://github.com/lyft/l5kit" target="_blank">
+                <a href="https://torchdrift.org" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">L5Kit</li>
+                        <li class="card-title">TorchDrift</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1123,19 +1123,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">ML Prediction, Planning and Simulation for Self-Driving built on PyTorch.</p>
+                    <p class="card-summary">TorchDrift is a data and concept drift library for PyTorch. It lets you monitor your PyTorch models to see if they operate within spec.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="pypose/pypose" data-title="PyPose">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="01/05/22" data-id="microsoft/torchgeo" data-title="torchgeo">
               <div class="card ecosystem-card">
-                <a href="https://pypose.org" target="_blank">
+                <a href="https://github.com/microsoft/torchgeo" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">PyPose</li>
+                        <li class="card-title">torchgeo</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1144,19 +1144,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">PyPose is a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization techniques, so that users can focus on their novel applications.</p>
+                    <p class="card-summary">Datasets, transforms, and models for geospatial data</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="02/23/22" data-id="unifyai/ivy" data-title="ivy">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/24/24" data-id="thuml/depyf" data-title="depyf">
               <div class="card ecosystem-card">
-                <a href="https://lets-unify.ai" target="_blank">
+                <a href="https://github.com/thuml/depyf" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">ivy</li>
+                        <li class="card-title">depyf</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1165,19 +1165,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">The Unified Machine Learning Framework</p>
+                    <p class="card-summary">depyf is a tool to help users understand and adapt to PyTorch compiler torch.compile.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="metaopt/TorchOpt" data-title="TorchOpt">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="07/19/21" data-id="asyml/forte" data-title="forte">
               <div class="card ecosystem-card">
-                <a href="https://torchopt.readthedocs.io/en/latest/#" target="_blank">
+                <a href="https://github.com/asyml/forte" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">TorchOpt</li>
+                        <li class="card-title">forte</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1186,19 +1186,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">TorchOpt is a PyTorch-based library for efficient differentiable optimization.</p>
+                    <p class="card-summary">Forte is a toolkit for building NLP pipelines featuring composable components, convenient data interfaces, and cross-task interaction.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="pyg-team/pytorch_geometric" data-title="PyTorch Geometric">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="4/6/20" data-id="optuna/optuna" data-title="Optuna">
               <div class="card ecosystem-card">
-                <a href="https://github.com/pyg-team/pytorch_geometric/" target="_blank">
+                <a href="https://optuna.org/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">PyTorch Geometric</li>
+                        <li class="card-title">Optuna</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1207,19 +1207,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">PyTorch Geometric is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds.</p>
+                    <p class="card-summary">An open source hyperparameter optimization framework to automate hyperparameter search.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="01/05/22" data-id="adap/flower" data-title="Flower">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="12/30/19" data-id="flairNLP/flair" data-title="Flair">
               <div class="card ecosystem-card">
-                <a href="https://flower.dev" target="_blank">
+                <a href="https://github.com/flairNLP/flair" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Flower</li>
+                        <li class="card-title">Flair</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1228,19 +1228,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Flower - A Friendly Federated Learning Framework</p>
+                    <p class="card-summary">Flair is a very simple framework for state-of-the-art natural language processing (NLP).</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="horovod/horovod" data-title="Horovod">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="8/14/19" data-id="PyTorchLightning/pytorch-lightning" data-title="PyTorch Lightning">
               <div class="card ecosystem-card">
-                <a href="http://horovod.ai" target="_blank">
+                <a href="https://github.com/williamFalcon/pytorch-lightning" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Horovod</li>
+                        <li class="card-title">PyTorch Lightning</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1249,19 +1249,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Horovod is a distributed training library for deep learning frameworks. Horovod aims to make distributed DL fast and easy to use.</p>
+                    <p class="card-summary">PyTorch Lightning is a Keras-like ML library for PyTorch. It leaves core training and validation logic to you and automates the rest.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="05/07/21" data-id="joeynmt/joeynmt" data-title="joeynmt">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="4/11/21" data-id="benedekrozemberczki/pytorch_geometric_temporal" data-title="PyTorch Geometric Temporal">
               <div class="card ecosystem-card">
-                <a href="https://joeynmt.readthedocs.io/en/latest/" target="_blank">
+                <a href="https://github.com/benedekrozemberczki/pytorch_geometric_temporal" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">joeynmt</li>
+                        <li class="card-title">PyTorch Geometric Temporal</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1270,19 +1270,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Minimalist Neural Machine Translation toolkit for educational purposes</p>
+                    <p class="card-summary">PyTorch Geometric Temporal is a temporal (dynamic) extension library for PyTorch Geometric.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="09/09/21" data-id="pykale/pykale" data-title="PyKale">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="10/29/20" data-id="pytorch/opacus" data-title="Opacus">
               <div class="card ecosystem-card">
-                <a href="https://github.com/pykale/pykale" target="_blank">
+                <a href="https://opacus.ai/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">PyKale</li>
+                        <li class="card-title">Opacus</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1291,19 +1291,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">PyKale is a PyTorch library for multimodal learning and transfer learning with deep learning and dimensionality reduction on graphs, images, texts, and videos.</p>
+                    <p class="card-summary">Train PyTorch models with Differential Privacy</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="4/6/20" data-id="PetrochukM/PyTorch-NLP" data-title="PyTorch-NLP">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="06/02/21" data-id="TorchEnsemble-Community/Ensemble-Pytorch" data-title="Ensemble-Pytorch">
               <div class="card ecosystem-card">
-                <a href="https://pytorchnlp.readthedocs.io" target="_blank">
+                <a href="https://ensemble-pytorch.readthedocs.io" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">PyTorch-NLP</li>
+                        <li class="card-title">Ensemble-Pytorch</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1312,19 +1312,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Basic Utilities for PyTorch Natural Language Processing (NLP).</p>
+                    <p class="card-summary">A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/31/21" data-id="TorchDrift/TorchDrift" data-title="TorchDrift">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="02/23/22" data-id="ContinualAI/avalanche" data-title="avalanche">
