diff --git "a/example_notebook.ipynb" "b/example_notebook.ipynb"
--- "a/example_notebook.ipynb"
+++ "b/example_notebook.ipynb"
@@ -1,1010 +1 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "id": "Mq5iNIZ9xWxt",
-      "metadata": {
-        "id": "Mq5iNIZ9xWxt"
-      },
-      "source": [
-        "# Empty Submission Example for S23DR Challenge\n",
-        "\n",
-        "### Helpful Links\n",
-        "[Challenge Page](https://huggingface.co/spaces/usm3d/S23DR)  \n",
-        "[Workshop Page](usm3d.github.io)  \n",
-        "\n",
-        "[HoHo Train Set](https://huggingface.co/datasets/usm3d/hoho-train-set)  \n",
-        "[Handcrafted Baseline Solution](https://huggingface.co/usm3d/handcrafted_baseline_submission) \n",
-        " "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "dua8UJOoxiDi",
-      "metadata": {
-        "id": "dua8UJOoxiDi"
-      },
-      "source": [
-        "## Setup\n",
-        "\n",
-        "We'll start by checking if we are running to Google Colab (and if we are setting `IN_COLAB = True` and installing the [hoho tools](https://huggingface.co/usm3d/tools))."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "id": "ItDDqoXop8bb",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ItDDqoXop8bb",
-        "outputId": "0c9d26a7-bf79-4452-c772-d5579a9cb2a9"
-      },
-      "outputs": [],
-      "source": [
-        "try:\n",
-        "  import google.colab\n",
-        "  IN_COLAB = True\n",
-        "except:\n",
-        "  IN_COLAB = False\n",
-        "\n",
-        "if IN_COLAB:\n",
-        "    !pip install git+http://hf.co/usm3d/tools.git"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "2tHX74Z-x1cU",
-      "metadata": {
-        "id": "2tHX74Z-x1cU"
-      },
-      "source": [
-        "We need to be logged into HF for this to work because the training dataset is gated. If you haven't already please go to the [dastaset page](https://huggingface.co/datasets/usm3d/hoho-train-set) to agree to our terms and request access to the dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "zq_ljluLqzzv",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "zq_ljluLqzzv",
-        "outputId": "b66806f1-b88a-47e0-8194-79515b73fa23"
-      },
-      "outputs": [],
-      "source": [
-        "if IN_COLAB:\n",
-        "    !huggingface-cli login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "Xf2PY79fywa5",
-      "metadata": {
-        "id": "Xf2PY79fywa5"
-      },
-      "source": [
-        "## Data Download, Analysis, and Visualization"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "e171b1ec-e861-4349-98fd-2eac4d080ff5",
-      "metadata": {
-        "id": "e171b1ec-e861-4349-98fd-2eac4d080ff5"
-      },
-      "outputs": [],
-      "source": [
-        "import hoho\n",
-        "from hoho import *\n",
-        "\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from pathlib import Path\n",
-        "from collections import Counter\n",
-        "import itertools\n",
-        "import datasets\n",
-        "import trimesh\n",
-        "from tqdm.notebook import tqdm\n",
-        "import webdataset as wds\n",
-        "import sys"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "83649a4c-fde7-4051-ba71-e596d382e76a",
-      "metadata": {
-        "id": "83649a4c-fde7-4051-ba71-e596d382e76a"
-      },
-      "source": [
-        "### Load the hoho package and point to the data folder\n",
-        "\n",
-        "We download only one shard of the data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "ffffc234",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ffffc234",
-        "outputId": "e969db58-e88e-457a-eee2-14e65c8117fb"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/Users/jack/dev/USM3D/comp/tools/hoho/hoho.py:309: UserWarning: streaming isn't using with 'all': changing `split` to 'train'\n",
-            "  warnings.warn('streaming isn\\'t using with \\'all\\': changing `split` to \\'train\\'')\n",
-            "/Users/jack/dev/USM3D/comp/tools/hoho/hoho.py:310: UserWarning: no tarfiles found in data/usm-training-data/data/val.\n",
-            "  warnings.warn(msg)\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "/Users/jack/dev/USM3D/comp/empty_submission\n",
-            "total 104\n",
-            "-rw-r--r--   1 jack  staff   1.5K Apr 26 12:51 .gitattributes\n",
-            "-rw-r--r--   1 jack  staff   855B Apr 26 12:51 README.md\n",
-            "drwxr-xr-x  10 jack  staff   320B Apr 26 12:55 \u001b[34m..\u001b[m\u001b[m\n",
-            "drwxr-xr-x   3 jack  staff    96B Apr 26 15:06 \u001b[34mdata\u001b[m\u001b[m\n",
-            "-rw-r--r--   1 jack  staff   5.8K Apr 26 15:42 submission.parquet\n",
