adaptive_federated_learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用Adaptive Optimization "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了调参，需要指定一个实验的名称`EXP_NAME`，表明不同的Adaptive优化器的名称以及参数。对应的`logs`, `models`, `eval`的会有一个相同的文件夹保存对应的文件\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "DOCKER_NAME='nvcr.io/nvidia/clara-train-sdk:v3.0'\n",
    "# 这个是物理机的数据集的路径\n",
    "DATASET_PREFIX='/home/liuyuan/shu_codes/datasets/brats'\n",
    "# RESNET 似乎不使用 pretrain\n",
    "PRETRAIN_PATH='/home/liuyuan/shu_codes/pretrain_models/for_clara'\n",
    "\n",
    "# FedAvg:\n",
    "# EXP_NAME='fedavg_server_lr_1.0_client_lr_0.0001'\n",
    "# FedAvgM\n",
    "# EXP_NAME='fedavgm_server_lr_1.0_client_lr_0.0001_moment_0.9'\n",
    "## FedAdam\n",
    "EXP_NAME='fedadam_server_lr_1.0_client_lr_0.0001_eps_0.01'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please enter the machine IP of local machine here (for tensorboard)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MMAR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Clara, we provide [MMAR (Model archive)](https://docs.nvidia.com/clara/tlt-mi/clara-train-sdk-v3.0/nvmidl/mmar.html) that contains everything necessary to get started. \n",
    "\n",
    "这个是为了确定使用那一个配置文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "MMAR_FOLDER=\"clara_seg_ct_brats_adaptive_fl\"\n",
    "\n",
    "!mkdir -p {MMAR_FOLDER}/logs/{EXP_NAME}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the folder structure and the files that are important for the FL setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 80K\n",
      "drwxr-xr-x 11 liuyuan liuyuan 4.0K Oct 10 09:56 .\n",
      "drwxrwxr-x 12 liuyuan liuyuan 4.0K Oct 10 10:22 ..\n",
      "drwxrwxr-x  3 liuyuan liuyuan 4.0K Oct  3 00:28 commands\n",
      "drwxrwxr-x  4 liuyuan liuyuan 4.0K Oct  2 23:57 components\n",
      "drwxrwxr-x  4 liuyuan liuyuan 4.0K Oct 10 10:29 config\n",
      "drwxrwxr-x  3 liuyuan liuyuan 4.0K Oct  9 22:05 eval\n",
      "drwxrwxr-x  2 liuyuan liuyuan 4.0K Oct  2 23:56 .ipynb_checkpoints\n",
      "drwxrwxr-x  5 liuyuan liuyuan 4.0K Oct 10 10:08 logs\n",
      "drwxrwxr-x  3 liuyuan liuyuan  40K Oct  9 22:05 models\n",
      "drwxrwxr-x  3 liuyuan liuyuan 4.0K Oct  3 00:12 resources\n",
      "drwxrwxr-x  3 liuyuan liuyuan 4.0K Oct 10 09:56 submit_brats_2020_validation\n"
     ]
    }
   ],
   "source": [
    "!ls -alh {MMAR_FOLDER}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tensorboard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During training, we want to check the training progress. We will do this with tensorboard and both clients are configured to write tensorboard logs to the same file folder. The following commands starts a separate docker instance to run tensorboard, and provide a URL for accessing it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash --bg\n",
    "docker stop tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47a63c8820f65936f0e273127d6a49c341d82b6106a40f38671ba4abb9414a9d\n"
     ]
    }
   ],
   "source": [
    "%%bash -s \"$MMAR_FOLDER\" \"$DOCKER_NAME\" \"$DATASET_PREFIX\" \"$EXP_NAME\"\n",
    "docker run --runtime=nvidia -dt --name tensorboard --rm --shm-size=10g --ulimit memlock=-1 --ulimit stack=67108864 \\\n",
    "    -v $PWD:/mmar \\\n",
    "    -v $3:/workspace/data \\\n",
    "    -p 19020:19020 \\\n",
    "    $2 \\\n",
    "    bash -c \"tensorboard --host 0.0.0.0 --port 19020 --logdir /mmar/$1/models/$4/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After executing the cell (it may take a while if you are pulling the container for the first time), you can click on the link to open tensorboard in a new tab."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we know how MMAR is structured and tensorboard is running, we can configure the server and the clients for FL. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Launch Server"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following diagram shows the server workflow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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x4sXt27eXB1vFxsa2adOGEBIQENC9e3dvb++8vLyzZ8+uXLly4cKFRUVFU6ZMKX+qRPlASg5P0/tJq9XS9MrT03PAgAG+vr537tw5c+ZMfn4+HepVr169GTNmXL9+/cCBA3qbsrKyIoQMHDhQqVR+8cUXgYGBHMcdOnRo/fr1Z86c2bVr15gxYwghHMfFx8eHhoYSQnx9fadMmeLm5nbjxo3Zs2enpqZ6eHiEhoba2NiUFeJwHHfp0iWaXn366afdunVzcXG5ffv2kiVLPv30U9rHjWXZFy9eDB48ODo62tXVtX///r6+vjzPx8TEbPtLSkoKrfnNMIyBgQEhZMOGDYmJiUOHDg0ODn7y5ElxcXGtWrXGjBlDZ9y7cOFC06ZNaasYhikoKFi8eDEhZMSIEXLB8h07dtA8JTQ0lF7N+/fvR0VFHT16dODAgVu2bOnYsSM9CrrHvLy8sLCwDh069OrVy8jI6MKFC40bNy4/i1y1atXXX39NCGnXrl1gYGCDBg3y8vJu37595syZkJAQehPyPB8bGxscHEwIadWqVVhYWOPGjVNSUk6cOLFhw4Zhw4YtX768X79+8jYJIfScdO7cOTg4uHr16rdu3Zo0aRIh5KuvvmrRooWFhYVCoWjfvr2rq+v27dtv375ta2s7YcIElUpVvXp12rOsWbNmM2fOPHXq1JkzZ3TvroyMjFGjRl29erVmzZoTJkwICAhITExcs2YNTdamTZtWrVq1snqciaJI77fExET64EydOlWpVNJOYaIoNmjQYPr06SkpKVu3brW3t3/t6Eue5xMTE1u3bk0ICQwMDAoKql+//osXL65evbpkyZIFCxbEx8dv27at1Mp3AAAAAAAAehBgweuNGTOGDnkTBIEOCqMvpTt27Pjf//6nVqvd3Ny+/fbbPXv26AVYzZs3t7e3X7x4cUFBQfPmzUNCQmgkIQgCzSC+/PLL1NTUa9euZWRkyCtKkmRjY7N///4aNWq4u7sLgsAwjFqt9vHxGTdu3IYNG0aOHGlpaVlW9EALA5VsD8MwsbGx9POCBQscHBwEQWjbtm337t3psEQ6wG316tWEkNDQ0NmzZzs6OtKhasHBwU2aNBk6dOiKFStatmzZoUOHdz+rDMM8f/6cfh40aNDEiRNVKlXLli179eqVnp4uiqIgCD4+Po0bN167dm3JAIsKCAiIjIz09/enaULLli0VCsXq1auPHz/epUsXd3d3tVpNO+O4urquXbvWw8NDEAQPDw8nJ6egoKCUlJT4+PjatWuX00i5592sWbN8fHzoSevcuTPLsnRwHMdx27dvpz2ndu3aVadOHXpx27dv36ZNm5CQkMLCwhUrVkyZMkWhUMhbTkxM/PLLL2fNmiV/Se8u+vncuXN+fn70M61lTq9dUFAQbdWNGzdoerVkyZL+/fvTUZYtWrTo2LHjjz/++PPPP/fv3z8xMdHR0VHeY1paWnBw8PLly2lRf9rXqazohOO45ORkml6FhYXNmDFD7nnXtWvXiIgIQRBouCOHa/b29suWLfPw8NBqtbVr127btq2RkdGKFStGjRrVrl073ZYQQhYuXBgeHm5mZiaKYmBgoIuLS1hY2PPnz8+cOdO9e3eO44KCgrp27RoXF3f79u1q1aqNHz9epVLRKQgJIU2bNm3durVKpdINsDiOO3z48NWrVwkhK1eubN68uVardXd3r1u3rre3N91pixYtyrrWgiDUr1/fz89v9+7d9MEZOXKklZUV7exG49QmTZocPHhw69atZW1ERp/Zrl27EkI6duy4ZMkSubNVt27dfHx8IiIijh49evjw4W7dupWVnwIAAAAAAMjY990A+A8YNGiQhYWFSqXSarXFxcUNGjRo1KgRIeTq1as0NxFFUa1Wl3wL1f5F97NWq5VTA41Go9Fo9AY00V4tbdq0cXFxUavVtMuJUqn89NNPabej1NTU8nt/lNUejuPoh0ePHhFCBEFQq9U1atSgPao4jvvzzz+3b99OCPnf//7n6Oio1WppkEQI6dKlC+3TdOHCheLi4ooU/34t2jOIEEJDItpsU1NTb29vulPaYa2c1/vAwEB/f3+6mEajMTAwoN2OYmNj8/PzWZZ9/vx5QkICIWTEiBFOTk40idBqtTVq1AgPDyeE0LJT5RyOfLHy8/MlSRIEQaVSOTs703SPZdnk5OQpU6YQQrZu3VqvXj160kRRVKlUjRo1ooHgzZs3MzMz5Q5oVEREBMdxmr9IkmRsbPzTTz8RQq5cuZKenk6Xp0Pb6Cp169YVRVGr1dLN0sGtBgYGNBLVarXW1tYDBw6kC2dkZOgd16RJk+zs7Oi9oVaryy/7dfjwYfrnmDFjHB0d6f2v1WpVKpWlpSWtfM9xXExMTFRUFCFk6dKlbm5uKpWKXg4TE5OIiAi6hf3798v3HiHEycmpW7duxsbGtCUqlaphw4b0p8LCQrl3Hr1Y8m2geyfQb/QyXJZlafDUqFEjPz8/tVpN73A3N7e2bdsSQu7fv69Sqcq60OSvJ0Lei0ajUavVco8tvV/Lx3HciRMnHj9+TAj5/vvvHR0d6ZNO47CQkJAvvvhCbtLf8jQBAAAAAMCHDQEWvJ6BgYFuxsTzPB0Odvr06X9up3T2vaysrISEhO3bty9evDgnJ4d2Y7l27ZpeFFIRgiD06tWLfm7duvWJEyfy8/Np5SAaZLAsK1cOMjExefjwYeZfMjIycnNzaaGfAwcOlFP2+40O0NzcvHfv3oSQ+fPnL1y4MCsri2XZcibRK4nneb0RWEZGRoSQV69e0aiiqKjozp07hBAXFxfaY4ju2tjYmM5smJGRoVe0SJcoit7e3vXq1SOEfPLJJ0ePHqW5mO4MdzQgI4RUrVr1wYMHmToeP35MQ7qTJ0++ePFCN6cYOXKk3uyHtEqUr6+vi4vL0aNHaQU0lmUfPnxIA6zff/9dbj/NGd3c3HJzcx89eiTvMT093crKivYpO336tG5sRAih+dprzyrDME+fPqVdmWbPnu3m5qa3lu4MejR8tLGx8fT01F1GEAR6hkmJyFWhUOg9UyzL0t5n79gX6dq1a4QQa2trhUIht5B2rSKEFBQU5Obm/jtpEcuyiYmJhBBfX19RFNPT03VvjPz8fC8vL0JIbGzss2fPEGABAAAAAMBrYQghvF7Jjirm5ub/6B5Zls3Kyjpz5oxuCXMZndbwTYmi6OzsvHnz5gEDBhBCunbtGhoaOnTo0Hr16hkbG9MhhPfu3aMLu7m5lbWdhw8f/i0le2ifr2nTphUUFPzxxx9Tpkz57rvvNm3a1KRJEycnp7cOMnRLiRFClEplzZo14+Pj4+Pj27VrR6uJMwyTn59P8wUfHx8azJVKFEV3d/f58+fTsXs9e/YMDw/v1KlTu3btlEqlPO6SLkwHlpZF74iqVatmaGhYcnc1atTo2bPnkiVLDh065OfnZ25ufuXKFa1W26JFC09PTzqi8+XLl3T5OXPmzJkzp6w96t0ndMBjBaPPoqKiK1eu0HbSIy25DJ09k3YyatCgAa0Zr7uAJEkBAQEXLlwoLCyk0wvI3+styXGcjY0NIYT2X6tIC0vl4eGRkpKSlZVVWFjIcRzdC+0MRQixsrKytbX9W+LX8jEMU1RUlJeXRwi5du0a7bBZqqdPn/4L7QEAAAAAgA8AAix4G/9ojwmWZXNzc0eMGPHnn38SQnr37h0YGFi9evVHjx6NHj1ab3K6NyJJUseOHS9dujR58uSzZ8/u2LFjx44dAwcOnD59Oi1k/uzZM0KIubl5u3btSo2QaHkjOYl4R6IoVq1adfXq1cuWLVuwYIEkSQMGDGjevPn//ve/gICAt8uwdC+NJEl2dnbNmjXbt2/f/Pnze/ToUadOHRpq0HrnhBAPDw+a35W1QY1G07Rp0ytXrixdulQuyj5ixIjPP/+cFuMvKioihPA8365dO1qLquRhWltbW1tb6/6kUChK3kWSJBkZGbVq1WrJkiXr1q2bPHmyKIq0utaQIUPkUXspKSl0eX9/f1rLrGSzCwoK6KSWcmJVkbrj8jnUaDQ0/zIwMChnLZVKRYej2traygNCdTk5ORFC8vPzCwoKLC0tS92I3AGQvNuTJQjC//73v4EDB96+ffvEiRMhISE0rExMTIyPjyeENGvW7N/p68QwzLNnzx48eEAIcXd39/b2LrVSO721LCwsUMQdAAAAAABeCwEWVDosyw4bNoymV6dPn27SpAntsaJUKletWhUTE/PWW6bvyd7e3gcPHrxy5cqmTZs2b968adOmc+fOXb582czMzMLCghCSl5e3a9eusqoF0bGNf9crtyRJZmZm06dPHzp06MGDB7/55puLFy8GBwefOXOmYcOG5eRKFdw4x3Gffvrp4sWLs7KymjVrtmHDBg8Pj5iYGDqxYHh4eKtWrV7b5UcURS8vr3Xr1kVERGzbtm3dunWrV68+cuTI6dOn7ezsaEcqrVa7fPnyqlWrlro1Wn1cb8BgqfvSaDS0txchZMeOHUFBQcuXL2/YsGGjRo1YlhUEQZIkeWje0KFDBw0aVFbSR6t9yblSxS8ZrcImt6esFWnc5u7