4_multicore_array_outofcore.ipynb

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   "source": [
    "# Array multicore outofcore\n",
    "\n",
    "Outofcore computing refers to processing data larger than memory by reading it in chunks from disk or creating chunks on the fly.\n",
    "\n",
    "Dask supports it natively, in this example we are still processing an array with multiple threads but the array would never fit in memory, we are creating chunks on the fly and processing them and then free the memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import dask.array as da"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# AA = da.random.normal(0,1,size=(200000,100000), chunks=(1000, 1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "AA = da.ones((200000,100000), chunks=(1000, 1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "(200, 100)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "AA.numblocks"
   ]
  },
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   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149.01161193847656"
      ]
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "AA.nbytes / 1024**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 38.8 s, sys: 12.1 s, total: 50.9 s\n",
      "Wall time: 23.6 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "20000000000.0"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "%time AA.sum().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.36 ms, sys: 3.4 ms, total: 6.77 ms\n",
      "Wall time: 5.42 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1000000.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time np.ones((1000, 1000)).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108.4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5.42 / 1000 * 200 * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Don't do this!\n",
    "# Just know you can save to disk data that does NOT fit in memory\n",
    "# AA.to_hdf5(\"AA.h5\", \"/data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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