pybench-training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Benchmarks with PyTorch and Standard Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==============NVSMI LOG==============\n",
      "\n",
      "Timestamp                           : Sat Jul  6 21:59:50 2019\n",
      "Driver Version                      : 418.67\n",
      "CUDA Version                        : 10.1\n",
      "\n",
      "Attached GPUs                       : 1\n",
      "GPU 00000000:28:00.0\n",
      "    Product Name                    : GeForce GTX 1080 Ti\n",
      "    Product Brand                   : GeForce\n",
      "    Display Mode                    : Enabled\n",
      "    Display Active                  : Enabled\n",
      "    Persistence Mode                : Enabled\n",
      "    Accounting Mode                 : Disabled\n",
      "    Accounting Mode Buffer Size     : 4000\n",
      "    Driver Model\n",
      "        Current                     : N/A\n",
      "        Pending                     : N/A\n",
      "    Serial Number                   : 0320917111888\n",
      "    GPU UUID                        : GPU-2d5cf167-db75-89ec-c6f7-5639237768ce\n",
      "    Minor Number                    : 0\n",
      "    VBIOS Version                   : 86.02.39.00.01\n",
      "    MultiGPU Board                  : No\n",
      "    Board ID                        : 0x2800\n",
      "    GPU Part Number                 : 900-1G611-2550-000\n",
      "    Inforom Version\n",
      "        Image Version               : G001.0000.01.04\n",
      "        OEM Object                  : 1.1\n",
      "        ECC Object                  : N/A\n",
      "        Power Management Object     : N/A\n",
      "    GPU Operation Mode\n",
      "        Current                     : N/A\n",
      "        Pending                     : N/A\n",
      "    GPU Virtualization Mode\n",
      "        Virtualization mode         : None\n",
      "    IBMNPU\n",
      "        Relaxed Ordering Mode       : N/A\n",
      "    PCI\n",
      "        Bus                         : 0x28\n",
      "        Device                      : 0x00\n",
      "        Domain                      : 0x0000\n",
      "        Device Id                   : 0x1B0610DE\n",
      "        Bus Id                      : 00000000:28:00.0\n",
      "        Sub System Id               : 0x120F10DE\n",
      "        GPU Link Info\n",
      "            PCIe Generation\n",
      "                Max                 : 3\n",
      "                Current             : 3\n",
      "            Link Width\n",
      "                Max                 : 16x\n",
      "                Current             : 16x\n",
      "        Bridge Chip\n",
      "            Type                    : N/A\n",
      "            Firmware                : N/A\n",
      "        Replays Since Reset         : 0\n",
      "        Replay Number Rollovers     : 0\n",
      "        Tx Throughput               : 0 KB/s\n",
      "        Rx Throughput               : 0 KB/s\n",
      "    Fan Speed                       : 32 %\n",
      "    Performance State               : P0\n",
      "    Clocks Throttle Reasons\n",
      "        Idle                        : Active\n",
      "        Applications Clocks Setting : Not Active\n",
      "        SW Power Cap                : Not Active\n",
      "        HW Slowdown                 : Not Active\n",
      "            HW Thermal Slowdown     : Not Active\n",
      "            HW Power Brake Slowdown : Not Active\n",
      "        Sync Boost                  : Not Active\n",
      "        SW Thermal Slowdown         : Not Active\n",
      "        Display Clock Setting       : Not Active\n",
      "    FB Memory Usage\n",
      "        Total                       : 11175 MiB\n",
      "        Used                        : 611 MiB\n",
      "        Free                        : 10564 MiB\n",
      "    BAR1 Memory Usage\n",
      "        Total                       : 256 MiB\n",
      "        Used                        : 6 MiB\n",
      "        Free                        : 250 MiB\n",
      "    Compute Mode                    : Default\n",
      "    Utilization\n",
      "        Gpu                         : 0 %\n",
      "        Memory                      : 0 %\n",
      "        Encoder                     : 0 %\n",
      "        Decoder                     : 0 %\n",
      "    Encoder Stats\n",
      "        Active Sessions             : 0\n",
      "        Average FPS                 : 0\n",
      "        Average Latency             : 0\n",
      "    FBC Stats\n",
      "        Active Sessions             : 0\n",
      "        Average FPS                 : 0\n",
      "        Average Latency             : 0\n",
      "    Ecc Mode\n",
      "        Current                     : N/A\n",
      "        Pending                     : N/A\n",
      "    ECC Errors\n",
      "        Volatile\n",
      "            Single Bit            \n",
      "                Device Memory       : N/A\n",
      "                Register File       : N/A\n",
      "                L1 Cache            : N/A\n",
      "                L2 Cache            : N/A\n",
      "                Texture Memory      : N/A\n",
      "                Texture Shared      : N/A\n",
      "                CBU                 : N/A\n",
