Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9ecd41d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "42c54bc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "download_dir = './ag_automm_tutorial'\n",
    "zip_file = 'https://automl-mm-bench.s3.amazonaws.com/petfinder_for_tutorial.zip'\n",
    "from autogluon.core.utils.loaders import load_zip\n",
    "load_zip.unzip(zip_file, unzip_dir=download_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "047394c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "dataset_path = download_dir + '/petfinder_for_tutorial'\n",
    "train_data = pd.read_csv(f'{dataset_path}/train.csv', index_col=0)\n",
    "test_data = pd.read_csv(f'{dataset_path}/test.csv', index_col=0)\n",
    "label_col = 'AdoptionSpeed'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6b68aff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/zl71/autogluon_lemda/ag_automm_tutorial/petfinder_for_tutorial/images/7d7a39d71-1.jpg'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_col = 'Images'\n",
    "train_data[image_col] = train_data[image_col].apply(lambda ele: ele.split(';')[0]) # Use the first image for a quick tutorial\n",
    "test_data[image_col] = test_data[image_col].apply(lambda ele: ele.split(';')[0])\n",
    "\n",
    "\n",
    "def path_expander(path, base_folder):\n",
    "    path_l = path.split(';')\n",
    "    return ';'.join([os.path.abspath(os.path.join(base_folder, path)) for path in path_l])\n",
    "\n",
    "train_data[image_col] = train_data[image_col].apply(lambda ele: path_expander(ele, base_folder=dataset_path))\n",
    "test_data[image_col] = test_data[image_col].apply(lambda ele: path_expander(ele, base_folder=dataset_path))\n",
    "\n",
    "train_data[image_col].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ac51323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type                                                             2\n",
       "Name                                                 Yumi Hamasaki\n",
       "Age                                                              4\n",
       "Breed1                                                         292\n",
       "Breed2                                                         265\n",
       "Gender                                                           2\n",
       "Color1                                                           1\n",
       "Color2                                                           5\n",
       "Color3                                                           7\n",
       "MaturitySize                                                     2\n",
       "FurLength                                                        2\n",
       "Vaccinated                                                       1\n",
       "Dewormed                                                         3\n",
       "Sterilized                                                       2\n",
       "Health                                                           1\n",
       "Quantity                                                         1\n",
       "Fee                                                              0\n",
       "State                                                        41326\n",
       "RescuerID                         bcc4e1b9557a8b3aaf545ea8e6e86991\n",
       "VideoAmt                                                         0\n",
       "Description      I rescued Yumi Hamasaki at a food stall far aw...\n",
       "PetID                                                    7d7a39d71\n",
       "PhotoAmt                                                       3.0\n",
       "AdoptionSpeed                                                    0\n",
       "Images           /home/zl71/autogluon_lemda/ag_automm_tutorial/...\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_row = train_data.iloc[0]\n",
    "\n",
    "example_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5b5cb3d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"I rescued Yumi Hamasaki at a food stall far away in Kelantan. At that time i was on my way back to KL, she was suffer from stomach problem and looking very2 sick.. I send her to vet & get the treatment + vaccinated and right now she's very2 healthy.. About yumi : - love to sleep with ppl - she will keep on meowing if she's hugry - very2 active, always seeking for people to accompany her playing - well trained (poo+pee in her own potty) - easy to bathing - I only feed her with these brands : IAMS, Kittenbites, Pro-formance Reason why i need someone to adopt Yumi: I just married and need to move to a new house where no pets are allowed :( As Yumi is very2 special to me, i will only give her to ppl that i think could take care of her just like i did (especially on her foods things)..\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_row['Description']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e2b25777",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_image = example_row[image_col]\n",
    "\n",
    "from IPython.display import Image, display\n",
    "pil_img = Image(filename=example_image)\n",
    "display(pil_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f95e3e83",
   "metadata": {},
   "outputs": [],
   "source": [
    "from autogluon.text.automm import AutoMMPredictor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e6dc0396",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 123\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image columns: ['Images']\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fcce191befe749cda81f741fcbe74f06",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/27.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81c2c72ca2e8499db3239384d98f3d7b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/666 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5eaa3078598044f7b8207c5cf1eadb9e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eed86a59eeff4b4cb1a9f0d99b953f6a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/455k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22kto1k.pth\" to /home/zl71/.cache/torch/hub/checkpoints/swin_base_patch4_window7_224_22kto1k.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00f12dc5c5f043daa192d6b4f52f72e4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/420M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializaing Augmentation Network\n",