uTgh5/PhxqTfMzZs3CSHW1tavjWne/Y4SBKF169ahoaE7duzo27fvtGnTgoKCEhMTaSkuBweHKVOm/Dvl0iVJsrS0pOGdr6/v8uXLTU1NSz1AWrkMARYAAAAAALwWamDBP+6NhkSxLPvo0aPz588TQhYtWtS0aVNaRprWz6aBzjv2IqG5hr+//6xZs3r06EEIuX///uXLl1mWlUcOqtVqVRnKKf5Nyc0ruVip49ckSVKr1c7OzhERETt27KBfXr58+R3TK0oQhJo1a+7du5fW/xoyZEiLFi1oetWnT5/Jkyebm5tXJD4QBKG4uNjX1zcyMnL8+PGEkAcPHly/fp1lWRMTE7pMcXFxWeeNFuOvYJtFUVyxYgUhZNeuXefOnSOENG/evEaNGvSESJJES7ARQu7evUuLypfqXcIapVL5ySefEEIePnxYTs1+AwMDOvTvxo0bGo1GbzGGYZKSkujW/oWuT5IkWVhYzJ49m87POHPmzICAAJpeBQQE7N69m04p+M81gGEYepiSJJmYmFhbWxNCdu7cWc7T9DdmwQAAAAAA8GFDgAX/LJZl6Rv+a3Mfefnk5GTameXTTz+V64vTUV1yhCEvL+cCWq22IhmBJEk0RdJoNDY2Nt988w39Pjs7W5KkNm3a0D937tzJ87zuBml59Ypsv0qVKvTz/fv35SLi9N1erneud8gMwwiCwHFckyZNAgMDCSEPHjzIy8v7W1IPtVptYGBQVFTUqFGj8PDwoUOHTp8+/fz58z///HO1atVeG5PRovL0QLRarYWFBZ2+kBBCB/fRLkiEkJkzZ+qdJYZhKnjedAmCQC/EjRs3xo0bRwgZOHCg3kWnJc/Pnj2bnJysO6KTYRiO497xvEmSZGNj06RJE0LIlClTHj16pDdoVDeIpL32cnNz09LSdJfhef7s2bP0c926df+dmIZOmilJkpeXV69evYYMGTJlypSoqKj9+/f7+Pj8QzuVD+358+e637u4uNAPJ0+e1BsxyrLsW8zDAAAAAAAAHzO8QsA/y9TU1NfXlxCyb9++7OxsQ0NDOj9aWREDLdtEP9++fdvQ0JDneQMDA5VKtXnz5lu3bhFC7t69S99+6bx1Dg4OhJArV668ePFCoVCU1RIaEu3ZsyctLU2hUCgUCp7nY2Nj5XZqtdrOnTv37NmTEPLLL79cuHCBEKL4y+PHj0+cOPHat25RFKtVq0Yzu8jIyIKCAgMDAwMDA0EQLl68OGnSJN32SJJ09uzZy5cvi6JIF8vLy6NzOzo5Ock9m94Fx3HXr19v3Lhx9erVt2zZsnbt2unTp/fs2dPY2Pjp06dynFcWhmFUKtWhQ4fu3bvHsqxCoeA4jvYqIoTQXlENGzacMWMGIWTXrl2HDh0qLCw0MDCgE+29evXqzz//1Jt/8LVoCDh//nz65x9Z7YsAACAASURBVCeffOLh4SF3HaLj++ger1y5snXr1rS0NHpf0TLqCQkJ169fL+dOqEgDTExMOnfuTP+cPHny/fv3eZ6ndwLHcTdu3IiLi5MkSRCEJk2a9OvXjxAyZsyY1NRUpVJJF3v06NFPP/1Et9C9e/d/YewenfrAy8srKipq586dGzZsmD17dr9+/VxdXXNycjQajd6cjH+jgIAAQsj58+fv3LlDsyqtVtutW7fq1asTQgYNGhQXFycIAr0xDA0NMzIyzpw5U7LPGgAAAAAAQFlQAwv+QaIoVqlSpVGjRtu2bdu3b5+9vX2DBg1UKtXTp0+HDx9uZmZW6ipeXl61a9dOTEwMDQ1dvXq1g4PD06dP4+Lili1bRpe5dOmSPMmara1tt27dVq1aNW/evGfPntEJ8koNC1iWjY+P/+yzz1q0aNGpUyczM7NXr17JiVL9+vUFQTA2Nh4+fPiePXvOnTvXuXPnb7/91sHBgWXZFy9eXLt2be/evQcOHGjTpk05YQSt/jNw4MDFixffuHHj+++/pxWs09LS5s6dW3Lh7t27E0K+++47R0dHrVZLSyYRQpydnZVK5btP0MayLC15np6evm/fvoKCgrS0tKSkJLVa7e7u3qxZs+7du5cz6SHLsgkJCYMHD/bz8wsJCXFwcMjNzT106BD91d/fn3bgCgsLi4yMpB++/PLLmjVrmpiY5Ofn37t37+effx4/fvzMmTPlznSvReud1a1bl/45duxYOoGg7gJ169adOXPmtGnT5s+ff/369ZCQEDMzM5VKlZ2dvWbNGmdn5x07dtjb27/1iDmtVuvj4zNlypQ5c+YcP3585MiRISEhVlZWkiQ9e/YsMjKyYcOGO3futLW1NTIyioiI2Lp1a3p6+sSJE7t27WpjY/PkyZMzZ87QLHLHjh3Gxsb/6Ng9GT3JHMcdOHBAo9HQa52Tk1OnTh3aJ6t27dp/+6x/giBERERcuHAhPz//66+/btOmzRdffMGyrKGh4fr169u2bUsI6dat2/Dhw52cnHiez8/PP3PmzOHDh/fv3x8YGPjvlOUCAAAAAID/OgRYQIjOICDdlEF+sSw5+ikvL6/kRspaPjw8PDk5ec2aNatWrZK/DAwMbNy4sSRJoig+fPiQECIPD+Q4bu/evXQWwhEjRsirLFu2zN/f38/PjxCyYMGC7777TqPRWFhYDBs2LD09/fDhw7/88outrW379u1LbQ/Lsrdv3yaEnD9/ntbYopo2bTpjxgxXV1f6Yh8QEHDx4sUff/xx//79s2bN0j0QOqtdOadR3u/XX3/99OnTzZs3L1++XP5+7Nix/v7+ffv2pU1iGCYjI6Nu3brx8fHff/+97hYiIyP79OkjXws5cdA9sXfu3CGElCwcLg8JpB8EQfD392/VqtWBAwcOHDhAf/L09ExLS7t169aBAweOHDmyZs2acrKewsJCCwuLq1evXr16Vf6yfv36c+fOdXV1pWvZ29snJydv3rx51qxZP//8s+7qTZo0MTY2llsuSVJBQYHuQZVKEISmTZuGhYXl5uZ6enrq/UpvkrFjx1apUmXVqlWnTp06deqU7gL9+vXTna2ysLCQEPL48eM3GscnCMLEiRPr168/b968ixcvXrx4UfdXDw8PeoG0Wm2jRo0uXrw4a9asP/74Q7clDRs2/P777wMCAuSLQld5+PChXkskSXr58iX5vw8gIYR+KfcT1KM3I6ckSebm5uHh4du2bZs6dSr9kpZ1o1d/3rx5MTEx7u7u5d/G5SdK9FgSExPlQxAEoX379tOnT58+fTq9Fp9//jnthOXr63vhwoUVK1Zs3bp1zpw5utsJCgqqeKYJAAAAAADAfGwFdDMzMwMDA9esWfPuU7x9MBiGKS4uvnTpEsdxdnZ23t7egiCwLPvgwYO0tDRRFBs3bmxmZibfKhzHXblypaioSJKktm3b0tddlmXv3buXnp4uSVKjRo10l2dZNi8v7+rVqwkJCSqVyt3dvUqVKp6eno6OjpIk5ebmJiYmqlQqDw8PZ2dnOWzKysq6fft2dHR0lSpVPDw8bG1tvby8GIaJj4+Pjo62sLAIDQ2lu+Z5PiMjIzEx8eHDh1ZWVnS4VlntuXXrVk5Ozr179zIzMx0dHT09Pd3d3eUghuJ5/unTp4mJiU+fPo2Pj3dxcXF3d1coFK6uri4uLhXpwMKybH5+fkJCQnJy8v3796tVq1a3bl0fH5+nT58mJydLktSwYUMLCwuGYdLT0x8+fPjo0aO7d+8aGhrWrl3b2dm5bt26LMvKp+LOnTu0RJe/vz+d8k+tVl+7dk2j0VSpUqVu3bryncyybHZ29p07dyRJqlWrlqOjoyiKCoXi7t27jx49ogsYGhqamJjQ3l5ffvklIWTXrl1BQUFlxRaSJNHVHz16dP/+fScnp2rVqnl6erq5udHZIeVdC4Jw5cqVgoKC+Ph4IyOjWrVqGRkZ2dvbu7u708UYhikqKkpISCgoKLC3t/fw8ChnACPP8ydPnlSr1UFBQaUGLrSsWEpKCj3kp0+fent729jYGBoa1q1bV6lU0tNCB1Hm5+ezLNu8efM3GrNGK2qlpKQ8evQoKysrKSmJ3gMWFha1a9c2MzOTzzy9CVNTU58+fXrz5s3atWs7ODg4Ojp6eXnJMQ3Hcbdv337y5AnP876+voaGhrqn5datW4WFhQ4ODrVq1aLfyw8az/MtWrTQ+/eK47i4uLgXL15IktSyZUt6XCzL5uTkpKSkCILAMIyBgYGpqSnDMHfv3v3yyy/z8vKWLFkydOjQciIq+uA8fPhQFMVmzZoplUrd/46gfQnT0tJ4nvfx8ZGnVmRZlh7CgwcPkpKSJk2axPM8/Ynn+WfPnt26devFixc3b96sXr26i4uLUqmsXr26g4MD/hGWMQyTl5fXu3fvAQMGjBo16n03BwAAAACg0kGABYT89aJOCKFlfeiXcqFlWhZad3m5Trbum3A5y9Osobi4WJIkAwMD+nJLUwm5zrcoiro5Bd1UUVERx3E0tRFFkXa9UavVgiDI7//krzroet+X2h56mHSKOp7n6cIl8xF6QrRabXFxMa3aQ8Oaig8Eo1ug86zRLdBMQa9JtJFqtVqtVjMMo1Qq6XA5vdRAby35pOleL72F5fNJWyKPwpO3QAhp2bJlXFzcV1999cMPP5TTHYaeNNpIWmqKlDa5JN2RKIpFRUU0KaM71V2ynJaXREuS08pWZS1Dj7e4uJheevnW0l2F3q4V2WM5u5APn9Z40tuFvBi9Z2hNLr1j122J3uplPQXlt7zkY0i3Q0fXyvEiffSWLVs2e/bsyZMnjx492sjI6LWnlJT2IMu/lnUIWq1WrVbTB1b3J92nid4/b/Q0fQwQYAEAAAAAlA9DCIEQQiRJKtkpo+Trt6zU1+lylqfv0vS1Vu9tvKyXc7oppVKptztBEHiel/t36C6s932p7aGbojFEWQdCdE6IkZERed2gqnK2wHEcncCObqHkSzttJMuydEelno2SB1JOFlPqUUdFRdG+ZnQvdLG7d++mp6cTQgICAsqPEui+6OklpUVXuodM/rpqpbbkjVIkOYspZxm6F3pBS72NSdlXuYL0rlFZhy8fL42H3qglZZ2W8lte6q1y/vx5MzMzT09PucYcx3EPHz6k4xAdHBxMTEzK32w5D3I5v9JDoCFsyQGS8tP01jEiAAAAAAB85BBgwb/nLbr7lbpKWdup+Pb/iSXfcQv/UF9InucvXbo0cOBAQsj3339frVo1MzMzrVabnZ29Y8eO58+fE0Lq1atXkb4w/+ZJe9NN/Ts9SStVY0piWfb+/fs9e/ZUq9WTJ0/28vKysrISRTE/P3/Pnj2HDx8mhNSpU+efbkY5h/+xdfgFAAAAAIC/EQIsgA+ZRqNJT0+3tLTMzc397rvv9H5t3br1tGnTnJycMJjrA0ADrOrVqycnJ5ec8tLe3n7p0qV+fn7oAAUAAAAAAP9FCLAAPmQMw4SFhdWrVy8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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image(filename='figures/fl_server_workflow.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Configuration \n",
    "A federated server is responsible for:\n",
    "1. Initialising a global model at federated round 0\n",
    "1. Sharing the global model with all clients\n",
    "1. Synchronising model updates from multiple clients\n",
    "1. Updating the global model when sufficient model updates received\n",
    "\n",
    "The configurations for the server are specified in the \"config_fed_server.json\" file: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"servers\": [\n",
      "        {\n",
      "            \"name\": \"brats_segmentation\",\n",
      "            \"service\": {\n",
      "                \"target\": \"192.168.1.101:28004\",\n",
      "                \"options\": [\n",
      "                    [\"grpc.max_send_message_length\",    1000000000],\n",
      "                    [\"grpc.max_receive_message_length\", 1000000000]\n",
      "                ]\n",
      "            },\n",
      "            \"ssl_private_key\": \"resources/certs/server.key\",\n",
      "            \"ssl_cert\": \"resources/certs/server.crt\",\n",
      "            \"ssl_root_cert\": \"resources/certs/rootCA.pem\",\n",
      "            \"min_num_clients\": 2,\n",
      "            \"max_num_clients\": 100,\n",
      "            \"wait_after_min_clients\": 10,\n",
      "            \"heart_beat_timeout\": 60000,\n",
      "            \"start_round\": 0,\n",
      "            \"num_rounds\": 200,\n",
      "            \"exclude_vars\": \"dummy\",\n",
      "            \"num_server_workers\": 100\n",
      "        }\n",
      "    ],\n",
      "    \"aggregator\":\n",
      "      {\n",
      "        \"name\": \"AdaptiveAggregator\",\n",
      "        \"path\": \"adaptive_optimizers.aggregator.AdaptiveAggregator\",\n",
      "        \"args\": {\n",
      "          \"exclude_vars\": \"dummy\",\n",
      "          \"algo_optimizer\": \"fedadam\",\n",
      "          \"algo_kws\": {\n",
      "            \"epsilon\": 0.01,\n",
      "            \"lr\": 1.0\n",
      "        }\n",
      "        }\n",
      "      }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "! cat {MMAR_FOLDER}/config/config_fed_server/{EXP_NAME}.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the \"config_fed_server.json\" shown above, the server is configured to\n",
    "1. listen on port 8002 on the localhost (`target` set to `localhost:8002`)\n",
    "2. wait for at least 2 clients' update submission at each federated round (`min_num_clients` set to `2`)\n",
    "3. Wait 10 seconds after the min no of clients join before starting rounds (`wait_after_min_clients` is set to 10).\n",
    "4. `ssl_*` parameters are related to authentication.\n",
    "5. Client sends heartbeat every minutes. `heart_beat_timeout` is set to detect if client is killed.\n",
    "6. `aggregator` defines the type of aggregation. You can also point here to your own aggregator.\n",
    "\n",
