{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Executor: scheduler=localhost:8786 workers=80 threads=80>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from distributed import Executor, progress, wait\n",
    "e = Executor('localhost:8786')\n",
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import dask.array as da\n",
    "x = da.random.randint(0, 2000, size=(10240, 1024000), \n",
    "                      chunks=(40, 1024000)).astype('u2')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda/lib/python3.5/site-packages/dask/imperative.py:5: UserWarning: dask.imperative has been moved to dask.delayed\n",
      "  warn(\"dask.imperative has been moved to dask.delayed\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 202 ms, sys: 166 µs, total: 202 ms\n",
      "Wall time: 2.84 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "x = e.persist(x)\n",
    "wait(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 75.8 ms, sys: 3.98 ms, total: 79.8 ms\n",
      "Wall time: 78.8 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "y = (x.rechunk((80, 512000))\n",
    "      .rechunk((160, 256000))\n",
    "      .rechunk((320, 128000))\n",
    "      .rechunk((640, 64000))\n",
    "      .rechunk((1280, 32000))\n",
    "      .rechunk((2560, 16000))\n",
    "      .rechunk((5120, 8000))\n",
    "      .rechunk((10240, 4000)))\n",
    "z = y.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 528 ms, sys: 28.6 ms, total: 556 ms\n",
      "Wall time: 1min 35s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[10480510468080]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "e.get(z.dask, z._keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}