      "feature_dims [('numerical_mlp', 128), ('hf_text', 768), ('timm_image', 1024)]\n",
      "after adapter dim 1024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "warmup steps: 15\n",
      "initilize augment optimizer\n",
      "augmentation learning rate:0.0001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 123\n",
      "initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/4\n",
      "Global seed set to 123\n",
      "initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/4\n",
      "Global seed set to 123\n",
      "initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/4\n",
      "Global seed set to 123\n",
      "initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/4\n",
      "----------------------------------------------------------------------------------------------------\n",
      "distributed_backend=nccl\n",
      "All distributed processes registered. Starting with 4 processes\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/torch/cuda/__init__.py:143: UserWarning: \n",
      "NVIDIA A100-SXM4-40GB with CUDA capability sm_80 is not compatible with the current PyTorch installation.\n",
      "The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70.\n",
      "If you want to use the NVIDIA A100-SXM4-40GB GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/\n",
      "\n",
      "  warnings.warn(incompatible_device_warn.format(device_name, capability, \" \".join(arch_list), device_name))\n",
      "/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/torch/cuda/__init__.py:143: UserWarning: \n",
      "NVIDIA A100-SXM4-40GB with CUDA capability sm_80 is not compatible with the current PyTorch installation.\n",
      "The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70.\n",
      "If you want to use the NVIDIA A100-SXM4-40GB GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/\n",
      "\n",
      "  warnings.warn(incompatible_device_warn.format(device_name, capability, \" \".join(arch_list), device_name))\n",
      "/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/torch/cuda/__init__.py:143: UserWarning: \n",
      "NVIDIA A100-SXM4-40GB with CUDA capability sm_80 is not compatible with the current PyTorch installation.\n",
      "The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70.\n",
      "If you want to use the NVIDIA A100-SXM4-40GB GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/\n",
      "\n",
      "  warnings.warn(incompatible_device_warn.format(device_name, capability, \" \".join(arch_list), device_name))\n",
      "/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/torch/cuda/__init__.py:143: UserWarning: \n",
      "NVIDIA A100-SXM4-40GB with CUDA capability sm_80 is not compatible with the current PyTorch installation.\n",
      "The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70.\n",
      "If you want to use the NVIDIA A100-SXM4-40GB GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/\n",
      "\n",
      "  warnings.warn(incompatible_device_warn.format(device_name, capability, \" \".join(arch_list), device_name))\n"
     ]
    },
    {
     "ename": "ProcessRaisedException",
     "evalue": "\n\n-- Process 1 terminated with the following error:\nTraceback (most recent call last):\n  File \"/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/torch/multiprocessing/spawn.py\", line 59, in _wrap\n    fn(i, *args)\n  File \"/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py\", line 208, in _wrapped_function\n    result = function(*args, **kwargs)\n  File \"/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py\", line 232, in new_process\n    self.configure_ddp()\n  File \"/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py\", line 291, in configure_ddp\n    self._model = self._setup_model(LightningDistributedModule(self.model))\n  File \"/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py\", line 159, in _setup_model\n    return DistributedDataParallel(module=model, device_ids=self.determine_ddp_device_ids(), **self._ddp_kwargs)\n  File \"/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/torch/nn/parallel/distributed.py\", line 578, in __init__\n    dist._verify_model_across_ranks(self.process_group, parameters)\nRuntimeError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:45, unhandled cuda error, NCCL version 21.0.3\nncclUnhandledCudaError: Call to CUDA function failed.\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mProcessRaisedException\u001b[0m                    Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[12], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m predictor \u001b[38;5;241m=\u001b[39m AutoMMPredictor(label\u001b[38;5;241m=\u001b[39mlabel_col)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mpredictor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrain_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtime_limit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m120\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# seconds\u001b[39;49;00m\n\u001b[1;32m      5\u001b[0m \u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.conda/envs/ag/lib/python3.9/site-packages/autogluon/text/automm/predictor.py:502\u001b[0m, in \u001b[0;36mAutoMMPredictor.fit\u001b[0;34m(self, train_data, config, tuning_data, time_limit, save_path, hyperparameters, column_types, holdout_frac, teacher_predictor, seed, hyperparameter_tune_kwargs)\u001b[0m\n\u001b[1;32m    495\u001b[0m     predictor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_hyperparameter_tune(\n\u001b[1;32m    496\u001b[0m         hyperparameter_tune_kwargs\u001b[38;5;241m=\u001b[39mhyperparameter_tune_kwargs,\n\u001b[1;32m    497\u001b[0m         resources\u001b[38;5;241m=\u001b[39mresources,\n\u001b[1;32m    498\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m_fit_args,\n\u001b[1;32m    499\u001b[0m     )\n\u001b[1;32m    500\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m predictor\n\u001b[0;32m--> 502\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m_fit_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    503\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[0;32m~/.conda/envs/ag/lib/python3.9/site-packages/autogluon/text/automm/predictor.py:1124\u001b[0m, in \u001b[0;36mAutoMMPredictor._fit\u001b[0;34m(self, train_df, val_df, validation_metric_name, minmax_mode, max_time, save_path, ckpt_path, resume, enable_progress_bar, config, hyperparameters, teacher_predictor, hpo_mode, **hpo_kwargs)\u001b[0m\n\u001b[1;32m   1115\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(\n\u001b[1;32m   1116\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1117\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.