    "<font color='red'>Note: In this tutorial, we'll be using insecure training so the `ssl_*` parameters are ignored.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**BEFORE YOU MOVE FORWARD, PLEASE REPLACE THE IP ADDRESS (localhost) IN \"target\": \"< IP_address > :8002\" with the IP ADDRESS OF YOUR MACHINE.**\n",
    "You can do this by navigating to the file explorer of this jupyter notebook and editing it there or overwriting the file here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Test the server \n",
    "\n",
    "Let's checkout `start_server_demo.sh` and start our server. We can start the server and run in the background:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "#!/usr/bin/env bash\n",
      "\n",
      "my_dir=\"$(dirname \"$0\")\"\n",
      ". $my_dir/set_env.sh\n",
      "\n",
      "echo \"MMAR_ROOT set to $MMAR_ROOT\"\n",
      "echo \"Experiment name is $1\"\n",
      "export CUDA_VISIBLE_DEVICES=\n",
      "\n",
      "# Data list containing all data\n",
      "CONFIG_FILE=config/config_train.json\n",
      "SERVER_FILE=config/config_fed_server/$1.json\n",
      "ENVIRONMENT_FILE=config/environment.json\n",
      "\n",
      "python3 -u  -m nvmidl.apps.fed_learn.server.fed_aggregate \\\n",
      "    -m $MMAR_ROOT \\\n",
      "    -c $CONFIG_FILE \\\n",
      "    -e $ENVIRONMENT_FILE \\\n",
      "    -s $SERVER_FILE \\\n",
      "    --set \\\n",
      "    DATASET_JSON=$MMAR_ROOT/config/fl_dataset_0.json \\\n",
      "    MMAR_CKPT_DIR=\"models/$1\" \\\n",
      "    secure_train=false\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!cat {MMAR_FOLDER}/commands/start_server_demo.sh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use `start_server_demo.sh` to start the container for the server (here named 'server_app'):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash --bg\n",
    "docker stop server_app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "508ebb68db8670590282d25c0a503bf53098fc6de37473e20d9979ccb859df32\n"
     ]
    }
   ],
   "source": [
    "%%bash -s \"$DOCKER_NAME\" \"$MMAR_FOLDER\" \"$PRETRAIN_PATH\" \"$DATASET_PREFIX\" \"$EXP_NAME\"\n",
    "docker run --name server_app --rm -dt --shm-size=10g --ulimit memlock=-1 --ulimit stack=67108864 \\\n",
    "-v $PWD:/mmar \\\n",
    "-v $3:/workspace/pretrain \\\n",
    "-v $4:/workspace/data \\\n",
    "--ipc=host --net=host \\\n",
    "$1 \\\n",
    "bash -c \"bash /mmar/$2/commands/start_server_demo.sh $5 | tee /mmar/$2/logs/$5/server_log.tmp\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Give it some time to start and then check the server's log:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replacing some pre-defined components...\n",
      "'/mmar/clara_seg_ct_brats_adaptive_fl/commands/../components/server_model_manager.py' -> '/opt/nvidia/medical/fed_learn/server/server_model_manager.py'\n",
      "'/mmar/clara_seg_ct_brats_adaptive_fl/commands/../components/fed_server.py' -> '/opt/nvidia/medical/fed_learn/server/fed_server.py'\n",
      "MMAR_ROOT set to /mmar/clara_seg_ct_brats_adaptive_fl/commands/..\n",
      "Experiment name is fedavgm_server_lr_1.0_client_lr_0.0001_moment_0.9\n",
      "2020-10-10 02:41:17,677 - __main__ - INFO - Starting aggregation server with config:\n",
      "2020-10-10 02:41:17,677 - __main__ - INFO - config/config_fed_server/fedavgm_server_lr_1.0_client_lr_0.0001_moment_0.9.json\n",
      "2020-10-10 02:41:18,045 - AdaptiveAggregator - INFO - optimizer select: fedavgm, initialized with parameters : {'momentum': 0.9}, server_lr: 1.0\n"
     ]
    }
   ],
   "source": [
    "!cat {MMAR_FOLDER}/logs/{EXP_NAME}/server_log.tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the output of the previous cell shows **FederatedServer - INFO - starting insecure server at < your_ip >:8002** it means that the configuration works fine and the server is up and running. \n",
    "If not, try to execute previous cell again. Maybe it was still in the process. Ready? Good, server is there, let's launch the clients.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Lauch Client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following diagram shows the client workflow:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Configuration\n",
    "A federated client will:\n",
    "1. download the global model\n",
    "1. train the model with local training data\n",
    "1. upload `delta_w` (the difference between the updated model and the global model) to the server"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's inspect the `config_fed_client.json`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"servers\": [\n",
      "        {\n",
      "            \"name\": \"brats_segmentation\",\n",
      "            \"service\": {\n",
      "                \"target\": \"192.168.1.101:28004\",\n",
      "                \"options\": [\n",
      "                    [\"grpc.max_send_message_length\",    1000000000],\n",
      "                    [\"grpc.max_receive_message_length\", 1000000000]\n",
      "                ]\n",
      "            }\n",
      "        }\n",
      "    ],\n",
      "    \"client\": {\n",
      "        \"local_epochs\": 1,\n",
      "        \"steps_aggregation\": 0,\n",
      "        \"exclude_vars\": \"dummy\",\n",
      "        \"privacy\": {\n",
      "        },\n",
      "        \"data_assembler\": {\n",
      "            \"name\": \"DataAssembler\"\n",
      "        },\n",
      "        \"retry_timeout\": 30,\n",
      "        \"ssl_private_key\": \"resources/certs/client1.key\",\n",
      "        \"ssl_cert\": \"resources/certs/client1.crt\",\n",
      "        \"ssl_root_cert\": \"resources/certs/rootCA.pem\"\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "! cat {MMAR_FOLDER}/config/config_fed_client.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the `config_fed_client.json` shown above, the client is configured to:\n",
    "* fetch global model from server at `localhost:8002` on local host (`target` set to `localhost:8002`)\n",
    "* run 5 local epochs before submitting the `delta_w` to the server (`local_epochs` set to `5`)\n",
    "* `privacy` can be used to define a custom privacy component. \n",
    "* `ssl_*` parameters are used for authentication.\n",
    "    * Since we are using insecure training, these parameters are not used. \n",
    "    * If we use secure training, we'll have two such configs with each containing the respective client's authentication files.\n",
    "    * For more details, please see [client configuration](https://docs.nvidia.com/clara/tlt-mi/clara-train-sdk-v3.0/nvmidl/additional_features/federated_learning.html#fl-client-configuration).\n",
    "\n",
    "**BEFORE YOU MOVE FORWARD, PLEASE REPLACE THE IP ADDRESS (localhost) IN \"target\": \"< IP_address > :8002\" with the IP ADDRESS OF YOUR MACHINE.**\n",
    "You can do this by navigating to the file explorer of this jupyter notebook and editing it there or overwriting the file here.\n",
    "\n",
    "The local training workflow is defined by `config/config_train.json`, similar to the not-FL Clara train workflow. \n",
    "\n",
    "In the next section, we'll start two federated training clients."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Test the client\n",
    "\n",
    "This mmar contains scripts to start two clients `client0_demo_train.sh` and `client1_demo_train.sh`. In other CLARA MMARs only one client training script is given (as clients are usually on different machines) and can be replicated many times.\n",
    "\n",
    "<font color='red'>Note: This MMAR uses _demo_ scripts that remove the requirement of secure training. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 这里服务端和客户端不要用 localhost!!! 这里使用的服务端的内网地址"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#!/usr/bin/env bash\n",
      "\n",
      "my_dir=\"$(dirname \"$0\")\"\n",
      ". $my_dir/set_env.sh\n",
      "\n",
      "echo \"MMAR_ROOT set to $MMAR_ROOT\"\n",
      "\n",
      "# Data list containing all data\n",
      "CONFIG_FILE=config/config_train.json\n",
      "CLIENT_FILE=config/config_fed_client.json\n",
      "ENVIRONMENT_FILE=config/environment.json\n",
      "\n",
      "python3 -u  -m nvmidl.apps.fed_learn.client.fed_local_train \\\n",
      "    -m $MMAR_ROOT \\\n",
      "    -c $CONFIG_FILE \\\n",
      "    -e $ENVIRONMENT_FILE \\\n",
      "    -s $CLIENT_FILE \\\n",
      "    --set \\\n",
      "    DATASET_JSON=$MMAR_ROOT/config/fl_dataset_0.json \\\n",
      "    DATA_LIST_KEY=validation \\\n",
      "    secure_train=false \\\n",
      "    uid=client0\n"
     ]
    }
   ],
   "source": [
    "!cat {MMAR_FOLDER}/commands/client0_demo_train.sh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run the two clients in separate dockers. If running on single machine, we should use separate GPU for each client. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash --bg\n",
