03，kerasmodel_tuning_demo.ipynb · zhulei632/torchkeras

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03，kerasmodel_tuning_demo.ipynb 118.99 KB

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8068f820",
   "metadata": {},
   "source": [
    "# torchkeras.KerasModel tune hyperparameters Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b84d3f83",
   "metadata": {},
   "source": [
    "You can install torchkeras using pip:\n",
    "`pip install torchkeras`\n",
    "\n",
    "Here is a mnist cnn example using torchkeras.KerasModel to auto tune the hyperparameters with help of wandb.sweep.\n",
    "\n",
    "\n",
    "`pip install wandb`\n",
    "\n",
    "\n",
    "Before you start run this demo, you should first create an account at wandb website  https://wandb.ai/ ,\n",
    "\n",
    "and get your API keys from the settings page。\n",
    "\n",
    "You can explore the wandb dashboard created in this demo from the url below:\n",
    "\n",
    "https://wandb.ai/lyhue1991/mnist_torchkeras\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6dec53e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0,'..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d52b297a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mlyhue1991\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
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      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wandb \n",
    "wandb.login() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1920bc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7.1\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "from matplotlib import pyplot as plt\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset,DataLoader\n",
    "\n",
    "import torchkeras #Attention this line \n",
    "print(torchkeras.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a739fd",
   "metadata": {},
   "source": [
    "## 0, set hyperparameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "358f728f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from argparse import Namespace\n",
    "\n",
    "\n",
    "config = Namespace(\n",
    "    project_name = \"mnist_torchkeras\",\n",
    "    \n",
    "    batch_size = 512,\n",
    "    \n",
    "    hidden_layer_width = 64,\n",
    "    dropout_p = 0.1,\n",
    "    \n",
    "    lr = 1e-4,\n",
    "    optim_type = 'Adam',\n",
    "    \n",
    "    epochs = 10,\n",
    "    ckpt_path = 'checkpoint.pt'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e73ca619",
   "metadata": {},
   "source": [
    "## 1, prepare data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "25ab4939",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision \n",
    "from torchvision import transforms\n",
    "\n",
    "transform = transforms.Compose([transforms.ToTensor()])\n",
    "ds_train = torchvision.datasets.MNIST(root=\"mnist/\",train=True,download=True,transform=transform)\n",
    "ds_val = torchvision.datasets.MNIST(root=\"mnist/\",train=False,download=True,transform=transform)\n",
    "    \n",
    "def create_dataloaders(config):\n",
    "    ds_train_sub = torch.utils.data.Subset(ds_train, indices=range(0, len(ds_train), 10))\n",
    "    dl_train =  torch.utils.data.DataLoader(ds_train_sub, batch_size=config.batch_size, shuffle=True, num_workers=2)\n",
    "    dl_val =  torch.utils.data.DataLoader(ds_val, batch_size=config.batch_size, shuffle=False, num_workers=2)\n",
    "    return dl_train,dl_val \n",
    "\n",
    "dl_train,dl_val = create_dataloaders(config)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "85a45abd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([512, 1, 28, 28])\n",
      "torch.Size([512])\n"
     ]
    }
   ],
   "source": [
    "for features,labels in dl_train:\n",
    "    break\n",
    "print(features.shape)\n",
    "print(labels.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c484c0b4",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 800x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "#visual some samples\n",
    "from matplotlib import pyplot as plt \n",
    "\n",
    "plt.figure(figsize=(8,8)) \n",
    "for i in range(9):\n",
    "    img,label = ds_train[i]\n",
    "    img = img.permute(1,2,0)\n",
    "    ax=plt.subplot(3,3,i+1)\n",
    "    ax.imshow(img.numpy())\n",
    "    ax.set_title(\"label = %d\"%label)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([]) \n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "485da125",
   "metadata": {},
   "source": [
    "## 2, create the  model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6174ae8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_net(config):\n",
    "    net = nn.Sequential()\n",
    "    net.add_module(\"conv1\",nn.Conv2d(in_channels=1,out_channels=config.hidden_layer_width,kernel_size = 3))\n",
    "    net.add_module(\"pool1\",nn.MaxPool2d(kernel_size = 2,stride = 2)) \n",
    "    net.add_module(\"conv2\",nn.Conv2d(in_channels=config.hidden_layer_width,\n",
    "                                     out_channels=config.hidden_layer_width,kernel_size = 5))\n",
    "    net.add_module(\"pool2\",nn.MaxPool2d(kernel_size = 2,stride = 2))\n",
    "    net.add_module(\"dropout\",nn.Dropout2d(p = config.dropout_p))\n",
    "    net.add_module(\"adaptive_pool\",nn.AdaptiveMaxPool2d((1,1)))\n",
    "    net.add_module(\"flatten\",nn.Flatten())\n",
    "    net.add_module(\"linear1\",nn.Linear(config.hidden_layer_width,config.hidden_layer_width))\n",
    "    net.add_module(\"relu\",nn.ReLU())\n",
    "    net.add_module(\"linear2\",nn.Linear(config.hidden_layer_width,10))\n",