               <div class="card ecosystem-card">
-                <a href="https://torchdrift.org" target="_blank">
+                <a href="http://avalanche.continualai.org" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">TorchDrift</li>
+                        <li class="card-title">avalanche</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1333,19 +1333,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">TorchDrift is a data and concept drift library for PyTorch. It lets you monitor your PyTorch models to see if they operate within spec.</p>
+                    <p class="card-summary">Avalanche: an End-to-End Library for Continual Learning</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="05/07/21" data-id="mlcommons/GaNDLF" data-title="GaNDLF">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="horovod/horovod" data-title="Horovod">
               <div class="card ecosystem-card">
-                <a href="https://mlcommons.github.io/GaNDLF/" target="_blank">
+                <a href="http://horovod.ai" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">GaNDLF</li>
+                        <li class="card-title">Horovod</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1354,19 +1354,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">A generalizable application framework for segmentation, regression, and classification using PyTorch</p>
+                    <p class="card-summary">Horovod is a distributed training library for deep learning frameworks. Horovod aims to make distributed DL fast and easy to use.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="10/28/19" data-id="catalyst-team/catalyst" data-title="Catalyst">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="02/16/22" data-id="intel/intel-extension-for-pytorch" data-title="intel-extension-for-pytorch">
               <div class="card ecosystem-card">
-                <a href="https://github.com/catalyst-team/catalyst" target="_blank">
+                <a href="https://intel.github.io/intel-extension-for-pytorch/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Catalyst</li>
+                        <li class="card-title">intel-extension-for-pytorch</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1375,19 +1375,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Catalyst helps you write compact, but full-featured deep learning and reinforcement learning pipelines with a few lines of code.</p>
+                    <p class="card-summary">A Python package for improving PyTorch performance on Intel platforms</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="8/14/19" data-id="tensorly/tensorly" data-title="TensorLy">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/24/24" data-id="IBM/simulai" data-title="SimulAI">
               <div class="card ecosystem-card">
-                <a href="http://tensorly.org/stable/home.html" target="_blank">
+                <a href="https://github.com/IBM/simulai" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">TensorLy</li>
+                        <li class="card-title">SimulAI</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1396,19 +1396,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">TensorLy is a high level API for tensor methods and deep tensorized neural networks in Python that aims to make tensor learning simple.</p>
+                    <p class="card-summary">SimulAI is basically a toolkit with pipelines for physics-informed machine learning.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="06/02/21" data-id="airaria/TextBrewer" data-title="TextBrewer">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="2/5/21" data-id="petuum/adaptdl" data-title="AdaptDL">
               <div class="card ecosystem-card">
-                <a href="http://textbrewer.hfl-rc.com" target="_blank">
+                <a href="https://github.com/petuum/adaptdl" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">TextBrewer</li>
+                        <li class="card-title">AdaptDL</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1417,19 +1417,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">A PyTorch-based knowledge distillation toolkit for natural language processing</p>
+                    <p class="card-summary">AdaptDL is a resource-adaptive deep learning training and scheduling framework.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/28/23" data-id="substra" data-title="Substra">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/16/20" data-id="NVIDIA/NeMo" data-title="NeMo">
               <div class="card ecosystem-card">
-                <a href="https://github.com/Substra" target="_blank">
+                <a href="https://github.com/NVIDIA/NeMo" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Substra</li>
+                        <li class="card-title">NeMo</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1438,19 +1438,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Substra is a federated learning Python library to run federated learning experiments at scale on real distributed data.</p>
+                    <p class="card-summary">NeMo: a toolkit for conversational AI.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="03/28/22" data-id="NVIDIA/Torch-TensorRT" data-title="Torch-TensorRT">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/27/20" data-id="facebookresearch/pytorch3d" data-title="PyTorch3D">
               <div class="card ecosystem-card">
-                <a href="https://pytorch.org/TensorRT/" target="_blank">
+                <a href="https://pytorch3d.org/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Torch-TensorRT</li>
+                        <li class="card-title">PyTorch3D</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1459,19 +1459,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">PyTorch/TorchScript compiler for NVIDIA GPUs using TensorRT</p>
+                    <p class="card-summary">PyTorch3D provides efficient, reusable components for 3D Computer Vision research with PyTorch.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="2/1/21" data-id="facebookresearch/vissl" data-title="VISSL">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="08/19/21" data-id="DeepGraphLearning/torchdrug" data-title="torchdrug">
               <div class="card ecosystem-card">
-                <a href="https://vissl.ai/" target="_blank">
+                <a href="https://torchdrug.ai/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">VISSL</li>
+                        <li class="card-title">torchdrug</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1480,19 +1480,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">A library for state-of-the-art self-supervised learning</p>
+                    <p class="card-summary">A powerful and flexible machine learning platform for drug discovery.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="08/19/21" data-id="DeepGraphLearning/torchdrug" data-title="torchdrug">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="OpenMined/PySyft" data-title="PySyft">
               <div class="card ecosystem-card">
-                <a href="https://torchdrug.ai/" target="_blank">
+                <a href="https://github.com/OpenMined/PySyft" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">torchdrug</li>
+                        <li class="card-title">PySyft</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1501,19 +1501,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">A powerful and flexible machine learning platform for drug discovery.</p>
+                    <p class="card-summary">PySyft is a Python library for encrypted, privacy preserving deep learning.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/14/19" data-id="BorealisAI/advertorch" data-title="AdverTorch">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="12/05/23" data-id="yoshitomo-matsubara/torchdistill" data-title="torchdistill">
               <div class="card ecosystem-card">
-                <a href="https://github.com/BorealisAI/advertorch" target="_blank">
+                <a href="https://github.com/yoshitomo-matsubara/torchdistill" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">AdverTorch</li>
+                        <li class="card-title">torchdistill</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1522,19 +1522,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">A toolbox for adversarial robustness research. It contains modules for generating adversarial examples and defending against attacks.</p>
