{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5eca6897-e43c-4358-9281-ab6b1ece871a",
   "metadata": {},
   "source": [
    "# Local Hyperparameter Optimization\n",
    "\n",
    "This notebook is similar to the `classifier_hyp` notebook, but it runs the hyperparameter optimization locally instead of on Google Colab. The definitive version is the Google Colab one because we run many more iterations and parameter combinations there due to GPU availability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "747ddcf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33me1527193\u001b[0m (\u001b[33mflower-classification\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wandb\n",
    "\n",
    "wandb.login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c37343d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "import torch.nn as nn\n",
    "from torchvision import datasets, transforms\n",
    "from torchvision.models import resnet50, ResNet50_Weights\n",
    "from torch.utils.data import Dataset, DataLoader, random_split, SubsetRandomSampler\n",
    "import numpy as np\n",
    "import os\n",
    "import time\n",
    "import copy\n",
    "import random\n",
    "from sklearn import metrics\n",
    "\n",
    "torch.manual_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "17b25dc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_dataset(batch_size):    \n",
    "    data_transforms = {\n",
    "        'train': transforms.Compose([\n",
    "            transforms.RandomResizedCrop(224),\n",
    "            transforms.RandomHorizontalFlip(),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "        ]),\n",
    "        'test': transforms.Compose([\n",
    "            transforms.Resize(256),\n",
    "            transforms.CenterCrop(224),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "        ]),\n",
    "    }\n",
    "\n",
    "    data_dir = 'plantsdata'\n",
    "    dataset = datasets.ImageFolder(os.path.join(data_dir))\n",
    "\n",
    "    # 90/10 split\n",
    "    train_dataset, test_dataset = random_split(dataset, [0.9, 0.1])\n",
    "\n",
    "    train_dataset.dataset.transform = data_transforms['train']\n",
    "    test_dataset.dataset.transform = data_transforms['test']\n",
    "\n",
    "    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,\n",
    "                                               shuffle=True, num_workers=4)\n",
    "    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size,\n",
    "                                             shuffle=True, num_workers=4)\n",
    "\n",
    "    dataloaders = {'train': train_loader, 'test': test_loader}\n",
    "    dataset_size = len(dataset)\n",
    "    dataset_sizes = {'train': len(train_dataset), 'test': len(test_dataset)}\n",
    "    class_names = dataset.classes\n",
    "\n",
    "    return (dataloaders, dataset_sizes)\n",
    "\n",
    "def build_network():\n",
    "    network = resnet50(weights=ResNet50_Weights.DEFAULT)\n",
    "    num_ftrs = network.fc.in_features\n",
    "\n",
    "    # Add linear layer with number of classes\n",
    "    network.fc = nn.Linear(num_ftrs, 2)\n",
    "\n",
    "    return network.to(device)\n",
    "\n",
    "def build_optimizer(network, optimizer, learning_rate, beta_one, beta_two, eps):\n",
    "    if optimizer == \"sgd\":\n",
    "        optimizer = optim.SGD(network.parameters(),\n",
    "                              lr=learning_rate, momentum=0.9)\n",
    "    elif optimizer == \"adam\":\n",
    "        optimizer = optim.Adam(network.parameters(),\n",
    "                               lr=learning_rate,\n",
    "                               betas=(beta_one, beta_two),\n",
    "                               eps=eps)\n",
    "    return optimizer\n",
    "\n",
    "def train_epoch(network, loader, optimizer, criterion, scheduler, dataset_sizes):\n",
    "    network.train()\n",
    "    running_loss = 0.0\n",
    "    running_corrects = 0\n",
    "    for _, (data, target) in enumerate(loader):\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # ➡ Forward pass\n",
    "        #loss = F.nll_loss(network(data), target)\n",
    "        with torch.set_grad_enabled(True):\n",
    "            outputs = network(data)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "            loss = criterion(outputs, target)\n",
    "        \n",