    "    return net \n",
    "\n",
    "net = create_net(config)\n",
    "\n",
    "class Accuracy(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        self.correct = nn.Parameter(torch.tensor(0.0),requires_grad=False)\n",
    "        self.total = nn.Parameter(torch.tensor(0.0),requires_grad=False)\n",
    "\n",
    "    def forward(self, preds: torch.Tensor, targets: torch.Tensor):\n",
    "        preds = preds.argmax(dim=-1)\n",
    "        m = (preds == targets).sum()\n",
    "        n = targets.shape[0] \n",
    "        self.correct += m \n",
    "        self.total += n\n",
    "        \n",
    "        return m/n\n",
    "\n",
    "    def compute(self):\n",
    "        return self.correct.float() / self.total \n",
    "    \n",
    "    def reset(self):\n",
    "        self.correct -= self.correct\n",
    "        self.total -= self.total\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ad17716f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------\n",
      "Layer (type)                            Output Shape              Param #\n",
      "==========================================================================\n",
      "Conv2d-1                            [-1, 64, 26, 26]                  640\n",
      "MaxPool2d-2                         [-1, 64, 13, 13]                    0\n",
      "Conv2d-3                              [-1, 64, 9, 9]              102,464\n",
      "MaxPool2d-4                           [-1, 64, 4, 4]                    0\n",
      "Dropout2d-5                           [-1, 64, 4, 4]                    0\n",
      "AdaptiveMaxPool2d-6                   [-1, 64, 1, 1]                    0\n",
      "Flatten-7                                   [-1, 64]                    0\n",
      "Linear-8                                    [-1, 64]                4,160\n",
      "ReLU-9                                      [-1, 64]                    0\n",
      "Linear-10                                   [-1, 10]                  650\n",
      "==========================================================================\n",
      "Total params: 107,914\n",
      "Trainable params: 107,914\n",
      "Non-trainable params: 0\n",
      "--------------------------------------------------------------------------\n",
      "Input size (MB): 0.000069\n",
      "Forward/backward pass size (MB): 0.469803\n",
      "Params size (MB): 0.411659\n",
      "Estimated Total Size (MB): 0.881531\n",
      "--------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "def create_model(config):\n",
    "    net = create_net(config)\n",
    "    optim_type = torch.optim.__dict__[config.optim_type]\n",
    "    model = torchkeras.KerasModel(net,\n",
    "          loss_fn = nn.CrossEntropyLoss(),\n",
    "          optimizer = optim_type(params=net.parameters(), lr=config.lr),\n",
    "          metrics_dict = {\"acc\":Accuracy()}\n",
    "        )\n",
    "    return model\n",
    "\n",
    "from torchkeras import summary\n",
    "model = create_model(config)\n",
    "summary(model,input_data=features);\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e98c79f5",
   "metadata": {},
   "source": [
    "## 3, train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "060ac38d-bab8-4cae-a0d5-17934206d055",
   "metadata": {},
   "source": [
    "we start train the model once using the default hyperparameters to ensure everything is right.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "54bcc553",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31m<<<<<< 🐌 cpu is used >>>>>>\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.13.9"
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    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/Users/liangyun2/CodeFiles/torchkeras/wandb/run-20230207_143245-5gsjfrw0</code>"
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    {
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       "Syncing run <strong><a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/5gsjfrw0\" target=\"_blank\">2023-02-07 14:32:45</a></strong> to <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
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       "<IPython.core.display.HTML object>"
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    {
     "data": {
      "text/html": [
       " View project at <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras</a>"
      ],
      "text/plain": [
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    {
     "data": {
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       " View run at <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/5gsjfrw0\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/runs/5gsjfrw0</a>"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2023-02-07 14:32:53\n",
      "Epoch 1 / 15\n",
      "\n",
      "100%|██████████████████| 12/12 [00:04<00:00,  2.65it/s, lr=0.0001, train_acc=0.573, train_loss=1.76]\n",
      "100%|█████████████████████████████████| 20/20 [00:02<00:00,  9.09it/s, val_acc=0.706, val_loss=1.67]\n",
      "\u001b[0;31m<<<<<< reach best val_acc : 0.7063000202178955 >>>>>>\u001b[0m\n",
      "\n",
      "================================================================================2023-02-07 14:33:00\n",
      "Epoch 2 / 15\n",
      "\n",
      "100%|██████████████████| 12/12 [00:04<00:00,  2.74it/s, lr=0.0001, train_acc=0.643, train_loss=1.68]\n",
      "100%|█████████████████████████████████| 20/20 [00:02<00:00,  9.17it/s, val_acc=0.765, val_loss=1.57]\n",
      "\u001b[0;31m<<<<<< reach best val_acc : 0.7651000022888184 >>>>>>\u001b[0m\n",