    "docker stop client_0\n",
    "docker stop client_1\n",
    "docker stop client_2\n",
    "docker stop client_3\n",
    "docker stop client_4\n",
    "docker stop client_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9b07ea4f953029104b624d0c4cad52abdc8d1aaf8a327820ad96be7129ce8e25\n"
     ]
    }
   ],
   "source": [
    "%%bash -s \"$DOCKER_NAME\" \"$DATASET_PREFIX\" \"$MMAR_FOLDER\" \"$PRETRAIN_PATH\" \"$EXP_NAME\"\n",
    "docker run --runtime=nvidia --name client_0 --rm -dt --shm-size=20g --ulimit memlock=-1 --ulimit stack=67108864 \\\n",
    "    -e NVIDIA_VISIBLE_DEVICES=3 \\\n",
    "    -v $PWD:/mmar \\\n",
    "    -v $2:/workspace/data \\\n",
    "    -v $4:/workspace/pretrain \\\n",
    "    $1 \\\n",
    "    bash -c \"bash /mmar/$3/commands/client0_demo_train.sh  2>&1 | tee /mmar/$3/logs/$5/client_0_log.tmp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d051c161c847fc0dd398a7b4783d34a1b143767f12417b102a7d99c8b80a4e44\n"
     ]
    }
   ],
   "source": [
    "%%bash -s \"$DOCKER_NAME\" \"$DATASET_PREFIX\" \"$MMAR_FOLDER\" \"$PRETRAIN_PATH\" \"$EXP_NAME\"\n",
    "docker run --runtime=nvidia --name client_1 --rm -dt --shm-size=20g --ulimit memlock=-1 --ulimit stack=67108864 \\\n",
    "    -e NVIDIA_VISIBLE_DEVICES=4 \\\n",
    "    -v $PWD:/mmar \\\n",
    "    -v $2:/workspace/data \\\n",
    "    -v $4:/workspace/pretrain \\\n",
    "    $1 \\\n",
    "    bash -c \"bash /mmar/$3/commands/client1_demo_train.sh  2>&1 | tee /mmar/$3/logs/$5/client_1_log.tmp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "576a5311b089a0c53ef65a4542edd4fbfc75579019759c7afadc23ec58e8a880\n"
     ]
    }
   ],
   "source": [
    "%%bash -s \"$DOCKER_NAME\" \"$DATASET_PREFIX\" \"$MMAR_FOLDER\" \"$PRETRAIN_PATH\" \"$EXP_NAME\"\n",
    "docker run --runtime=nvidia --name client_2 --rm -dt --shm-size=20g --ulimit memlock=-1 --ulimit stack=67108864 \\\n",
    "    -e NVIDIA_VISIBLE_DEVICES=5 \\\n",
    "    -v $PWD:/mmar \\\n",
    "    -v $2:/workspace/data \\\n",
    "    -v $4:/workspace/pretrain \\\n",
    "    $1 \\\n",
    "    bash -c \"bash /mmar/$3/commands/client2_demo_train.sh  2>&1 | tee /mmar/$3/logs/$5/client_2_log.tmp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f00b09054cda8e58412d9c780ca276c5ab1164303389f1cb22ee382b1b5be0eb\n"
     ]
    }
   ],
   "source": [
    "%%bash -s \"$DOCKER_NAME\" \"$DATASET_PREFIX\" \"$MMAR_FOLDER\" \"$PRETRAIN_PATH\" \"$EXP_NAME\"\n",
    "docker run --runtime=nvidia --name client_3 --rm -dt --shm-size=20g --ulimit memlock=-1 --ulimit stack=67108864 \\\n",
    "    -e NVIDIA_VISIBLE_DEVICES=6 \\\n",
    "    -v $PWD:/mmar \\\n",
    "    -v $2:/workspace/data \\\n",
    "    -v $4:/workspace/pretrain \\\n",
    "    $1 \\\n",
    "    bash -c \"bash /mmar/$3/commands/client3_demo_train.sh  2>&1 | tee /mmar/$3/logs/$5/client_3_log.tmp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eeb3db19e93e245964eaefb89f7b274696a18a37a702e7dd41333762b5bc3e67\n"
     ]
    }
   ],
   "source": [
    "%%bash -s \"$DOCKER_NAME\" \"$DATASET_PREFIX\" \"$MMAR_FOLDER\" \"$PRETRAIN_PATH\" \"$EXP_NAME\"\n",
    "docker run --runtime=nvidia --name client_4 --rm -dt --shm-size=20g --ulimit memlock=-1 --ulimit stack=67108864 \\\n",
    "    -e NVIDIA_VISIBLE_DEVICES=7 \\\n",
    "    -v $PWD:/mmar \\\n",
    "    -v $2:/workspace/data \\\n",
    "    -v $4:/workspace/pretrain \\\n",
    "    $1 \\\n",
    "    bash -c \"bash /mmar/$3/commands/client4_demo_train.sh  2>&1 | tee /mmar/$3/logs/$5/client_4_log.tmp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5435413b7e5cd3977d3ae75449ab97873a241b5520aed395ab37e0a804d14a04\n"
     ]
    }
   ],
   "source": [
    "%%bash -s \"$DOCKER_NAME\" \"$DATASET_PREFIX\" \"$MMAR_FOLDER\" \"$PRETRAIN_PATH\" \"$EXP_NAME\"\n",
    "docker run --runtime=nvidia --name client_5 --rm -dt --shm-size=20g --ulimit memlock=-1 --ulimit stack=67108864 \\\n",
    "    -e NVIDIA_VISIBLE_DEVICES=2 \\\n",
    "    -v $PWD:/mmar \\\n",
    "    -v $2:/workspace/data \\\n",
    "    -v $4:/workspace/pretrain \\\n",
    "    $1 \\\n",
    "    bash -c \"bash /mmar/$3/commands/client5_demo_train.sh  2>&1 | tee /mmar/$3/logs/$5/client_5_log.tmp\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After running the federated clients for a few minutes, we can check the output messages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replacing some pre-defined components...\n",
      "'/mmar/clara_seg_ct_brats_adaptive_fl/commands/../components/server_model_manager.py' -> '/opt/nvidia/medical/fed_learn/server/server_model_manager.py'\n",
      "'/mmar/clara_seg_ct_brats_adaptive_fl/commands/../components/fed_server.py' -> '/opt/nvidia/medical/fed_learn/server/fed_server.py'\n",
      "MMAR_ROOT set to /mmar/clara_seg_ct_brats_adaptive_fl/commands/..\n",
      "2020-10-02 16:32:55.019096: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/horovod-0.18.1-py3.6-linux-x86_64.egg/horovod/tensorflow/__init__.py:117: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/horovod-0.18.1-py3.6-linux-x86_64.egg/horovod/tensorflow/__init__.py:143: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "--------------------------------------------------------------------------\n",
      "[[28650,1],0]: A high-performance Open MPI point-to-point messaging module\n",
      "was unable to find any relevant network interfaces:\n",
      "\n",
      "Module: OpenFabrics (openib)\n",
      "  Host: 62169f3fb56c\n",
      "\n",
      "Another transport will be used instead, although this may result in\n",
      "lower performance.\n",
      "\n",
      "NOTE: You can disable this warning by setting the MCA parameter\n",
      "btl_base_warn_component_unused to 0.\n",
      "--------------------------------------------------------------------------\n",
      "2020-10-02 16:32:56,714 - __main__ - INFO - Starting FL client with config:\n",
      "2020-10-02 16:32:56,714 - __main__ - INFO - config/config_fed_client.json\n",
      "2020-10-02 16:32:56,715 - __main__ - INFO - Automatic Mixed Precision status: Disabled\n",
      "Using TensorFlow backend.\n",
      "Number of samples: 74\n",
      "Data Property: {'task': 'segmentation', 'num_channels': 4, 'num_label_channels': 3, 'data_format': 'channels_first', 'label_format': None, 'crop_size': [128, 128, 128], 'num_data_dims': 3}\n",
      "Number of samples: 14\n",
      "Data Property: {'task': 'segmentation', 'num_channels': 4, 'num_label_channels': 1, 'data_format': 'channels_first', 'label_format': None, 'crop_size': [128, 128, 128], 'num_data_dims': 3}\n",
      "SegResnet init, num_classes: 3 data_format: channels_first\n",
      "SegResnet net_config:\n",
      "num_classes      => 3\n",
      "data_format      => channels_first\n",
      "blocks_down      => 1,2,2,4\n",
      "blocks_up        => 1,1,1\n",
      "init_filters     => 16\n",
      "use_batchnorm    => False\n",
      "use_groupnorm    => True\n",
      "use_groupnormG   => 8\n",
      "reg_weight       => 1e-05\n",
      "dropout_prob     => 0.2\n",
      "final_activation => sigmoid\n",
      "use_vae          => False\n",
      "-----------------\n",
      "SegResnet inputs  [None, 4, 128, 128, 128] use_bias False filters 16 blocks_down  [1, 2, 2, 4] blocks_up [1, 1, 1]\n",
      "LayerDown [ 0 : 0 ] shape [None, 16, 128, 128, 128]\n",
      "LayerDown [ 1 : 0 ] shape [None, 32, 64, 64, 64]\n",
      "LayerDown [ 1 : 1 ] shape [None, 32, 64, 64, 64]\n",
      "LayerDown [ 2 : 0 ] shape [None, 64, 32, 32, 32]\n",
      "LayerDown [ 2 : 1 ] shape [None, 64, 32, 32, 32]\n",
      "LayerDown [ 3 : 0 ] shape [None, 128, 16, 16, 16]\n",
      "LayerDown [ 3 : 1 ] shape [None, 128, 16, 16, 16]\n",
      "LayerDown [ 3 : 2 ] shape [None, 128, 16, 16, 16]\n",
      "LayerDown [ 3 : 3 ] shape [None, 128, 16, 16, 16]\n",
      "LayerUp [ 0 : 0 ] shape [None, 64, 32, 32, 32]\n",
      "LayerUp [ 1 : 0 ] shape [None, 32, 64, 64, 64]\n",
      "LayerUp [ 2 : 0 ] shape [None, 16, 128, 128, 128]\n",
      "Final activation sigmoid\n",
      "dice_loss targets [None, 3, 128, 128, 128] predictions [None, 3, 128, 128, 128] targets.dtype <dtype: 'float32'> predictions.dtype <dtype: 'float32'>\n",
      "dice_loss is_channels_first: True skip_background: False is_onehot_targets True\n",
      "metric_dice targets (?, 3, 128, 128, 128) predictions (?, 3, 128, 128, 128) targets.dtype <dtype: 'float32'> predictions.dtype <dtype: 'float32'>\n",
      "metric_dice is_channels_first: True skip_background: False is_independent_predictions: True is_onehot_targets True threshold 0.5\n",