-            "-rw-r--r--   1 jack  staff   2.3K Apr 26 15:50 script.py\n",
-            "drwxr-xr-x  15 jack  staff   480B Apr 26 15:50 \u001b[34m.git\u001b[m\u001b[m\n",
-            "-rw-r--r--   1 jack  staff    32K Apr 26 18:26 example_notebook.ipynb\n",
-            "drwxr-xr-x   9 jack  staff   288B Apr 28 10:32 \u001b[34m.\u001b[m\u001b[m\n",
-            "Using data/usm-training-data/data as the data directory (we are running locally)\n",
-            "------------ Loading dataset------------ \n",
-            "params.json not found (this means we probably aren't in the test env). Using example params.\n",
-            "{'competition_id': 'usm3d/S23DR', 'competition_type': 'script', 'metric': 'custom', 'token': 'hf_**********************************', 'team_id': 'local-test-team_id', 'submission_id': 'local-test-submission_id', 'submission_id_col': '__key__', 'submission_cols': ['__key__', 'wf_edges', 'wf_vertices', 'edge_semantics'], 'submission_rows': 180, 'output_path': '.', 'submission_repo': '<THE HF MODEL ID of THIS REPO', 'time_limit': 7200, 'dataset': 'usm3d/usm-test-data-x', 'submission_filenames': ['submission.parquet']}\n",
-            "------------ Now you can do your solution ---------------\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "0it [00:00, ?it/s]"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "2it [00:34, 17.31s/it]\n"
-          ]
-        },
-        {
-          "ename": "KeyboardInterrupt",
-          "evalue": "",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-            "Cell \u001b[0;32mIn[4], line 47\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m------------ Now you can do your solution ---------------\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     46\u001b[0m solution \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, sample \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tqdm(dataset)):\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;66;03m# replace this with your solution\u001b[39;00m\n\u001b[1;32m     49\u001b[0m     pred_vertices, pred_edges \u001b[38;5;241m=\u001b[39m empty_solution(sample)\n\u001b[1;32m     51\u001b[0m     solution\u001b[38;5;241m.\u001b[39mappend({\n\u001b[1;32m     52\u001b[0m                     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__key__\u001b[39m\u001b[38;5;124m'\u001b[39m: sample[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__key__\u001b[39m\u001b[38;5;124m'\u001b[39m], \n\u001b[1;32m     53\u001b[0m                     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwf_vertices\u001b[39m\u001b[38;5;124m'\u001b[39m: pred_vertices\u001b[38;5;241m.\u001b[39mtolist(),\n\u001b[1;32m     54\u001b[0m                     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwf_edges\u001b[39m\u001b[38;5;124m'\u001b[39m: pred_edges\n\u001b[1;32m     55\u001b[0m                 })\n",
-            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/tqdm/std.py:1181\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1178\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m   1180\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1181\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m   1182\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m   1183\u001b[0m         \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m   1184\u001b[0m         \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
-            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/pipeline.py:70\u001b[0m, in \u001b[0;36mDataPipeline.iterator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Create an iterator through the entire dataset, using the given number of repetitions.\"\"\"\u001b[39;00m\n\u001b[1;32m     69\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrepetitions):\n\u001b[0;32m---> 70\u001b[0m     \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterator1()\n",
-            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/filters.py:302\u001b[0m, in \u001b[0;36m_map\u001b[0;34m(data, f, handler)\u001b[0m\n\u001b[1;32m    300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_map\u001b[39m(data, f, handler\u001b[38;5;241m=\u001b[39mreraise_exception):\n\u001b[1;32m    301\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Map samples.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 302\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m sample \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[1;32m    303\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    304\u001b[0m             result \u001b[38;5;241m=\u001b[39m f(sample)\n",