      "\n",
      "================================================================================2023-02-07 14:33:06\n",
      "Epoch 3 / 15\n",
      "\n",
      "100%|██████████████████| 12/12 [00:04<00:00,  2.55it/s, lr=0.0001, train_acc=0.645, train_loss=1.59]\n",
      "100%|█████████████████████████████████| 20/20 [00:02<00:00,  8.56it/s, val_acc=0.786, val_loss=1.48]\n",
      "\u001b[0;31m<<<<<< reach best val_acc : 0.7857000231742859 >>>>>>\u001b[0m\n",
      "\n",
      "================================================================================2023-02-07 14:33:14\n",
      "Epoch 4 / 15\n",
      "\n",
      "100%|██████████████████| 12/12 [00:04<00:00,  2.54it/s, lr=0.0001, train_acc=0.686, train_loss=1.51]\n",
      "100%|█████████████████████████████████| 20/20 [00:02<00:00,  8.29it/s, val_acc=0.818, val_loss=1.39]\n",
      "\u001b[0;31m<<<<<< reach best val_acc : 0.8184999823570251 >>>>>>\u001b[0m\n",
      "\n",
      "================================================================================2023-02-07 14:33:21\n",
      "Epoch 5 / 15\n",
      "\n",
      "100%|███████████████████| 12/12 [00:04<00:00,  2.66it/s, lr=0.0001, train_acc=0.71, train_loss=1.41]\n",
      "100%|██████████████████████████████████| 20/20 [00:02<00:00,  8.71it/s, val_acc=0.826, val_loss=1.3]\n",
      "\u001b[0;31m<<<<<< reach best val_acc : 0.8263999819755554 >>>>>>\u001b[0m\n",
      "\n",
      "================================================================================2023-02-07 14:33:28\n",
      "Epoch 6 / 15\n",
      "\n",
      "100%|██████████████████| 12/12 [00:04<00:00,  2.71it/s, lr=0.0001, train_acc=0.733, train_loss=1.33]\n",
      "100%|█████████████████████████████████| 20/20 [00:02<00:00,  8.40it/s, val_acc=0.845, val_loss=1.21]\n",
      "\u001b[0;31m<<<<<< reach best val_acc : 0.8450999855995178 >>>>>>\u001b[0m\n",
      "\n",
      "================================================================================2023-02-07 14:33:34\n",
      "Epoch 7 / 15\n",
      "\n",
      "100%|██████████████████| 12/12 [00:04<00:00,  2.66it/s, lr=0.0001, train_acc=0.748, train_loss=1.25]\n",
      "100%|█████████████████████████████████| 20/20 [00:02<00:00,  9.12it/s, val_acc=0.849, val_loss=1.12]\n",
      "\u001b[0;31m<<<<<< reach best val_acc : 0.8490999937057495 >>>>>>\u001b[0m\n",
      "\n",
      "================================================================================2023-02-07 14:33:41\n",
      "Epoch 8 / 15\n",
      "\n",
      "100%|██████████████████| 12/12 [00:04<00:00,  2.51it/s, lr=0.0001, train_acc=0.762, train_loss=1.17]\n",
      "100%|█████████████████████████████████| 20/20 [00:02<00:00,  8.67it/s, val_acc=0.857, val_loss=1.04]\n",
      "\u001b[0;31m<<<<<< reach best val_acc : 0.8574000000953674 >>>>>>\u001b[0m\n",
      "\n",
      "================================================================================2023-02-07 14:33:48\n",
      "Epoch 9 / 15\n",
      "\n",
      "100%|███████████████████| 12/12 [00:04<00:00,  2.83it/s, lr=0.0001, train_acc=0.769, train_loss=1.1]\n",
      "100%|████████████████████████████████| 20/20 [00:02<00:00,  8.65it/s, val_acc=0.866, val_loss=0.964]\n",
      "\u001b[0;31m<<<<<< reach best val_acc : 0.8664000034332275 >>>>>>\u001b[0m\n",
      "\n",
      "================================================================================2023-02-07 14:33:55\n",
      "Epoch 10 / 15\n",
      "\n",
      "100%|██████████████████| 12/12 [00:04<00:00,  2.71it/s, lr=0.0001, train_acc=0.785, train_loss=1.02]\n",
      "100%|████████████████████████████████| 20/20 [00:02<00:00,  8.69it/s, val_acc=0.871, val_loss=0.893]\n",
      "\u001b[0;31m<<<<<< reach best val_acc : 0.8712999820709229 >>>>>>\u001b[0m\n",
      "\n",
      "================================================================================2023-02-07 14:34:01\n",
      "Epoch 11 / 15\n",
      "\n",
      "100%|██████████████████| 12/12 [00:04<00:00,  2.75it/s, lr=0.0001, train_acc=0.79, train_loss=0.969]\n",
      "100%|████████████████████████████████| 20/20 [00:02<00:00,  9.08it/s, val_acc=0.874, val_loss=0.829]\n",
      "\u001b[0;31m<<<<<< reach best val_acc : 0.8743000030517578 >>>>>>\u001b[0m\n",
      "\n",
      "================================================================================2023-02-07 14:34:08\n",
      "Epoch 12 / 15\n",
      "\n",
      "100%|█████████████████| 12/12 [00:04<00:00,  2.64it/s, lr=0.0001, train_acc=0.807, train_loss=0.905]\n",
      "100%|████████████████████████████████| 20/20 [00:02<00:00,  9.05it/s, val_acc=0.879, val_loss=0.774]\n",
      "\u001b[0;31m<<<<<< reach best val_acc : 0.8787000179290771 >>>>>>\u001b[0m\n",
      "\n",
      "================================================================================2023-02-07 14:34:15\n",
      "Epoch 13 / 15\n",
      "\n",
      "100%|█████████████████| 12/12 [00:04<00:00,  2.73it/s, lr=0.0001, train_acc=0.816, train_loss=0.851]\n",
      "100%|█████████████████████████████████| 20/20 [00:02<00:00,  8.29it/s, val_acc=0.884, val_loss=0.72]\n",
      "\u001b[0;31m<<<<<< reach best val_acc : 0.883899986743927 >>>>>>\u001b[0m\n",
      "\n",
      "================================================================================2023-02-07 14:34:22\n",
      "Epoch 14 / 15\n",
      "\n",
      "100%|█████████████████| 12/12 [00:04<00:00,  2.45it/s, lr=0.0001, train_acc=0.822, train_loss=0.801]\n",
      "100%|████████████████████████████████| 20/20 [00:02<00:00,  8.01it/s, val_acc=0.886, val_loss=0.674]\n",
      "\u001b[0;31m<<<<<< reach best val_acc : 0.8859999775886536 >>>>>>\u001b[0m\n",