      "                Total               : N/A\n",
      "            Double Bit            \n",
      "                Device Memory       : N/A\n",
      "                Register File       : N/A\n",
      "                L1 Cache            : N/A\n",
      "                L2 Cache            : N/A\n",
      "                Texture Memory      : N/A\n",
      "                Texture Shared      : N/A\n",
      "                CBU                 : N/A\n",
      "                Total               : N/A\n",
      "        Aggregate\n",
      "            Single Bit            \n",
      "                Device Memory       : N/A\n",
      "                Register File       : N/A\n",
      "                L1 Cache            : N/A\n",
      "                L2 Cache            : N/A\n",
      "                Texture Memory      : N/A\n",
      "                Texture Shared      : N/A\n",
      "                CBU                 : N/A\n",
      "                Total               : N/A\n",
      "            Double Bit            \n",
      "                Device Memory       : N/A\n",
      "                Register File       : N/A\n",
      "                L1 Cache            : N/A\n",
      "                L2 Cache            : N/A\n",
      "                Texture Memory      : N/A\n",
      "                Texture Shared      : N/A\n",
      "                CBU                 : N/A\n",
      "                Total               : N/A\n",
      "    Retired Pages\n",
      "        Single Bit ECC              : N/A\n",
      "        Double Bit ECC              : N/A\n",
      "        Pending                     : N/A\n",
      "    Temperature\n",
      "        GPU Current Temp            : 50 C\n",
      "        GPU Shutdown Temp           : 96 C\n",
      "        GPU Slowdown Temp           : 93 C\n",
      "        GPU Max Operating Temp      : N/A\n",
      "        Memory Current Temp         : N/A\n",
      "        Memory Max Operating Temp   : N/A\n",
      "    Power Readings\n",
      "        Power Management            : Supported\n",
      "        Power Draw                  : 76.93 W\n",
      "        Power Limit                 : 250.00 W\n",
      "        Default Power Limit         : 250.00 W\n",
      "        Enforced Power Limit        : 250.00 W\n",
      "        Min Power Limit             : 125.00 W\n",
      "        Max Power Limit             : 300.00 W\n",
      "    Clocks\n",
      "        Graphics                    : 1797 MHz\n",
      "        SM                          : 1797 MHz\n",
      "        Memory                      : 5508 MHz\n",
      "        Video                       : 1518 MHz\n",
      "    Applications Clocks\n",
      "        Graphics                    : N/A\n",
      "        Memory                      : N/A\n",
      "    Default Applications Clocks\n",
      "        Graphics                    : N/A\n",
      "        Memory                      : N/A\n",
      "    Max Clocks\n",
      "        Graphics                    : 1911 MHz\n",
      "        SM                          : 1911 MHz\n",
      "        Memory                      : 5505 MHz\n",
      "        Video                       : 1620 MHz\n",
      "    Max Customer Boost Clocks\n",
      "        Graphics                    : N/A\n",
      "    Clock Policy\n",
      "        Auto Boost                  : N/A\n",
      "        Auto Boost Default          : N/A\n",
      "    Processes\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from importlib import reload\n",
    "import os\n",
    "import numpy as np\n",
    "import time\n",
    "import torch\n",
    "from torch import optim\n",
    "import torch.nn as nn\n",
    "import torch.nn.parallel\n",
    "import torch.backends.cudnn as cudnn\n",
    "import torch.distributed as dist\n",
    "import torch.optim\n",
    "from torchvision import models\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from natsort import natsorted, ns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resnet18 resnet34 resnet50 resnet101 resnet152 vgg11 vgg11_bn vgg13 vgg13_bn vgg16 vgg16_bn vgg19 vgg19_bn\n"
     ]
    }
   ],
   "source": [
    "model_names = natsorted([m for m in dir(models) if \"vgg\" in m or \"resnet\" in m])\n",
    "model_names = [m for m in model_names if callable(getattr(models, m))]\n",
    "print(\" \".join(model_names))\n",
    "model_colors = [mpl.cm.spectral(s) for s in np.arange(len(model_names))*1.0/len(model_names)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "time.sleep(3.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def experiment(inference=True, batchsize=300, mname=\"resnet18\", mintime=5, mintrials=3, maxtrials=10000):\n",
    "    xbytes = 4 # assume float32 inputs\n",
    "    ybytes = 4 # assume int32 outputs\n",
    "    generator = getattr(models, mname)\n",
    "    try:\n",
    "        model = generator().to(\"cuda\")\n",
    "        if not inference:\n",
    "            criterion = nn.CrossEntropyLoss().cuda()\n",
    "            optimizer = optim.SGD(model.parameters(), lr=0.00001, momentum=0.9)\n",
    "            model.train()\n",
    "        else:\n",
    "            model.eval()\n",
    "        xs = torch.randn(batchsize, 3, 224, 224).type(torch.float32).cuda()*0.01\n",
    "        targets = (torch.rand(batchsize)*1000).type(torch.int64).cuda()\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        return None\n",
