train-uxml-adam-optuna.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyTorch version: 1.5.0\n",
      "CUDA GPU 1: GeForce RTX 2080 Ti [Compute Capability: 7.5]\n",
      "PyTorch Lightning version: 0.7.6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG:root:Logging enabled at DEBUG level.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optuna version: 1.5.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\" \n",
    "import time\n",
    "import numpy as np\n",
    "import torch\n",
    "print(\"PyTorch version:\",torch.__version__)\n",
    "if torch.cuda.is_available():\n",
    "    for i in range(torch.cuda.device_count()):\n",
    "      print(f\"CUDA GPU {i+1}: {torch.cuda.get_device_name(i)} [Compute Capability: {torch.cuda.get_device_capability(0)[0]}.{torch.cuda.get_device_capability(0)[1]}]\")\n",
    "    device = torch.device('cuda')\n",
    "    kwargs = {'num_workers': 8, 'pin_memory': True}\n",
    "    torch.backends.cudnn.benchmark = True\n",
    "else:\n",
    "    device = torch.device('cpu')\n",
    "    print(\"CUDA GPU is not available. :(\")  \n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "\n",
    "import pytorch_lightning as pl\n",
    "from pytorch_lightning.loggers import TensorBoardLogger\n",
    "print (\"PyTorch Lightning version:\",pl.__version__)\n",
    "    \n",
    "import scipy.sparse as sp\n",
    "from argparse import Namespace\n",
    "\n",
    "from utilities.custom_lightning import CSVProfiler\n",
    "\n",
    "import logging\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.DEBUG)\n",
    "logging.debug(\"Logging enabled at DEBUG level.\")\n",
    "\n",
    "from constants import (SEED, DATA_DIR, LOG_DIR, TRAIN_DATA_PATH, VAL_DATA_PATH, TEST_DATA_PATH)\n",
    "torch.manual_seed(SEED)\n",
    "torch.cuda.manual_seed(SEED)\n",
    "\n",
    "import optuna\n",
    "from optuna.integration import PyTorchLightningPruningCallback\n",
    "print(\"Optuna version:\",optuna.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "NAME = r'AdamOptunaUXML' \n",
    "SAVE_PATH = DATA_DIR+r'/'+NAME+r'.pt'\n",
    "PROFILE_PATH = LOG_DIR+'\\\\'+NAME+'\\\\profile.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Interactions(Dataset):\n",
    "    \"\"\"\n",
    "    Create interactions matrix.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, matrix):\n",
    "        self.matrix = matrix\n",
    "        self.n_users = self.matrix.shape[0]\n",
    "        self.n_items = self.matrix.shape[1]\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        row = self.matrix.row[index]\n",
    "        col = self.matrix.col[index]\n",
    "        val = self.matrix.data[index]\n",
    "        return (row, col), val\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.matrix.nnz\n",
    "    \n",
    "interaction = Interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TestingCallbacks(pl.Callback):\n",
    "    def on_test_start(self, trainer, pl_module):\n",
    "        global y_hat \n",
    "        y_hat = sp.dok_matrix((hparams.total_users, hparams.total_items), dtype=np.float32) \n",
    "    def on_test_end(self, trainer, pl_module):\n",
    "        logging.debug(f\"Non-zero values in prediction matrix: {y_hat.nnz:,}\")\n",
    "        sp.save_npz(DATA_DIR+NAME+r'-y_hat.npz',y_hat.tocoo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MetricsCallback(pl.Callback):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.metrics = []\n",
    "    def on_validation_end(self, trainer, pl_module):\n",
    "        self.metrics.append(trainer.callback_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AdamUXML(pl.LightningModule):\n",
    "    def __init__(self, trial, hparams):\n",
    "        super(AdamUXML, self).__init__() \n",
    "        self.hparams = hparams\n",
    "        \n",
    "        n_factors = trial.suggest_int(\"n_factors\", 5, 40)\n",
    "        dropout_p = trial.suggest_uniform(\"dropout_p\", 0.0, 0.8)\n",
    "        \n",
    "        self.user_factors = nn.Embedding(hparams.total_users, n_factors, sparse=hparams.sparse)\n",
    "        self.item_factors = nn.Embedding(hparams.total_items, n_factors, sparse=hparams.sparse)\n",
    "        self.user_biases = nn.Embedding(hparams.total_users, 1, sparse=hparams.sparse)\n",
    "        self.item_biases = nn.Embedding(hparams.total_items, 1, sparse=hparams.sparse)\n",
    "        self.dropout = nn.Dropout(p=dropout_p)\n",
    "        \n",
    "    def forward(self, users, items):            \n",
    "        \n",
    "        user_factors_users = self.user_factors(users)\n",
    "        item_factors_items = self.item_factors(items)        \n",
    "        predictions = self.user_biases(users)\n",
    "        predictions += self.item_biases(items)\n",
    "        predictions += (self.dropout(user_factors_users) * self.dropout(item_factors_items)).sum(dim=1, keepdim=True)              \n",
    "        return predictions.squeeze()\n",
    "    \n",
    "    def MSELoss(self, logits, labels):\n",
    "        return nn.functional.mse_loss(logits, labels)\n",
    "    \n",
    "    def training_step(self, train_batch, batch_idx):\n",
    "        x, y = train_batch\n",
    "        row, column = x\n",
    "        row = row.long()\n",
    "        column = column.long()\n",
    "        logits = self.forward(row,column)\n",
    "        loss = self.MSELoss(logits, y)\n",
    "        logs = {'train_loss': loss}\n",
    "        return {'loss': loss}\n",
    "\n",
    "    def validation_step(self, val_batch, batch_idx):\n",
    "        x, y = val_batch\n",
    "        row, column = x\n",
    "        row = row.long()\n",
    "        column = column.long()\n",
    "        logits = self.forward(row,column)                \n",
    "        loss = self.MSELoss(logits, y)\n",
    "        return {'val_loss': loss}\n",
    "    \n",
    "    def validation_epoch_end(self, outputs):\n",
    "        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()\n",
    "        return {\"log\": {\"avg_val_loss\": avg_loss}}\n",
    "    \n",
    "    def test_step(self, test_batch, batch_idx):\n",
    "        x, y = test_batch\n",
    "        row, column = x\n",
    "        row = row.long()\n",
    "        column = column.long()\n",
    "        logits = self.forward(row,column)                \n",
    "        loss = self.MSELoss(logits, y)        \n",
    "       \n",
    "        logits_array = logits.cpu().numpy()\n",
    "        r = row.cpu().numpy()\n",
    "        c = column.cpu().numpy()\n",
    "        for i in range(len(logits_array)):\n",
    "            y_hat[r[i],c[i]]=logits_array[i]      \n",
    "        return {'test_loss': loss}\n",
    "    \n",
    "    def test_epoch_end(self, outputs):\n",
