Centralized-PS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Centralized parameter server\n",
    "This notebook shows a centralized parameter server. Discussion can be found in this issue: https://github.com/dask/dask-ml/issues/171\n",
    "\n",
    "Currently, this notebook runs into a serialization issue with Torch models.\n",
    "\n",
    "This notebook does have more advanced structure than the centralized example in https://gist.github.com/stsievert/ff8a1df9300a82f15a2704e913469522."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {
      "image/png": {
       "width": 400
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('imgs/centralized.png', width=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import argparse\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "import torchvision\n",
    "import numpy as np\n",
    "\n",
    "import itertools\n",
    "from time import sleep, time\n",
    "import toolz\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1.23.0',\n",
       " '/Users/ssievert/Developer/dask/distributed/distributed/__init__.py')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import distributed\n",
    "distributed.__version__, distributed.__file__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
    "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
    "        self.conv2_drop = nn.Dropout2d()\n",
    "        self.fc1 = nn.Linear(320, 50)\n",
    "        self.fc2 = nn.Linear(50, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
    "        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
    "        x = x.view(-1, 320)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.dropout(x, training=self.training)\n",
    "        x = self.fc2(x)\n",
    "        return F.log_softmax(x, dim=1)\n",
    "    \n",
    "def num_params(model):\n",
    "    param_sizes = [np.prod(tuple(p.size())) for p in model.parameters()]\n",
    "    return sum(param_sizes)\n",
    "    \n",
    "#models = {num_params(model): model for model in models}\n",
    "\n",
    "# ResNets are larger models; they range between 18 and 150ish layers with millions of parameters\n",
    "import models\n",
    "\n",
    "# These register_generic lines shouldn't be necessary; [1]\n",
    "# defines a serialization scheme for torch.nn.Module, which\n",
    "# every PyTorch model inherits from.\n",
    "# [1]:https://github.com/mrocklin/distributed/blob/88045547f3978d028504f1caa9d1cbd1b5885c50/distributed/protocol/torch.py\n",
    "#\n",
    "# from distributed.protocol import register_generic\n",
    "# register_generic(models.ResNet)\n",
    "resnets = [models.resnet(layers=n) for n in [18, 34, 50, 101, 152]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from types import SimpleNamespace\n",
    "args = SimpleNamespace(batch_size=64, test_batch_size=1000,\n",
    "                       epochs=2, lr=0.01, momentum=0.5,\n",
    "                       no_cuda=True, seed=42, log_interval=80)\n",
    "    \n",
    "use_cuda = not args.no_cuda and torch.cuda.is_available()\n",
    "torch.manual_seed(args.seed)\n",
    "\n",
    "kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3>Client</h3>\n",
       "<ul>\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:50723\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3>Cluster</h3>\n",
       "<ul>\n",
       "  <li><b>Workers: </b>8</li>\n",
       "  <li><b>Cores: </b>8</li>\n",
       "  <li><b>Memory: </b>17.18 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: scheduler='tcp://127.0.0.1:50723' processes=8 cores=8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.distributed import Client, wait\n",
    "client = Client()\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter Server\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "import copy\n",
    "\n",
    "def clone(model):\n",
    "    return copy.deepcopy(model)\n",
    "\n",
    "class PS:\n",
    "    model = None\n",
    "    n_steps = 0\n",
    "\n",
    "    def __init__(self, model, args, device, num_workers=1, max_iter=30):\n",
    "        model = model.to(device)\n",
    "        self.model = model\n",
    "        self._model = clone(model)\n",
    "        self.optimizer = optim.SGD(self._model.parameters(), lr=args.lr, momentum=args.momentum)\n",
    "        self.n_steps = 0\n",
    "        self._grads_recvd = 0\n",
    "        self._updating = False\n",
    "        self.max_iter = max_iter\n",
    "        self.num_workers = num_workers\n",
    "        self.start_time = time()\n",
    "        \n",
    "        transform_test = transforms.Compose([\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "        ])\n",
    "\n",
    "        test_set = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)\n",
    "        test_loader = torch.utils.data.DataLoader(test_set, batch_size=1000, shuffle=False)\n",
    "        self.test_loader = test_loader\n",
    "\n",
    "    def pull(self):\n",
    "        \"\"\"\n",
    "        For a worker to pull a model from this PS\n",
    "        \"\"\"\n",
    "        return self.model\n",
    "\n",
    "    def push(self, grads, key=None):\n",
    "        \"\"\"\n",
    "        For a worker to push some gradients to this PS\n",
    "        \"\"\"\n",
    "        if grads:\n",
    "            if self.n_steps is not None and self.n_steps > self.max_iter:\n",
    "                self.n_steps = None\n",
    "            if self.n_steps is None:\n",
    "                return None\n",
    "            if key != self.n_steps or self._updating:\n",
    "                assert key < self.n_steps\n",
    "                return self.n_steps\n",
    "            \n",
    "            assert not self._updating\n",
    "            if self._grads_recvd == 0:\n",
    "                self.optimizer.zero_grad()\n",
    "            \n",
    "            self._grads_recvd += 1\n",
    "            self.aggregate(grads)\n",
    "            if self._grads_recvd == self.num_workers:\n",