    "    total = 0\n",
    "    nbytes = 0\n",
    "    start = time.time()\n",
    "    #print(nsamples, batchsize, ntrials)\n",
    "    try:\n",
    "        for trial in range(maxtrials):\n",
    "            if inference:\n",
    "                with torch.no_grad():\n",
    "                    ys = model(xs)\n",
    "            else:\n",
    "                optimizer.zero_grad()\n",
    "                ys = model(xs)\n",
    "                loss = criterion(ys, targets)\n",
    "                loss.backward()\n",
    "            total += xs.size(0)\n",
    "            nbytes += xbytes * xs.numel() + ybytes * ys.numel()\n",
    "            finish = time.time()\n",
    "            if finish-start >= mintime and trial >= mintrials:\n",
    "                break\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        return None\n",
    "    return dict(model=mname, \n",
    "                bs=batchsize,\n",
    "                ntrials=trial+1,\n",
    "                #total=total,\n",
    "                #nbytes=nbytes,\n",
    "                time=finish-start,\n",
    "                inference=inference,\n",
    "                srate=total/(finish-start),\n",
    "                mrate=nbytes/(finish-start)/1e6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== True resnet18\n",
      "{'model': 'resnet18', 'bs': 1, 'ntrials': 1870, 'time': 5.000935792922974, 'inference': True, 'srate': 373.9300157875077, 'mrate': 226.6434697289979}\n",
      "{'model': 'resnet18', 'bs': 3, 'ntrials': 1891, 'time': 5.00175929069519, 'inference': True, 'srate': 1134.2009221742285, 'mrate': 687.4527893408659}\n",
      "{'model': 'resnet18', 'bs': 10, 'ntrials': 920, 'time': 5.00035834312439, 'inference': True, 'srate': 1839.8681391805083, 'mrate': 1115.1661575749763}\n",
      "{'model': 'resnet18', 'bs': 30, 'ntrials': 355, 'time': 5.007247447967529, 'inference': True, 'srate': 2126.917055861279, 'mrate': 1289.1499505621914}\n",
      "{'model': 'resnet18', 'bs': 100, 'ntrials': 117, 'time': 5.032979726791382, 'inference': True, 'srate': 2324.666625958966, 'mrate': 1409.0083379932407}\n",
      "{'model': 'resnet18', 'bs': 300, 'ntrials': 48, 'time': 5.122859954833984, 'inference': True, 'srate': 2810.9298569468037, 'mrate': 1703.738317453741}\n",
      "{'model': 'resnet18', 'bs': 1000, 'ntrials': 22, 'time': 5.249723672866821, 'inference': True, 'srate': 4190.696762518554, 'mrate': 2540.0315961236456}\n",
      "=== True resnet34\n",
      "{'model': 'resnet34', 'bs': 1, 'ntrials': 1042, 'time': 5.0043017864227295, 'inference': True, 'srate': 208.22085567002992, 'mrate': 126.20515927187316}\n",
      "{'model': 'resnet34', 'bs': 3, 'ntrials': 1028, 'time': 5.000590562820435, 'inference': True, 'srate': 616.7271567741714, 'mrate': 373.80573044670655}\n",
      "{'model': 'resnet34', 'bs': 10, 'ntrials': 533, 'time': 5.006452560424805, 'inference': True, 'srate': 1064.6260871685442, 'mrate': 645.2826469459006}\n",
      "{'model': 'resnet34', 'bs': 30, 'ntrials': 191, 'time': 5.02510142326355, 'inference': True, 'srate': 1140.275492445415, 'mrate': 691.1346592770755}\n",
      "{'model': 'resnet34', 'bs': 100, 'ntrials': 64, 'time': 5.004988193511963, 'inference': True, 'srate': 1278.7242951534652, 'mrate': 775.0501399840571}\n",
      "{'model': 'resnet34', 'bs': 300, 'ntrials': 26, 'time': 5.018954515457153, 'inference': True, 'srate': 1554.108525187448, 'mrate': 941.9638264184146}\n",
      "{'model': 'resnet34', 'bs': 1000, 'ntrials': 12, 'time': 5.026312589645386, 'inference': True, 'srate': 2387.4360748515683, 'mrate': 1447.0536542004338}\n",
      "=== True resnet50\n",
      "{'model': 'resnet50', 'bs': 1, 'ntrials': 739, 'time': 5.0013813972473145, 'inference': True, 'srate': 147.75917717587677, 'mrate': 89.55861039642501}\n",
      "{'model': 'resnet50', 'bs': 3, 'ntrials': 697, 'time': 5.001614570617676, 'inference': True, 'srate': 418.0650009066515, 'mrate': 253.39421382953236}\n",
      "{'model': 'resnet50', 'bs': 10, 'ntrials': 291, 'time': 5.016079425811768, 'inference': True, 'srate': 580.1343545370727, 'mrate': 351.6263938971742}\n",
      "{'model': 'resnet50', 'bs': 30, 'ntrials': 106, 'time': 5.041433095932007, 'inference': True, 'srate': 630.7730241557664, 'mrate': 382.3190992170999}\n",
      "{'model': 'resnet50', 'bs': 100, 'ntrials': 36, 'time': 5.022353887557983, 'inference': True, 'srate': 716.7953673910514, 'mrate': 434.45827372012496}\n",
      "{'model': 'resnet50', 'bs': 300, 'ntrials': 16, 'time': 5.3810646533966064, 'inference': True, 'srate': 892.0167864866983, 'mrate': 540.6620784910257}\n",
      "CUDA out of memory. Tried to allocate 2.99 GiB (GPU 0; 10.91 GiB total capacity; 7.39 GiB already allocated; 1.81 GiB free; 541.42 MiB cached)\n",
      "=== True resnet101\n",
      "{'model': 'resnet101', 'bs': 1, 'ntrials': 386, 'time': 5.00537896156311, 'inference': True, 'srate': 77.11703808325785, 'mrate': 46.741562186719584}\n",
      "{'model': 'resnet101', 'bs': 3, 'ntrials': 387, 'time': 5.005185842514038, 'inference': True, 'srate': 231.95941899668708, 'mrate': 140.59338736691998}\n",
      "{'model': 'resnet101', 'bs': 10, 'ntrials': 174, 'time': 5.012632131576538, 'inference': True, 'srate': 347.12301926946856, 'mrate': 210.39542745545614}\n",
