03，kerasmodel_tuning_demo.ipynb · 算法美食屋/torchkeras

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03，kerasmodel_tuning_demo.ipynb 278.87 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": null,
   "id": "1cd4c020-883c-4d2f-8ac1-4a90135f0ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install wandb "
   ]
  },
  {
   "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": [
      "3.9.3\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'\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": 10,
   "id": "54bcc553",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31m<<<<<< 🚀 mps is used >>>>>>\u001b[0m\n"
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       "      <br>\n",
       "      ████████████████████100.00% [20/20] [val_loss=1.6845, val_acc=0.7016]\n",
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       "Run data is saved locally in <code>/Users/liangyun2/CodeFiles/torchkeras/wandb/run-20230802_100417-cy886ype</code>"
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       "Syncing run <strong><a href='https://wandb.ai/lyhue1991/mnist_torchkeras/runs/cy886ype' target=\"_blank\">2023-08-02 10:04:17</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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     "text": [
      "wandb: WARNING Source type is set to 'repo' but some required information is missing from the environment. A job will not be created from this run. See https://docs.wandb.ai/guides/launch/create-job\n"
     ]
    },
    {
     "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",
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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>0.7016</td></tr><tr><td>best_val_acc</td><td>0.7016</td></tr><tr><td>epoch</td><td>10.0</td></tr><tr><td>lr</td><td>0.0001</td></tr><tr><td>train_acc</td><td>0.57617</td></tr><tr><td>train_loss</td><td>1.75727</td></tr><tr><td>val_acc</td><td>0.7016</td></tr><tr><td>val_loss</td><td>1.68454</td></tr></table><br/></div></div>"
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       " View run <strong style=\"color:#cdcd00\">2023-08-02 10:04:17</strong> at: <a href='https://wandb.ai/lyhue1991/mnist_torchkeras/runs/cy886ype' target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/runs/cy886ype</a><br/>Synced 6 W&B file(s), 0 media file(s), 1 artifact file(s) and 2 other file(s)"
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   ],
   "source": [
    "from torchkeras.kerascallbacks import WandbCallback\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": 15,
   "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": 16,
   "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": 17,
   "id": "c3cb80a5-7427-42fd-b964-33e423e8ada4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create sweep with ID: ad8y5buz\n",
      "Sweep URL: https://wandb.ai/lyhue1991/mnist_torchkeras/sweeps/ad8y5buz\n"
     ]
    }
   ],
   "source": [
    "sweep_id = wandb.sweep(sweep_config, project='mnist_torchkeras')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "262140c7-8030-470c-841e-ac9616b2a13e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchkeras.kerascallbacks import WandbCallback\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",
    "        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]) \n",
    "        return model\n",
    "    "
   ]
  },
  {
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   "execution_count": 19,
   "id": "e620f434-7176-4226-b9cd-dc24ea9a29c3",
   "metadata": {},
   "outputs": [
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      "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: r9w1exdd with config:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 40\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tckpt_path: checkpoint.pt\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \tdropout_p: 0.32243880949259884\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: 0.0902581402613245\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"
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      "\u001b[0;31m<<<<<< 🚀 mps is used >>>>>>\u001b[0m\n"
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       "<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>0.929</td></tr><tr><td>best_val_acc</td><td>0.929</td></tr><tr><td>epoch</td><td>10.0</td></tr><tr><td>lr</td><td>0.09026</td></tr><tr><td>train_acc</td><td>0.87183</td></tr><tr><td>train_loss</td><td>0.39538</td></tr><tr><td>val_acc</td><td>0.929</td></tr><tr><td>val_loss</td><td>0.23253</td></tr></table><br/></div></div>"
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      "wandb: WARNING Source type is set to 'repo' but some required information is missing from the environment. A job will not be created from this run. See https://docs.wandb.ai/guides/launch/create-job\n"
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       "<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>0.1108</td></tr><tr><td>best_val_acc</td><td>0.1108</td></tr><tr><td>epoch</td><td>10.0</td></tr><tr><td>lr</td><td>5e-05</td></tr><tr><td>train_acc</td><td>0.10917</td></tr><tr><td>train_loss</td><td>2.30389</td></tr><tr><td>val_acc</td><td>0.1108</td></tr><tr><td>val_loss</td><td>2.30594</td></tr></table><br/></div></div>"
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       " View run <strong style=\"color:#cdcd00\">2023-08-02 10:11:21</strong> at: <a href='https://wandb.ai/lyhue1991/mnist_torchkeras/runs/irjh3ia1' target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/runs/irjh3ia1</a><br/>Synced 6 W&B file(s), 0 media file(s), 1 artifact file(s) and 1 other file(s)"
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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>0.2972</td></tr><tr><td>best_val_acc</td><td>0.2972</td></tr><tr><td>epoch</td><td>10.0</td></tr><tr><td>lr</td><td>1e-05</td></tr><tr><td>train_acc</td><td>0.235</td></tr><tr><td>train_loss</td><td>2.24357</td></tr><tr><td>val_acc</td><td>0.2972</td></tr><tr><td>val_loss</td><td>2.23061</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\">2023-08-02 10:12:56</strong> at: <a href='https://wandb.ai/lyhue1991/mnist_torchkeras/runs/lyuf94na' target=\"_blank\">https://wandb.ai/lyhue1991/mnist_torchkeras/runs/lyuf94na</a><br/>Synced 6 W&B file(s), 0 media file(s), 1 artifact file(s) and 1 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>./wandb/run-20230802_101258-lyuf94na/logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try 3 times\n",
    "wandb.agent(sweep_id, train, count=3)"
   ]
  },
  {
   "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": [
      "['v49', 'v48', 'v47', 'v46', 'v45', 'v44', 'v43', 'v42', 'v41', 'v40', 'v39', 'v38', 'v37', 'v36', 'v35', 'v34', 'v33', '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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

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提交于 2024-09-04 10:37 +08:00 . update 4.0.0

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