      "\n",
      "================================================================================2023-02-07 14:34:29\n",
      "Epoch 15 / 15\n",
      "\n",
      "100%|█████████████████| 12/12 [00:04<00:00,  2.68it/s, lr=0.0001, train_acc=0.831, train_loss=0.755]\n",
      "100%|████████████████████████████████| 20/20 [00:02<00:00,  8.56it/s, val_acc=0.891, val_loss=0.632]\n",
      "\u001b[0;31m<<<<<< reach best val_acc : 0.8909000158309937 >>>>>>\u001b[0m\n",
      "    epoch  train_loss  train_acc      lr  val_loss  val_acc\n",
      "0       1    1.757595   0.573000  0.0001  1.667398   0.7063\n",
      "1       2    1.677442   0.643000  0.0001  1.574891   0.7651\n",
      "2       3    1.590015   0.645000  0.0001  1.484543   0.7857\n",
      "3       4    1.508809   0.686000  0.0001  1.388418   0.8185\n",
      "4       5    1.414975   0.710000  0.0001  1.299086   0.8264\n",
      "5       6    1.327756   0.733333  0.0001  1.208950   0.8451\n",
      "6       7    1.253490   0.748000  0.0001  1.120964   0.8491\n",
      "7       8    1.171132   0.761667  0.0001  1.040663   0.8574\n",
      "8       9    1.101371   0.768833  0.0001  0.964349   0.8664\n",
      "9      10    1.024419   0.784833  0.0001  0.892811   0.8713\n",
      "10     11    0.968760   0.790333  0.0001  0.829149   0.8743\n",
      "11     12    0.905306   0.806500  0.0001  0.774353   0.8787\n",
      "12     13    0.851196   0.816167  0.0001  0.719902   0.8839\n",
      "13     14    0.801464   0.821667  0.0001  0.673553   0.8860\n",
      "14     15    0.755362   0.830667  0.0001  0.632009   0.8909\n"
     ]
    },
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       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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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>best_val_acc</td><td>▁▃▄▅▆▆▆▇▇▇▇████</td></tr><tr><td>epoch</td><td>▁▁▂▃▃▃▄▅▅▅▆▇▇▇█</td></tr><tr><td>lr</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_acc</td><td>▁▃▃▄▅▅▆▆▆▇▇▇███</td></tr><tr><td>train_loss</td><td>█▇▇▆▆▅▄▄▃▃▂▂▂▁▁</td></tr><tr><td>val_acc</td><td>▁▃▄▅▆▆▆▇▇▇▇████</td></tr><tr><td>val_loss</td><td>█▇▇▆▆▅▄▄▃▃▂▂▂▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>best_score</td><td>0.8909</td></tr><tr><td>best_val_acc</td><td>0.8909</td></tr><tr><td>epoch</td><td>15.0</td></tr><tr><td>lr</td><td>0.0001</td></tr><tr><td>train_acc</td><td>0.83067</td></tr><tr><td>train_loss</td><td>0.75536</td></tr><tr><td>val_acc</td><td>0.8909</td></tr><tr><td>val_loss</td><td>0.63201</td></tr></table><br/></div></div>"
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     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">2023-02-07 14:32:45</strong> at: <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/5gsjfrw0\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/runs/5gsjfrw0</a><br/>Synced 7 W&B file(s), 2 media file(s), 1 artifact file(s) and 3 other file(s)"
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       "<IPython.core.display.HTML object>"
      ]
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    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>./wandb/run-20230207_143245-5gsjfrw0/logs</code>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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   ],
   "source": [
    "from torchkeras.kerascallbacks import WandbCallback,MiniLogCallback\n",
    "\n",
    "wandb_cb = WandbCallback(project=config.project_name,\n",
    "                         config=config,\n",
    "                         name=None,\n",
    "                         save_code=True,\n",
    "                         save_ckpt=True)\n",
    "\n",
    "dfhistory=model.fit(train_data=dl_train, \n",
    "                    val_data=dl_val, \n",
    "                    epochs=config.epochs, \n",
    "                    patience=3, \n",
    "                    monitor=\"val_acc\",\n",
    "                    mode=\"max\",\n",
    "                    ckpt_path='checkpoint.pt',\n",
    "                    callbacks = [wandb_cb]\n",
    "                   )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "679e03b8-4d70-4691-b760-94e7d9ae5fcc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "627a59f4-e8c8-4ce2-92ad-a8feda19aa8a",
   "metadata": {},
   "source": [
    "## 4, tune hyperparameters \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f9955f78-37e8-48cc-89c4-c0b87b0089dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from argparse import Namespace\n",
    "\n",
    "config = Namespace(\n",
    "    project_name = 'mnist_torchkeras',\n",
    "    \n",
    "    batch_size = 512,\n",
    "    \n",
    "    hidden_layer_width = 64,\n",
    "    dropout_p = 0.1,\n",
    "    \n",
    "    lr = 1e-4,\n",
    "    optim_type = 'Adam',\n",
    "    \n",
    "    epochs = 10,\n",
    "    ckpt_path = 'checkpoint.pt'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6429a417-96ae-4561-bf21-d4fde55fb1b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'early_terminate': {'eta': 2, 'min_iter': 3, 's': 3, 'type': 'hyperband'},\n",
      " 'method': 'random',\n",
      " 'metric': {'goal': 'maximize', 'name': 'val_acc'},\n",
      " 'parameters': {'batch_size': {'distribution': 'q_log_uniform_values',\n",
      "                               'max': 256,\n",
      "                               'min': 32,\n",
      "                               'q': 8},\n",
      "                'ckpt_path': {'value': 'checkpoint.pt'},\n",
      "                'dropout_p': {'distribution': 'uniform', 'max': 0.6, 'min': 0},\n",