      "{'model': 'resnet101', 'bs': 30, 'ntrials': 61, 'time': 5.063853025436401, 'inference': True, 'srate': 361.38489620604486, 'mrate': 219.03972220923822}\n",
      "{'model': 'resnet101', 'bs': 100, 'ntrials': 21, 'time': 5.084444046020508, 'inference': True, 'srate': 413.02450788963404, 'mrate': 250.33911052600186}\n",
      "{'model': 'resnet101', 'bs': 300, 'ntrials': 9, 'time': 5.340259075164795, 'inference': True, 'srate': 505.5934481824144, 'mrate': 306.4462560647396}\n",
      "CUDA out of memory. Tried to allocate 2.99 GiB (GPU 0; 10.91 GiB total capacity; 7.46 GiB already allocated; 1.78 GiB free; 501.50 MiB cached)\n",
      "=== True resnet152\n",
      "{'model': 'resnet152', 'bs': 1, 'ntrials': 252, 'time': 5.010340213775635, 'inference': True, 'srate': 50.29598575105556, 'mrate': 30.48500051554379}\n",
      "{'model': 'resnet152', 'bs': 3, 'ntrials': 259, 'time': 5.016691446304321, 'inference': True, 'srate': 154.88295589165597, 'mrate': 93.8764181614034}\n",
      "{'model': 'resnet152', 'bs': 10, 'ntrials': 122, 'time': 5.024232625961304, 'inference': True, 'srate': 242.82315147909245, 'mrate': 147.17802598929566}\n",
      "{'model': 'resnet152', 'bs': 30, 'ntrials': 43, 'time': 5.122485637664795, 'inference': True, 'srate': 251.8308671311525, 'mrate': 152.6377105385971}\n",
      "{'model': 'resnet152', 'bs': 100, 'ntrials': 15, 'time': 5.289210796356201, 'inference': True, 'srate': 283.5961843368707, 'mrate': 171.8910504807894}\n",
      "{'model': 'resnet152', 'bs': 300, 'ntrials': 6, 'time': 5.135024309158325, 'inference': True, 'srate': 350.53388097690146, 'mrate': 212.4627916666717}\n",
      "CUDA out of memory. Tried to allocate 2.99 GiB (GPU 0; 10.91 GiB total capacity; 7.52 GiB already allocated; 1.75 GiB free; 474.12 MiB cached)\n",
      "=== True vgg11\n",
      "{'model': 'vgg11', 'bs': 1, 'ntrials': 1482, 'time': 5.001999855041504, 'inference': True, 'srate': 296.2814959913075, 'mrate': 179.57977009828338}\n",
      "{'model': 'vgg11', 'bs': 3, 'ntrials': 460, 'time': 5.006743431091309, 'inference': True, 'srate': 275.62826395903505, 'mrate': 167.06159832473864}\n",
      "{'model': 'vgg11', 'bs': 10, 'ntrials': 322, 'time': 5.005663871765137, 'inference': True, 'srate': 643.2713187480841, 'mrate': 389.8944655490388}\n",
      "{'model': 'vgg11', 'bs': 30, 'ntrials': 140, 'time': 5.015994310379028, 'inference': True, 'srate': 837.3215239318387, 'mrate': 507.5106235133746}\n",
      "{'model': 'vgg11', 'bs': 100, 'ntrials': 53, 'time': 5.128957748413086, 'inference': True, 'srate': 1033.3483448249958, 'mrate': 626.324831978568}\n",
      "{'model': 'vgg11', 'bs': 300, 'ntrials': 20, 'time': 5.0956199169158936, 'inference': True, 'srate': 1177.4818565415057, 'mrate': 713.6858830320851}\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 1.06 GiB already allocated; 6.97 GiB free; 1.70 GiB cached)\n",
      "=== True vgg11_bn\n",
      "{'model': 'vgg11_bn', 'bs': 1, 'ntrials': 1402, 'time': 5.000927448272705, 'inference': True, 'srate': 280.3479983466354, 'mrate': 169.9222859738759}\n",
      "{'model': 'vgg11_bn', 'bs': 3, 'ntrials': 436, 'time': 5.0104289054870605, 'inference': True, 'srate': 261.0554953823559, 'mrate': 158.2288684171905}\n",
      "{'model': 'vgg11_bn', 'bs': 10, 'ntrials': 292, 'time': 5.017944097518921, 'inference': True, 'srate': 581.9116242135437, 'mrate': 352.7036183753194}\n",
      "{'model': 'vgg11_bn', 'bs': 30, 'ntrials': 124, 'time': 5.004324436187744, 'inference': True, 'srate': 743.3570799486109, 'mrate': 450.55764644181244}\n",
      "{'model': 'vgg11_bn', 'bs': 100, 'ntrials': 47, 'time': 5.072819232940674, 'inference': True, 'srate': 926.5065014499731, 'mrate': 561.5667086068461}\n",
      "{'model': 'vgg11_bn', 'bs': 300, 'ntrials': 18, 'time': 5.078930616378784, 'inference': True, 'srate': 1063.2159420697371, 'mrate': 644.4279410797726}\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 1.06 GiB already allocated; 5.18 GiB free; 3.49 GiB cached)\n",
      "=== True vgg13\n",
      "{'model': 'vgg13', 'bs': 1, 'ntrials': 1230, 'time': 5.003106594085693, 'inference': True, 'srate': 245.84725047713675, 'mrate': 149.0109686811983}\n",
      "{'model': 'vgg13', 'bs': 3, 'ntrials': 388, 'time': 5.008512020111084, 'inference': True, 'srate': 232.4043538931516, 'mrate': 140.8630677468859}\n",
      "{'model': 'vgg13', 'bs': 10, 'ntrials': 228, 'time': 5.007981300354004, 'inference': True, 'srate': 455.2732654650351, 'mrate': 275.94658947754334}\n",
      "{'model': 'vgg13', 'bs': 30, 'ntrials': 95, 'time': 5.012223958969116, 'inference': True, 'srate': 568.6098672626295, 'mrate': 344.6412638662868}\n",
      "{'model': 'vgg13', 'bs': 100, 'ntrials': 39, 'time': 5.140133619308472, 'inference': True, 'srate': 758.7351397539519, 'mrate': 459.87847302654734}\n",
      "{'model': 'vgg13', 'bs': 300, 'ntrials': 15, 'time': 5.012746810913086, 'inference': True, 'srate': 897.711408484306, 'mrate': 544.1136572192397}\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 1.06 GiB already allocated; 5.18 GiB free; 3.49 GiB cached)\n",
      "=== True vgg13_bn\n",
      "{'model': 'vgg13_bn', 'bs': 1, 'ntrials': 1138, 'time': 5.002187490463257, 'inference': True, 'srate': 227.50046897874452, 'mrate': 137.8907642536448}\n",
      "{'model': 'vgg13_bn', 'bs': 3, 'ntrials': 362, 'time': 5.012792587280273, 'inference': True, 'srate': 216.64570817385786, 'mrate': 131.31156347267333}\n",