*does not have many workers which may be a bottleneck. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1118\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConsider increasing the value of the `num_workers` argument` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1119\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.* in the `DataLoader` init to improve performance.*\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1120\u001b[0m     )\n\u001b[1;32m   1121\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(\n\u001b[1;32m   1122\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCheckpoint directory .* exists and is not empty.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1123\u001b[0m     )\n\u001b[0;32m-> 1124\u001b[0m     \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1125\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1126\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_dm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1127\u001b[0m \u001b[43m        \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\n\u001b[1;32m   1128\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mresume\u001b[49m\n\u001b[1;32m   1129\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# this is to resume training that was broken accidentally\u001b[39;49;00m\n\u001b[1;32m   1130\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1132\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mglobal_rank \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   1133\u001b[0m     \u001b[38;5;66;03m# We do not perform averaging checkpoint in the case of hpo for each trial\u001b[39;00m\n\u001b[1;32m   1134\u001b[0m     \u001b[38;5;66;03m# We only averaging the checkpoint of the best trial in the end in the master process\u001b[39;00m\n\u001b[1;32m   1135\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m hpo_mode:\n",
      "File \u001b[0;32m~/.conda/envs/ag/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:740\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, train_dataloader, ckpt_path)\u001b[0m\n\u001b[1;32m    735\u001b[0m     rank_zero_deprecation(\n\u001b[1;32m    736\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`trainer.fit(train_dataloader)` is deprecated in v1.4 and will be removed in v1.6.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    737\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m Use `trainer.fit(train_dataloaders)` instead. HINT: added \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    738\u001b[0m     )\n\u001b[1;32m    739\u001b[0m     train_dataloaders \u001b[38;5;241m=\u001b[39m train_dataloader\n\u001b[0;32m--> 740\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    741\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[1;32m    742\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.conda/envs/ag/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:685\u001b[0m, in \u001b[0;36mTrainer._call_and_handle_interrupt\u001b[0;34m(self, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    675\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    676\u001b[0m \u001b[38;5;124;03mError handling, intended to be used only for main trainer function entry points (fit, validate, test, predict)\u001b[39;00m\n\u001b[1;32m    677\u001b[0m \u001b[38;5;124;03mas all errors should funnel through them\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    682\u001b[0m \u001b[38;5;124;03m    **kwargs: keyword arguments to be passed to `trainer_fn`\u001b[39;00m\n\u001b[1;32m    683\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    684\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 685\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\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[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    686\u001b[0m \u001b[38;5;66;03m# TODO: treat KeyboardInterrupt as BaseException (delete the code below) in v1.7\u001b[39;00m\n\u001b[1;32m    687\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exception:\n",
      "File \u001b[0;32m~/.conda/envs/ag/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:777\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m    775\u001b[0m \u001b[38;5;66;03m# TODO: ckpt_path only in v1.7\u001b[39;00m\n\u001b[1;32m    776\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m ckpt_path \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresume_from_checkpoint\n\u001b[0;32m--> 777\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    779\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n\u001b[1;32m    780\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m~/.conda/envs/ag/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1199\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m   1196\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcheckpoint_connector\u001b[38;5;241m.\u001b[39mresume_end()\n\u001b[1;32m   1198\u001b[0m \u001b[38;5;66;03m# dispatch `start_training` or `start_evaluating` or `start_predicting`\u001b[39;00m\n\u001b[0;32m-> 1199\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1201\u001b[0m \u001b[38;5;66;03m# plugin will finalized fitting (e.g. ddp_spawn will load trained model)\u001b[39;00m\n\u001b[1;32m   1202\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_post_dispatch()\n",