      "                'epochs': {'value': 10},\n",
      "                'hidden_layer_width': {'values': [16,\n",
      "                                                  32,\n",
      "                                                  48,\n",
      "                                                  64,\n",
      "                                                  80,\n",
      "                                                  96,\n",
      "                                                  112,\n",
      "                                                  128]},\n",
      "                'lr': {'distribution': 'log_uniform_values',\n",
      "                       'max': 0.1,\n",
      "                       'min': 1e-06},\n",
      "                'optim_type': {'values': ['Adam', 'SGD', 'AdamW']},\n",
      "                'project_name': {'value': 'mnist_torchkeras'}}}\n"
     ]
    }
   ],
   "source": [
    "#1，search method\n",
    "sweep_config = {'method': 'random'} # one of ['grid','random','bayes']\n",
    "\n",
    "#2，search target\n",
    "sweep_config['metric'] =  {'name': 'val_acc', 'goal': 'maximize'}\n",
    "\n",
    "#3，hyperparameters space\n",
    "sweep_config['parameters'] = {}\n",
    "\n",
    "#fixed hyperparameters\n",
    "sweep_config['parameters'].update({\n",
    "    'project_name':{'value':config.project_name},\n",
    "    'epochs': {'value': 10},\n",
    "    'ckpt_path': {'value':'checkpoint.pt'}})\n",
    "\n",
    "#discrete hyperparameters\n",
    "sweep_config['parameters'].update({\n",
    "    'optim_type': {\n",
    "        'values': ['Adam', 'SGD','AdamW']\n",
    "        },\n",
    "    'hidden_layer_width': {\n",
    "        'values': [16,32,48,64,80,96,112,128]\n",
    "        }\n",
    "    })\n",
    "\n",
    "#continuous hyperparameters\n",
    "sweep_config['parameters'].update({\n",
    "    \n",
    "    'lr': {\n",
    "        'distribution': 'log_uniform_values',\n",
    "        'min': 1e-6,\n",
    "        'max': 0.1\n",
    "      },\n",
    "    \n",
    "    'batch_size': {\n",
    "        'distribution': 'q_log_uniform_values',\n",
    "        'q': 8,\n",
    "        'min': 32,\n",
    "        'max': 256,\n",
    "      },\n",
    "    \n",
    "    'dropout_p': {\n",
    "        'distribution': 'uniform',\n",
    "        'min': 0,\n",
    "        'max': 0.6,\n",
    "      }\n",
    "})\n",
    "\n",
    "\n",
    "\n",
    "#4, prune strategy\n",
    "\n",
    "#decide prune or not at epoch 3,6,12\n",
    "sweep_config['early_terminate'] = {'type':'hyperband','min_iter':3, 'eta': 2, 's':3} \n",
    "\n",
    "from pprint import pprint \n",
    "pprint(sweep_config)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c3cb80a5-7427-42fd-b964-33e423e8ada4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create sweep with ID: ne3wz8k3\n",
      "Sweep URL: https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3\n"
     ]
    }
   ],
   "source": [
    "sweep_id = wandb.sweep(sweep_config, project='mnist_torchkeras')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "262140c7-8030-470c-841e-ac9616b2a13e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchkeras.kerascallbacks import WandbCallback,MiniLogCallback\n",
    "\n",
    "def train(config=None):\n",
    "    \n",
    "    wandb_cb = WandbCallback(project='mnist_torchkeras',\n",
    "                            config=config,\n",
    "                            save_code=True)\n",
    "    \n",
    "    with wandb.init(project = wandb_cb.project,\n",
    "                    config = wandb_cb.config,\n",
    "                    name = wandb_cb.name):\n",
    "        \n",
    "        config = wandb.config\n",
    "        \n",
    "        #1，prepare datasets\n",
    "        dl_train, dl_val =  create_dataloaders(config)\n",
    "        \n",
    "        #2，prepare model\n",
    "        model = create_model(config)\n",
    "        \n",
    "        #2，fit the model\n",
    "        mini_log_cb = MiniLogCallback()\n",
    "        import contextlib\n",
    "        with open('training.log','w') as f, contextlib.redirect_stdout(f):\n",
    "            dfhistory = model.fit(\n",
    "                train_data = dl_train,\n",
    "                val_data=dl_val,\n",
    "                epochs=config.epochs,\n",
    "                ckpt_path=config.ckpt_path,\n",
    "                patience=3,\n",
    "                monitor='val_acc',\n",
    "                mode='max',\n",
    "                mixed_precision='no',\n",
    "                callbacks= [wandb_cb,mini_log_cb])\n",
    "            \n",
    "        return model\n",
    "    "
   ]
  },
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   "execution_count": 17,
   "id": "e620f434-7176-4226-b9cd-dc24ea9a29c3",
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: kci98cyx with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 48\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tckpt_path: checkpoint.pt\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tdropout_p: 0.008867025716607512\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \thidden_layer_width: 32\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlr: 0.0007143031691417582\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptim_type: Adam\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tproject_name: mnist_torchkeras\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Ignored wandb.init() arg project when running a sweep.\n"