    "        #cumu_loss += loss.item()\n",
    "        \n",
    "        running_loss += loss.item() * data.size(0)\n",
    "        running_corrects += torch.sum(preds == target.data)\n",
    "\n",
    "        # ⬅ Backward pass + weight update\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        wandb.log({'train/batch_loss': loss.item()})\n",
    "\n",
    "    scheduler.step()\n",
    "\n",
    "    epoch_loss = running_loss / dataset_sizes['train']\n",
    "    epoch_acc = running_corrects.double() / dataset_sizes['train']\n",
    "    \n",
    "    return (epoch_loss, epoch_acc)\n",
    "\n",
    "def test(network, loader, optimizer, criterion, dataset_sizes):\n",
    "    network.eval()\n",
    "    confusion = torch.empty([0, 1])\n",
    "    confusion = confusion.to(device)\n",
    "    running_loss = 0.0\n",
    "    test_corrects = 0\n",
    "    for _, (data, target) in enumerate(loader):\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # ➡ Forward pass\n",
    "        with torch.set_grad_enabled(False):\n",
    "            outputs = network(data)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "            loss = criterion(outputs, target)\n",
    "\n",
    "        running_loss += loss.item() * data.size(0)\n",
    "        test_corrects += torch.sum(preds == target.data)\n",
    "        \n",
    "        confusion = torch.cat((confusion, preds[:, None] / target.data[:, None]))\n",
    "\n",
    "    tp = torch.sum(confusion == 1).item()\n",
    "    fp = torch.sum(confusion == float('inf')).item()\n",
    "    tn = torch.sum(torch.isnan(confusion)).item()\n",
    "    fn = torch.sum(confusion == 0).item()\n",
    "    \n",
    "    precision = tp / (tp + fp)\n",
    "    recall = tp / (tp + fn)\n",
    "    f = 2 * ((precision * recall) / (precision + recall))\n",
    "    \n",
    "    epoch_loss = running_loss / dataset_sizes['test']\n",
    "    epoch_acc = test_corrects.double() / dataset_sizes['test']\n",
    "    \n",
    "    return (epoch_loss, epoch_acc, precision, recall, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5eff68bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(config=None):\n",
    "    # Initialize a new wandb run\n",
    "    with wandb.init(config=config):\n",
    "        # If called by wandb.agent, as below,\n",
    "        # this config will be set by Sweep Controller\n",
    "        config = wandb.config\n",
    "\n",
    "        (dataloaders, dataset_sizes) = build_dataset(config.batch_size)\n",
    "        network = build_network()\n",
    "        optimizer = build_optimizer(network, config.optimizer, config.learning_rate, config.beta_one,\n",
    "                                    config.beta_two, config.eps)\n",
    "        criterion = nn.CrossEntropyLoss()\n",
    "        # Decay LR by a factor of 0.1 every 7 epochs\n",
    "        exp_lr_scheduler = optim.lr_scheduler.StepLR(optimizer, config.step_size, config.gamma)\n",
    "\n",
    "        for epoch in range(config.epochs):            \n",
    "            (epoch_loss, epoch_acc) = train_epoch(network, dataloaders['train'], optimizer,\n",
    "                                                  criterion, exp_lr_scheduler,\n",
    "                                                  dataset_sizes)\n",
    "            wandb.log({\"epoch\": epoch, 'train/epoch_loss': epoch_loss, 'train/epoch_acc': epoch_acc})\n",
    "            \n",
    "            (test_loss, test_acc, test_precision, test_recall, test_f) = test(network, dataloaders['test'],\n",
    "                                                                              optimizer, criterion,\n",
    "                                                                              dataset_sizes)\n",
    "            wandb.log({'test/epoch_loss': test_loss, 'test/epoch_acc': test_acc,\n",
    "                       'test/precision': test_precision, 'test/recall': test_recall,\n",
    "                       'test/f1-score': test_f})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "732a83df",
   "metadata": {},
   "outputs": [],
   "source": [
    "sweep_config = {\n",
    "    'method': 'random'\n",
    "}\n",
    "\n",
    "metric = {\n",
    "    'name': 'test/epoch_acc',\n",
    "    'goal': 'maximize'   \n",
    "}\n",
    "\n",
    "sweep_config['metric'] = metric\n",
    "\n",