    "        avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()\n",
    "        tensorboard_logs = {'MSE': avg_loss}\n",
    "        print(f\"Test Mean Squared Error (MSE): {avg_loss}\")                   \n",
    "        return {\"log\": {\"avg_test_loss\": avg_loss}}\n",
    "      \n",
    "    def prepare_data(self):\n",
    "        self.train_dataset = sp.load_npz(TRAIN_DATA_PATH)\n",
    "        self.val_dataset = sp.load_npz(VAL_DATA_PATH)\n",
    "        self.test_dataset = sp.load_npz(TEST_DATA_PATH)\n",
    "        \n",
    "    def train_dataloader(self):\n",
    "        return DataLoader(interaction(self.train_dataset), batch_size=self.hparams.batch_size, shuffle=True)\n",
    "    \n",
    "    def val_dataloader(self):\n",
    "        return DataLoader(interaction(self.val_dataset), batch_size=self.hparams.batch_size, shuffle=False)\n",
    "    \n",
    "    def test_dataloader(self):\n",
    "        return DataLoader(interaction(self.test_dataset), batch_size=self.hparams.batch_size, shuffle=False)\n",
    "      \n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.Adam(self.parameters(),\n",
    "                                     lr=self.hparams.learning_rate,\n",
    "                                     betas=self.hparams.betas,\n",
    "                                     amsgrad=self.hparams.amsgrad)\n",
    "        return optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "uxm = sp.load_npz(TRAIN_DATA_PATH)\n",
    "total_users = uxm.shape[0]\n",
    "total_items = uxm.shape[1]\n",
    "del uxm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(trial):\n",
    "    checkpoint_callback = pl.callbacks.ModelCheckpoint(\n",
    "        os.path.join(LOG_DIR+\"\\Optuna\",\"trial_{}\".format(trial.number), \"{epoch}\"), monitor=\"avg_val_loss\"\n",
    "    )\n",
    "\n",
    "    metrics_callback = MetricsCallback()\n",
    "    trainer = pl.Trainer(\n",
    "        logger=False,\n",
    "        val_percent_check=hparams.val_percent_check,\n",
    "        checkpoint_callback=checkpoint_callback,\n",
    "        max_epochs=hparams.max_epochs,\n",
    "        gpus=1,\n",
    "        callbacks=[metrics_callback],\n",
    "        early_stop_callback=PyTorchLightningPruningCallback(trial, monitor=\"avg_val_loss\"),\n",
    "    )\n",
    "\n",
    "    model = AdamUXML(trial, hparams)\n",
    "    trainer.fit(model)\n",
    "\n",
    "    return metrics_callback.metrics[-1][\"avg_val_loss\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "hparams = Namespace(**{\n",
    "    'batch_size': 1024,\n",
    "    'val_percent_check': 1.0, # set this to <1.0 to use data subset for validation\n",
    "    'learning_rate': 0.001, \n",
    "    'betas': (0.9, 0.999),\n",
    "#     'dropout_p': 0.2, ### delegated to Optuna\n",
    "#     'n_factors': 20, ### delegated to Optuna    \n",
    "    'sparse': False,\n",
    "    'max_epochs': 10,\n",
    "    'amsgrad': False,\n",
    "    'action':\"store_true\", # do we need this?\n",
    "    'pruning': True,\n",
    "    'total_users': total_users,\n",
    "    'total_items': total_items\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 2 M   \n",
      "1 | item_factors | Embedding | 716 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 2 M   \n",
      "1 | item_factors | Embedding | 716 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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      "\n"
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     "text": [
      "[I 2020-06-06 19:28:06,860] Finished trial#0 with value: 1.360329508781433 with parameters: {'n_factors': 16, 'dropout_p': 0.28339097027307975}. Best is trial#0 with value: 1.360329508781433.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 2 M   \n",
      "1 | item_factors | Embedding | 582 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 2 M   \n",
      "1 | item_factors | Embedding | 582 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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      "[I 2020-06-06 19:30:56,563] Finished trial#1 with value: 1.0264251232147217 with parameters: {'n_factors': 13, 'dropout_p': 0.20906809319775768}. Best is trial#1 with value: 1.0264251232147217.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 19:33:47,272] Finished trial#2 with value: 0.42526593804359436 with parameters: {'n_factors': 6, 'dropout_p': 0.003723607692885711}. Best is trial#2 with value: 0.42526593804359436.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 4 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 4 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 19:36:39,306] Finished trial#3 with value: 4.232080459594727 with parameters: {'n_factors': 25, 'dropout_p': 0.6280046733967967}. Best is trial#2 with value: 0.42526593804359436.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 19:39:29,511] Finished trial#4 with value: 1.0625261068344116 with parameters: {'n_factors': 8, 'dropout_p': 0.5639956502009288}. Best is trial#2 with value: 0.42526593804359436.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 6 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 6 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 19:40:04,580] Setting status of trial#5 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 6 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 6 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 19:40:39,670] Setting status of trial#6 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
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      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 5 M   \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 5 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 19:41:14,544] Setting status of trial#7 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
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      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 3 M   \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 3 M   \n",
      "1 | item_factors | Embedding | 985 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 19:41:49,120] Setting status of trial#8 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
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      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 6 M   \n",
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      "INFO:lightning:\n",
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      "---------------------------------------\n",
      "0 | user_factors | Embedding | 6 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
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      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 19:42:24,352] Setting status of trial#9 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
