{
 "cells": [
  {
   "metadata": {},
   "cell_type": "code",
   "source": "!pip install tensorboard",
   "id": "add52c783768e27a",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-20T09:23:44.953818Z",
     "start_time": "2025-06-20T09:23:44.941696Z"
    }
   },
   "cell_type": "code",
   "source": [
    "%load_ext tensorboard\n",
    "%tensorboard --logdir runs --host 0.0.0.0 --port 6006"
   ],
   "id": "6d148c29e2c0fbbe",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tensorboard extension is already loaded. To reload it, use:\n",
      "  %reload_ext tensorboard\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 6006 (pid 414), started 0:24:42 ago. (Use '!kill 414' to kill it.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-42c02cf9641fd4a7\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-42c02cf9641fd4a7\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-20T09:23:36.713794Z",
     "start_time": "2025-06-20T09:23:32.977971Z"
    }
   },
   "source": [
    "from torch.utils.tensorboard import SummaryWriter\n",
    "import numpy as np\n",
    "\n",
    "# 创建一个SummaryWriter对象\n",
    "writer = SummaryWriter()\n",
    "\n",
    "for n_iter in range(100):\n",
    "    writer.add_scalar('Loss/train', np.random.random(), n_iter)\n",
    "    writer.add_scalar('Loss/test', np.random.random(), n_iter)\n",
    "    writer.add_scalar('Accuracy/train', np.random.random(), n_iter)\n",
    "    writer.add_scalar('Accuracy/test', np.random.random(), n_iter)\n",
    "\n",
    "img = np.zeros((3, 100, 100))\n",
    "img[0] = np.arange(0, 10000).reshape(100, 100) / 10000\n",
    "img[1] = np.arange(0, 10000).reshape(100, 100) / 10000\n",
    "\n",
    "writer.add_image('my_image', img, 0)\n",
    "writer.close()"
   ],
   "outputs": [],
   "execution_count": 11
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}