      "{'model': 'vgg13_bn', 'bs': 10, 'ntrials': 204, 'time': 5.008368492126465, 'inference': True, 'srate': 407.31827204947774, 'mrate': 246.88049250845305}\n",
      "{'model': 'vgg13_bn', 'bs': 30, 'ntrials': 84, 'time': 5.019959211349487, 'inference': True, 'srate': 501.99611070595984, 'mrate': 304.2658666522107}\n",
      "{'model': 'vgg13_bn', 'bs': 100, 'ntrials': 34, 'time': 5.143555402755737, 'inference': True, 'srate': 661.0213624175991, 'mrate': 400.6529800176558}\n",
      "{'model': 'vgg13_bn', 'bs': 300, 'ntrials': 14, 'time': 5.070330619812012, 'inference': True, 'srate': 828.3483494328265, 'mrate': 502.0718747714293}\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 1.06 GiB already allocated; 5.18 GiB free; 3.49 GiB cached)\n",
      "=== True vgg16\n",
      "{'model': 'vgg16', 'bs': 1, 'ntrials': 1021, 'time': 5.003169298171997, 'inference': True, 'srate': 204.07064785375977, 'mrate': 123.68966851193804}\n",
      "{'model': 'vgg16', 'bs': 3, 'ntrials': 339, 'time': 5.00860333442688, 'inference': True, 'srate': 203.05061752636723, 'mrate': 123.07141589014151}\n",
      "{'model': 'vgg16', 'bs': 10, 'ntrials': 184, 'time': 5.0159523487091064, 'inference': True, 'srate': 366.82964112957296, 'mrate': 222.33984744432772}\n",
      "{'model': 'vgg16', 'bs': 30, 'ntrials': 77, 'time': 5.001100540161133, 'inference': True, 'srate': 461.89833246695196, 'mrate': 279.9621220882092}\n",
      "{'model': 'vgg16', 'bs': 100, 'ntrials': 31, 'time': 5.066259384155273, 'inference': True, 'srate': 611.8912919648864, 'mrate': 370.8746547554212}\n",
      "{'model': 'vgg16', 'bs': 300, 'ntrials': 13, 'time': 5.259144306182861, 'inference': True, 'srate': 741.5655043758741, 'mrate': 449.47175098826983}\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 1.08 GiB already allocated; 5.18 GiB free; 3.47 GiB cached)\n",
      "=== True vgg16_bn\n",
      "{'model': 'vgg16_bn', 'bs': 1, 'ntrials': 951, 'time': 5.0030646324157715, 'inference': True, 'srate': 190.08349279325654, 'mrate': 115.21188598390631}\n",
      "{'model': 'vgg16_bn', 'bs': 3, 'ntrials': 317, 'time': 5.014657735824585, 'inference': True, 'srate': 189.64404952427373, 'mrate': 114.9455341452566}\n",
      "{'model': 'vgg16_bn', 'bs': 10, 'ntrials': 166, 'time': 5.016430854797363, 'inference': True, 'srate': 330.912564739628, 'mrate': 200.5700764394654}\n",
      "{'model': 'vgg16_bn', 'bs': 30, 'ntrials': 69, 'time': 5.047579050064087, 'inference': True, 'srate': 410.09758925394505, 'mrate': 248.56507001788714}\n",
      "{'model': 'vgg16_bn', 'bs': 100, 'ntrials': 28, 'time': 5.210115909576416, 'inference': True, 'srate': 537.416066858221, 'mrate': 325.73432711557}\n",
      "{'model': 'vgg16_bn', 'bs': 300, 'ntrials': 12, 'time': 5.161460876464844, 'inference': True, 'srate': 697.47695200311, 'mrate': 422.74915033250903}\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 1.08 GiB already allocated; 5.18 GiB free; 3.47 GiB cached)\n",
      "=== True vgg19\n",
      "{'model': 'vgg19', 'bs': 1, 'ntrials': 875, 'time': 5.002733945846558, 'inference': True, 'srate': 174.9043641879967, 'mrate': 106.01163398671505}\n",
      "{'model': 'vgg19', 'bs': 3, 'ntrials': 300, 'time': 5.007230520248413, 'inference': True, 'srate': 179.74007714654812, 'mrate': 108.94261763944857}\n",
      "{'model': 'vgg19', 'bs': 10, 'ntrials': 153, 'time': 5.001521110534668, 'inference': True, 'srate': 305.9069363472988, 'mrate': 185.413865003334}\n",
      "{'model': 'vgg19', 'bs': 30, 'ntrials': 65, 'time': 5.038410425186157, 'inference': True, 'srate': 387.026827003271, 'mrate': 234.58160416860662}\n",
      "{'model': 'vgg19', 'bs': 100, 'ntrials': 26, 'time': 5.051849365234375, 'inference': True, 'srate': 514.6630099251536, 'mrate': 311.9434262717547}\n",
      "{'model': 'vgg19', 'bs': 300, 'ntrials': 12, 'time': 5.493974447250366, 'inference': True, 'srate': 655.2633315944406, 'mrate': 397.16296843936954}\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 1.10 GiB already allocated; 5.18 GiB free; 3.45 GiB cached)\n",
      "=== True vgg19_bn\n",
      "{'model': 'vgg19_bn', 'bs': 1, 'ntrials': 818, 'time': 5.001080751419067, 'inference': True, 'srate': 163.56464545546294, 'mrate': 99.13849438630156}\n",
      "{'model': 'vgg19_bn', 'bs': 3, 'ntrials': 282, 'time': 5.011765003204346, 'inference': True, 'srate': 168.80280688721388, 'mrate': 102.31340688802297}\n",
      "{'model': 'vgg19_bn', 'bs': 10, 'ntrials': 140, 'time': 5.034754514694214, 'inference': True, 'srate': 278.06718200738914, 'mrate': 168.5398558208626}\n",
      "{'model': 'vgg19_bn', 'bs': 30, 'ntrials': 58, 'time': 5.039553642272949, 'inference': True, 'srate': 345.2686732817912, 'mrate': 209.27148610017306}\n",
      "{'model': 'vgg19_bn', 'bs': 100, 'ntrials': 24, 'time': 5.277406454086304, 'inference': True, 'srate': 454.7688378524789, 'mrate': 275.64084984844175}\n",
      "{'model': 'vgg19_bn', 'bs': 300, 'ntrials': 11, 'time': 5.824952840805054, 'inference': True, 'srate': 566.5281917619636, 'mrate': 343.3795353652273}\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 1.10 GiB already allocated; 5.18 GiB free; 3.45 GiB cached)\n",
      "=== False resnet18\n",
      "{'model': 'resnet18', 'bs': 1, 'ntrials': 418, 'time': 5.005057334899902, 'inference': False, 'srate': 83.51552680232378, 'mrate': 50.619762981210066}\n",
      "{'model': 'resnet18', 'bs': 3, 'ntrials': 429, 'time': 5.001381158828735, 'inference': False, 'srate': 257.3289175787194, 'mrate': 155.9701448914728}\n",