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       "Syncing run <strong><a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/kci98cyx\" target=\"_blank\">2023-02-07 14:36:57</a></strong> to <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>Sweep page: <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3</a>"
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       " View sweep at <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3</a>"
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     "text": [
      "epoch【10】@2023-02-07 14:37:56 --> best_val_acc = 0.9476000070571899\r"
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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",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>best_val_acc</td><td>▁▆▆▇▇█████</td></tr><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>lr</td><td>▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_acc</td><td>▁▆▇▇██████</td></tr><tr><td>train_loss</td><td>█▄▂▂▁▁▁▁▁▁</td></tr><tr><td>val_acc</td><td>▁▆▆▇▇█████</td></tr><tr><td>val_loss</td><td>█▃▂▂▂▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>best_score</td><td>0.9476</td></tr><tr><td>best_val_acc</td><td>0.9476</td></tr><tr><td>epoch</td><td>10.0</td></tr><tr><td>lr</td><td>0.00071</td></tr><tr><td>train_acc</td><td>0.95233</td></tr><tr><td>train_loss</td><td>0.17249</td></tr><tr><td>val_acc</td><td>0.9476</td></tr><tr><td>val_loss</td><td>0.17224</td></tr></table><br/></div></div>"
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       " View run <strong style=\"color:#cdcd00\">2023-02-07 14:36:57</strong> at: <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/kci98cyx\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/runs/kci98cyx</a><br/>Synced 6 W&B file(s), 2 media file(s), 1 artifact file(s) and 2 other file(s)"
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     "text": [
      "\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: 5nsggpfq with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 168\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tckpt_path: checkpoint.pt\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tdropout_p: 0.3647552723480366\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \thidden_layer_width: 128\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlr: 0.0013760447138439903\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptim_type: Adam\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tproject_name: mnist_torchkeras\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Ignored wandb.init() arg project when running a sweep.\n"
     ]
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       "Syncing run <strong><a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/5nsggpfq\" target=\"_blank\">2023-02-07 14:38:30</a></strong> to <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>Sweep page: <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3</a>"
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       " View sweep at <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3</a>"
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       " View run at <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/5nsggpfq\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/runs/5nsggpfq</a>"
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      "epoch【10】@2023-02-07 14:40:38 --> best_val_acc = 0.9707000255584717\r"
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    },
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     "data": {
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       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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      "text/html": [
       "<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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       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>best_val_acc</td><td>▁▄▇▇▇▇▇███</td></tr><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>lr</td><td>▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_acc</td><td>▁▇▇███████</td></tr><tr><td>train_loss</td><td>█▃▂▂▁▁▁▁▁▁</td></tr><tr><td>val_acc</td><td>▁▄▇▇▇▇▇███</td></tr><tr><td>val_loss</td><td>█▄▂▂▁▁▂▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>best_score</td><td>0.9707</td></tr><tr><td>best_val_acc</td><td>0.9707</td></tr><tr><td>epoch</td><td>10.0</td></tr><tr><td>lr</td><td>0.00138</td></tr><tr><td>train_acc</td><td>0.97233</td></tr><tr><td>train_loss</td><td>0.08129</td></tr><tr><td>val_acc</td><td>0.9681</td></tr><tr><td>val_loss</td><td>0.09655</td></tr></table><br/></div></div>"
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       " View run <strong style=\"color:#cdcd00\">2023-02-07 14:38:30</strong> at: <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/5nsggpfq\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/runs/5nsggpfq</a><br/>Synced 6 W&B file(s), 2 media file(s), 1 artifact file(s) and 2 other file(s)"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: rtz82tiq with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 224\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tckpt_path: checkpoint.pt\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tdropout_p: 0.029987727612876425\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \thidden_layer_width: 48\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlr: 8.557824417088818e-05\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptim_type: Adam\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tproject_name: mnist_torchkeras\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Ignored wandb.init() arg project when running a sweep.\n"