+                    <p class="card-summary">torchdistill is a coding-free framework built on PyTorch for reproducible deep learning and knowledge distillation studies.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="05/07/21" data-id="aramis-lab/AD-DL" data-title="ClinicaDL">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="8/20/20" data-id="ray-project/ray" data-title="Ray">
               <div class="card ecosystem-card">
-                <a href="https://clinicadl.readthedocs.io/" target="_blank">
+                <a href="https://github.com/ray-project/ray" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">ClinicaDL</li>
+                        <li class="card-title">Ray</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1543,19 +1543,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Framework for reproducible classification of Alzheimer's Disease</p>
+                    <p class="card-summary">Ray is a fast and simple framework for building and running distributed applications.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="huggingface/diffusers" data-title="Diffusers">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="02/23/22" data-id="unifyai/ivy" data-title="ivy">
               <div class="card ecosystem-card">
-                <a href="https://huggingface.co/docs/diffusers" target="_blank">
+                <a href="https://lets-unify.ai" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Diffusers</li>
+                        <li class="card-title">ivy</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1564,19 +1564,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Diffusers provides pretrained diffusion models across multiple modalities, such as vision and audio, and serves as a modular toolbox for inference and training of diffusion models.</p>
+                    <p class="card-summary">The Unified Machine Learning Framework</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="4/6/20" data-id="optuna/optuna" data-title="Optuna">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="9/17/20" data-id="polyaxon/polyaxon" data-title="Polyaxon">
               <div class="card ecosystem-card">
-                <a href="https://optuna.org/" target="_blank">
+                <a href="https://github.com/polyaxon/polyaxon" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Optuna</li>
+                        <li class="card-title">Polyaxon</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1585,19 +1585,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">An open source hyperparameter optimization framework to automate hyperparameter search.</p>
+                    <p class="card-summary">Polyaxon is a platform for building, training, and monitoring large-scale deep learning applications.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="12/06/20" data-id="fepegar/torchio" data-title="TorchIO">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="02/16/23" data-id="BiomedSciAI/fuse-med-ml" data-title="FuseMedML">
               <div class="card ecosystem-card">
-                <a href="https://github.com/fepegar/torchio" target="_blank">
+                <a href="https://github.com/BiomedSciAI/fuse-med-ml" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">TorchIO</li>
+                        <li class="card-title">FuseMedML</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1606,19 +1606,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">TorchIO is a set of tools to efficiently read, preprocess, sample, augment, and write 3D medical images in deep learning applications written in PyTorch.</p>
+                    <p class="card-summary">FuseMedML is a python framework accelerating ML based discovery in the medical field by encouraging code reuse</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/3/20" data-id="dmlc/dgl" data-title="DGL">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="mit-han-lab/torchquantum" data-title="TorchQuantum">
               <div class="card ecosystem-card">
-                <a href="https://www.dgl.ai" target="_blank">
+                <a href="https://hanruiwanghw.wixsite.com/torchquantum" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">DGL</li>
+                        <li class="card-title">TorchQuantum</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1627,19 +1627,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Deep Graph Library (DGL) is a Python package built for easy implementation of graph neural network model family, on top of PyTorch and other frameworks.</p>
+                    <p class="card-summary">TorchQuantum is a quantum classical simulation framework based on PyTorch. It supports statevector, density matrix simulation and pulse simulation on different hardware platforms such as CPUs and GPUs.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="09/13/21" data-id="huggingface/accelerate" data-title="accelerate">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="huggingface/diffusers" data-title="Diffusers">
               <div class="card ecosystem-card">
-                <a href="https://huggingface.co/docs/accelerate" target="_blank">
+                <a href="https://huggingface.co/docs/diffusers" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">accelerate</li>
+                        <li class="card-title">Diffusers</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1648,19 +1648,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision</p>
+                    <p class="card-summary">Diffusers provides pretrained diffusion models across multiple modalities, such as vision and audio, and serves as a modular toolbox for inference and training of diffusion models.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="03/28/22" data-id="lf1-io/padl" data-title="padl">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="pypose/pypose" data-title="PyPose">
               <div class="card ecosystem-card">
-                <a href="https://lf1-io.github.io/padl/" target="_blank">
+                <a href="https://pypose.org" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">padl</li>
+                        <li class="card-title">PyPose</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1669,19 +1669,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Pipeline Abstractions for Deep Learning in PyTorch</p>
+                    <p class="card-summary">PyPose is a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization techniques, so that users can focus on their novel applications.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/22/21" data-id="facebookresearch/fairscale" data-title="FairScale">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="09/08/21" data-id="pytorchfi/pytorchfi" data-title="pytorchfi">
               <div class="card ecosystem-card">
-                <a href="https://github.com/facebookresearch/fairscale" target="_blank">
+                <a href="https://github.com/pytorchfi/pytorchfi" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">FairScale</li>
+                        <li class="card-title">pytorchfi</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1690,19 +1690,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">FairScale is a PyTorch extension library for high performance and large scale training on one or multiple machines/nodes.</p>
+                    <p class="card-summary">A runtime fault injection tool for PyTorch.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="2/5/21" data-id="petuum/adaptdl" data-title="AdaptDL">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="pyro-ppl/pyro" data-title="Pyro">
               <div class="card ecosystem-card">
-                <a href="https://github.com/petuum/adaptdl" target="_blank">
+                <a href="http://pyro.ai/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">AdaptDL</li>
+                        <li class="card-title">Pyro</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1711,19 +1711,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">AdaptDL is a resource-adaptive deep learning training and scheduling framework.</p>
+                    <p class="card-summary">Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="10/29/19" data-id="kornia/kornia" data-title="Kornia">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/14/19" data-id="pytorch/botorch" data-title="BoTorch">
               <div class="card ecosystem-card">
-                <a href="https://kornia.github.io/" target="_blank">
+                <a href="https://botorch.org/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Kornia</li>