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      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
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      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 19:45:14,858] Finished trial#10 with value: 0.34879061579704285 with parameters: {'n_factors': 5, 'dropout_p': 0.04722129819514774}. Best is trial#10 with value: 0.34879061579704285.\n",
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      "[I 2020-06-06 19:48:04,986] Finished trial#11 with value: 0.3506661653518677 with parameters: {'n_factors': 5, 'dropout_p': 0.02859825028223103}. Best is trial#10 with value: 0.34879061579704285.\n",
      "GPU available: True, used: True\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
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      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 19:50:55,682] Finished trial#12 with value: 0.35171303153038025 with parameters: {'n_factors': 5, 'dropout_p': 0.006507567256365886}. Best is trial#10 with value: 0.34879061579704285.\n",
      "GPU available: True, used: True\n",
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      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 2 M   \n",
      "1 | item_factors | Embedding | 537 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 2 M   \n",
      "1 | item_factors | Embedding | 537 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
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      "[I 2020-06-06 19:51:29,956] Setting status of trial#13 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 19:54:21,047] Finished trial#14 with value: 0.548665463924408 with parameters: {'n_factors': 5, 'dropout_p': 0.48104152812114354}. Best is trial#10 with value: 0.34879061579704285.\n",
      "GPU available: True, used: True\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 3 M   \n",
      "1 | item_factors | Embedding | 761 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 3 M   \n",
      "1 | item_factors | Embedding | 761 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 19:54:55,434] Setting status of trial#15 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 403 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 403 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 19:55:29,707] Setting status of trial#16 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
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      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 492 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 492 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 19:56:04,040] Setting status of trial#17 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 3 M   \n",
      "1 | item_factors | Embedding | 940 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 3 M   \n",
      "1 | item_factors | Embedding | 940 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 19:56:38,566] Setting status of trial#18 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 5 M   \n",
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      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 5 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 19:57:13,670] Setting status of trial#19 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 3 M   \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 3 M   \n",
      "1 | item_factors | Embedding | 761 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 19:57:48,088] Setting status of trial#20 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
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      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:00:39,039] Finished trial#21 with value: 0.3495282530784607 with parameters: {'n_factors': 5, 'dropout_p': 0.02153682004124429}. Best is trial#10 with value: 0.34879061579704285.\n",
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      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
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      "[I 2020-06-06 20:03:30,247] Finished trial#22 with value: 0.34588390588760376 with parameters: {'n_factors': 5, 'dropout_p': 0.06047423235336517}. Best is trial#22 with value: 0.34588390588760376.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:04:04,806] Setting status of trial#23 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 447 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 447 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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      "[I 2020-06-06 20:04:39,455] Setting status of trial#24 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 2 M   \n",
      "1 | item_factors | Embedding | 582 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 2 M   \n",
      "1 | item_factors | Embedding | 582 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:05:14,035] Setting status of trial#25 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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     "text": [
      "[I 2020-06-06 20:05:48,488] Setting status of trial#26 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 2 M   \n",
      "1 | item_factors | Embedding | 671 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 2 M   \n",
      "1 | item_factors | Embedding | 671 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:06:23,072] Setting status of trial#27 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
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      "INFO:lightning:\n",
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      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
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      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 20:09:14,710] Finished trial#28 with value: 0.3460424244403839 with parameters: {'n_factors': 5, 'dropout_p': 0.06411071529749876}. Best is trial#22 with value: 0.34588390588760376.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 3 M   \n",
      "1 | item_factors | Embedding | 895 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 3 M   \n",
      "1 | item_factors | Embedding | 895 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 20:09:49,292] Setting status of trial#29 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 447 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 447 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 20:10:23,886] Setting status of trial#30 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