      "2020-10-02 16:33:02,028 - ClientModelManager - INFO - privacy module disabled.\n",
      "2020-10-02 16:33:02.384596: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2100000000 Hz\n",
      "2020-10-02 16:33:02.391033: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4795cf0 executing computations on platform Host. Devices:\n",
      "2020-10-02 16:33:02.391058: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>\n",
      "2020-10-02 16:33:02.393917: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1\n",
      "2020-10-02 16:33:04.276128: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x25883a0 executing computations on platform CUDA. Devices:\n",
      "2020-10-02 16:33:04.276194: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5\n",
      "2020-10-02 16:33:04.278680: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
      "name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545\n",
      "pciBusID: 0000:3e:00.0\n",
      "2020-10-02 16:33:04.278742: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1\n",
      "2020-10-02 16:33:04.285569: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10\n",
      "2020-10-02 16:33:04.288855: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10\n",
      "2020-10-02 16:33:04.289936: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10\n",
      "2020-10-02 16:33:04.295611: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10\n",
      "2020-10-02 16:33:04.297038: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10\n",
      "2020-10-02 16:33:04.303917: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
      "2020-10-02 16:33:04.306663: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
      "2020-10-02 16:33:04.306705: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1\n",
      "2020-10-02 16:33:09.558449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2020-10-02 16:33:09.558489: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
      "2020-10-02 16:33:09.558497: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
      "2020-10-02 16:33:09.560783: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10230 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:3e:00.0, compute capability: 7.5)\n",
      "Fitting with single gpu\n",
      "2020-10-02 16:33:09,570 - SupervisedFitter - INFO - CLEAN START (global_variables_initializer)\n",
      "2020-10-02 16:33:16.134828: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set.  If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU.  To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.\n",
      "Requested train epochs: 1; iterations: 74\n",
      "2020-10-02 16:33:16,557 - FederatedClient - INFO - Successfully registered client:client0 for brats_segmentation. Got token:158f1dcf-4623-4f06-8320-0f5f6adcd59f\n",
      "2020-10-02 16:33:16,839 - FederatedClient - INFO - Received brats_segmentation model at round 0 (56441874 Bytes)\n",
      "2020-10-02 16:33:20,769 - AssignVariables - INFO - Vars from remote 252, Vars from local 252, vars matched 252 of 252 local\n",
      "2020-10-02 16:33:20,789 - ClientModelManager - INFO - Setting graph with global federated model data (14106684 elements)\n",
      "2020-10-02 16:33:20,790 - ClientModelManager - INFO - Round 0: local model updated\n",
      "2020-10-02 16:33:26.252856: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
      "Epoch: 1/1, mean_dice: 0.0417  mean_dice_et: 0.0554  mean_dice_tc: 0.0045  mean_dice_wt: 0.0652  val_time: 11.40s    \n",
      "2020-10-02 16:33:32,192 - SupervisedFitter - INFO - New best val metric: 0.04170284792780876\n",
      "2020-10-02 16:33:32,192 - SupervisedFitter - INFO - Saving model checkpoint at: /mmar/clara_seg_ct_brats_adaptive_fl/commands/../models/model.ckpt\n"
     ]
    }
   ],
   "source": [
    "# !tail {MMAR_FOLDER}/logs/client_0_log.tmp -n 20\n",
    "!cat {MMAR_FOLDER}/logs/{EXP_NAME}/client_0_log.tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-20 12:49:23.025333: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
      "2020-09-20 12:49:23.025374: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1\n",
      "2020-09-20 12:49:23.361621: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2020-09-20 12:49:23.361662: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
      "2020-09-20 12:49:23.361668: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
      "2020-09-20 12:49:23.363324: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10230 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:b1:00.0, compute capability: 7.5)\n",
      "Fitting with single gpu\n",
      "2020-09-20 12:49:23,372 - SupervisedFitter - INFO - CLEAN START (global_variables_initializer)\n",
      "2020-09-20 12:49:28.625076: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set.  If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU.  To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.\n",
      "Requested train epochs: 5; iterations: 96\n"
     ]
    }
   ],
   "source": [
    "!tail {MMAR_FOLDER}/logs/{EXP_NAME}/client_1_log.tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-20 12:41:13.383595: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1881] Running auto_mixed_precision graph optimizer\n",
      "2020-09-20 12:41:13.383930: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1331] No whitelist ops found, nothing to do\n",
      "2020-09-20 12:41:13.388691: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1331] No whitelist ops found, nothing to do\n",
      "2020-09-20 12:41:13.390743: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1331] No whitelist ops found, nothing to do\n",
      "2020-09-20 12:41:13.392273: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1331] No whitelist ops found, nothing to do\n",
      "2020-09-20 12:41:13.393273: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1331] No whitelist ops found, nothing to do\n",
      "2020-09-20 12:41:13.394914: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1331] No whitelist ops found, nothing to do\n",
      "2020-09-20 12:41:13.462328: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1881] Running auto_mixed_precision graph optimizer\n",
      "2020-09-20 12:41:13.462587: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1331] No whitelist ops found, nothing to do\n",
      "Requested train epochs: 5; iterations: 71\n"
     ]
    }
   ],
   "source": [
    "!tail {MMAR_FOLDER}/logs/{EXP_NAME}/client_2_log.tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 检查模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "!md5sum {MMAR_FOLDER}/models/{EXP_NAME}/*.data-00000-of-00001\n",
    "# 似乎 FL model 的结果和 final 的结果相同"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 输出分割后的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#!/usr/bin/env bash\n",
      "\n",
      "my_dir=\"$(dirname \"$0\")\"\n",
      ". $my_dir/set_env.sh\n",
      "\n",
      "echo \"MMAR_ROOT set to $MMAR_ROOT\"\n",
      "echo \"Inference on round range: start=$1, end=$2, step=$3\"\n",
      "echo \"Experiment name is $4\"\n",
      "\n",
      "start=$1\n",
      "end=$2\n",
      "step=$3\n",
      "for ((i=$start;i<$end;i=i+$step))\n",
      "do\n",
      "  echo \"Validation on round $i\"\n",
      "  report_path=${MMAR_ROOT}/eval/$4/result_on_round_$i\n",
      "  mkdir -vp $report_path\n",
      "  # Data list containing all data\n",
      "  CONFIG_FILE=config/config_validation.json\n",
      "  ENVIRONMENT_FILE=config/environment.json\n",
      "\n",
      "  python3 -u  -m nvmidl.apps.evaluate \\\n",
      "      -m $MMAR_ROOT \\\n",
      "      -c $CONFIG_FILE \\\n",
      "      -e $ENVIRONMENT_FILE \\\n",
      "      --set \\\n",
      "      DATASET_JSON=$MMAR_ROOT/config/2018train_2019test.json \\\n",
      "      do_validation=false \\\n",
      "      output_infer_result=true \\\n",
      "      MMAR_EVAL_OUTPUT_PATH=$report_path \\\n",
      "      ROUND_NUM=$i\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "!cat {MMAR_FOLDER}/commands/infer.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash --bg\n",
    "docker stop infer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c30d58e9c1d25afbe0f3b37191ac3024c25b27217dba7cf059e51e4f6a66a1c9\n"
     ]
    }
   ],
   "source": [
    "%%bash -s \"$DOCKER_NAME\" \"$DATASET_PREFIX\" \"$MMAR_FOLDER\" \"$PRETRAIN_PATH\" \"$EXP_NAME\"\n",
    "docker run --runtime=nvidia --name infer --rm -dt --shm-size=20g --ulimit memlock=-1 --ulimit stack=67108864 \\\n",
    "    -e NVIDIA_VISIBLE_DEVICES='4' \\\n",
    "    -v $PWD:/mmar \\\n",
    "    -v $2:/workspace/data \\\n",
    "    -v $4:/workspace/pretrain \\\n",