-            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/filters.py:302\u001b[0m, in \u001b[0;36m_map\u001b[0;34m(data, f, handler)\u001b[0m\n\u001b[1;32m    300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_map\u001b[39m(data, f, handler\u001b[38;5;241m=\u001b[39mreraise_exception):\n\u001b[1;32m    301\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Map samples.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 302\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m sample \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[1;32m    303\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    304\u001b[0m             result \u001b[38;5;241m=\u001b[39m f(sample)\n",
-            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/tariterators.py:219\u001b[0m, in \u001b[0;36mgroup_by_keys\u001b[0;34m(data, keys, lcase, suffixes, handler)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Group tarfile contents by keys and yield samples.\u001b[39;00m\n\u001b[1;32m    204\u001b[0m \n\u001b[1;32m    205\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;124;03m    iterator over samples.\u001b[39;00m\n\u001b[1;32m    217\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    218\u001b[0m current_sample \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 219\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m filesample \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    221\u001b[0m         \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(filesample, \u001b[38;5;28mdict\u001b[39m)\n",
-            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/tariterators.py:177\u001b[0m, in \u001b[0;36mtar_file_expander\u001b[0;34m(data, handler, select_files, rename_files)\u001b[0m\n\u001b[1;32m    175\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(source, \u001b[38;5;28mdict\u001b[39m)\n\u001b[1;32m    176\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m source\n\u001b[0;32m--> 177\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sample \u001b[38;5;129;01min\u001b[39;00m tar_file_iterator(\n\u001b[1;32m    178\u001b[0m     source[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m    179\u001b[0m     handler\u001b[38;5;241m=\u001b[39mhandler,\n\u001b[1;32m    180\u001b[0m     select_files\u001b[38;5;241m=\u001b[39mselect_files,\n\u001b[1;32m    181\u001b[0m     rename_files\u001b[38;5;241m=\u001b[39mrename_files,\n\u001b[1;32m    182\u001b[0m ):\n\u001b[1;32m    183\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[1;32m    184\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(sample, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m sample \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfname\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m sample\n\u001b[1;32m    185\u001b[0m     )\n\u001b[1;32m    186\u001b[0m     sample[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__url__\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m url\n",
-            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/tariterators.py:142\u001b[0m, in \u001b[0;36mtar_file_iterator\u001b[0;34m(fileobj, skip_meta, handler, select_files, rename_files)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m select_files \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m select_files(fname):\n\u001b[1;32m    141\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m--> 142\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mstream\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextractfile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarinfo\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    143\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(fname\u001b[38;5;241m=\u001b[39mfname, data\u001b[38;5;241m=\u001b[39mdata)\n\u001b[1;32m    144\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m result\n",
-            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/tarfile.py:689\u001b[0m, in \u001b[0;36m_FileInFile.read\u001b[0;34m(self, size)\u001b[0m\n\u001b[1;32m    687\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data:\n\u001b[1;32m    688\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfileobj\u001b[38;5;241m.\u001b[39mseek(offset \u001b[38;5;241m+\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposition \u001b[38;5;241m-\u001b[39m start))\n\u001b[0;32m--> 689\u001b[0m     b \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfileobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlength\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    690\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(b) \u001b[38;5;241m!