    "                self._updating = True\n",
    "                self._grads_recvd = 0\n",
    "                self.step()\n",
    "                self.n_steps += 1\n",
    "                self._updating = False\n",
    "            return self.n_steps\n",
    "\n",
    "    def aggregate(self, grads):\n",
    "        for name, param in self._model.named_parameters():\n",
    "            if param.grad is None:\n",
    "                param.grad = 0 * param\n",
    "            param.grad += grads[name]\n",
    "            \n",
    "    def test(self, model, loader, device):\n",
    "        model.eval()\n",
    "        test_loss = 0\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        num = 0\n",
    "        with torch.no_grad():\n",
    "            for batch_idx, (inputs, targets) in enumerate(loader):\n",
    "                inputs, targets = inputs.to(device), targets.to(device)\n",
    "                outputs = net(inputs)\n",
    "                loss = criterion(outputs, targets)\n",
    "\n",
    "                test_loss += loss.item()\n",
    "                _, predicted = outputs.max(1)\n",
    "                total += targets.size(0)\n",
    "                num += 1\n",
    "                correct += predicted.eq(targets).sum().item()\n",
    "        return {'step': self.n_steps, \n",
    "                'time': time() - self.start_time,\n",
    "                'test_loss': test_loss / num,\n",
    "                'test_acc': correct / total}\n",
    "\n",
    "    def step(self):\n",
    "        self.optimizer.step()\n",
    "        if self.n_steps % 5 == 0:\n",
    "            self.scores += [test(self.model, self.test_loader, self.device)]\n",
    "                    \n",
    "        self.model = clone(self._model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, device, data, target):\n",
    "    model.train()\n",
    "    data, target = data.to(device), target.to(device)\n",
    "    output = model(data)\n",
    "    loss = F.nll_loss(output, target)\n",
    "    loss.backward()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import toolz\n",
    "def worker(ps, device, train_loader, worker_id=0):\n",
    "    meta = {'comm_grad': 0, 'comm_model': 0, 'comp_grad': 0,\n",
    "            'n_comm_grad': 0, 'n_steps': 0,\n",
    "            'worker_id': worker_id,\n",
    "            'scores': []}\n",
    "    last_step = -1\n",
    "    step = 0\n",
    "    train_loader = iter(train_loader)\n",
    "    start_step = ps.push(None).result()\n",
    "    \n",
    "    loop_start = time()\n",
    "    while True:\n",
    "        if step != last_step:\n",
    "            start = time()\n",
    "            model = ps.pull().result()\n",
    "            last_step = step\n",
    "            meta['comm_model'] += time() - start\n",
    "            meta['n_steps'] += 1\n",
    "            \n",
    "        start = time()\n",
    "        data, target = next(train_loader)\n",
    "        model = train(model, device, data, target)\n",
    "        meta['comp_grad'] += time() - start\n",
    "        \n",
    "        if True:\n",
    "            check = toolz.first(model.parameters())\n",
    "            check = check.detach().numpy().flat[:3]\n",
    "            print(step, check)\n",
    "        \n",
    "        grads = {name: p.grad.data for name, p in model.named_parameters()}\n",
    "        \n",
    "        start = time()\n",
    "        next_step = ps.push(grads, key=step).result()\n",
    "        meta['n_comm_grad'] += 1\n",
    "        meta['comm_grad'] += time() - start\n",
    "        \n",
    "        if step == next_step:\n",
    "            sleep(10e-3)\n",
    "            step = ps.n_steps\n",
    "        else:\n",
    "            step = next_step\n",
    "        \n",
    "        if step is None:\n",
    "            break\n",
    "            \n",
    "    meta['avg_comm_grad'] = meta['comm_grad'] / meta['n_comm_grad']\n",
    "    meta['avg_comp_grad'] = meta['comp_grad'] / meta['n_comm_grad']\n",
    "    meta['avg_comm_model'] = meta['comm_model'] / meta['n_steps']\n",
    "    meta['opt_time'] = time() - loop_start\n",
    "    return meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(model, batch_size=32, num_workers=4, iters=30, scaling='weak'):\n",
    "    assert scaling in {'weak', 'strong'}\n",
    "    if scaling == 'weak':\n",
    "        batch_size //= num_workers\n",
    "    n_params = [np.prod(list(p.size())) for p in model.parameters()]\n",
    "    test_loader = torch.utils.data.DataLoader(\n",
    "        datasets.MNIST('../data', train=False, transform=transforms.Compose([\n",
    "                           transforms.ToTensor(),\n",
    "                           transforms.Normalize((0.1307,), (0.3081,))\n",
    "                       ])),\n",
    "    batch_size=1000, shuffle=True, **kwargs)\n",
    "    \n",
    "    transform_train = transforms.Compose([\n",
    "        transforms.RandomCrop(32, padding=4),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "    ])\n",
    "    train_set = torchvision.datasets.CIFAR10('./data', train=True, download=False, transform=transform_train)\n",
    "    train_loader = torch.utils.data.DataLoader(train_set,\n",
    "                                              batch_size=batch_size,\n",
    "                                              shuffle=True)\n",
    "                                             #num_workers=1)\n",
    "    #train_loader = torch.utils.data.DataLoader(\n",
    "    #    datasets.MNIST('../data', train=True, download=True,\n",
    "    #                   transform=transforms.Compose([\n",
    "    #                       transforms.ToTensor(),\n",
    "    #                       transforms.Normalize((0.1307,), (0.3081,))\n",
    "    #                   ])),\n",
    "    #    batch_size=batch_size, shuffle=True, **kwargs)\n",
    "\n",