    "    $1 \\\n",
    "    bash -c \"bash /mmar/$3/commands/infer.sh 199 2>&1 | tee /mmar/$3/logs/$5/infer.tmp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat: clara_seg_ct_brats_adaptive_fl/logs/fedavg_server_lr_1.0_client_lr_0.0001/infer.tmp: No such file or directory\n"
     ]
    }
   ],
   "source": [
    "!cat {MMAR_FOLDER}/logs/{EXP_NAME}/infer.tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# INFER 提交相关结果\n",
    "这里使用的是 BRATS2020 Validation 的数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#!/usr/bin/env bash\n",
      "\n",
      "my_dir=\"$(dirname \"$0\")\"\n",
      ". $my_dir/set_env.sh\n",
      "\n",
      "echo \"MMAR_ROOT set to $MMAR_ROOT\"\n",
      "echo \"Inference on round range: start=$1, end=$2, step=$3\"\n",
      "echo \"Using data list file: $4\"\n",
      "echo \"Experiment name is $5\"\n",
      "\n",
      "start=$1\n",
      "end=$2\n",
      "step=$3\n",
      "for ((i=$start;i<$end;i=i+$step))\n",
      "do\n",
      "  echo \"Validation on round $i\"\n",
      "  report_path=${MMAR_ROOT}/eval/$5/result_on_round_$i\n",
      "  mkdir -vp $report_path\n",
      "  # Data list containing all data\n",
      "  CONFIG_FILE=config/config_validation.json\n",
      "  ENVIRONMENT_FILE=config/environment.json\n",
      "\n",
      "  python3 -u  -m nvmidl.apps.evaluate \\\n",
      "      -m $MMAR_ROOT \\\n",
      "      -c $CONFIG_FILE \\\n",
      "      -e $ENVIRONMENT_FILE \\\n",
      "      --set \\\n",
      "      DATASET_JSON=$4 \\\n",
      "      do_validation=false \\\n",
      "      output_infer_result=true \\\n",
      "      MMAR_CKPT_DIR=models/$5 \\\n",
      "      MMAR_EVAL_OUTPUT_PATH=$report_path \\\n",
      "      ROUND_NUM=$i\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "!cat {MMAR_FOLDER}/commands/infer_submit.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash --bg\n",
    "docker stop infer_submit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`infer_submit` 的参数：\n",
    "1. 起始的 Round\n",
    "2. 终止的 Round\n",
    "3. 间隔 Round\n",
    "4. 使用的配置文件（在训练的过程中，使用的 2019作为验证）\n",
    "5. 实验名（`EXP_NAME`）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2b34630efd195517f56b66a12daa0d83272b92c182bf371eab8e4372f6273890\n"
     ]
    }
   ],
   "source": [
    "%%bash -s \"$DOCKER_NAME\" \"$DATASET_PREFIX\" \"$MMAR_FOLDER\" \"$PRETRAIN_PATH\" \"$EXP_NAME\"\n",
    "docker run --runtime=nvidia --name infer_submit --rm -dt --shm-size=20g --ulimit memlock=-1 --ulimit stack=67108864 \\\n",
    "    -e NVIDIA_VISIBLE_DEVICES='4' \\\n",
    "    -v $PWD:/mmar \\\n",
    "    -v $2:/workspace/data \\\n",
    "    -v $4:/workspace/pretrain \\\n",
    "    $1 \\\n",
    "    bash -c \"bash /mmar/$3/commands/infer_submit.sh 199 200 5 /mmar/$3/config/2020_validation_submit.json $5 2>&1 | tee /mmar/$3/logs/$5/infer_submit.tmp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replacing some pre-defined components...\n",
      "'/mmar/clara_seg_ct_brats_adaptive_fl/commands/../components/server_model_manager.py' -> '/opt/nvidia/medical/fed_learn/server/server_model_manager.py'\n",
      "'/mmar/clara_seg_ct_brats_adaptive_fl/commands/../components/fed_server.py' -> '/opt/nvidia/medical/fed_learn/server/fed_server.py'\n",
      "MMAR_ROOT set to /mmar/clara_seg_ct_brats_adaptive_fl/commands/..\n",
      "Inference on round range: start=199, end=200, step=5\n",
      "Using data list file: /mmar/clara_seg_ct_brats_adaptive_fl/config/2020_validation_submit.json\n",
      "Experiment name is fedadam_server_lr_1.0_client_lr_0.0001_eps_0.01\n",
      "Validation on round 199\n",
      "mkdir: created directory '/mmar/clara_seg_ct_brats_adaptive_fl/commands/../eval/fedadam_server_lr_1.0_client_lr_0.0001_eps_0.01'\n",
      "mkdir: created directory '/mmar/clara_seg_ct_brats_adaptive_fl/commands/../eval/fedadam_server_lr_1.0_client_lr_0.0001_eps_0.01/result_on_round_199'\n",
      "2020-10-10 13:30:22.973485: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/horovod-0.18.1-py3.6-linux-x86_64.egg/horovod/tensorflow/__init__.py:117: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/horovod-0.18.1-py3.6-linux-x86_64.egg/horovod/tensorflow/__init__.py:143: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "--------------------------------------------------------------------------\n",
      "[[29385,1],0]: A high-performance Open MPI point-to-point messaging module\n",
      "was unable to find any relevant network interfaces:\n",
      "\n",
      "Module: OpenFabrics (openib)\n",
      "  Host: 2b34630efd19\n",
      "\n",
      "Another transport will be used instead, although this may result in\n",
      "lower performance.\n",
      "\n",
      "NOTE: You can disable this warning by setting the MCA parameter\n",
      "btl_base_warn_component_unused to 0.\n",
      "--------------------------------------------------------------------------\n",
      "Using TensorFlow backend.\n",
      "2020-10-10 13:30:24,911 - nvmidl.utils.train_conf - INFO - Automatic Mixed Precision status: Disabled\n",
      "Previously evaluated: 0 ; To be evaluated: 125\n",
      "2020-10-10 13:30:24.912700: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1\n",
      "2020-10-10 13:30:26.096912: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
      "name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545\n",
      "pciBusID: 0000:88:00.0\n",
      "2020-10-10 13:30:26.096955: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1\n",
      "2020-10-10 13:30:26.097033: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10\n",
      "2020-10-10 13:30:26.097062: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10\n",
      "2020-10-10 13:30:26.097091: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10\n",
      "2020-10-10 13:30:26.100177: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10\n",
      "2020-10-10 13:30:26.101687: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10\n",
      "2020-10-10 13:30:26.101763: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
      "2020-10-10 13:30:26.103962: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
      "2020-10-10 13:30:26.143776: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2100000000 Hz\n",
      "2020-10-10 13:30:26.155253: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2d01490 executing computations on platform Host. Devices:\n",
      "2020-10-10 13:30:26.155303: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>\n",
      "2020-10-10 13:30:26.279692: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2064880 executing computations on platform CUDA. Devices:\n",
      "2020-10-10 13:30:26.279766: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5\n",
      "2020-10-10 13:30:26.282562: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
      "name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545\n",
      "pciBusID: 0000:88:00.0\n",
      "2020-10-10 13:30:26.282642: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1\n",
      "2020-10-10 13:30:26.282731: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10\n",
      "2020-10-10 13:30:26.282783: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10\n",
      "2020-10-10 13:30:26.282835: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10\n",
      "2020-10-10 13:30:26.282902: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10\n",
      "2020-10-10 13:30:26.282961: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10\n",
      "2020-10-10 13:30:26.283012: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
      "2020-10-10 13:30:26.287777: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
      "2020-10-10 13:30:26.287869: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1\n",
      "2020-10-10 13:30:26.863549: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2020-10-10 13:30:26.863588: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
      "2020-10-10 13:30:26.863594: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
      "2020-10-10 13:30:26.865161: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10156 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:88:00.0, compute capability: 7.5)\n",
      "2020-10-10 13:30:30.563052: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
      "Batch 1 / 125: 7.77s; pre-process: 1.14s; infer: 6.31s; post-process: 0.32s\n",
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      "Batch 115 / 125: 5.72s; pre-process: 1.10s; infer: 4.30s; post-process: 0.33s\n",
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      "Batch 117 / 125: 5.69s; pre-process: 1.08s; infer: 4.29s; post-process: 0.32s\n",