=\u001b[39m length:\n\u001b[1;32m    691\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ReadError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munexpected end of data\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/tarfile.py:526\u001b[0m, in \u001b[0;36m_Stream.read\u001b[0;34m(self, size)\u001b[0m\n\u001b[1;32m    524\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return the next size number of bytes from the stream.\"\"\"\u001b[39;00m\n\u001b[1;32m    525\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 526\u001b[0m buf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43msize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    527\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpos \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(buf)\n\u001b[1;32m    528\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m buf\n",
-            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/tarfile.py:544\u001b[0m, in \u001b[0;36m_Stream._read\u001b[0;34m(self, size)\u001b[0m\n\u001b[1;32m    542\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuf \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    543\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 544\u001b[0m     buf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfileobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbufsize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    545\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m buf:\n\u001b[1;32m    546\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/gopen.py:87\u001b[0m, in \u001b[0;36mPipe.read\u001b[0;34m(self, *args, **kw)\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw):\n\u001b[1;32m     86\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Wrap stream.read and checks status.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 87\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     88\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcheck_status()\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-          ]
-        }
-      ],
-      "source": [
-        "# %load script.py\n",
-        "### This is example of the script that will be run in the test environment.\n",
-        "### Some parts of the code are compulsory and you should NOT CHANGE THEM.\n",
-        "### They are between '''---compulsory---''' comments.\n",
-        "### You can change the rest of the code to define and test your solution.\n",
-        "### However, you should not change the signature of the provided function.\n",
-        "### The script would save \"submission.parquet\" file in the current directory.\n",
-        "### You can use any additional files and subdirectories to organize your code.\n",
-        "\n",
-        "'''---compulsory---'''\n",
-        "import hoho; hoho.setup() # YOU MUST CALL hoho.setup() BEFORE ANYTHING ELSE\n",
-        "'''---compulsory---'''\n",
-        "\n",
-        "from pathlib import Path\n",
-        "from tqdm import tqdm\n",
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "\n",
-        "\n",
-        "def empty_solution(sample):\n",
-        "    '''Return a minimal valid solution, i.e. 2 vertices and 1 edge.'''\n",
-        "    return np.zeros((2,3)), [(0, 1)]\n",
-        "\n",
-        "\n",
-        "if __name__ == \"__main__\":\n",
-        "    print (\"------------ Loading dataset------------ \")\n",
-        "    params = hoho.get_params()\n",
-        "    \n",
-        "    # by default it is usually better to use `get_dataset()` like this\n",
-        "    # \n",
-        "    # dataset = hoho.get_dataset(split='all')\n",
-        "    # \n",
-        "    # but in this case (because we don't do anything with the sample \n",
-        "    # anyway) we set `decode=None`. We can set the `split` argument \n",
-        "    # to 'train' or 'val' ('all' defaults back to 'train') if we are \n",
-        "    # testing ourselves locally. \n",
-        "    # \n",
-        "    # dataset = hoho.get_dataset(split='val', decode=None)\n",
-        "    #\n",
-        "    # On the test server *`split` must be set to 'all'* \n",
-        "    # to compute both the public and private leaderboards.\n",
-        "    # \n",
-        "    dataset = hoho.get_dataset(split='all', decode=None)\n",
-        "    \n",
-        "    print('------------ Now you can do your solution ---------------')\n",
-        "    solution = []\n",
-        "    for i, sample in enumerate(tqdm(dataset)):\n",
-        "        # replace this with your solution\n",
-        "        pred_vertices, pred_edges = empty_solution(sample)\n",
-        "        \n",
-        "        solution.append({\n",
-        "                        '__key__': sample['__key__'], \n",