    "parameters_dict = {\n",
    "    'optimizer': {\n",
    "        'values': ['adam', 'sgd']\n",
    "    },\n",
    "}\n",
    "\n",
    "sweep_config['parameters'] = parameters_dict\n",
    "\n",
    "parameters_dict.update({\n",
    "    'epochs': {\n",
    "        'value': 10},\n",
    "    'batch_size': {\n",
    "        'values': [4, 8]},\n",
    "    'learning_rate': {\n",
    "        'values': [0.1, 0.01, 0.003, 0.001, 0.0003, 0.0001]},\n",
    "    'step_size': {\n",
    "        'values': [2, 3, 5, 7]},\n",
    "    'gamma': {\n",
    "        'values': [0.1, 0.5]},\n",
    "    'beta_one': {\n",
    "        'values': [0.9, 0.99]},\n",
    "    'beta_two': {\n",
    "        'values': [0.5, 0.9, 0.99, 0.999]},\n",
    "    'eps': {\n",
    "        'values': [1e-08, 0.1, 1]}\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9a01fef6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create sweep with ID: eqwnoagh\n",
      "Sweep URL: https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh\n"
     ]
    }
   ],
   "source": [
    "sweep_id = wandb.sweep(sweep_config, project=\"pytorch-sweeps-demo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e80d1730",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: znahtehx with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 4\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.5\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 2\n"
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       " View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
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       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▁▂▅▆▅▇▄▇▃█</td></tr><tr><td>test/epoch_loss</td><td>█▃▂▂▃▂▂▁▂▂</td></tr><tr><td>test/f1-score</td><td>▁▃▅▆▄▆▄▇▃█</td></tr><tr><td>test/precision</td><td>▁▁▃▅▆▇▄█▃▇</td></tr><tr><td>test/recall</td><td>▁▆▇▆▁▄▃▄▃█</td></tr><tr><td>train/batch_loss</td><td>█▇▆▆▆▆▅▄▄▃▅▅█▆▃█▆▇▂▆▅▅▁▃▆▄▃██▅▆▄▆▅▄▂▂▇▇▆</td></tr><tr><td>train/epoch_acc</td><td>▁▆▇▇▇███▇█</td></tr><tr><td>train/epoch_loss</td><td>█▄▂▂▂▁▁▁▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.85556</td></tr><tr><td>test/epoch_loss</td><td>0.6166</td></tr><tr><td>test/f1-score</td><td>0.86022</td></tr><tr><td>test/precision</td><td>0.81633</td></tr><tr><td>test/recall</td><td>0.90909</td></tr><tr><td>train/batch_loss</td><td>0.66533</td></tr><tr><td>train/epoch_acc</td><td>0.75676</td></tr><tr><td>train/epoch_loss</td><td>0.61072</td></tr></table><br/></div></div>"
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       " View run <strong style=\"color:#cdcd00\">sparkling-sweep-1</strong> at: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/znahtehx' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/znahtehx</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: qutqx8ux with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 4\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.9\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1e-08\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 2\n"
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       "Run data is saved locally in <code>/home/zenon/Documents/master-thesis/classification/classifier/wandb/run-20230313_210951-qutqx8ux</code>"
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       "Syncing run <strong><a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/qutqx8ux' target=\"_blank\">stoic-sweep-2</a></strong> to <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▁▂▁▆▆█▇▆▃▅</td></tr><tr><td>test/epoch_loss</td><td>█▅▃▁▁▁▁▂▂▂</td></tr><tr><td>test/f1-score</td><td>▄▁▆█▇███▆▇</td></tr><tr><td>test/precision</td><td>▁▃▁▅▆█▆▅▃▄</td></tr><tr><td>test/recall</td><td>▅▁██▆▆██▆█</td></tr><tr><td>train/batch_loss</td><td>▄▁▃█▃▇▅▁▄▄▅▃▃▂▂▅▅▂▂▅▃▃▅▄▂▃▃▂▄▃▃▆▅▂▂▄▅▁▂▂</td></tr><tr><td>train/epoch_acc</td><td>▁▃▄▇▆▇█▇▇█</td></tr><tr><td>train/epoch_loss</td><td>█▄▃▂▂▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.7</td></tr><tr><td>test/epoch_loss</td><td>0.55412</td></tr><tr><td>test/f1-score</td><td>0.71579</td></tr><tr><td>test/precision</td><td>0.59649</td></tr><tr><td>test/recall</td><td>0.89474</td></tr><tr><td>train/batch_loss</td><td>0.78966</td></tr><tr><td>train/epoch_acc</td><td>0.66585</td></tr><tr><td>train/epoch_loss</td><td>0.61866</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.99\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.5\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 0.