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      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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      "[I 2020-06-06 20:13:15,667] Finished trial#31 with value: 0.3486556112766266 with parameters: {'n_factors': 5, 'dropout_p': 0.05440794455480602}. Best is trial#22 with value: 0.34588390588760376.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 20:13:50,292] Setting status of trial#32 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
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      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:14:25,092] Setting status of trial#33 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
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      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 20:14:59,952] Setting status of trial#34 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 20:15:34,573] Setting status of trial#35 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
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      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 2 M   \n",
      "1 | item_factors | Embedding | 582 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 2 M   \n",
      "1 | item_factors | Embedding | 582 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 20:16:09,217] Setting status of trial#36 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
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      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 403 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 403 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 20:16:43,938] Setting status of trial#37 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
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      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 6 M   \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 6 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:17:19,392] Setting status of trial#38 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
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      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 4 M   \n",
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      "---------------------------------------\n",
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      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:17:54,573] Setting status of trial#39 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
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      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
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     "text": [
      "[I 2020-06-06 20:18:29,212] Setting status of trial#40 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "[I 2020-06-06 20:20:47,180] Setting status of trial#41 as TrialState.PRUNED. Trial was pruned at epoch 7.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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     "text": [
      "[I 2020-06-06 20:23:39,079] Finished trial#42 with value: 0.3493475914001465 with parameters: {'n_factors': 5, 'dropout_p': 0.034713701752816614}. Best is trial#22 with value: 0.34588390588760376.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 403 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 403 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 20:24:13,664] Setting status of trial#43 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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     "text": [
      "[I 2020-06-06 20:24:48,493] Setting status of trial#44 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 492 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 492 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:25:23,079] Setting status of trial#45 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:25:57,749] Setting status of trial#46 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:26:32,321] Setting status of trial#47 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:27:07,025] Setting status of trial#48 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 2 M   \n",
      "1 | item_factors | Embedding | 537 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 2 M   \n",
      "1 | item_factors | Embedding | 537 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:27:41,697] Setting status of trial#49 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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     "text": [
      "[I 2020-06-06 20:28:16,346] Setting status of trial#50 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
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      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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    {
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     "output_type": "stream",
     "text": [
      "[I 2020-06-06 20:28:51,185] Setting status of trial#51 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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     "text": [
      "[I 2020-06-06 20:31:43,752] Finished trial#52 with value: 0.3491765856742859 with parameters: {'n_factors': 5, 'dropout_p': 0.03809047009528191}. Best is trial#22 with value: 0.34588390588760376.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
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      "2 | user_biases  | Embedding | 177 K \n",
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    {
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     "text": [
      "[I 2020-06-06 20:34:02,937] Setting status of trial#53 as TrialState.PRUNED. Trial was pruned at epoch 7.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 447 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 447 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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    {
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     "text": [
      "[I 2020-06-06 20:34:37,797] Setting status of trial#54 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 7 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 7 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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    {
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     "text": [
      "[I 2020-06-06 20:35:13,599] Setting status of trial#55 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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    {
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     "text": [
      "[I 2020-06-06 20:35:48,276] Setting status of trial#56 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 403 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 403 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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    {
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     "output_type": "stream",
     "text": [