-        "                        'wf_vertices': pred_vertices.tolist(),\n",
-        "                        'wf_edges': pred_edges\n",
-        "                    })\n",
-        "    print('------------ Saving results ---------------')\n",
-        "    sub = pd.DataFrame(solution, columns=[\"__key__\", \"wf_vertices\", \"wf_edges\"])\n",
-        "    sub.to_parquet(Path(params['output_path']) / \"submission.parquet\")\n",
-        "    print(\"------------ Done ------------ \")"
-      ]
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+{"cells":[{"cell_type":"markdown","id":"Mq5iNIZ9xWxt","metadata":{"id":"Mq5iNIZ9xWxt"},"source":["# Example Submission for S23DR Challenge\n","\n","### Helpful Links\n","[Challenge Page](https://huggingface.co/spaces/usm3d/S23DR)  \n","[Workshop Page](usm3d.github.io)  \n","\n","[HoHo Train Set](https://huggingface.co/datasets/usm3d/hoho-train-set)  \n","[Handcrafted Baseline Solution](https://huggingface.co/usm3d/handcrafted_baseline_submission)\n"]},{"cell_type":"markdown","id":"dua8UJOoxiDi","metadata":{"id":"dua8UJOoxiDi"},"source":["## Setup\n","\n","We'll start by checking if we are running to Google Colab (and if we are setting `IN_COLAB = True` and installing the [hoho tools](https://huggingface.co/usm3d/tools))."]},{"cell_type":"code","execution_count":1,"id":"ItDDqoXop8bb","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ItDDqoXop8bb","outputId":"e05ad9d9-cd34-46bc-df5d-6c91bcd30cf6","executionInfo":{"status":"ok","timestamp":1714452318340,"user_tz":240,"elapsed":27497,"user":{"displayName":"Jack Langerman","userId":"09781760878184723424"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting git+http://hf.co/usm3d/tools.git\n","  Cloning http://hf.co/usm3d/tools.git to /tmp/pip-req-build-j6lfpwgr\n","  Running command git clone --filter=blob:none --quiet http://hf.co/usm3d/tools.git /tmp/pip-req-build-j6lfpwgr\n","  warning: redirecting to https://huggingface.co/usm3d/tools.git/\n","  Resolved http://hf.co/usm3d/tools.git to commit edc78606eec1fe10e90d8f6cb834a24395b95f94\n","  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: numpy in 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requests>=2.19.0->datasets->hoho==0.0.2) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets->hoho==0.0.2) (3.7)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets->hoho==0.0.2) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets->hoho==0.0.2) (2024.2.2)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->hoho==0.0.2) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->hoho==0.0.2) (2023.4)\n","Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->hoho==0.0.2) (2024.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets->hoho==0.0.2) (1.16.0)\n","Building wheels for collected packages: hoho\n","  Building wheel for hoho (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for hoho: filename=hoho-0.0.2-py3-none-any.whl size=19721 sha256=212cd2bd9aa807da5bc2523e81be659f3bf475ac6c84be2df9415bb7db8fdece\n","  Stored in directory: /tmp/pip-ephem-wheel-cache-lk5prg74/wheels/d3/de/c1/ac1615d96ac4623ed9c567bfa6301ed76edb6a75261fb9d292\n","Successfully built hoho\n","Installing collected packages: braceexpand, xxhash, webdataset, trimesh, dill, multiprocess, huggingface-hub, datasets, hoho\n","  Attempting uninstall: huggingface-hub\n","    Found existing installation: huggingface-hub 0.20.3\n","    Uninstalling huggingface-hub-0.20.3:\n","      Successfully uninstalled huggingface-hub-0.20.3\n","Successfully installed braceexpand-0.1.7 datasets-2.19.0 dill-0.3.8 hoho-0.0.2 huggingface-hub-0.22.2 multiprocess-0.70.16 trimesh-4.3.2 webdataset-0.2.86 xxhash-3.4.1\n"]}],"source":["try:\n","  import google.colab\n","  IN_COLAB = True\n","except:\n","  IN_COLAB = False\n","\n","if IN_COLAB:\n","    !pip install git+http://hf.co/usm3d/tools.git"]},{"cell_type":"markdown","id":"2tHX74Z-x1cU","metadata":{"id":"2tHX74Z-x1cU"},"source":["We need to be logged into HF for this to work because the training dataset is gated. If you haven't already please go to the [dastaset page](https://huggingface.co/datasets/usm3d/hoho-train-set) to agree to our terms and request access to the dataset."]