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0001\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▁█▇▇▇▆▆▆▆▆</td></tr><tr><td>test/epoch_loss</td><td>█▂▃▁▂▂▂▁▁▁</td></tr><tr><td>test/f1-score</td><td>▁█▆▇▇▅▆▅▅▅</td></tr><tr><td>test/precision</td><td>▁█▇█▇▆▆▇▇▇</td></tr><tr><td>test/recall</td><td>▄█▄▁█▁▄▁▁▁</td></tr><tr><td>train/batch_loss</td><td>▄▆██▄▃▃▅▂▃▄▅▃▄▂▃▁▁▁▄▂▃▄▁▄▂▂▁▁▃▃▄▂▂▂▅▂▃▃▄</td></tr><tr><td>train/epoch_acc</td><td>▁▅█▇▇███▇▇</td></tr><tr><td>train/epoch_loss</td><td>█▄▂▂▁▁▁▁▂▂</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.75556</td></tr><tr><td>test/epoch_loss</td><td>0.6144</td></tr><tr><td>test/f1-score</td><td>0.76087</td></tr><tr><td>test/precision</td><td>0.67308</td></tr><tr><td>test/recall</td><td>0.875</td></tr><tr><td>train/batch_loss</td><td>0.65108</td></tr><tr><td>train/epoch_acc</td><td>0.7543</td></tr><tr><td>train/epoch_loss</td><td>0.62678</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.99\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1e-08\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0003\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 5\n"
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       "<style>\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▅▁▂▅▇▄▅▇█▇</td></tr><tr><td>test/epoch_loss</td><td>█▆▄▂▁▂▂▁▁▁</td></tr><tr><td>test/f1-score</td><td>▆▁▂▅▇▃▅▇█▇</td></tr><tr><td>test/precision</td><td>▁▁▄▅▇▆█▇█▆</td></tr><tr><td>test/recall</td><td>█▁▁▅▆▂▃▆▆▅</td></tr><tr><td>train/batch_loss</td><td>█▇▇▇▇▇▇▆▆▄▆▇▆▆▅▆▄▃▃▃▁▄▃▃▄▃▂▄▂▁▂▃▇▁▃▄▃▄▆▄</td></tr><tr><td>train/epoch_acc</td><td>▁▅▆▆▇█▇█▇█</td></tr><tr><td>train/epoch_loss</td><td>█▆▄▃▂▁▂▁▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.88889</td></tr><tr><td>test/epoch_loss</td><td>0.30289</td></tr><tr><td>test/f1-score</td><td>0.86486</td></tr><tr><td>test/precision</td><td>0.91429</td></tr><tr><td>test/recall</td><td>0.82051</td></tr><tr><td>train/batch_loss</td><td>0.27111</td></tr><tr><td>train/epoch_acc</td><td>0.89681</td></tr><tr><td>train/epoch_loss</td><td>0.28549</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.99\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1e-08\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.5\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▁▅▄▆▇█▇▇▆▆</td></tr><tr><td>test/epoch_loss</td><td>█▅▄▃▃▁▂▃▂▁</td></tr><tr><td>test/f1-score</td><td>▁▅▄▆▇██▇▆▇</td></tr><tr><td>test/precision</td><td>███▄▁▂▁▅▁▁</td></tr><tr><td>test/recall</td><td>▁▄▄▅▇██▇▆▇</td></tr><tr><td>train/batch_loss</td><td>██▇▇▅▅▃▆▂▃▂▂▂▂▄▄▂▂▂▂▄▁▂▅▃▁▁▁▂▆▂▃▃▁▁▁▂▂▁▂</td></tr><tr><td>train/epoch_acc</td><td>▁▅▆▇▇█████</td></tr><tr><td>train/epoch_loss</td><td>█▅▄▂▂▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.88889</td></tr><tr><td>test/epoch_loss</td><td>0.26007</td></tr><tr><td>test/f1-score</td><td>0.8913</td></tr><tr><td>test/precision</td><td>0.95349</td></tr><tr><td>test/recall</td><td>0.83673</td></tr><tr><td>train/batch_loss</td><td>0.01167</td></tr><tr><td>train/epoch_acc</td><td>0.98034</td></tr><tr><td>train/epoch_loss</td><td>0.08153</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.5\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▇▆▆▁▅▆▃▃▃█</td></tr><tr><td>test/epoch_loss</td><td>█▄▃▄▄▅▂▄▃▁</td></tr><tr><td>test/f1-score</td><td>▇▆▆▁▅▆▄▃▄█</td></tr><tr><td>test/precision</td><td>▅▂█▃▁▂▁▄▁▅</td></tr><tr><td>test/recall</td><td>▇▇▅▁▆▇▅▃▅█</td></tr><tr><td>train/batch_loss</td><td>▆▆▆▆▃▂▂▄▂▂▁▃▂█▂▁▂▅▄▁▁▂▁▁▅▁▁▃▂▂▄▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train/epoch_acc</td><td>▁▄▆▇▇▇████</td></tr><tr><td>train/epoch_loss</td><td>█▅▄▃▂▂▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.9</td></tr><tr><td>test/epoch_loss</td><td>0.22746</td></tr><tr><td>test/f1-score</td><td>0.89655</td></tr><tr><td>test/precision</td><td>0.92857</td></tr><tr><td>test/recall</td><td>0.86667</td></tr><tr><td>train/batch_loss</td><td>0.13858</td></tr><tr><td>train/epoch_acc</td><td>0.98403</td></tr><tr><td>train/epoch_loss</td><td>0.07075</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.99\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.5\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0001\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▁▄▆██▇▇▇▇▆</td></tr><tr><td>test