+                        <li class="card-title">BoTorch</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1732,19 +1732,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Kornia is a differentiable computer vision library that consists of a set of routines and differentiable modules to solve generic CV problems.</p>
+                    <p class="card-summary">BoTorch is a library for Bayesian Optimization. It provides a modular, extensible interface for composing Bayesian optimization primitives.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/27/20" data-id="facebookresearch/pytorch3d" data-title="PyTorch3D">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="01/18/21" data-id="huggingface/transformers" data-title="Transformers">
               <div class="card ecosystem-card">
-                <a href="https://pytorch3d.org/" target="_blank">
+                <a href="https://github.com/huggingface/transformers" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">PyTorch3D</li>
+                        <li class="card-title">Transformers</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1753,19 +1753,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">PyTorch3D provides efficient, reusable components for 3D Computer Vision research with PyTorch.</p>
+                    <p class="card-summary">State-of-the-art Natural Language Processing for PyTorch.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="2/1/21" data-id="microsoft/onnxruntime" data-title="ONNX Runtime">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="octoml/octoml-profile" data-title="OctoML Profile">
               <div class="card ecosystem-card">
-                <a href="https://github.com/microsoft/onnxruntime" target="_blank">
+                <a href="https://github.com/octoml/octoml-profile" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">ONNX Runtime</li>
+                        <li class="card-title">OctoML Profile</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1774,19 +1774,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">ONNX Runtime is a cross-platform inferencing and training accelerator.</p>
+                    <p class="card-summary">octoml-profile is a python library and cloud service designed to provide a simple experience for assessing and optimizing the performance of PyTorch models.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="octoml/octoml-profile" data-title="OctoML Profile">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="5/20/20" data-id="nicolas-chaulet/torch-points3d" data-title="PyTorch-Points3d">
               <div class="card ecosystem-card">
-                <a href="https://github.com/octoml/octoml-profile" target="_blank">
+                <a href="https://torch-points3d.readthedocs.io/en/latest/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">OctoML Profile</li>
+                        <li class="card-title">PyTorch-Points3d</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1795,19 +1795,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">octoml-profile is a python library and cloud service designed to provide a simple experience for assessing and optimizing the performance of PyTorch models.</p>
+                    <p class="card-summary">A PyTorch framework for deep learning on point clouds.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="TissueImageAnalytics/tiatoolbox" data-title="TIAToolbox">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="06/22/21" data-id="PyTorchLightning/metrics" data-title="TorchMetrics">
               <div class="card ecosystem-card">
-                <a href="https://github.com/TissueImageAnalytics/tiatoolbox" target="_blank">
+                <a href="https://github.com/PyTorchLightning/metrics" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">TIAToolbox</li>
+                        <li class="card-title">TorchMetrics</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1816,19 +1816,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">TIAToolbox provides an easy-to-use API where researchers can use, adapt and create models for CPath.</p>
+                    <p class="card-summary">Machine learning metrics for distributed, scalable PyTorch applications.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/29/21" data-id="DLR-RM/stable-baselines3" data-title="Stable Baselines3">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="microsoft/Semi-supervised-learning" data-title="USB">
               <div class="card ecosystem-card">
-                <a href="https://github.com/DLR-RM/stable-baselines3" target="_blank">
+                <a href="https://usb.readthedocs.io/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Stable Baselines3</li>
+                        <li class="card-title">USB</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1837,19 +1837,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch.</p>
+                    <p class="card-summary">USB is a Pytorch-based Python package for Semi-Supervised Learning (SSL). It is easy-to-use/extend, affordable to small groups, and comprehensive for developing and evaluating SSL algorithms.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/20/21" data-id="arogozhnikov/einops" data-title="einops">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="4/6/20" data-id="PetrochukM/PyTorch-NLP" data-title="PyTorch-NLP">
               <div class="card ecosystem-card">
-                <a href="https://github.com/arogozhnikov/einops" target="_blank">
+                <a href="https://pytorchnlp.readthedocs.io" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">einops</li>
+                        <li class="card-title">PyTorch-NLP</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1858,19 +1858,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Flexible and powerful tensor operations for readable and reliable code.</p>
+                    <p class="card-summary">Basic Utilities for PyTorch Natural Language Processing (NLP).</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="cornellius-gp/gpytorch" data-title="GPyTorch">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="8/6/20" data-id="pfnet/pfrl" data-title="PFRL">
               <div class="card ecosystem-card">
-                <a href="https://cornellius-gp.github.io/" target="_blank">
+                <a href="https://github.com/pfnet/pfrl" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">GPyTorch</li>
+                        <li class="card-title">PFRL</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1879,19 +1879,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">GPyTorch is a Gaussian process library implemented using PyTorch, designed for creating scalable, flexible Gaussian process models.</p>
+                    <p class="card-summary">PFRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using PyTorch.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="12/05/23" data-id="yoshitomo-matsubara/torchdistill" data-title="torchdistill">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/20/21" data-id="KevinMusgrave/pytorch-metric-learning" data-title="PyTorch Metric Learning">
               <div class="card ecosystem-card">
-                <a href="https://github.com/yoshitomo-matsubara/torchdistill" target="_blank">
+                <a href="https://github.com/KevinMusgrave/pytorch-metric-learning" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">torchdistill</li>
+                        <li class="card-title">PyTorch Metric Learning</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1900,19 +1900,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">torchdistill is a coding-free framework built on PyTorch for reproducible deep learning and knowledge distillation studies.</p>
+                    <p class="card-summary">The easiest way to use deep metric learning in your application. Modular, flexible, and extensible.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/28/23" data-id="WenjieDu/PyPOTS" data-title="PyPOTS">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="fastai/fastai" data-title="fastai">
               <div class="card ecosystem-card">
-                <a href="https://github.com/WenjieDu/PyPOTS" target="_blank">
+                <a href="https://docs.fast.ai" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">PyPOTS</li>
+                        <li class="card-title">fastai</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1921,19 +1921,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">A Python toolbox for data mining on Partially-Observed Time Series (POTS) and helps engineers focus more on the core problems in rather than missing parts in their data.</p>