    "    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
    "    test_loader = None\n",
    "    train_loader, test_loader, model = client.scatter([train_loader, test_loader, model])\n",
    "\n",
    "    ps = client.submit(PS, model, args, device, \n",
    "                       max_iter=iters, num_workers=num_workers,\n",
    "                       actor=True).result()\n",
    "    \n",
    "    futures = [client.submit(worker, ps, device, train_loader, worker_id=i)\n",
    "               for i in range(num_workers)]\n",
    "    metas = client.gather(futures)\n",
    "    metas = [{'worker_batch_size': batch_size,\n",
    "              'num_workers': num_workers,\n",
    "              'iters': iters,\n",
    "              'scaling': scaling,\n",
    "              'params': sum(n_params),\n",
    "              **m} for m in metas]\n",
    "    return metas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_workers = 4\n",
    "# model = resnets[0]\n",
    "model = Net()\n",
    "# data = [test(model, num_workers=num_workers, iters=5, scaling='weak') \n",
    "#         for num_workers in [2, 4, 6]]\n",
    "#         #for model in resnets[:3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<class '__main__.Net'>, <class 'torch.nn.modules.module.Module'>, <class 'object'>]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "distributed.core - ERROR - No dispatch for <class '__main__.Net'>\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/ssievert/Developer/dask/distributed/distributed/core.py\", line 340, in handle_comm\n",
      "    result = yield result\n",
      "  File \"/Users/ssievert/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
      "    value = future.result()\n",
      "  File \"/Users/ssievert/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
      "    raise_exc_info(self._exc_info)\n",
      "  File \"<string>\", line 4, in raise_exc_info\n",
      "  File \"/Users/ssievert/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
      "    yielded = self.gen.throw(*exc_info)\n",
      "  File \"/Users/ssievert/Developer/dask/distributed/distributed/scheduler.py\", line 2214, in scatter\n",
      "    report=False)\n",
      "  File \"/Users/ssievert/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
      "    value = future.result()\n",
      "  File \"/Users/ssievert/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
      "    raise_exc_info(self._exc_info)\n",
      "  File \"<string>\", line 4, in raise_exc_info\n",
      "  File \"/Users/ssievert/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
      "    yielded = self.gen.throw(*exc_info)\n",
      "  File \"/Users/ssievert/Developer/dask/distributed/distributed/utils_comm.py\", line 129, in scatter_to_workers\n",
      "    for address, v in d.items()])\n",
      "  File \"/Users/ssievert/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
      "    value = future.result()\n",
      "  File \"/Users/ssievert/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
      "    raise_exc_info(self._exc_info)\n",
      "  File \"<string>\", line 4, in raise_exc_info\n",
      "  File \"/Users/ssievert/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
      "    yielded = self.gen.throw(*exc_info)\n",
      "  File \"/Users/ssievert/Developer/dask/distributed/distributed/utils.py\", line 214, in All\n",
      "    result = yield tasks.next()\n",
      "  File \"/Users/ssievert/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
      "    value = future.result()\n",
      "  File \"/Users/ssievert/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
      "    raise_exc_info(self._exc_info)\n",
      "  File \"<string>\", line 4, in raise_exc_info\n",
      "  File \"/Users/ssievert/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
      "    yielded = self.gen.throw(*exc_info)\n",
      "  File \"/Users/ssievert/Developer/dask/distributed/distributed/core.py\", line 636, in send_recv_from_rpc\n",
      "    result = yield send_recv(comm=comm, op=key, **kwargs)\n",
      "  File \"/Users/ssievert/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
      "    value = future.result()\n",
      "  File \"/Users/ssievert/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
      "    raise_exc_info(self._exc_info)\n",
      "  File \"<string>\", line 4, in raise_exc_info\n",
      "  File \"/Users/ssievert/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
      "    yielded = self.gen.send(value)\n",
      "  File \"/Users/ssievert/Developer/dask/distributed/distributed/core.py\", line 468, in send_recv\n",
      "    raise Exception(response['text'])\n",
      "Exception: No dispatch for <class '__main__.Net'>\n"
     ]
    },
    {
     "ename": "Exception",
     "evalue": "No dispatch for <class '__main__.Net'>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-d552f61c6e61>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmro\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-11-89345b71a9d4>\u001b[0m in \u001b[0;36mtest\u001b[0;34m(model, batch_size, num_workers, iters, scaling)\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cuda\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0muse_cuda\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0mtest_loader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m     \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     ps = client.submit(PS, model, args, device, \n",