/epoch_loss</td><td>█▇▆▄▃▂▁▁▁▁</td></tr><tr><td>test/f1-score</td><td>▁▄▆█▇▆▆▆▆▃</td></tr><tr><td>test/precision</td><td>▁▃▄▆▇▇▇███</td></tr><tr><td>test/recall</td><td>▇▇██▆▅▅▄▄▁</td></tr><tr><td>train/batch_loss</td><td>▇▇▇▆▇▆▆▆▆▅▆▅▆▆▆▇▅▅▄▅█▅▃▄▅▃▅▇▃▅▅▅▅▂▄▁▅▄▄▅</td></tr><tr><td>train/epoch_acc</td><td>▁▄▆▇▇███▇█</td></tr><tr><td>train/epoch_loss</td><td>█▇▆▅▄▃▂▁▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.77778</td></tr><tr><td>test/epoch_loss</td><td>0.47685</td></tr><tr><td>test/f1-score</td><td>0.72222</td></tr><tr><td>test/precision</td><td>0.83871</td></tr><tr><td>test/recall</td><td>0.63415</td></tr><tr><td>train/batch_loss</td><td>0.37919</td></tr><tr><td>train/epoch_acc</td><td>0.82924</td></tr><tr><td>train/epoch_loss</td><td>0.45283</td></tr></table><br/></div></div>"
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▃▃▁▃▃▆▆█▆▃</td></tr><tr><td>test/epoch_loss</td><td>█▆▄▃▂▂▁▁▁▁</td></tr><tr><td>test/f1-score</td><td>▄▃▁▃▃▆▆█▆▃</td></tr><tr><td>test/precision</td><td>▁█▇███████</td></tr><tr><td>test/recall</td><td>▆▃▁▃▃▆▆█▆▃</td></tr><tr><td>train/batch_loss</td><td>█▇██▇▆▇▃▅▃▇▄▄▄▃▆▃▅▃▅▃▁▇▅▃▄▄▆▂▅▂▂▃▁▁▂▂▁▃▁</td></tr><tr><td>train/epoch_acc</td><td>▁▅▅▆▇▇▇▇██</td></tr><tr><td>train/epoch_loss</td><td>█▆▅▃▃▂▂▂▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.88889</td></tr><tr><td>test/epoch_loss</td><td>0.26282</td></tr><tr><td>test/f1-score</td><td>0.87179</td></tr><tr><td>test/precision</td><td>0.97143</td></tr><tr><td>test/recall</td><td>0.7907</td></tr><tr><td>train/batch_loss</td><td>0.1486</td></tr><tr><td>train/epoch_acc</td><td>0.88698</td></tr><tr><td>train/epoch_loss</td><td>0.31064</td></tr></table><br/></div></div>"
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▁█▅▇▇▇▇▇▇█</td></tr><tr><td>test/epoch_loss</td><td>█▁▄▃▆▅▆▅▅▄</td></tr><tr><td>test/f1-score</td><td>▁█▅▇▇▇▇▇▇█</td></tr><tr><td>test/precision</td><td>▆▅▇▄█▄█▄▁▅</td></tr><tr><td>test/recall</td><td>▁█▅▇▇▇▇▇▇█</td></tr><tr><td>train/batch_loss</td><td>▅▅▆▃▄▃▃▂▂▂▂▂▁▃█▁▅▁▂▂▂▁▁▂▃▁▁▁▁▃▁▁▁▁▁▂▁▁▄▁</td></tr><tr><td>train/epoch_acc</td><td>▁▄▆▆▇█████</td></tr><tr><td>train/epoch_loss</td><td>█▆▄▃▂▂▂▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.86667</td></tr><tr><td>test/epoch_loss</td><td>0.37958</td></tr><tr><td>test/f1-score</td><td>0.875</td></tr><tr><td>test/precision</td><td>0.95455</td></tr><tr><td>test/recall</td><td>0.80769</td></tr><tr><td>train/batch_loss</td><td>0.077</td></tr><tr><td>train/epoch_acc</td><td>0.9656</td></tr><tr><td>train/epoch_loss</td><td>0.09797</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: j93p9uxm with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.5\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1e-08\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▅█</td></tr><tr><td>test/epoch_acc</td><td>▁█</td></tr><tr><td>test/epoch_loss</td><td>█▁</td></tr><tr><td>test/f1-score</td><td>▁█</td></tr><tr><td>test/precision</td><td>▁█</td></tr><tr><td>test/recall</td><td>█▁</td></tr><tr><td>train/batch_loss</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁█▁▁▆▅▁▁</td></tr><tr><td>train/epoch_acc</td><td>█▃▁</td></tr><tr><td>train/epoch_loss</td><td>▁▁█</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>2</td></tr><tr><td>test/epoch_acc</td><td>0.56667</td></tr><tr><td>test/epoch_loss</td><td>92431672.3021</td></tr><tr><td>test/f1-score</td><td>0.62136</td></tr><tr><td>test/precision</td><td>0.47761</td></tr><tr><td>test/recall</td><td>0.88889</td></tr><tr><td>train/batch_loss</td><td>7666.14648</td></tr><tr><td>train/epoch_acc</td><td>0.46929</td></tr><tr><td>train/epoch_loss</td><td>4618.08651</td></tr></table><br/></div></div>"
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     "text": [
      "Run j93p9uxm errored: ZeroDivisionError('division by zero')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run j93p9uxm errored: ZeroDivisionError('division by zero')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: pb5m44k2 with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 4\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.999\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1e-08\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
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       "<style>\n",
