{
 "cells": [
  {
   "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",
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    "scrolled": true
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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",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; 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: \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: \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: \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",
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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>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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