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       " View sweep at <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3</a>"
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       " View run at <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/rtz82tiq\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/runs/rtz82tiq</a>"
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    {
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     "output_type": "stream",
     "text": [
      "epoch【10】@2023-02-07 14:42:00 --> best_val_acc = 0.7857000231742859\r"
     ]
    },
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<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",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>best_val_acc</td><td>▁▂▃▅▆▆▇▇██</td></tr><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>lr</td><td>▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_acc</td><td>▁▂▃▄▅▆▇▇██</td></tr><tr><td>train_loss</td><td>██▇▇▆▅▄▃▂▁</td></tr><tr><td>val_acc</td><td>▁▂▃▅▆▆▇▇██</td></tr><tr><td>val_loss</td><td>██▇▆▆▅▄▃▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>best_score</td><td>0.7857</td></tr><tr><td>best_val_acc</td><td>0.7857</td></tr><tr><td>epoch</td><td>10.0</td></tr><tr><td>lr</td><td>9e-05</td></tr><tr><td>train_acc</td><td>0.70433</td></tr><tr><td>train_loss</td><td>1.51818</td></tr><tr><td>val_acc</td><td>0.7857</td></tr><tr><td>val_loss</td><td>1.43522</td></tr></table><br/></div></div>"
      ],
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">2023-02-07 14:40:56</strong> at: <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/rtz82tiq\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/runs/rtz82tiq</a><br/>Synced 6 W&B file(s), 2 media file(s), 1 artifact file(s) and 2 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>./wandb/run-20230207_144058-rtz82tiq/logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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: t2en6p3w with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 88\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tckpt_path: checkpoint.pt\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tdropout_p: 0.43865740655519714\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \thidden_layer_width: 80\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlr: 0.0021908568878640225\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptim_type: SGD\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tproject_name: mnist_torchkeras\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Ignored wandb.init() arg project when running a sweep.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9680219576664cc9969bf776731f386c",
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      "text/plain": [
       "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.01675115903333335, max=1.0)…"
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    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.13.9"
      ],
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/Users/liangyun2/CodeFiles/torchkeras/wandb/run-20230207_144233-t2en6p3w</code>"
      ],
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/t2en6p3w\" target=\"_blank\">2023-02-07 14:42:30</a></strong> to <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>Sweep page: <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3</a>"
      ],
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       "<IPython.core.display.HTML object>"
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    {
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      "text/html": [
       " View project at <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       " View sweep at <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       " View run at <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/t2en6p3w\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/runs/t2en6p3w</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch【10】@2023-02-07 14:44:06 --> best_val_acc = 0.24799999594688416\r"
     ]
    },
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<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",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>best_val_acc</td><td>▁▁▂▂▂▂▃▅▆█</td></tr><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>lr</td><td>▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_acc</td><td>▁▁▂▃▄▄▅▆▇█</td></tr><tr><td>train_loss</td><td>█▇▆▅▅▄▃▃▂▁</td></tr><tr><td>val_acc</td><td>▁▁▂▂▂▂▃▅▆█</td></tr><tr><td>val_loss</td><td>█▇▆▆▅▄▄▃▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>best_score</td><td>0.248</td></tr><tr><td>best_val_acc</td><td>0.248</td></tr><tr><td>epoch</td><td>10.0</td></tr><tr><td>lr</td><td>0.00219</td></tr><tr><td>train_acc</td><td>0.18617</td></tr><tr><td>train_loss</td><td>2.27504</td></tr><tr><td>val_acc</td><td>0.248</td></tr><tr><td>val_loss</td><td>2.27004</td></tr></table><br/></div></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">2023-02-07 14:42:30</strong> at: <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/t2en6p3w\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/runs/t2en6p3w</a><br/>Synced 6 W&B file(s), 2 media file(s), 1 artifact file(s) and 2 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>./wandb/run-20230207_144233-t2en6p3w/logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: f4a284vy with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 176\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tckpt_path: checkpoint.pt\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tdropout_p: 0.0477474801587983\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \thidden_layer_width: 96\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tlr: 0.0010393795785333295\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \toptim_type: AdamW\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tproject_name: mnist_torchkeras\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Ignored wandb.init() arg project when running a sweep.