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▅▁▆▃▆▆▇▇▇█</td></tr><tr><td>test/epoch_loss</td><td>▅█▄▅▃▃▂▁▁▁</td></tr><tr><td>test/f1-score</td><td>▄▄▄▁▅▆▇▇▇█</td></tr><tr><td>test/precision</td><td>▆▁█▃▆▆█▇██</td></tr><tr><td>test/recall</td><td>▃█▂▁▄▆▅▆▅▇</td></tr><tr><td>train/batch_loss</td><td>▆▄▂▅▅▄▃▆█▃▂▄▂▁▃▂▄▁▂▂▄▃▅▂▂▅▂▂▃▄▁▄▃▁▂▄▂▂▃▄</td></tr><tr><td>train/epoch_acc</td><td>▁▁▃▃▄▄▆▆▇█</td></tr><tr><td>train/epoch_loss</td><td>█▇▇▆▅▅▃▃▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.78889</td></tr><tr><td>test/epoch_loss</td><td>0.51027</td></tr><tr><td>test/f1-score</td><td>0.78161</td></tr><tr><td>test/precision</td><td>0.7234</td></tr><tr><td>test/recall</td><td>0.85</td></tr><tr><td>train/batch_loss</td><td>0.42048</td></tr><tr><td>train/epoch_acc</td><td>0.82555</td></tr><tr><td>train/epoch_loss</td><td>0.40512</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 0.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
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       " View run at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/q8m1yt6d' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/q8m1yt6d</a>"
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       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train/batch_loss</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>train/batch_loss</td><td>0.67379</td></tr></table><br/></div></div>"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run q8m1yt6d errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 1.95 GiB total capacity; 1.30 GiB already allocated; 11.31 MiB free; 1.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Sweep Agent: Waiting for job.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Job received.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: f3kiw40d with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.99\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.5\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run f3kiw40d errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 3.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: i0xsie8j with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 0.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.5\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: sgd\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run i0xsie8j errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Sweep Agent: Waiting for job.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Job received.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: bi477kch with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run bi477kch errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 7jmkpkmh with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.99\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.99\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 0.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0001\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run 7jmkpkmh errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: pc0kaw45 with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 4\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.99\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 0.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0003\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run pc0kaw45 errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: o04kggii with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 4\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run o04kggii errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: mr7zxx8m with config:\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run 292ds63r errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run fdlwffsj errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run 3s4wltdw errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: kv0nxhmk with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run kv0nxhmk errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run ixbulpc8 errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: lfi2onyo with config:\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run 4uvn2tnq errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run hxampiva errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run q1v8qruc errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n"
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