+                    <p class="card-summary">fastai is a library that simplifies training fast and accurate neural nets using modern best practices.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="9/17/20" data-id="polyaxon/polyaxon" data-title="Polyaxon">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="cornellius-gp/gpytorch" data-title="GPyTorch">
               <div class="card ecosystem-card">
-                <a href="https://github.com/polyaxon/polyaxon" target="_blank">
+                <a href="https://cornellius-gp.github.io/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Polyaxon</li>
+                        <li class="card-title">GPyTorch</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1942,19 +1942,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Polyaxon is a platform for building, training, and monitoring large-scale deep learning applications.</p>
+                    <p class="card-summary">GPyTorch is a Gaussian process library implemented using PyTorch, designed for creating scalable, flexible Gaussian process models.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="5/7/21" data-id="pystiche/pystiche" data-title="pystiche">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="10/18/19" data-id="pytorch/captum" data-title="Captum">
               <div class="card ecosystem-card">
-                <a href="https://github.com/pystiche/pystiche" target="_blank">
+                <a href="https://captum.ai/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">pystiche</li>
+                        <li class="card-title">Captum</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1963,19 +1963,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">pystiche is a framework for Neural Style Transfer (NST) built upon PyTorch.</p>
+                    <p class="card-summary">Captum (“comprehension” in Latin) is an open source, extensible library for model interpretability built on PyTorch.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="01/04/23" data-id="hpcaitech/ColossalAI" data-title="ColossalAI">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/23/21" data-id="graphcore/poptorch" data-title="PopTorch">
               <div class="card ecosystem-card">
-                <a href="https://www.colossalai.org/" target="_blank">
+                <a href="https://docs.graphcore.ai/projects/poptorch-user-guide/en/latest/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">ColossalAI</li>
+                        <li class="card-title">PopTorch</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -1984,19 +1984,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Colossal-AI is a Unified Deep Learning System for Big Model Era</p>
+                    <p class="card-summary">The PopTorch interface library is a simple wrapper for running PyTorch programs directly on Graphcore IPUs.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="8/14/19" data-id="PyTorchLightning/pytorch-lightning" data-title="PyTorch Lightning">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="8/14/19" data-id="skorch-dev/skorch" data-title="skorch">
               <div class="card ecosystem-card">
-                <a href="https://github.com/williamFalcon/pytorch-lightning" target="_blank">
+                <a href="https://github.com/skorch-dev/skorch" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">PyTorch Lightning</li>
+                        <li class="card-title">skorch</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2005,19 +2005,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">PyTorch Lightning is a Keras-like ML library for PyTorch. It leaves core training and validation logic to you and automates the rest.</p>
+                    <p class="card-summary">skorch is a high-level library for PyTorch that provides full scikit-learn compatibility.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="4/11/21" data-id="benedekrozemberczki/pytorch_geometric_temporal" data-title="PyTorch Geometric Temporal">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/6/20" data-id="facebookresearch/hydra" data-title="Hydra">
               <div class="card ecosystem-card">
-                <a href="https://github.com/benedekrozemberczki/pytorch_geometric_temporal" target="_blank">
+                <a href="https://hydra.cc/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">PyTorch Geometric Temporal</li>
+                        <li class="card-title">Hydra</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2026,19 +2026,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">PyTorch Geometric Temporal is a temporal (dynamic) extension library for PyTorch Geometric.</p>
+                    <p class="card-summary">A framework for elegantly configuring complex applications.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="02/23/22" data-id="ContinualAI/avalanche" data-title="avalanche">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="05/07/21" data-id="aramis-lab/AD-DL" data-title="ClinicaDL">
               <div class="card ecosystem-card">
-                <a href="http://avalanche.continualai.org" target="_blank">
+                <a href="https://clinicadl.readthedocs.io/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">avalanche</li>
+                        <li class="card-title">ClinicaDL</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2047,19 +2047,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Avalanche: an End-to-End Library for Continual Learning</p>
+                    <p class="card-summary">Framework for reproducible classification of Alzheimer's Disease</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/17/20" data-id="microsoft/hummingbird" data-title="Hummingbird">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/28/23" data-id="WenjieDu/PyPOTS" data-title="PyPOTS">
               <div class="card ecosystem-card">
-                <a href="https://github.com/microsoft/hummingbird" target="_blank">
+                <a href="https://github.com/WenjieDu/PyPOTS" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Hummingbird</li>
+                        <li class="card-title">PyPOTS</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2068,19 +2068,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Hummingbird compiles trained ML models into tensor computation for faster inference.</p>
+                    <p class="card-summary">A Python toolbox for data mining on Partially-Observed Time Series (POTS) and helps engineers focus more on the core problems in rather than missing parts in their data.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="fastai/fastai" data-title="fastai">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="09/08/21" data-id="fidelity/stoke" data-title="stoke">
               <div class="card ecosystem-card">
-                <a href="https://docs.fast.ai" target="_blank">
+                <a href="https://fidelity.github.io/stoke/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">fastai</li>
+                        <li class="card-title">stoke</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2089,19 +2089,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">fastai is a library that simplifies training fast and accurate neural nets using modern best practices.</p>
+                    <p class="card-summary">A lightweight declarative PyTorch wrapper for context switching between devices, distributed modes, mixed-precision, and PyTorch extensions.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="awslabs/renate" data-title="Renate">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="TissueImageAnalytics/tiatoolbox" data-title="TIAToolbox">
               <div class="card ecosystem-card">
-                <a href="https://renate.readthedocs.io/en/latest/" target="_blank">
+                <a href="https://github.com/TissueImageAnalytics/tiatoolbox" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Renate</li>
+                        <li class="card-title">TIAToolbox</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2110,19 +2110,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Renate is a library providing tools for re-training pytorch models over time as new data becomes available.</p>
+                    <p class="card-summary">TIAToolbox provides an easy-to-use API where researchers can use, adapt and create models for CPath.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="01/05/22" data-id="microsoft/torchgeo" data-title="torchgeo">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="3/29/21" data-id="DLR-RM/stable-baselines3" data-title="Stable Baselines3">