},{"cell_type":"code","execution_count":2,"id":"zq_ljluLqzzv","metadata":{"id":"zq_ljluLqzzv","executionInfo":{"status":"ok","timestamp":1714452318978,"user_tz":240,"elapsed":643,"user":{"displayName":"Jack Langerman","userId":"09781760878184723424"}}},"outputs":[],"source":["import os\n","from pathlib import Path\n","\n","\n","if IN_COLAB:\n","    from google.colab import userdata\n","    hf_token = os.environ.get('HF_TOKEN')\n","    if hf_token is None:\n","        hf_home = os.environ.get('HF_HOME', '~/.cache/huggingface')\n","        token_path = (Path(hf_home) / 'token')\n","        if token_path.exists():\n","            with token_path.open() as f:\n","                hf_token = f.read()\n","        else:\n","            hf_token = userdata.get('HF_TOKEN')\n","    if hf_token is not None:\n","        os.environ['HF_TOKEN'] = hf_token\n","    else:\n","        !huggingface-cli login"]},{"cell_type":"code","source":[],"metadata":{"id":"RGxnc7-zSUd2","executionInfo":{"status":"ok","timestamp":1714452318978,"user_tz":240,"elapsed":2,"user":{"displayName":"Jack Langerman","userId":"09781760878184723424"}}},"id":"RGxnc7-zSUd2","execution_count":2,"outputs":[]},{"cell_type":"markdown","source":["Now that we are logged in, let's create a repo for our solution"],"metadata":{"id":"hx857th3Ne1Z"},"id":"hx857th3Ne1Z"},{"cell_type":"code","source":["from huggingface_hub import HfApi\n","from pathlib import Path\n","\n","hf_api = HfApi()\n","\n","submission_name = 'my_cool_submission'\n","\n","# Set the repository IDs\n","source_repo_id = \"usm3d/empty_submission\"\n","target_repo_id = f\"{hf_api.whoami()['name']}/{submission_name}\"\n","upstream = Path('upstream')\n","upstream.mkdir(exist_ok=True)\n","\n","# Download the source repository locally\n","local_dir = hf_api.snapshot_download(repo_id=source_repo_id, local_dir=upstream, local_dir_use_symlinks=False)\n","print(local_dir)\n","\n","# Create a new repository in the user's account\n","new_repo = hf_api.create_repo(repo_id=target_repo_id, exist_ok=True)\n","print(new_repo)\n","\n","# Upload the files to the new repository\n","operations = [\n","    hf_api.upload_folder(\n","        folder_path=upstream,\n","        path_in_repo=\".\",\n","        repo_id=target_repo_id,\n","    )\n","]\n","print(operations)\n","\n","# Download the new repo\n","my_cool_submission = hf_api.snapshot_download(repo_id=target_repo_id, local_dir=submission_name, local_dir_use_symlinks=False)\n","print(my_cool_submission)\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":478,"referenced_widgets":["c710e106b78d48fba72ddf646389ed04","772db963d54048b8a5731ac46dc66108","d842d41db5d4443e8f84e9ece371b5e2","18b434ad3e264dccb1d3a6bc2ca2a44c","721aa92741c7446bb63f18be89d0b06c","10a4d0aaeb084bde84392748a6cf94d4","601b4e47f0984f60b16e92edad15f399","e6a95941e4334236b509e2740602282b","2bf7eb164d814da39e845cc810e0c6e3","fcb08c0bc7204137a3a865fbdd74f1c8","ff878a0cfc3c49a18965067d30953aa9","abea9e04ff0a4b8f94de6b57d6c1e910","77163de0845d40f884a6054cc4395b0c","0ad1e06be78e49cfbe4bcebf2096d490","18ca9923c52a4f1fa752b65eefb6c2e4","6002dc1dfed54423b354600b05463bf3","6fe1212d760f40f7ab56a0efd6892a6b","27fbb4cba4ac40ad8f39d7019dfa91a7","b81d5b9cd03a4d4c8ca6cf99661c3ced","0a0c642367ab4c4e97ebab052fc2e6c0","2e957076778d4163976d4a87efb6c27b","ff7b99ffcbbe402087654e45c33872de","285af1c25835478eaddbfe6f409fc58e","30b13108629c470890776ac101708e99","f391f9935d454deeb1873bb68d6d33b5","404850eab54d49bdafc229973ea5f439","a53a06de7d2446888b7a8c4a3d8e0f8e","f53d5e38a8034438a6fecd273a169e3b","c56e225b1bdd448fb904a9d8f2f49a9b","c30fadd696054a85b78aa68731cc7ab6","72363a43c6354397b75e2a1afb681532","4051821ed76e438181c90b87f4215b34","d769d5a388f84c6892af37bcb3f45d7c","0ff13f176fa7402f95678b3fb2783447","ca2fd125a4dd4be482133bfd021caa8c","f632690a764e4eb4b9b1f311eac123b8","b01e4005249a46728cb726dbeb586463","7353188ea2514d408923a4182c3bf525","e9ab79994f574579b9ca31912b01d808","a3e808e6c5014a5a972c1583fd501e85","e1cb197f8d0545508a4094f677c30dfe","cfd5b350581f494b88e861db63453b42","4b522599967b4ddba2b11d8002561b24","2ff7cbee5a1f411db974eab474f811ed","3eb6c8018fc14706b009f1df8f2dea00","050d19e326cb41348660d5a6d67bfe57","aac33d3d85084be4aebc8f4436a19a51","60cea387a9d04d439984667093d956e8","d01c5541737f448fb9f095a7508731d2","7fdec7a83f504e2b882ef52379314e08","3d270f901f334202a85406bef49576d9","c443e33f99764c50aae4ebf660f60686","8b93970233f04dbb8f7f4a34aeb264d2","b58a06a807f8431e933d79d3c6080e61","1027ae6f50ca4594bc3560b37bafb495","47f69711b02444bd9af85c8f9ca17095","9b2a99f2172f468dac504244032c0b8d","26721dfad2ab4a3db6199f4a9a30e595","bbc2d859b37741af91d229ef44fc98e2","766abdef67a54845b2c18a078ed1fae5","4bf296c4b060469984ff6f4e4cbbb5c3","c215874c7a194bc88d767448ca5fa336","526786c760614bbeb20282517422caf3","98a66d3233074ba6ada19a1a630e7e0b","666bcaa7aa0a4fe48ed3e0421fd5461a","53487e7967db4e4ab82b5de5cb373431","feefa5c5a17143dc98e2770d8952a5db","c151d0b782a3450b99818de0cd90758e","e4346ad43b294ad4a87411fde1bf7665","77280ea912f6491cba724577cd35bc76","e142394d10344fbb8400fed84775b9a9","d158691fef2c483aa4f85892728ba967","2ada4f17