      "File \u001b[0;32m~/.conda/envs/ag/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1279\u001b[0m, in \u001b[0;36mTrainer._dispatch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1277\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining_type_plugin\u001b[38;5;241m.\u001b[39mstart_predicting(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   1278\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1279\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_type_plugin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart_training\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.conda/envs/ag/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py:173\u001b[0m, in \u001b[0;36mDDPSpawnPlugin.start_training\u001b[0;34m(self, trainer)\u001b[0m\n\u001b[1;32m    172\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstart_training\u001b[39m(\u001b[38;5;28mself\u001b[39m, trainer: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpl.Trainer\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 173\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mspawn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnew_process\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmp_queue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_result\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    174\u001b[0m     \u001b[38;5;66;03m# reset optimizers, since main process is never used for training and thus does not have a valid optim state\u001b[39;00m\n\u001b[1;32m    175\u001b[0m     trainer\u001b[38;5;241m.\u001b[39moptimizers \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/.conda/envs/ag/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py:201\u001b[0m, in \u001b[0;36mDDPSpawnPlugin.spawn\u001b[0;34m(self, function, return_result, *args, **kwargs)\u001b[0m\n\u001b[1;32m    199\u001b[0m context \u001b[38;5;241m=\u001b[39m mp\u001b[38;5;241m.\u001b[39mget_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspawn\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    200\u001b[0m return_queue \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mSimpleQueue() \u001b[38;5;28;01mif\u001b[39;00m return_result \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 201\u001b[0m \u001b[43mmp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mspawn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wrapped_function\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfunction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_queue\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnprocs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_processes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    202\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m return_queue\u001b[38;5;241m.\u001b[39mget() \u001b[38;5;28;01mif\u001b[39;00m return_result \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.conda/envs/ag/lib/python3.9/site-packages/torch/multiprocessing/spawn.py:230\u001b[0m, in \u001b[0;36mspawn\u001b[0;34m(fn, args, nprocs, join, daemon, start_method)\u001b[0m\n\u001b[1;32m    226\u001b[0m     msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThis method only supports start_method=spawn (got: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m).\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    227\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTo use a different start_method use:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    228\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m torch.multiprocessing.start_processes(...)\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m start_method)\n\u001b[1;32m    229\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(msg)\n\u001b[0;32m--> 230\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mstart_processes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnprocs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdaemon\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mspawn\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.conda/envs/ag/lib/python3.9/site-packages/torch/multiprocessing/spawn.py:188\u001b[0m, in \u001b[0;36mstart_processes\u001b[0;34m(fn, args, nprocs, join, daemon, start_method)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m context\n\u001b[1;32m    187\u001b[0m \u001b[38;5;66;03m# Loop on join until it returns True or raises an exception.\u001b[39;00m\n\u001b[0;32m--> 188\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    189\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n",
      "File \u001b[0;32m~/.conda/envs/ag/lib/python3.9/site-packages/torch/multiprocessing/spawn.py:150\u001b[0m, in \u001b[0;36mProcessContext.join\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    148\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m-- Process \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m terminated with the following error:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m error_index\n\u001b[1;32m    149\u001b[0m msg \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m original_trace\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ProcessRaisedException(msg, error_index, failed_process\u001b[38;5;241m.\u001b[39mpid)\n",
      "\u001b[0;31mProcessRaisedException\u001b[0m: \n\n-- Process 1 terminated with the following error:\nTraceback (most recent call last):\n  File \"/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/torch/multiprocessing/spawn.py\", line 59, in _wrap\n    fn(i, *args)\n  File \"/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py\", line 208, in _wrapped_function\n    result = function(*args, **kwargs)\n  File \"/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py\", line 232, in new_process\n    self.configure_ddp()\n  File \"/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py\", line 291, in configure_ddp\n    self._model = self._setup_model(LightningDistributedModule(self.model))\n  File \"/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py\", line 159, in _setup_model\n    return DistributedDataParallel(module=model, device_ids=self.determine_ddp_device_ids(), **self._ddp_kwargs)\n  File \"/home/zl71/.conda/envs/ag/lib/python3.9/site-packages/torch/nn/parallel/distributed.py\", line 578, in __init__\n    dist._verify_model_across_ranks(self.process_group, parameters)\nRuntimeError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:45, unhandled cuda error, NCCL version 21.0.3\nncclUnhandledCudaError: Call to CUDA function failed.\n"
     ]
    }
   ],
   "source": [
    "predictor = AutoMMPredictor(label=label_col)\n",
    "predictor.fit(\n",
    "    train_data=train_data,\n",
    "    time_limit=120, # seconds\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eacac6f7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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