               <div class="card ecosystem-card">
-                <a href="https://github.com/microsoft/torchgeo" target="_blank">
+                <a href="https://github.com/DLR-RM/stable-baselines3" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">torchgeo</li>
+                        <li class="card-title">Stable Baselines3</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2131,19 +2131,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Datasets, transforms, and models for geospatial data</p>
+                    <p class="card-summary">Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="02/16/23" data-id="BiomedSciAI/fuse-med-ml" data-title="FuseMedML">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="06/02/21" data-id="airaria/TextBrewer" data-title="TextBrewer">
               <div class="card ecosystem-card">
-                <a href="https://github.com/BiomedSciAI/fuse-med-ml" target="_blank">
+                <a href="http://textbrewer.hfl-rc.com" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">FuseMedML</li>
+                        <li class="card-title">TextBrewer</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2152,19 +2152,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">FuseMedML is a python framework accelerating ML based discovery in the medical field by encouraging code reuse</p>
+                    <p class="card-summary">A PyTorch-based knowledge distillation toolkit for natural language processing</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="10/28/19" data-id="albumentations-team/albumentations" data-title="Albumentations">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="10/28/19" data-id="catalyst-team/catalyst" data-title="Catalyst">
               <div class="card ecosystem-card">
-                <a href="https://github.com/albu/albumentations" target="_blank">
+                <a href="https://github.com/catalyst-team/catalyst" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Albumentations</li>
+                        <li class="card-title">Catalyst</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2173,19 +2173,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Fast and extensible image augmentation library for different CV tasks like classification, segmentation, object detection and pose estimation.</p>
+                    <p class="card-summary">Catalyst helps you write compact, but full-featured deep learning and reinforcement learning pipelines with a few lines of code.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="OpenMined/PySyft" data-title="PySyft">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="05/07/21" data-id="joeynmt/joeynmt" data-title="joeynmt">
               <div class="card ecosystem-card">
-                <a href="https://github.com/OpenMined/PySyft" target="_blank">
+                <a href="https://joeynmt.readthedocs.io/en/latest/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">PySyft</li>
+                        <li class="card-title">joeynmt</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2194,19 +2194,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">PySyft is a Python library for encrypted, privacy preserving deep learning.</p>
+                    <p class="card-summary">Minimalist Neural Machine Translation toolkit for educational purposes</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="07/19/21" data-id="asyml/forte" data-title="forte">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="09/13/21" data-id="huggingface/accelerate" data-title="accelerate">
               <div class="card ecosystem-card">
-                <a href="https://github.com/asyml/forte" target="_blank">
+                <a href="https://huggingface.co/docs/accelerate" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">forte</li>
+                        <li class="card-title">accelerate</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2215,19 +2215,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Forte is a toolkit for building NLP pipelines featuring composable components, convenient data interfaces, and cross-task interaction.</p>
+                    <p class="card-summary">🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="9/21/23" data-id="" data-title="RoMa">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/11/20" data-id="facebookresearch/mmf" data-title="MMF">
               <div class="card ecosystem-card">
-                <a href="https://github.com/naver/roma" target="_blank">
+                <a href="https://mmf.sh/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">RoMa</li>
+                        <li class="card-title">MMF</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2236,19 +2236,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">RoMa is a standalone library to handle rotation representations with PyTorch (rotation matrices, quaternions, rotation vectors, etc). It aims for robustness, ease-of-use, and efficiency.</p>
+                    <p class="card-summary">A modular framework for vision & language multimodal research from Facebook AI Research (FAIR).</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="10/18/19" data-id="pytorch/captum" data-title="Captum">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="5/7/21" data-id="pystiche/pystiche" data-title="pystiche">
               <div class="card ecosystem-card">
-                <a href="https://captum.ai/" target="_blank">
+                <a href="https://github.com/pystiche/pystiche" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Captum</li>
+                        <li class="card-title">pystiche</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2257,19 +2257,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Captum (“comprehension” in Latin) is an open source, extensible library for model interpretability built on PyTorch.</p>
+                    <p class="card-summary">pystiche is a framework for Neural Style Transfer (NST) built upon PyTorch.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="mit-han-lab/torchquantum" data-title="TorchQuantum">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="03/28/22" data-id="mosaicml/composer" data-title="composer">
               <div class="card ecosystem-card">
-                <a href="https://hanruiwanghw.wixsite.com/torchquantum" target="_blank">
+                <a href="https://github.com/mosaicml/composer" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">TorchQuantum</li>
+                        <li class="card-title">composer</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2278,19 +2278,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">TorchQuantum is a quantum classical simulation framework based on PyTorch. It supports statevector, density matrix simulation and pulse simulation on different hardware platforms such as CPUs and GPUs.</p>
+                    <p class="card-summary">library of algorithms to speed up neural network training</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="06/02/21" data-id="TorchEnsemble-Community/Ensemble-Pytorch" data-title="Ensemble-Pytorch">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="8/14/19" data-id="tensorly/tensorly" data-title="TensorLy">
               <div class="card ecosystem-card">
-                <a href="https://ensemble-pytorch.readthedocs.io" target="_blank">
+                <a href="http://tensorly.org/stable/home.html" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Ensemble-Pytorch</li>
+                        <li class="card-title">TensorLy</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2299,19 +2299,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.</p>
+                    <p class="card-summary">TensorLy is a high level API for tensor methods and deep tensorized neural networks in Python that aims to make tensor learning simple.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/1/23" data-id="microsoft/Semi-supervised-learning" data-title="USB">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="1/20/21" data-id="arogozhnikov/einops" data-title="einops">
               <div class="card ecosystem-card">
-                <a href="https://usb.readthedocs.io/" target="_blank">
+                <a href="https://github.com/arogozhnikov/einops" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">USB</li>