      "\u001b[0;32m~/Developer/dask/distributed/distributed/client.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, data, workers, broadcast, direct, hash, maxsize, timeout, asynchronous)\u001b[0m\n\u001b[1;32m   1780\u001b[0m                              \u001b[0mbroadcast\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbroadcast\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdirect\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1781\u001b[0m                              \u001b[0mlocal_worker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_worker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1782\u001b[0;31m                              asynchronous=asynchronous, hash=hash)\n\u001b[0m\u001b[1;32m   1783\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1784\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoroutine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Developer/dask/distributed/distributed/client.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    643\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    644\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 645\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Developer/dask/distributed/distributed/utils.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(loop, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m             \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 277\u001b[0;31m         \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    278\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/six.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m    691\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    692\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 693\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    694\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    695\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Developer/dask/distributed/distributed/utils.py\u001b[0m in \u001b[0;36mf\u001b[0;34m()\u001b[0m\n\u001b[1;32m    260\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m                 \u001b[0mfuture\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimedelta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseconds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    263\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m             \u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1053\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1054\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1055\u001b[0;31m                         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1056\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1057\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhad_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m                 \u001b[0mraise_exc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m                 \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/util.py\u001b[0m in \u001b[0;36mraise_exc_info\u001b[0;34m(exc_info)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1061\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1062\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1063\u001b[0;31m                             \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1064\u001b[0m                         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1065\u001b[0m                             \u001b[0;31m# Break up a reference to itself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Developer/dask/distributed/distributed/client.py\u001b[0m in \u001b[0;36m_scatter\u001b[0;34m(self, data, workers, broadcast, direct, local_worker, timeout, hash)\u001b[0m\n\u001b[1;32m   1648\u001b[0m                                              \u001b[0mclient\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1649\u001b[0m                                              \u001b[0mbroadcast\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbroadcast\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1650\u001b[0;31m                                              timeout=timeout)\n\u001b[0m\u001b[1;32m   1651\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1652\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFuture\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1053\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1054\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1055\u001b[0;31m                         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1056\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1057\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhad_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m                 \u001b[0mraise_exc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m                 \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/util.py\u001b[0m in \u001b[0;36mraise_exc_info\u001b[0;34m(exc_info)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1061\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1062\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1063\u001b[0;31m                             \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1064\u001b[0m                         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1065\u001b[0m                             \u001b[0;31m# Break up a reference to itself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Developer/dask/distributed/distributed/core.py\u001b[0m in \u001b[0;36msend_recv_from_rpc\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    572\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m                 \u001b[0mcomm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlive_comm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 574\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0msend_recv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    575\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mRPCClosed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCommClosedError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m                 raise e.