      "{'model': 'resnet18', 'bs': 10, 'ntrials': 262, 'time': 5.0149548053741455, 'inference': False, 'srate': 522.4374100425283, 'mrate': 316.6555834756969}\n",
      "{'model': 'resnet18', 'bs': 30, 'ntrials': 102, 'time': 5.038429021835327, 'inference': False, 'srate': 607.3321638031822, 'mrate': 368.1113124670744}\n",
      "{'model': 'resnet18', 'bs': 100, 'ntrials': 35, 'time': 5.01019549369812, 'inference': False, 'srate': 698.5755355060176, 'mrate': 423.4150149766233}\n",
      "{'model': 'resnet18', 'bs': 300, 'ntrials': 14, 'time': 5.036125183105469, 'inference': False, 'srate': 833.9745036699263, 'mrate': 505.48195436838637}\n",
      "CUDA out of memory. Tried to allocate 1.50 GiB (GPU 0; 10.91 GiB total capacity; 7.33 GiB already allocated; 1.43 GiB free; 980.08 MiB cached)\n",
      "=== False resnet34\n",
      "{'model': 'resnet34', 'bs': 1, 'ntrials': 206, 'time': 5.011734485626221, 'inference': False, 'srate': 41.10353423366963, 'mrate': 24.913345341437967}\n",
      "{'model': 'resnet34', 'bs': 3, 'ntrials': 213, 'time': 5.026875734329224, 'inference': False, 'srate': 127.11672891298693, 'mrate': 77.04697479490832}\n",
      "{'model': 'resnet34', 'bs': 10, 'ntrials': 154, 'time': 5.024162292480469, 'inference': False, 'srate': 306.5187608101111, 'mrate': 185.78469915213802}\n",
      "{'model': 'resnet34', 'bs': 30, 'ntrials': 56, 'time': 5.074334621429443, 'inference': False, 'srate': 331.07789007551554, 'mrate': 200.67028210945088}\n",
      "{'model': 'resnet34', 'bs': 100, 'ntrials': 21, 'time': 5.160285949707031, 'inference': False, 'srate': 406.95419216433635, 'mrate': 246.6598193211102}\n",
      "CUDA out of memory. Tried to allocate 116.00 MiB (GPU 0; 10.91 GiB total capacity; 9.52 GiB already allocated; 26.50 MiB free; 180.68 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 2.99 GiB (GPU 0; 10.91 GiB total capacity; 6.62 GiB already allocated; 2.06 GiB free; 1.04 GiB cached)\n",
      "=== False resnet50\n",
      "{'model': 'resnet50', 'bs': 1, 'ntrials': 175, 'time': 5.014038324356079, 'inference': False, 'srate': 34.90200686140031, 'mrate': 21.154525182777064}\n",
      "{'model': 'resnet50', 'bs': 3, 'ntrials': 172, 'time': 5.013043403625488, 'inference': False, 'srate': 102.93148462006594, 'mrate': 62.38800800603742}\n",
      "{'model': 'resnet50', 'bs': 10, 'ntrials': 84, 'time': 5.041838884353638, 'inference': False, 'srate': 166.6058791776897, 'mrate': 100.98182264014787}\n",
      "{'model': 'resnet50', 'bs': 30, 'ntrials': 31, 'time': 5.000745058059692, 'inference': False, 'srate': 185.97228796959376, 'mrate': 112.72003540582642}\n",
      "CUDA out of memory. Tried to allocate 78.00 MiB (GPU 0; 10.91 GiB total capacity; 9.62 GiB already allocated; 64.00 MiB free; 44.77 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 920.00 MiB (GPU 0; 10.91 GiB total capacity; 8.41 GiB already allocated; 320.00 MiB free; 1022.69 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 2.99 GiB (GPU 0; 10.91 GiB total capacity; 6.64 GiB already allocated; 2.11 GiB free; 1001.05 MiB cached)\n",
      "=== False resnet101\n",
      "{'model': 'resnet101', 'bs': 1, 'ntrials': 106, 'time': 5.051900625228882, 'inference': False, 'srate': 20.982202118276536, 'mrate': 12.717564490312828}\n",
      "{'model': 'resnet101', 'bs': 3, 'ntrials': 103, 'time': 5.043389320373535, 'inference': False, 'srate': 61.26832183106462, 'mrate': 37.13546508167024}\n",
      "{'model': 'resnet101', 'bs': 10, 'ntrials': 51, 'time': 5.044085264205933, 'inference': False, 'srate': 101.10852082915513, 'mrate': 61.283087776800876}\n",
      "{'model': 'resnet101', 'bs': 30, 'ntrials': 19, 'time': 5.255215883255005, 'inference': False, 'srate': 108.46366974499061, 'mrate': 65.74113179647576}\n",
      "CUDA out of memory. Tried to allocate 78.00 MiB (GPU 0; 10.91 GiB total capacity; 9.61 GiB already allocated; 56.75 MiB free; 58.15 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 920.00 MiB (GPU 0; 10.91 GiB total capacity; 8.48 GiB already allocated; 884.75 MiB free; 385.76 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 2.99 GiB (GPU 0; 10.91 GiB total capacity; 6.71 GiB already allocated; 2.04 GiB free; 1002.12 MiB cached)\n",
      "=== False resnet152\n",
      "{'model': 'resnet152', 'bs': 1, 'ntrials': 78, 'time': 5.044686794281006, 'inference': False, 'srate': 15.461812235484276, 'mrate': 9.371589937673845}\n",
      "{'model': 'resnet152', 'bs': 3, 'ntrials': 73, 'time': 5.053965330123901, 'inference': False, 'srate': 43.332311500963755, 'mrate': 26.264233988472142}\n",
      "{'model': 'resnet152', 'bs': 10, 'ntrials': 37, 'time': 5.124684810638428, 'inference': False, 'srate': 72.19956225052323, 'mrate': 43.761021074789134}\n",
      "{'model': 'resnet152', 'bs': 30, 'ntrials': 13, 'time': 5.110706329345703, 'inference': False, 'srate': 76.31039133683302, 'mrate': 46.252643913950536}\n",
      "CUDA out of memory. Tried to allocate 154.00 MiB (GPU 0; 10.91 GiB total capacity; 9.56 GiB already allocated; 14.25 MiB free; 149.76 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 920.00 MiB (GPU 0; 10.91 GiB total capacity; 8.55 GiB already allocated; 840.25 MiB free; 365.39 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 2.99 GiB (GPU 0; 10.91 GiB total capacity; 6.77 GiB already allocated; 1.99 GiB free; 981.75 MiB cached)\n",
      "=== False vgg11\n",