+                        <li class="card-title">einops</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2320,19 +2320,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">USB is a Pytorch-based Python package for Semi-Supervised Learning (SSL). It is easy-to-use/extend, affordable to small groups, and comprehensive for developing and evaluating SSL algorithms.</p>
+                    <p class="card-summary">Flexible and powerful tensor operations for readable and reliable code.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="pyro-ppl/pyro" data-title="Pyro">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/14/19" data-id="BorealisAI/advertorch" data-title="AdverTorch">
               <div class="card ecosystem-card">
-                <a href="http://pyro.ai/" target="_blank">
+                <a href="https://github.com/BorealisAI/advertorch" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Pyro</li>
+                        <li class="card-title">AdverTorch</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2341,19 +2341,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend.</p>
+                    <p class="card-summary">A toolbox for adversarial robustness research. It contains modules for generating adversarial examples and defending against attacks.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="05/20/22" data-id="ludwig-ai/ludwig" data-title="ludwig">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="pytorch/ignite" data-title="Ignite">
               <div class="card ecosystem-card">
-                <a href="http://ludwig.ai" target="_blank">
+                <a href="https://github.com/pytorch/ignite" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">ludwig</li>
+                        <li class="card-title">Ignite</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2362,19 +2362,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Data-centric declarative deep learning framework</p>
+                    <p class="card-summary">Ignite is a high-level library for training neural networks in PyTorch. It helps with writing compact, but full-featured training loops.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="7/14/19" data-id="facebookresearch/ParlAI" data-title="ParlAI">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="03/28/22" data-id="intel/neural-compressor" data-title="neural-compressor">
               <div class="card ecosystem-card">
-                <a href="http://parl.ai/" target="_blank">
+                <a href="https://intel.github.io/neural-compressor/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">ParlAI</li>
+                        <li class="card-title">neural-compressor</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2383,19 +2383,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">ParlAI is a unified platform for sharing, training, and evaluating dialog models across many tasks.</p>
+                    <p class="card-summary">Intel® Neural Compressor provides unified APIs for network compression technologies for faster inference</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="08/15/21" data-id="facebookresearch/pytorchvideo" data-title="PyTorchVideo">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="03/28/22" data-id="lf1-io/padl" data-title="padl">
               <div class="card ecosystem-card">
-                <a href="https://pytorchvideo.org/" target="_blank">
+                <a href="https://lf1-io.github.io/padl/" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">PyTorchVideo</li>
+                        <li class="card-title">padl</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2404,19 +2404,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">A deep learning library for video understanding research. Hosts various video-focused models, datasets, training pipelines and more.</p>
+                    <p class="card-summary">Pipeline Abstractions for Deep Learning in PyTorch</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="12/30/19" data-id="flairNLP/flair" data-title="Flair">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="6/17/20" data-id="microsoft/hummingbird" data-title="Hummingbird">
               <div class="card ecosystem-card">
-                <a href="https://github.com/flairNLP/flair" target="_blank">
+                <a href="https://github.com/microsoft/hummingbird" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Flair</li>
+                        <li class="card-title">Hummingbird</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2425,19 +2425,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Flair is a very simple framework for state-of-the-art natural language processing (NLP).</p>
+                    <p class="card-summary">Hummingbird compiles trained ML models into tensor computation for faster inference.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="06/22/21" data-id="PyTorchLightning/metrics" data-title="TorchMetrics">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="06/27/21" data-id="open-mmlab" data-title="OpenMMLab">
               <div class="card ecosystem-card">
-                <a href="https://github.com/PyTorchLightning/metrics" target="_blank">
+                <a href="https://github.com/open-mmlab" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">TorchMetrics</li>
+                        <li class="card-title">OpenMMLab</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2446,19 +2446,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Machine learning metrics for distributed, scalable PyTorch applications.</p>
+                    <p class="card-summary">OpenMMLab covers a wide range of computer vision research topics including classification, detection, segmentation, and super-resolution.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="2/13/20" data-id="GRAAL-Research/poutyne" data-title="Poutyne">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="09/09/21" data-id="pykale/pykale" data-title="PyKale">
               <div class="card ecosystem-card">
-                <a href="https://poutyne.org/" target="_blank">
+                <a href="https://github.com/pykale/pykale" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">Poutyne</li>
+                        <li class="card-title">PyKale</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2467,19 +2467,19 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">Poutyne is a Keras-like framework for PyTorch and handles much of the boilerplating code needed to train neural networks.</p>
+                    <p class="card-summary">PyKale is a PyTorch library for multimodal learning and transfer learning with deep learning and dimensionality reduction on graphs, images, texts, and videos.</p>
                   </div>
                 </a>
               </div>
             </div>
           
-            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="06/27/21" data-id="open-mmlab" data-title="OpenMMLab">
+            <div class="col-md-6 ecosystem-card-wrapper" id="ecosystemCards" data-categories="" data-date-added="05/07/21" data-id="azavea/raster-vision" data-title="raster-vision">
               <div class="card ecosystem-card">
-                <a href="https://github.com/open-mmlab" target="_blank">
+                <a href="https://docs.rastervision.io" target="_blank">
                   <div class="card-body  ">
                     <div class="ecosystem-card-title-container">
                       <ul class="star-list">
-                        <li class="card-title">OpenMMLab</li>
+                        <li class="card-title">raster-vision</li>
                           <div class="icon-count-container">
                             <li><img class="github-logo" alt="GitHub logo" src="/assets/images/logo-github.svg"></li>
                             <li class="github-stars-count"></li>
@@ -2488,7 +2488,7 @@ <h1>
                       </ul>
                         
                     </div>
-                    <p class="card-summary">OpenMMLab covers a wide range of computer vision research topics including classification, detection, segmentation, and super-resolution.</p>
+                    <p class="card-summary">An open source framework for deep learning on satellite and aerial imagery.</p>
                   </div>
                 </a>
               </div>
diff --git a/feed.xml b/feed.xml
index 07674302eaea..8c46ef7d74c2 100644
--- a/feed.xml
+++ b/feed.xml
@@ -4,7 +4,7 @@
   <generator uri="https://jekyllrb.com/" version="3.9.0">Jekyll</generator>
   <link href="https://pytorch.org/feed.xml" rel="self" type="application/atom+xml" />