ccae4f1f8d2419617d44bdf7","e23e1e79aa3844348152b75183d94a05","13a3c5a9194f4e14a631e9532a28e45d","fa0a3edba52e43e28b055d46158f601d","6ff5c5ae7668435fbd126236420f1ff0","b0720acfd7784aa0be9df8519eb218c8","0b2a787f7fe54c4c95eaf2020fa54211","6a97ae750448428fbbfa2b0a6c9751aa","0faea59162f1433c95373f6f320efad1","d53880b512364451b33a20cb8fb3d2cf","f24d3506fd9e4a868a95b841d01752c4","6950ff9eef6b473aa49765a2896e0ad5","39070b41ac3b43ee8c2811cec8e8137d","4eddbb96df6b4319a84754e1a4905f8f","4c8a75218c084e0187e11906bc0fccec","58afd5d8adbe4b96b84a6f746ce1b523","2c606c7851624713a0cc2bfb20d8d542","9f2629e967c346b297fef041f4aa95ec","6c26c9cb3e0c4921bb7333b52f710454","a9d0d7d29a8c49e1b9995532094f1f01","6b336967f63f461b9ce8f083b31567d5","3ac6e60b3c0d44f0a29938cc96543bc6","6aa0917c604049c5aef97b9aeb154eac","5b0054bb2b7f487aa8cc767f806cdc62","d6848bce0e204dccb3ca99db25d7e4cb","b5893262d8f9444dad5d22f3a6fae7c6","9e52a4f1ae5042f78b8dd9b030d5b5c3","85a00b4159fc45dabb54e5cb15b87a2e","b5d7032384b34f7a9d00dac8c18aa0c7","8bbe4fa9af8744a9bc670c6c5fb6ec39","52a70585b3cf45cfaecf0a5a6c51835d","af73cbe1d1834f269f0eda616b777758","447f6931edec437db73c9ef7b5c66d9d","dd3ef53b3a65443b9c07cff6cbd7e139","ccc2947bb0d34f19a8582ac99c1cf30b","4231086d422648979f3a6e08365ed07a","fb499bf5fe9840369ae6d247dc286551","258f1782bdc84a41a5372f33a9a1adf2"]},"id":"U0J1w3kCZqMD","executionInfo":{"status":"ok","timestamp":1714452323248,"user_tz":240,"elapsed":4272,"user":{"displayName":"Jack Langerman","userId":"09781760878184723424"}},"outputId":"866a4304-8696-4521-a7d5-9b7e3b4fcef7"},"id":"U0J1w3kCZqMD","execution_count":3,"outputs":[{"output_type":"display_data","data":{"text/plain":["Fetching 4 files:   0%|          | 0/4 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c710e106b78d48fba72ddf646389ed04"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[".gitattributes:   0%|          | 0.00/1.52k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"abea9e04ff0a4b8f94de6b57d6c1e910"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["README.md:   0%|          | 0.00/779 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"285af1c25835478eaddbfe6f409fc58e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["example_notebook.ipynb:   0%|         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README.md:\\n{message}\")\n"]},{"output_type":"stream","name":"stdout","text":["[CommitInfo(commit_url='https://huggingface.co/jacklangerman/my_cool_submission/commit/b4aff5061b2c1ae17a4497c46e49280a1adda703', commit_message='Upload folder using huggingface_hub', commit_description='', oid='b4aff5061b2c1ae17a4497c46e49280a1adda703', pr_url=None, pr_revision=None, pr_num=None)]\n"]},{"output_type":"display_data","data":{"text/plain":["Fetching 4 files:   0%|          | 0/4 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"47f69711b02444bd9af85c8f9ca17095"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["README.md:   0%|          | 0.00/779 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"feefa5c5a17143dc98e2770d8952a5db"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[".gitattributes:   0%|          | 0.00/1.52k [00:00<?, 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Langerman","userId":"09781760878184723424"}}},"outputs":[],"source":[]},{"cell_type":"code","source":["# change directories into our submission folder\n","%cd $submission_name\n","\n","# update the readme\n","with open('README.md', 'w') as f:\n","    f.write(f\"# {' '.join(submission_name.split('_')).title()}\\n\\nThis repo contains a submission to the [S23DR Challenge](https://huggingface.co/spaces/usm3d/S23DR) (part of the [USM3D](https://usm3d.github.io/) workshop at CVPR2024). It was prepared by [{hf_api.whoami()['name']}](https://huggingface.co/{hf_api.whoami()['name']}).\\n\\n\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NfxHRGClBYQE","executionInfo":{"status":"ok","timestamp":1714452323462,"user_tz":240,"elapsed":6,"user":{"displayName":"Jack Langerman","userId":"09781760878184723424"}},"outputId":"ab2d8aa2-62e7-4922-b0ad-2899fa93cffa"},"id":"NfxHRGClBYQE","execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/my_cool_submission\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"viNhQ5JOBYbs","executionInfo":{"status":"ok","timestamp":1714452323462,"user_tz":240,"elapsed":4,"user":{"displayName":"Jack Langerman","userId":"09781760878184723424"}}},"id":"viNhQ5JOBYbs","execution_count":4,"outputs":[]},{"cell_type":"markdown","source":["## Edit the solution\n","\n","**Now you can open up the example solution `script.py` and edit it with your cool ideas.