      "[I 2020-06-06 20:36:22,896] Setting status of trial#57 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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    {
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     "output_type": "stream",
     "text": [
      "[I 2020-06-06 20:36:57,680] Setting status of trial#58 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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    {
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     "output_type": "stream",
     "text": [
      "[I 2020-06-06 20:37:32,420] Setting status of trial#59 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 492 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
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      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 492 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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    {
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     "output_type": "stream",
     "text": [
      "[I 2020-06-06 20:38:07,133] Setting status of trial#60 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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    },
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      "[I 2020-06-06 20:41:04,491] Finished trial#61 with value: 0.3463299572467804 with parameters: {'n_factors': 5, 'dropout_p': 0.026124045613427658}. Best is trial#22 with value: 0.34588390588760376.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 20:41:41,128] Setting status of trial#62 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 20:42:16,991] Setting status of trial#63 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:44:35,685] Setting status of trial#64 as TrialState.PRUNED. Trial was pruned at epoch 7.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:45:11,830] Setting status of trial#65 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 20:45:48,164] Setting status of trial#66 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 447 K \n",
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      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
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      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 447 K \n",
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      "[I 2020-06-06 20:46:24,354] Setting status of trial#67 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
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      "---------------------------------------\n",
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      "[I 2020-06-06 20:47:00,898] Setting status of trial#68 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
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      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
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      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 403 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 20:47:37,137] Setting status of trial#69 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "[I 2020-06-06 20:48:13,865] Setting status of trial#70 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
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      "  | Name         | Type      | Params\n",
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      "---------------------------------------\n",
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      "[I 2020-06-06 20:51:08,769] Setting status of trial#71 as TrialState.PRUNED. Trial was pruned at epoch 9.\n",
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      "[I 2020-06-06 20:52:53,785] Setting status of trial#72 as TrialState.PRUNED. Trial was pruned at epoch 5.\n",
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      "---------------------------------------\n",
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      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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     "text": [
      "[I 2020-06-06 20:53:29,577] Setting status of trial#73 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:54:05,902] Setting status of trial#74 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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    {
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     "text": [
      "[I 2020-06-06 20:54:40,839] Setting status of trial#75 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 3 M   \n",
      "1 | item_factors | Embedding | 850 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 3 M   \n",
      "1 | item_factors | Embedding | 850 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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    {
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     "output_type": "stream",
     "text": [
      "[I 2020-06-06 20:55:15,958] Setting status of trial#76 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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    {
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     "text": [
      "[I 2020-06-06 20:55:50,876] Setting status of trial#77 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 4 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 4 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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    {
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     "output_type": "stream",
     "text": [
      "[I 2020-06-06 20:56:26,229] Setting status of trial#78 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 5 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 5 M   \n",
      "1 | item_factors | Embedding | 1 M   \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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    {
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     "output_type": "stream",
     "text": [
      "[I 2020-06-06 20:57:01,692] Setting status of trial#79 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 403 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
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      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 403 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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    {
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     "output_type": "stream",
     "text": [
      "[I 2020-06-06 20:57:36,467] Setting status of trial#80 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 20:58:11,337] Setting status of trial#81 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
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      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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     "text": [