   <link href="https://pytorch.org/" rel="alternate" type="text/html" />
-  <updated>2024-11-07T12:57:53-08:00</updated>
+  <updated>2024-11-08T13:27:09-08:00</updated>
   <id>https://pytorch.org/feed.xml</id>
 
   
diff --git a/join.html b/join.html
index 32f362fd8b47..9468e979abb1 100644
--- a/join.html
+++ b/join.html
@@ -318,7 +318,7 @@ <h1 class="small">Join the PyTorch Foundation</h1>
   <div class="container">
     <div class="row content  border-bottom">
       <div class="col-md-5 body-side-text">
-        <p>According to statistics from MIT Sloan, <a href="https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/">75%</a> of top executives believe AI will help their organizations grow and gain a competitive edge. Since 2020, there has been a <a href="https://twitter.com/intent/tweet?url=http%3A%2F%2Fwww.forbes.com%2Fsites%2Flouiscolumbus%2F2018%2F01%2F12%2F10-charts-that-will-change-your-perspective-on-artificial-intelligences-growth%2F&text=There%20has%20been%20a%2014X%20increase%20in%20the%20number%20of%20active%20AI%20startups%20since%202000.%20%23AI">14X increase</a> in the number of active AI startups, and <a href="https://twitter.com/intent/tweet?url=http%3A%2F%2Fwww.forbes.com%2Fsites%2Flouiscolumbus%2F2018%2F01%2F12%2F10-charts-that-will-change-your-perspective-on-artificial-intelligences-growth%2F&text=Investment%20into%20AI%20start-ups%20by%20venture%20capitalists%20has%20increased%206X%20since%202000.%20%23AI">venture capitalist-funded startups have increased by 6X</a>.  The <a href="https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html">PwC Global Artificial Intelligence Study</a> indicates that AI has the potential to contribute $15.7 trillion to the global economy by 2030, with 45% of the total economic gains coming from product enhancements that stimulate consumer demand.</p>
+        <p>According to statistics from MIT Sloan, 75% of top executives believe AI will help their organizations grow and gain a competitive edge. Since 2020, there has been a 14X increase in the number of active AI startups, and venture capitalist-funded startups have increased by 6X. The PwC Global Artificial Intelligence Study indicates that AI has the potential to contribute $15.7 trillion to the global economy by 2030, with 45% of the total economic gains coming from product enhancements that stimulate consumer demand.</p>
         <p>By joining the PyTorch Foundation, you can help build and shape the future of end-to-end machine learning frameworks alongside your industry peers. PyTorch offers a user-friendly front-end, distributed training, and an ecosystem of tools and libraries that enable fast, flexible experimentation and efficient production.</p>
         <p>As a member of the PyTorch Foundation, you'll have access to resources that allow you to be stewards of stable, secure, and long-lasting codebases. You can collaborate on training, local and regional events, open-source developer tooling, academic research, and guides to help new users and contributors have a productive experience.</p>        
       </div>
@@ -417,72 +417,96 @@ <h2 class="upper">Join the Membership that fits your goals</h2>
           </thead>
           <tbody>
             <tr>
-              <td class="benefit">Hold one (1) seat on PTF Governing Board + one alternate (1) representative</td>
+              <td class="benefit">Guaranteed one (1) seat on the PTF Governing Board
+              </td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
               <td></td>
               <td></td>
             </tr>
             <tr>
-              <td class="benefit">Appoint one (1) voting representative in any subcommittees or activities of the PTF Governing Board</td>
+              <td class="benefit">Guaranteed one (1) voting representative on the PyTorch Technical Advisory Committee (TAC)
+              </td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
               <td></td>
               <td></td>
             </tr>
             <tr>
-              <td class="benefit">Enjoy most prominent placement in displays of membership including website, landscape and marketing materials</td>
+              <td class="benefit">Guaranteed one (1) voting representative in any other subcommittees  of the PTF
+              </td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
               <td></td>
               <td></td>
             </tr>
             <tr>
-              <td class="benefit">Receive greater insight into PTF strategy and activities through engagement with the PTF leadership team</td>
-              <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
+              <td class="benefit">Enjoy most prominent placement in displays of membership including website, landscape and marketing materials
+              </td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
               <td></td>
+              <td></td>
             </tr>
             <tr>
-              <td class="benefit">Opportunity to host “PyTorch Day”, including on-demand webinars and livestream</td>
+              <td class="benefit">Add resources to the PyTorch Infrastructure team and get the ability to execute self hosted runners for tests on your own hardware/soc/systems
+              </td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
+              <td></td>
+              <td></td>
+            </tr>
+            <tr>
+              <td class="benefit">Receive greater insight into PTF strategy and activities through engagement with the PTF leadership team
+              </td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
               <td></td>
+              <td></td>
             </tr>
             <tr>
-              <td class="benefit">Create an individualized amplification plan upon membership announcement with the PTF team</td>
+              <td class="benefit">Opportunity to be considered for a PTF Board seat (Premier are guaranteed a seat)
+              </td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
               <td></td>
             </tr>
             <tr>
-              <td class="benefit">Participate in Marketing, Community, Thought Leadership opportunities</td>
+              <td class="benefit">Opportunity to be considered for a PTF TAC seat (Premier are guaranteed a seat)
+              </td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
               <td></td>
             </tr>
             <tr>
-              <td class="benefit">Receive discounts on PTF event sponsorships and training courses</td>
+              <td class="benefit">Create an individualized amplification plan  with the PTF team
+              </td>
+              <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
+              <td></td>
+            </tr>
+            <tr>
+              <td class="benefit">Participate in Marketing, Community, and Thought Leadership opportunities
+              </td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
+              <td></td>
+            </tr>
+            <tr>
+              <td class="benefit">Receive discounts on PTF event sponsorships and training courses
+              </td>
+                <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
+                <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
+                <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
             </tr>
             <tr>
-              <td class="benefit">Demonstrate your support for the PTF by displaying your logo on the PTF website and in marketing materials</td>
+              <td class="benefit">Your company logo displayed on the PTF website and in marketing materials
+              </td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
             </tr>
             <tr>
-              <td class="benefit">Internship opportunities </td>
+              <td class="benefit">Opportunity to host PyTorch on-demand webinars and livestreams
+              </td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
               <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
             </tr>
-            <tr>
-              <td class="benefit">Opportunity to be considered for a PTF Board position<br/> 
-                <span class="lighter">(Note that premier members automatically receive a seat on the board)</span></td>
-                <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
-                <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
-                <td><img alt="Checkmark icon" src="/assets/images/join/check.svg" /></td>
-              </tr>
           </tbody>
           <tfoot>
             <tr>