**\n","\n","<br>\n","\n","<img src='https://huggingface.co/datasets/jacklangerman/hosted_images/resolve/main/open_script/step1.png' width=374 style=\"display: inline;\">\n","<img src='https://huggingface.co/datasets/jacklangerman/hosted_images/resolve/main/open_script/step2.png' width=374 style=\"display: inline;\">\n","<img src='https://huggingface.co/datasets/jacklangerman/hosted_images/resolve/main/open_script/step3.png' width=374 style=\"display: inline;\">\n","<img src='https://huggingface.co/datasets/jacklangerman/hosted_images/resolve/main/open_script/step4.png' width=374 style=\"display: inline;\">\n","\n","**Once you get it open, edit `script.py`. Make sure to save your changes and upload them to your repo using the following snippet.**"],"metadata":{"id":"QbKlhw5p_t26"},"id":"QbKlhw5p_t26"},{"cell_type":"code","source":[],"metadata":{"id":"ZymO55T4HGSU","executionInfo":{"status":"ok","timestamp":1714452323462,"user_tz":240,"elapsed":4,"user":{"displayName":"Jack Langerman","userId":"09781760878184723424"}}},"id":"ZymO55T4HGSU","execution_count":4,"outputs":[]},{"cell_type":"code","source":["# Upload our changes to the solution\n","operations = [\n","    hf_api.upload_folder(\n","        folder_path='.',\n","        path_in_repo=\".\",\n","        repo_id=target_repo_id,\n","    )\n","]\n","print(operations)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4Y0eTe7M_vIJ","executionInfo":{"status":"ok","timestamp":1714452323463,"user_tz":240,"elapsed":5,"user":{"displayName":"Jack Langerman","userId":"09781760878184723424"}},"outputId":"e49c869c-9ee8-4298-c848-e0137a31be5b"},"id":"4Y0eTe7M_vIJ","execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["[CommitInfo(commit_url='https://huggingface.co/jacklangerman/my_cool_submission/commit/a10f1668aa1dab26e3cfdd8bccf533c776e6e0a8', commit_message='Upload folder using huggingface_hub', commit_description='', oid='a10f1668aa1dab26e3cfdd8bccf533c776e6e0a8', pr_url=None, pr_revision=None, pr_num=None)]\n"]}]},{"cell_type":"code","source":["# @title Submit your solution to the competition\n","from IPython.display import Markdown as md\n","\n","# Follow the instructions below\n","md(f'''\n","1. Follow this link to the competition space for the [S23DR Challenge](https://huggingface.co/spaces/usm3d/S23DR)\n","<br>\n","2. Login to Hugging Face. <br>\n","<img src=\"https://huggingface.co/datasets/jacklangerman/hosted_images/resolve/main/submit_to_s23rd/login.png\" width=512>\n","<br>\n","\n","3. Click \"New submission\" <br>\n","<img src=\"https://huggingface.co/datasets/jacklangerman/hosted_images/resolve/main/submit_to_s23rd/click_new_submission.png\" width=512>\n","<br>\n","\n","4. Enter in the repo_id of your submission ([{hf_api.whoami()[\"name\"]}/{submission_name}](https://huggingface.co/{hf_api.whoami()[\"name\"]}/{submission_name}))<br>\n","<br>\n","<img src=\"https://huggingface.co/datasets/jacklangerman/hosted_images/resolve/main/submit_to_s23rd/name_and_desc.png\" width=512>\n","<br>\n","\n","''')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"cellView":"form","id":"Rf59SEZ6Cp4U","executionInfo":{"status":"ok","timestamp":1714452323635,"user_tz":240,"elapsed":175,"user":{"displayName":"Jack Langerman","userId":"09781760878184723424"}},"outputId":"3473603f-0f68-4ed0-f95e-8001ffec7b66"},"id":"Rf59SEZ6Cp4U","execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<IPython.core.display.Markdown object>"],"text/markdown":"\n1. Follow this link to the competition space for the [S23DR Challenge](https://huggingface.co/spaces/usm3d/S23DR)  \n<br>\n2. Login to Hugging Face. <br>\n<img src=\"https://huggingface.co/datasets/jacklangerman/hosted_images/resolve/main/submit_to_s23rd/login.png\" width=512>  \n<br>\n\n3. Click \"New submission\" <br>\n<img src=\"https://huggingface.co/datasets/jacklangerman/hosted_images/resolve/main/submit_to_s23rd/click_new_submission.png\" width=512> \n<br>\n\n4. Enter in the repo_id of your submission ([jacklangerman/my_cool_submission](https://huggingface.co/jacklangerman/my_cool_submission))<br>\n<br>\n<img src=\"https://huggingface.co/datasets/jacklangerman/hosted_images/resolve/main/submit_to_s23rd/name_and_desc.png\" width=512> \n<br>\n\n"},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":[],"metadata":{"id":"aKwpCW2LT0Se","executionInfo":{"status":"ok","timestamp":1714452323636,"user_tz":240,"elapsed":4,"user":{"displayName":"Jack Langerman","userId":"09781760878184723424"}}},"id":"aKwpCW2LT0Se","execution_count":6,"outputs":[]}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/usm3d/empty_submission/blob/main/example_notebook.ipynb","timestamp":1714317826676}]},"kernelspec":{"display_name":"Python 3 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