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.13.9"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/Users/liangyun2/CodeFiles/torchkeras/wandb/run-20230207_144426-f4a284vy</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/f4a284vy\" target=\"_blank\">2023-02-07 14:44:24</a></strong> to <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>Sweep page: <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View sweep at <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ne3wz8k3</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       " View run at <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/f4a284vy\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/runs/f4a284vy</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch【10】@2023-02-07 14:46:08 --> best_val_acc = 0.9652000069618225\r"
     ]
    },
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<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",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>best_val_acc</td><td>▁▆▇███████</td></tr><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>lr</td><td>▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_acc</td><td>▁▆▇▇██████</td></tr><tr><td>train_loss</td><td>█▃▂▂▁▁▁▁▁▁</td></tr><tr><td>val_acc</td><td>▁▆▇███████</td></tr><tr><td>val_loss</td><td>█▃▂▂▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>best_score</td><td>0.9652</td></tr><tr><td>best_val_acc</td><td>0.9652</td></tr><tr><td>epoch</td><td>10.0</td></tr><tr><td>lr</td><td>0.00104</td></tr><tr><td>train_acc</td><td>0.97483</td></tr><tr><td>train_loss</td><td>0.07928</td></tr><tr><td>val_acc</td><td>0.9652</td></tr><tr><td>val_loss</td><td>0.11169</td></tr></table><br/></div></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">2023-02-07 14:44:24</strong> at: <a href=\"https://wandb.ai/lyhue1991/mnist_torchkeras/runs/f4a284vy\" target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/runs/f4a284vy</a><br/>Synced 6 W&B file(s), 2 media file(s), 1 artifact file(s) and 2 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>./wandb/run-20230207_144426-f4a284vy/logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try 5 times\n",
    "wandb.agent(sweep_id, train, count=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a413621-859d-4c83-95a2-9146362e5c59",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "46479e12-1c56-47f1-b36e-edee38912b72",
   "metadata": {},
   "source": [
    "## 5, load the best checkpoint \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "43fd8747-377b-424d-be4f-d547f90f88a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import wandb\n",
    "api = wandb.Api()\n",
    "    \n",
    "entity, project = \"lyhue1991\", \"mnist_torchkeras\"  # set to your entity and project \n",
    "\n",
    "def get_best_run_id(entity,project,metric_mode='max'):\n",
    "    runs = api.runs(entity + \"/\" + project) \n",
    "    data = []\n",
    "    for run in runs:\n",
    "        run_info = {'run_id':run.id,'run_name':run.name,'best_score':run.summary['best_score']}\n",
    "        data.append(run_info)\n",
    "    dfruns = pd.DataFrame(data) \n",
    "    dfruns.sort_values(by = 'best_score', \n",
    "                   ascending= False if metric_mode=='max' else True,\n",
    "                   inplace = True)\n",
    "    best_run_id = dfruns['run_id'].iloc[0]\n",
    "    return best_run_id \n",
    "\n",
    "def get_model_versions(entity,project):\n",
    "    model_versions = api.artifact_versions('model',\n",
    "        name = entity + \"/\" + project+'/' + 'checkpoint')\n",
    "    return [x.version for x in model_versions]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "171dd497-eec3-4477-b90a-008c1fa9e024",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2a61qmcd\n"
     ]
    }
   ],
   "source": [
    "best_run_id = get_best_run_id(entity,project)\n",
    "print(best_run_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "254c492f-9384-4959-8556-499d3430a1df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['v32', 'v31', 'v30', 'v29', 'v28', 'v27', 'v26', 'v20', 'v14', 'v13', 'v9', 'v3', 'v2', 'v1', 'v0']\n"
     ]
    }
   ],
   "source": [
    "model_versions = get_model_versions(entity,project)\n",
    "print(model_versions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "57c4f304-1aa3-4744-88be-83c374da8ff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run = api.run(path= entity + \"/\" + project+'/'+ best_run_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e9ad3402-3596-46b4-82c6-615f9e347c4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'best_ckpt'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model_arti = None\n",
    "for arti in best_run.logged_artifacts():\n",
    "    if arti.type=='model':\n",
    "        best_model_arti = arti\n",
    "\n",
    "print(best_model_arti.version) \n",
    "\n",
    "best_model_arti.download(root='best_ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4229ccce",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

一键复制编辑原始数据按行查看历史

提交于 2023-05-12 18:35 . update 3.8.4

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