      "Batch 118 / 125: 5.92s; pre-process: 1.30s; infer: 4.29s; post-process: 0.33s\n",
      "Batch 119 / 125: 5.97s; pre-process: 1.33s; infer: 4.31s; post-process: 0.33s\n",
      "Batch 120 / 125: 5.98s; pre-process: 1.36s; infer: 4.30s; post-process: 0.32s\n",
      "Batch 121 / 125: 5.94s; pre-process: 1.32s; infer: 4.29s; post-process: 0.33s\n",
      "Batch 122 / 125: 5.97s; pre-process: 1.33s; infer: 4.31s; post-process: 0.33s\n",
      "Batch 123 / 125: 5.97s; pre-process: 1.35s; infer: 4.30s; post-process: 0.33s\n",
      "Batch 124 / 125: 5.96s; pre-process: 1.35s; infer: 4.29s; post-process: 0.32s\n",
      "Batch 125 / 125: 5.95s; pre-process: 1.35s; infer: 4.28s; post-process: 0.33s\n",
      "\n",
      "\n",
      "Total Inference Time: 540.8690512180328s\n",
      "2020-10-10 13:42:35,346 - nvmidl.utils.train_conf - INFO - Total Evaluation Time 730.9218306541443\n"
     ]
    }
   ],
   "source": [
    "!cat {MMAR_FOLDER}/logs/{EXP_NAME}/infer_submit.tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 输出在测试集上的结果\n",
    "这里使用的是与 BRATS2019 的训练数据集（客户端均是通过 BRATS2018 进行的训练）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#!/usr/bin/env bash\n",
      "\n",
      "my_dir=\"$(dirname \"$0\")\"\n",
      ". $my_dir/set_env.sh\n",
      "\n",
      "echo \"MMAR_ROOT set to $MMAR_ROOT\"\n",
      "echo \"Validation on round range: start=$1, end=$2, step=$3\"\n",
      "echo \"Experiment name is $4\"\n",
      "\n",
      "start=$1\n",
      "end=$2\n",
      "step=$3\n",
      "for ((i=$start;i<$end;i=i+$step))\n",
      "do\n",
      "  echo \"Validation on round $i\"\n",
      "  report_path=${MMAR_ROOT}/eval/result_on_round_$i\n",
      "  mkdir -vp $report_path\n",
      "  # Data list containing all data\n",
      "  CONFIG_FILE=config/config_validation.json\n",
      "  ENVIRONMENT_FILE=config/environment.json\n",
      "\n",
      "  python3 -u  -m nvmidl.apps.evaluate \\\n",
      "      -m $MMAR_ROOT \\\n",
      "      -c $CONFIG_FILE \\\n",
      "      -e $ENVIRONMENT_FILE \\\n",
      "      --set \\\n",
      "      DATASET_JSON=$MMAR_ROOT/config/2018train_2019test.json \\\n",
      "      do_validation=true \\\n",
      "      output_infer_result=false \\\n",
      "      MMAR_EVAL_OUTPUT_PATH=$report_path \\\n",
      "      MMAR_CKPT_DIR=\"${MMAR_ROOT}/models/$4\" \\\n",
      "      ROUND_NUM=$i\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "!cat {MMAR_FOLDER}/commands/validate.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash --bg\n",
    "docker stop validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ebd5149083815808fbfc7196e910641f7ad31df90b0c4a7db2a6cd1464b66041\n"
     ]
    }
   ],
   "source": [
    "%%bash -s \"$DOCKER_NAME\" \"$DATASET_PREFIX\" \"$MMAR_FOLDER\" \"$PRETRAIN_PATH\" \"$EXP_NAME\"\n",
    "docker run --runtime=nvidia --name validate --rm -dt --shm-size=20g --ulimit memlock=-1 --ulimit stack=67108864 \\\n",
    "    -e NVIDIA_VISIBLE_DEVICES='7' \\\n",
    "    -v $PWD:/mmar \\\n",
    "    -v $2:/workspace/data \\\n",
    "    -v $4:/workspace/pretrain \\\n",
    "    $1 \\\n",
    "    bash -c \"bash /mmar/$3/commands/validate.sh 199 200 5 $5 2>&1 | tee /mmar/$3/logs/$5/validate.tmp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replacing some pre-defined components...\n",
      "'/mmar/clara_seg_ct_brats_adaptive_fl/commands/../components/server_model_manager.py' -> '/opt/nvidia/medical/fed_learn/server/server_model_manager.py'\n",
      "'/mmar/clara_seg_ct_brats_adaptive_fl/commands/../components/fed_server.py' -> '/opt/nvidia/medical/fed_learn/server/fed_server.py'\n",
      "MMAR_ROOT set to /mmar/clara_seg_ct_brats_adaptive_fl/commands/..\n",
      "Validation on round range: start=199, end=200, step=5\n",
      "Validation on round 199\n",
      "mkdir: created directory '/mmar/clara_seg_ct_brats_adaptive_fl/commands/../eval/result_on_round_199'\n",
      "2020-10-03 00:20:52.225296: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/horovod-0.18.1-py3.6-linux-x86_64.egg/horovod/tensorflow/__init__.py:117: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/horovod-0.18.1-py3.6-linux-x86_64.egg/horovod/tensorflow/__init__.py:143: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "--------------------------------------------------------------------------\n",
      "[[59326,1],0]: A high-performance Open MPI point-to-point messaging module\n",
      "was unable to find any relevant network interfaces:\n",
      "\n",
      "Module: OpenFabrics (openib)\n",
      "  Host: ebd514908381\n",
      "\n",
      "Another transport will be used instead, although this may result in\n",
      "lower performance.\n",
      "\n",
      "NOTE: You can disable this warning by setting the MCA parameter\n",
      "btl_base_warn_component_unused to 0.\n",
      "--------------------------------------------------------------------------\n",
      "Using TensorFlow backend.\n",
      "2020-10-03 00:20:54,462 - nvmidl.utils.train_conf - INFO - Automatic Mixed Precision status: Disabled\n",
      "Previously evaluated: 0 ; To be evaluated: 84\n",
      "2020-10-03 00:20:54.463771: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1\n",
      "2020-10-03 00:20:54.487104: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
      "name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545\n",
      "pciBusID: 0000:b2:00.0\n",
      "2020-10-03 00:20:54.487142: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1\n",
      "2020-10-03 00:20:54.487225: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10\n",
      "2020-10-03 00:20:54.487258: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10\n",
      "2020-10-03 00:20:54.487293: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10\n",
      "2020-10-03 00:20:54.491994: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10\n",
      "2020-10-03 00:20:54.493433: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10\n",
      "2020-10-03 00:20:54.493507: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
      "2020-10-03 00:20:54.495459: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
      "2020-10-03 00:20:54.511662: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2100000000 Hz\n",
      "2020-10-03 00:20:54.521470: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3129cc0 executing computations on platform Host. Devices:\n",
      "2020-10-03 00:20:54.521521: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>\n",
      "2020-10-03 00:20:54.655993: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x31aa5c0 executing computations on platform CUDA. Devices:\n",
      "2020-10-03 00:20:54.656029: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5\n",
      "2020-10-03 00:20:54.657285: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
      "name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545\n",
      "pciBusID: 0000:b2:00.0\n",
      "2020-10-03 00:20:54.657322: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1\n",
      "2020-10-03 00:20:54.657366: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10\n",
      "2020-10-03 00:20:54.657381: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10\n",
      "2020-10-03 00:20:54.657395: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10\n",
      "2020-10-03 00:20:54.657419: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10\n",
      "2020-10-03 00:20:54.657439: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10\n",
      "2020-10-03 00:20:54.657453: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
      "2020-10-03 00:20:54.660706: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
      "2020-10-03 00:20:54.660782: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1\n",
      "2020-10-03 00:20:55.294742: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2020-10-03 00:20:55.294781: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
      "2020-10-03 00:20:55.294787: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
      "2020-10-03 00:20:55.296328: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10156 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:b2:00.0, compute capability: 7.5)\n",
      "2020-10-03 00:20:59.147837: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
      "Batch 1 / 84: 8.88s; pre-process: 1.33s; infer: 6.72s; post-process: 0.83s\n",
      "Batch 2 / 84: 6.41s; pre-process: 1.31s; infer: 4.33s; post-process: 0.77s\n",
      "Batch 3 / 84: 6.38s; pre-process: 1.28s; infer: 4.32s; post-process: 0.77s\n",
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      "Batch 17 / 84: 6.53s; pre-process: 1.38s; infer: 4.38s; post-process: 0.77s\n",
