wip example notebook

example_notebook.ipynb

@@ -0,0 +1,1010 @@
+{
+  "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 ------------ \")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f077cbd7",
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b65ed78e",
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    },
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+      "execution_count": null,
+      "id": "a4584502",
+      "metadata": {},
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+      "language": "python",
+      "name": "python3"
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+        "name": "ipython",
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+      "mimetype": "text/x-python",
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