__class__(\"%s: while trying to call remote method %r\"\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1053\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1054\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1055\u001b[0;31m                         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1056\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1057\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhad_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m                 \u001b[0mraise_exc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m                 \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/util.py\u001b[0m in \u001b[0;36mraise_exc_info\u001b[0;34m(exc_info)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1067\u001b[0m                             \u001b[0mexc_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1068\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1069\u001b[0;31m                         \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1070\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1071\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mstack_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontexts\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0morig_stack_contexts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Developer/dask/distributed/distributed/core.py\u001b[0m in \u001b[0;36msend_recv\u001b[0;34m(comm, reply, serializers, deserializers, **kwargs)\u001b[0m\n\u001b[1;32m    464\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'status'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'uncaught-error'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcomm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeserialize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 466\u001b[0;31m             \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mclean_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    467\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    468\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/six.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m    690\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    691\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 692\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    693\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Developer/dask/distributed/distributed/core.py\u001b[0m in \u001b[0;36mhandle_comm\u001b[0;34m()\u001b[0m\n\u001b[1;32m    338\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFuture\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m                             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ongoing_coroutines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 340\u001b[0;31m                             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    341\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCommClosedError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    342\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'running'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1053\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1054\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1055\u001b[0;31m                         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1056\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1057\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhad_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m()\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m                 \u001b[0mraise_exc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m                 \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/util.py\u001b[0m in \u001b[0;36mraise_exc_info\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1061\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1062\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1063\u001b[0;31m                             \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1064\u001b[0m                         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1065\u001b[0m                             \u001b[0;31m# Break up a reference to itself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Developer/dask/distributed/distributed/scheduler.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m()\u001b[0m\n\u001b[1;32m   2212\u001b[0m         keys, who_has, nbytes = yield scatter_to_workers(ncores, data,\n\u001b[1;32m   2213\u001b[0m                                                          \u001b[0mrpc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrpc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2214\u001b[0;31m                                                          report=False)\n\u001b[0m\u001b[1;32m   2215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2216\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwho_has\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwho_has\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1053\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1054\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1055\u001b[0;31m                         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1056\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1057\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhad_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m()\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m                 \u001b[0mraise_exc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m                 \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/util.py\u001b[0m in \u001b[0;36mraise_exc_info\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1061\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1062\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1063\u001b[0;31m                             \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1064\u001b[0m                         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1065\u001b[0m                             \u001b[0;31m# Break up a reference to itself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Developer/dask/distributed/distributed/utils_comm.py\u001b[0m in \u001b[0;36mscatter_to_workers\u001b[0;34m()\u001b[0m\n\u001b[1;32m    127\u001b[0m         out = yield