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      "Batch 32 / 84: 6.81s; pre-process: 1.47s; infer: 4.56s; post-process: 0.79s\n",
      "Batch 33 / 84: 6.61s; pre-process: 1.33s; infer: 4.51s; post-process: 0.77s\n",
      "Batch 34 / 84: 6.70s; pre-process: 1.40s; infer: 4.51s; post-process: 0.78s\n",
      "Batch 35 / 84: 6.62s; pre-process: 1.33s; infer: 4.50s; post-process: 0.79s\n",
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      "Batch 37 / 84: 6.66s; pre-process: 1.35s; infer: 4.51s; post-process: 0.80s\n",
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      "Batch 39 / 84: 6.69s; pre-process: 1.35s; infer: 4.55s; post-process: 0.79s\n",
      "Batch 40 / 84: 6.62s; pre-process: 1.32s; infer: 4.52s; post-process: 0.78s\n",
      "Batch 41 / 84: 6.52s; pre-process: 1.35s; infer: 4.39s; post-process: 0.78s\n",
      "Batch 42 / 84: 6.44s; pre-process: 1.27s; infer: 4.41s; post-process: 0.76s\n",
      "Batch 43 / 84: 6.53s; pre-process: 1.31s; infer: 4.44s; post-process: 0.78s\n",
      "Batch 44 / 84: 6.55s; pre-process: 1.33s; infer: 4.43s; post-process: 0.78s\n",
      "Batch 45 / 84: 6.59s; pre-process: 1.39s; infer: 4.43s; post-process: 0.77s\n",
      "Batch 46 / 84: 6.63s; pre-process: 1.31s; infer: 4.54s; post-process: 0.78s\n",
      "Batch 47 / 84: 6.62s; pre-process: 1.30s; infer: 4.55s; post-process: 0.77s\n",
      "Batch 48 / 84: 6.60s; pre-process: 1.27s; infer: 4.56s; post-process: 0.77s\n",
      "Batch 49 / 84: 6.65s; pre-process: 1.33s; infer: 4.55s; post-process: 0.77s\n",
      "Batch 50 / 84: 6.64s; pre-process: 1.30s; infer: 4.57s; post-process: 0.78s\n",
      "Batch 51 / 84: 6.96s; pre-process: 1.60s; infer: 4.60s; post-process: 0.77s\n",
      "Batch 52 / 84: 6.90s; pre-process: 1.58s; infer: 4.56s; post-process: 0.76s\n",
      "Batch 53 / 84: 6.85s; pre-process: 1.57s; infer: 4.51s; post-process: 0.77s\n",
      "Batch 54 / 84: 6.87s; pre-process: 1.55s; infer: 4.58s; post-process: 0.75s\n",
      "Batch 55 / 84: 6.98s; pre-process: 1.59s; infer: 4.61s; post-process: 0.78s\n",
      "Batch 56 / 84: 6.94s; pre-process: 1.59s; infer: 4.57s; post-process: 0.77s\n",
      "Batch 57 / 84: 6.88s; pre-process: 1.58s; infer: 4.52s; post-process: 0.78s\n",
      "Batch 58 / 84: 6.99s; pre-process: 1.62s; infer: 4.59s; post-process: 0.78s\n",
      "Batch 59 / 84: 6.93s; pre-process: 1.58s; infer: 4.56s; post-process: 0.78s\n",
      "Batch 60 / 84: 6.92s; pre-process: 1.56s; infer: 4.60s; post-process: 0.77s\n",
      "Batch 61 / 84: 6.96s; pre-process: 1.60s; infer: 4.59s; post-process: 0.77s\n",
      "Batch 62 / 84: 6.82s; pre-process: 1.50s; infer: 4.55s; post-process: 0.76s\n",
      "Batch 63 / 84: 6.97s; pre-process: 1.56s; infer: 4.65s; post-process: 0.76s\n",
      "Batch 64 / 84: 6.71s; pre-process: 1.43s; infer: 4.52s; post-process: 0.76s\n",
      "Batch 65 / 84: 6.85s; pre-process: 1.55s; infer: 4.55s; post-process: 0.76s\n",
      "Batch 66 / 84: 6.92s; pre-process: 1.53s; infer: 4.62s; post-process: 0.76s\n",
      "Batch 67 / 84: 6.93s; pre-process: 1.53s; infer: 4.65s; post-process: 0.75s\n",
      "Batch 68 / 84: 7.18s; pre-process: 1.53s; infer: 4.89s; post-process: 0.76s\n",
      "Batch 69 / 84: 6.96s; pre-process: 1.57s; infer: 4.63s; post-process: 0.76s\n",
      "Batch 70 / 84: 6.87s; pre-process: 1.46s; infer: 4.65s; post-process: 0.76s\n",
      "Batch 71 / 84: 7.01s; pre-process: 1.57s; infer: 4.68s; post-process: 0.76s\n",
      "Batch 72 / 84: 6.84s; pre-process: 1.45s; infer: 4.64s; post-process: 0.75s\n",
      "Batch 73 / 84: 6.94s; pre-process: 1.50s; infer: 4.67s; post-process: 0.77s\n",
      "Batch 74 / 84: 6.94s; pre-process: 1.53s; infer: 4.65s; post-process: 0.76s\n",
      "Batch 75 / 84: 6.84s; pre-process: 1.42s; infer: 4.66s; post-process: 0.76s\n",
      "Batch 76 / 84: 6.96s; pre-process: 1.53s; infer: 4.67s; post-process: 0.76s\n",
      "Batch 77 / 84: 6.95s; pre-process: 1.53s; infer: 4.66s; post-process: 0.76s\n",
      "Batch 78 / 84: 6.85s; pre-process: 1.48s; infer: 4.63s; post-process: 0.74s\n",
      "Batch 79 / 84: 6.90s; pre-process: 1.54s; infer: 4.61s; post-process: 0.75s\n",
      "Batch 80 / 84: 6.85s; pre-process: 1.49s; infer: 4.63s; post-process: 0.74s\n",
      "Batch 81 / 84: 6.78s; pre-process: 1.42s; infer: 4.61s; post-process: 0.74s\n",
      "Batch 82 / 84: 6.95s; pre-process: 1.54s; infer: 4.65s; post-process: 0.76s\n",
      "Batch 83 / 84: 6.88s; pre-process: 1.51s; infer: 4.61s; post-process: 0.76s\n",
      "Batch 84 / 84: 7.00s; pre-process: 1.57s; infer: 4.66s; post-process: 0.77s\n",
      "\n",
      "\n",
      "Total Inference Time: 381.4252851009369s\n",
      "2020-10-03 00:30:24,751 - nvmidl.utils.train_conf - INFO - Total Evaluation Time 570.8249261379242\n"
     ]
    }
   ],
   "source": [
    "!cat {MMAR_FOLDER}/logs/{EXP_NAME}/validate.tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Demo Results\n",
    "\n",
    "The training curves in the form of tensorboard logs are saved at `clara_seg_ct_spleen_fl_demo/models`.\n",
    "\n",
    "Assuming you have started the tensorboard server at the beginning of this demo. This section reads the tensorboard values and visualises the validation curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " # open tab into Tensorboard to check training progress\n",
    "import webbrowser\n",
    "import os\n",
    "from IPython.core.display import display, HTML\n",
    "\n",
    "%env MACHINE_IP=$MY_IP\n",
    "%env TB_PORT=6006\n",
    "\n",
    "# generate an URL\n",
    "url = 'http://' + os.environ['MACHINE_IP'] + ':' + os.environ['TB_PORT']\n",
    "display(HTML('<a href=\"' + url + '\" target=\"_blank\">Open Tensorboard</a>'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>Since this is a simple demo run, the results will not show great accuracy. </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Longer Training & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook comes with a `clara_seg_ct_spleen_fl` folder that contains a full clara MMAR for federated spleen training. Running this will take several hours so you can do so on your own time. We will show how to use this MMAR and the results obtained from it. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running \n",
    "\n",
    "Running this MMAR is very similar to this demo. Navigate to commands directory and use:\n",
    "1. **server_train.sh**: To run federated server.\n",
    "2. **client_train.sh**: To run federated clients.\n",
    "3. The configs are same as described above. For secure training, see CLARA documentation on setting up ssl. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "\n",
    "Below are the tensorboard logs of training runs from the above MMAR. The event files of one such run are included in the MMAR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 250
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image(filename='figures/normal_run/mean_dice.png', height = 250, width=250))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 250
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image(filename='figures/client_0_run/mean_dice.png', height = 250, width=250))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display(Image(filename='figures/client_1_run/mean_dice.png', height = 250, width=250))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary\n",
    "In this demo, we have configured and trained a federated model.\n",
    "\n",
    "Two federated clients train the model with their own training data, and share model updates within the FL system;\n",
    "the server hosts model parameters and synchronise model updates submitted by the two clients.\n",
    "\n",
    "But this is only a simple demo. There are several features that we haven't investigated in this simple notebook, such as defining your own aggregation method or privacy method. But try it out! You can find all the information that you need in the [Clara documentation] (https://docs.nvidia.com/clara/tlt-mi/clara-train-sdk-v3.0/nvmidl/additional_features/federated_learning.html(. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "server_app\n",
      "client_0\n",
      "client_1\n",
      "client_2\n",
      "tensorboard\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "docker stop server_app\n",
    "docker stop client_0\n",
    "docker stop client_1\n",
    "docker stop client_2\n",
    "docker stop tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONTAINER ID        IMAGE               COMMAND             CREATED             STATUS              PORTS               NAMES\n"
     ]
    }
   ],
   "source": [
    "!docker ps "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}