Tornado yielding list

timing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/scott/anaconda3/lib/python3.6/site-packages/distributed/bokeh/core.py:57: UserWarning: \n",
      "Port 8787 is already in use. \n",
      "Perhaps you already have a cluster running?\n",
      "Hosting the diagnostics dashboard on a random port instead.\n",
      "  warnings.warn('\\n' + msg)\n"
     ]
    }
   ],
   "source": [
    "from tornado import gen\n",
    "from dask.distributed import Future, default_client, futures_of, wait\n",
    "import time\n",
    "\n",
    "from distributed import Client\n",
    "client = Client()\n",
    "\n",
    "class SHA:\n",
    "    def __init__(self, x=1, use_time=False):\n",
    "        self.x = x\n",
    "        self.use_time = use_time\n",
    "        \n",
    "    @gen.coroutine\n",
    "    def _fit(self):\n",
    "        if self.use_time:\n",
    "            time.sleep(self.x)\n",
    "        else:\n",
    "            yield gen.sleep(self.x)\n",
    "        raise gen.Return(self)\n",
    "        \n",
    "    def fit(self):\n",
    "        return default_client().sync(self._fit)\n",
    "\n",
    "class Hyperband:\n",
    "    def __init__(self, use_time=False):\n",
    "        self.use_time = use_time\n",
    "        \n",
    "    @gen.coroutine\n",
    "    def _fit(self):\n",
    "        SHAs = [SHA(x=x, use_time=self.use_time) for x in [1, 2]]\n",
    "        SHAs = yield [SHA.fit() for SHA in SHAs]\n",
    "        raise gen.Return(self)\n",
    "    \n",
    "    def fit(self):\n",
    "        return default_client().sync(self._fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 453 ms, sys: 91.4 ms, total: 544 ms\n",
      "Wall time: 2.01 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<__main__.Hyperband at 0x116a452e8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "Hyperband().fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 591 ms, sys: 115 ms, total: 706 ms\n",
      "Wall time: 3.05 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<__main__.Hyperband at 0x116ad1080>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "Hyperband(use_time=True).fit()"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}