All([rpcs[address].update_data(data=v, report=report,\n\u001b[1;32m    128\u001b[0m                                                    serializers=serializers)\n\u001b[0;32m--> 129\u001b[0;31m                          for address, v in d.items()])\n\u001b[0m\u001b[1;32m    130\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrpcs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1053\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1054\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1055\u001b[0;31m                         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1056\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1057\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhad_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m()\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m                 \u001b[0mraise_exc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m                 \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/util.py\u001b[0m in \u001b[0;36mraise_exc_info\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1061\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1062\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1063\u001b[0;31m                             \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1064\u001b[0m                         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1065\u001b[0m                             \u001b[0;31m# Break up a reference to itself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Developer/dask/distributed/distributed/utils.py\u001b[0m in \u001b[0;36mAll\u001b[0;34m()\u001b[0m\n\u001b[1;32m    212\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtasks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mtasks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    215\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m             \u001b[0;34m@\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoroutine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1053\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1054\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1055\u001b[0;31m                         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1056\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1057\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhad_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m()\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m                 \u001b[0mraise_exc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m                 \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/util.py\u001b[0m in \u001b[0;36mraise_exc_info\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1061\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1062\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1063\u001b[0;31m                             \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1064\u001b[0m                         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1065\u001b[0m                             \u001b[0;31m# Break up a reference to itself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Developer/dask/distributed/distributed/core.py\u001b[0m in \u001b[0;36msend_recv_from_rpc\u001b[0;34m()\u001b[0m\n\u001b[1;32m    634\u001b[0m             \u001b[0mcomm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    635\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 636\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0msend_recv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    637\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    638\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreuse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcomm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1053\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1054\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1055\u001b[0;31m                         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1056\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1057\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhad_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m()\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m                 \u001b[0mraise_exc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m                 \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/util.py\u001b[0m in \u001b[0;36mraise_exc_info\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1067\u001b[0m                             \u001b[0mexc_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1068\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1069\u001b[0;31m                         \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1070\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1071\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mstack_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontexts\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0morig_stack_contexts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Developer/dask/distributed/distributed/core.py\u001b[0m in \u001b[0;36msend_recv\u001b[0;34m()\u001b[0m\n\u001b[1;32m    466\u001b[0m             \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mclean_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    467\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 468\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    469\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReturn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    470\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mException\u001b[0m: No dispatch for <class '__main__.Net'>"
     ]
    }
   ],
   "source": [
    "print(type(model).mro())\n",
    "test(model, num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_comm_grad</th>\n",
       "      <th>avg_comm_model</th>\n",
       "      <th>avg_comp_grad</th>\n",
       "      <th>comm_grad</th>\n",