      "[I 2020-06-06 21:00:30,034] Setting status of trial#82 as TrialState.PRUNED. Trial was pruned at epoch 7.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 21:01:04,901] Setting status of trial#83 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 21:01:39,670] Setting status of trial#84 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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     "text": [
      "[I 2020-06-06 21:02:14,531] Setting status of trial#85 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
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      "INFO:lightning:\n",
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      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 21:02:49,194] Setting status of trial#86 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "---------------------------------------\n",
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      "---------------------------------------\n",
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      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 21:03:24,060] Setting status of trial#87 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
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      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 492 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 21:03:58,750] Setting status of trial#88 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
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      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
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      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
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      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 21:06:16,675] Setting status of trial#89 as TrialState.PRUNED. Trial was pruned at epoch 7.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 403 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 403 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "[I 2020-06-06 21:06:51,340] Setting status of trial#90 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
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      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 21:07:26,049] Setting status of trial#91 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
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      "3 | item_biases  | Embedding | 44 K  \n",
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      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 21:10:18,344] Finished trial#92 with value: 0.34575051069259644 with parameters: {'n_factors': 5, 'dropout_p': 0.062377213677543994}. Best is trial#92 with value: 0.34575051069259644.\n",
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      "---------------------------------------\n",
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      "---------------------------------------\n",
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      "[I 2020-06-06 21:10:53,111] Setting status of trial#93 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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     "text": [
      "[I 2020-06-06 21:13:45,546] Finished trial#94 with value: 0.34302350878715515 with parameters: {'n_factors': 5, 'dropout_p': 0.08822302993566566}. Best is trial#94 with value: 0.34302350878715515.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
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      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 358 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
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     "text": [
      "[I 2020-06-06 21:14:19,656] Setting status of trial#95 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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     "text": [
      "[I 2020-06-06 21:14:53,644] Setting status of trial#96 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 887 K \n",
      "1 | item_factors | Embedding | 223 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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     "text": [
      "[I 2020-06-06 21:15:27,496] Setting status of trial#97 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 268 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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    {
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     "output_type": "stream",
     "text": [
      "[I 2020-06-06 21:16:02,937] Setting status of trial#98 as TrialState.PRUNED. Trial was pruned at epoch 1.\n",
      "GPU available: True, used: True\n",
      "INFO:lightning:GPU available: True, used: True\n",
      "No environment variable for node rank defined. Set as 0.\n",
      "WARNING:lightning:No environment variable for node rank defined. Set as 0.\n",
      "CUDA_VISIBLE_DEVICES: [0]\n",
      "INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n",
      "INFO:lightning:\n",
      "  | Name         | Type      | Params\n",
      "---------------------------------------\n",
      "0 | user_factors | Embedding | 1 M   \n",
      "1 | item_factors | Embedding | 313 K \n",
      "2 | user_biases  | Embedding | 177 K \n",
      "3 | item_biases  | Embedding | 44 K  \n",
      "4 | dropout      | Dropout   | 0     \n"
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    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "[I 2020-06-06 21:16:39,260] Setting status of trial#99 as TrialState.PRUNED. Trial was pruned at epoch 1.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of finished trials: 100\n",
      "Best trial:\n",
      "  Value: 0.34302350878715515\n",
      "  Params: \n",
      "    n_factors: 5\n",
      "    dropout_p: 0.08822302993566566\n",
      "Elapsed time: 6685.85 seconds\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time() \n",
    "\n",
    "pruner = optuna.pruners.MedianPruner() if hparams.pruning else optuna.pruners.NopPruner()\n",
    "\n",
    "study = optuna.create_study(direction='minimize', pruner=pruner)\n",
    "study.optimize(objective, n_trials=100, timeout=None)\n",
    "\n",
    "print(\"Number of finished trials: {}\".format(len(study.trials)))\n",
    "\n",
    "print(\"Best trial:\")\n",
    "trial = study.best_trial\n",
    "\n",
    "print(\"  Value: {}\".format(trial.value))\n",
    "\n",
    "print(\"  Params: \")\n",
    "for key, value in trial.params.items():\n",
    "    print(\"    {}: {}\".format(key, value))\n",
    "    \n",
    "print(f\"Elapsed time: {time.time()-start_time:.2f} seconds\") "
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     "text": [
      "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost:51110\n",
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