      "{'model': 'vgg11', 'bs': 1, 'ntrials': 308, 'time': 5.0035176277160645, 'inference': False, 'srate': 61.55669329391201, 'mrate': 37.310250485759596}\n",
      "{'model': 'vgg11', 'bs': 3, 'ntrials': 180, 'time': 5.024990558624268, 'inference': False, 'srate': 107.4628884771159, 'mrate': 65.13454626064167}\n",
      "{'model': 'vgg11', 'bs': 10, 'ntrials': 106, 'time': 5.037964582443237, 'inference': False, 'srate': 210.40243190553295, 'mrate': 127.52743880712639}\n",
      "{'model': 'vgg11', 'bs': 30, 'ntrials': 48, 'time': 5.118884801864624, 'inference': False, 'srate': 281.31127300920315, 'mrate': 170.50613830615413}\n",
      "CUDA out of memory. Tried to allocate 1.20 GiB (GPU 0; 10.91 GiB total capacity; 6.43 GiB already allocated; 720.75 MiB free; 2.59 GiB cached)\n",
      "CUDA out of memory. Tried to allocate 1.79 GiB (GPU 0; 10.91 GiB total capacity; 8.14 GiB already allocated; 720.75 MiB free; 900.45 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 7.64 GiB already allocated; 1.49 GiB free; 609.24 MiB cached)\n",
      "=== False vgg11_bn\n",
      "{'model': 'vgg11_bn', 'bs': 1, 'ntrials': 286, 'time': 5.001382350921631, 'inference': False, 'srate': 57.18419027637375, 'mrate': 34.66002393679344}\n",
      "{'model': 'vgg11_bn', 'bs': 3, 'ntrials': 165, 'time': 5.008268594741821, 'inference': False, 'srate': 98.83655212096657, 'mrate': 59.90602027914329}\n",
      "{'model': 'vgg11_bn', 'bs': 10, 'ntrials': 92, 'time': 5.033897876739502, 'inference': False, 'srate': 182.76095831246616, 'mrate': 110.77360996468549}\n",
      "CUDA out of memory. Tried to allocate 392.00 MiB (GPU 0; 10.91 GiB total capacity; 9.35 GiB already allocated; 186.25 MiB free; 196.34 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 308.00 MiB (GPU 0; 10.91 GiB total capacity; 9.53 GiB already allocated; 118.25 MiB free; 84.00 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 3.59 GiB (GPU 0; 10.91 GiB total capacity; 8.44 GiB already allocated; 118.25 MiB free; 1.17 GiB cached)\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 8.83 GiB already allocated; 118.25 MiB free; 793.19 MiB cached)\n",
      "=== False vgg13\n",
      "{'model': 'vgg13', 'bs': 1, 'ntrials': 269, 'time': 5.016334056854248, 'inference': False, 'srate': 53.62481783533578, 'mrate': 32.502645587811045}\n",
      "{'model': 'vgg13', 'bs': 3, 'ntrials': 146, 'time': 5.031284809112549, 'inference': False, 'srate': 87.05529832195235, 'mrate': 52.76526097651518}\n",
      "CUDA out of memory. Tried to allocate 392.00 MiB (GPU 0; 10.91 GiB total capacity; 9.41 GiB already allocated; 114.00 MiB free; 210.95 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 184.00 MiB (GPU 0; 10.91 GiB total capacity; 9.46 GiB already allocated; 119.00 MiB free; 155.16 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 1.20 GiB (GPU 0; 10.91 GiB total capacity; 9.53 GiB already allocated; 119.00 MiB free; 83.34 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 3.59 GiB (GPU 0; 10.91 GiB total capacity; 8.44 GiB already allocated; 119.00 MiB free; 1.17 GiB cached)\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 8.84 GiB already allocated; 118.75 MiB free; 792.54 MiB cached)\n",
      "=== False vgg13_bn\n",
      "{'model': 'vgg13_bn', 'bs': 1, 'ntrials': 243, 'time': 5.01740288734436, 'inference': False, 'srate': 48.43143065368156, 'mrate': 29.354871296364237}\n",
      "{'model': 'vgg13_bn', 'bs': 3, 'ntrials': 130, 'time': 5.005452394485474, 'inference': False, 'srate': 77.91503529824088, 'mrate': 47.22523787468737}\n",
      "CUDA out of memory. Tried to allocate 392.00 MiB (GPU 0; 10.91 GiB total capacity; 9.44 GiB already allocated; 116.50 MiB free; 171.44 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 368.00 MiB (GPU 0; 10.91 GiB total capacity; 9.37 GiB already allocated; 118.50 MiB free; 246.98 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 1.20 GiB (GPU 0; 10.91 GiB total capacity; 9.53 GiB already allocated; 118.50 MiB free; 83.29 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 3.59 GiB (GPU 0; 10.91 GiB total capacity; 8.44 GiB already allocated; 118.25 MiB free; 1.17 GiB cached)\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 8.84 GiB already allocated; 118.25 MiB free; 792.48 MiB cached)\n",
      "=== False vgg16\n",
      "{'model': 'vgg16', 'bs': 1, 'ntrials': 233, 'time': 5.0001137256622314, 'inference': False, 'srate': 46.59894010093555, 'mrate': 28.244176782458247}\n",
      "{'model': 'vgg16', 'bs': 3, 'ntrials': 126, 'time': 5.027570486068726, 'inference': False, 'srate': 75.18542028350049, 'mrate': 45.57078545887305}\n",
      "CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 10.91 GiB total capacity; 9.49 GiB already allocated; 12.00 MiB free; 227.54 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 184.00 MiB (GPU 0; 10.91 GiB total capacity; 9.48 GiB already allocated; 12.00 MiB free; 237.08 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 1.20 GiB (GPU 0; 10.91 GiB total capacity; 9.55 GiB already allocated; 12.00 MiB free; 165.26 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 3.59 GiB (GPU 0; 10.91 GiB total capacity; 8.47 GiB already allocated; 197.25 MiB free; 1.07 GiB cached)\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 8.86 GiB already allocated; 197.25 MiB free; 690.45 MiB cached)\n",
      "=== False vgg16_bn\n",