       "      <th>comm_model</th>\n",
       "      <th>comp_grad</th>\n",
       "      <th>iters</th>\n",
       "      <th>n_comm_grad</th>\n",
       "      <th>n_steps</th>\n",
       "      <th>num_workers</th>\n",
       "      <th>opt_time</th>\n",
       "      <th>params</th>\n",
       "      <th>scaling</th>\n",
       "      <th>worker_batch_size</th>\n",
       "      <th>worker_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.431208</td>\n",
       "      <td>0.424327</td>\n",
       "      <td>1.617879</td>\n",
       "      <td>4.312082</td>\n",
       "      <td>2.545961</td>\n",
       "      <td>16.178787</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>23.279888</td>\n",
       "      <td>11173962</td>\n",
       "      <td>weak</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.500862</td>\n",
       "      <td>0.428205</td>\n",
       "      <td>1.697454</td>\n",
       "      <td>4.507757</td>\n",
       "      <td>2.997432</td>\n",
       "      <td>15.277085</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>22.902149</td>\n",
       "      <td>11173962</td>\n",
       "      <td>weak</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.675556</td>\n",
       "      <td>0.764108</td>\n",
       "      <td>1.192803</td>\n",
       "      <td>8.106667</td>\n",
       "      <td>4.584646</td>\n",
       "      <td>14.313631</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>27.309265</td>\n",
       "      <td>11173962</td>\n",
       "      <td>weak</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.885406</td>\n",
       "      <td>1.089637</td>\n",
       "      <td>1.391538</td>\n",
       "      <td>7.083248</td>\n",
       "      <td>7.627456</td>\n",
       "      <td>11.132301</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>26.068309</td>\n",
       "      <td>11173962</td>\n",
       "      <td>weak</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.739191</td>\n",
       "      <td>0.703265</td>\n",
       "      <td>1.170361</td>\n",
       "      <td>8.131097</td>\n",
       "      <td>4.219589</td>\n",
       "      <td>12.873971</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>25.687755</td>\n",
       "      <td>11173962</td>\n",
       "      <td>weak</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_comm_grad  avg_comm_model  avg_comp_grad  comm_grad  comm_model  \\\n",
       "0       0.431208        0.424327       1.617879   4.312082    2.545961   \n",
       "1       0.500862        0.428205       1.697454   4.507757    2.997432   \n",
       "2       0.675556        0.764108       1.192803   8.106667    4.584646   \n",
       "3       0.885406        1.089637       1.391538   7.083248    7.627456   \n",
       "4       0.739191        0.703265       1.170361   8.131097    4.219589   \n",
       "\n",
       "   comp_grad  iters  n_comm_grad  n_steps  num_workers   opt_time    params  \\\n",
       "0  16.178787      5           10        6            2  23.279888  11173962   \n",
       "1  15.277085      5            9        7            2  22.902149  11173962   \n",
       "2  14.313631      5           12        6            4  27.309265  11173962   \n",
       "3  11.132301      5            8        7            4  26.068309  11173962   \n",
       "4  12.873971      5           11        6            4  25.687755  11173962   \n",
       "\n",
       "  scaling  worker_batch_size  worker_id  \n",
       "0    weak                 16          0  \n",
       "1    weak                 16          1  \n",
       "2    weak                  8          0  \n",
       "3    weak                  8          1  \n",
       "4    weak                  8          2  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(sum(data, []))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "show_var = 'params'\n",
    "show_var = 'num_workers'\n",
    "show = df.pivot_table(index=show_var, values='opt_time',\n",
    "                      aggfunc=np.median)\n",
    "show.plot.bar()\n",
    "plt.ylabel('Total optimization time')\n",
    "plt.savefig('total_opt_time.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[((2, 0), 0    23.279888\n",
       "  Name: opt_time, dtype: float64), ((2, 1), 1    22.902149\n",
       "  Name: opt_time, dtype: float64), ((4, 0), 2    27.309265\n",
       "  Name: opt_time, dtype: float64), ((4, 1), 3    26.068309\n",
       "  Name: opt_time, dtype: float64), ((4, 2), 4    25.687755\n",
       "  Name: opt_time, dtype: float64), ((4, 3), 5    26.111374\n",
       "  Name: opt_time, dtype: float64), ((6, 0), 6    30.799753\n",
       "  Name: opt_time, dtype: float64), ((6, 1), 7    26.25958\n",
       "  Name: opt_time, dtype: float64), ((6, 2), 8    26.862038\n",
       "  Name: opt_time, dtype: float64), ((6, 3), 9    26.650819\n",
       "  Name: opt_time, dtype: float64), ((6, 4), 10    26.952295\n",
       "  Name: opt_time, dtype: float64), ((6, 5), 11    27.486288\n",
       "  Name: opt_time, dtype: float64)]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = df.groupby([show_var, 'worker_id'])['opt_time']\n",
    "list(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "variables = {'avg worker time (s)': ['avg_comm_grad', 'avg_comm_model', 'avg_comp_grad'],\n",
    "             'total worker time (s)': ['comm_grad', 'comm_model', 'comp_grad'],\n",
    "             'number': ['n_steps', 'n_comm_grad']}\n",
    "\n",
    "w = 6\n",
    "plt, axs = plt.subplots(ncols=3, figsize=(3*w, w))\n",
    "for (label, values), ax in zip(variables.items(), axs):\n",
    "    show = df.melt(id_vars=[show_var], value_vars=values, value_name=label)\n",
    "    sns.lineplot(data=show, x=show_var, hue='variable', y=label,\n",
    "                 estimator=np.median, ax=ax, marker='o')\n",
    "    if 'workers' in show_var:\n",
    "        ax.set_xscale('log', basex=2)\n",
    "plt.savefig('times.png', dpi=300)"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}