      "{'model': 'vgg16_bn', 'bs': 1, 'ntrials': 211, 'time': 5.002600193023682, 'inference': False, 'srate': 42.17806577752258, 'mrate': 25.56463180454577}\n",
      "CUDA out of memory. Tried to allocate 392.00 MiB (GPU 0; 10.91 GiB total capacity; 9.17 GiB already allocated; 195.00 MiB free; 369.56 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 392.00 MiB (GPU 0; 10.91 GiB total capacity; 9.56 GiB already allocated; 131.00 MiB free; 34.29 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 368.00 MiB (GPU 0; 10.91 GiB total capacity; 9.39 GiB already allocated; 133.00 MiB free; 208.88 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 1.20 GiB (GPU 0; 10.91 GiB total capacity; 8.35 GiB already allocated; 132.75 MiB free; 1.24 GiB cached)\n",
      "CUDA out of memory. Tried to allocate 3.59 GiB (GPU 0; 10.91 GiB total capacity; 8.47 GiB already allocated; 132.75 MiB free; 1.13 GiB cached)\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 8.86 GiB already allocated; 132.75 MiB free; 754.38 MiB cached)\n",
      "=== False vgg19\n",
      "{'model': 'vgg19', 'bs': 1, 'ntrials': 206, 'time': 5.000938892364502, 'inference': False, 'srate': 41.19226497938686, 'mrate': 24.967126111186133}\n",
      "{'model': 'vgg19', 'bs': 3, 'ntrials': 110, 'time': 5.018542528152466, 'inference': False, 'srate': 65.7561429735431, 'mrate': 39.85558732998016}\n",
      "CUDA out of memory. Tried to allocate 392.00 MiB (GPU 0; 10.91 GiB total capacity; 9.59 GiB already allocated; 22.50 MiB free; 117.57 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 184.00 MiB (GPU 0; 10.91 GiB total capacity; 9.50 GiB already allocated; 26.25 MiB free; 202.82 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 1.20 GiB (GPU 0; 10.91 GiB total capacity; 8.37 GiB already allocated; 132.25 MiB free; 1.22 GiB cached)\n",
      "CUDA out of memory. Tried to allocate 3.59 GiB (GPU 0; 10.91 GiB total capacity; 8.49 GiB already allocated; 132.25 MiB free; 1.11 GiB cached)\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 8.88 GiB already allocated; 132.25 MiB free; 734.20 MiB cached)\n",
      "=== False vgg19_bn\n",
      "{'model': 'vgg19_bn', 'bs': 1, 'ntrials': 187, 'time': 5.005307197570801, 'inference': False, 'srate': 37.36034425434581, 'mrate': 22.644552976690047}\n",
      "CUDA out of memory. Tried to allocate 392.00 MiB (GPU 0; 10.91 GiB total capacity; 9.24 GiB already allocated; 131.00 MiB free; 362.80 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 392.00 MiB (GPU 0; 10.91 GiB total capacity; 9.68 GiB already allocated; 15.00 MiB free; 28.70 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 368.00 MiB (GPU 0; 10.91 GiB total capacity; 9.41 GiB already allocated; 16.75 MiB free; 304.60 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 1.20 GiB (GPU 0; 10.91 GiB total capacity; 9.57 GiB already allocated; 80.75 MiB free; 76.91 MiB cached)\n",
      "CUDA out of memory. Tried to allocate 3.59 GiB (GPU 0; 10.91 GiB total capacity; 8.49 GiB already allocated; 80.75 MiB free; 1.16 GiB cached)\n",
      "CUDA out of memory. Tried to allocate 11.96 GiB (GPU 0; 10.91 GiB total capacity; 8.88 GiB already allocated; 80.50 MiB free; 786.10 MiB cached)\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "for inference in [True, False]:\n",
    "    for mname in model_names:\n",
    "        print(\"===\", inference, mname)\n",
    "        for batchsize in [1, 3, 10, 30, 100, 300, 1000]:\n",
    "            result = experiment(inference=inference, mname=mname, batchsize=batchsize)\n",
    "            if result is not None:\n",
    "                print(result)\n",
    "                results.append(result)\n",
    "            time.sleep(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc83237f668>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc8826ecda0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(results)\n",
    "df = df[df[\"inference\"]==True]\n",
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_title(\"I/O Rate by Batch Size and Architecture for Inference\")\n",
    "for i, m in enumerate(natsorted(list(set(df[\"model\"])))):\n",
    "    subset = df[df[\"model\"]==m]\n",
    "    if \"resnet\" in m: linewidth = 3\n",
    "    else: linewidth = 1\n",
    "    ax.plot(subset[\"bs\"], subset[\"mrate\"]*1e6, label=m, \n",
    "            linewidth=linewidth,\n",
    "            color=model_colors[i])\n",
    "ax.plot([1, 1000], [1e9, 1e9], label=\"1 Gbyte/s\", color=\"gray\")\n",
    "ax.plot([1, 1000], [1e8, 1e8], label=\"100 Mbytes/s\", color=\"black\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc8321126a0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc8323660b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(results)\n",
    "df = df[df[\"inference\"]==False]\n",
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_title(\"I/O Rate by Batch Size and Architecture for Inference\")\n",
    "for i, m in enumerate(natsorted(list(set(df[\"model\"])))):\n",
    "    subset = df[df[\"model\"]==m]\n",
    "    if \"resnet\" in m: linewidth = 3\n",
    "    else: linewidth = 1\n",
    "    ax.plot(subset[\"bs\"], subset[\"mrate\"]*1e6, label=m, \n",
    "            linewidth=linewidth,\n",
    "            color=model_colors[i])\n",
    "ax.plot([1, 1000], [1e9, 1e9], label=\"1 Gbyte/s\", color=\"gray\")\n",
    "ax.plot([1, 1000], [1e8, 1e8], label=\"100 Mbytes/s\", color=\"black\")\n",
    "ax.legend()"
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}