Dask array vs lists

dask_list_vs_array.ipynb

{
 "cells": [
  {
   "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": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask import delayed, visualize\n",
    "\n",
    "\n",
    "my_list = []\n",
    "\n",
    "my_list.append(1)\n",
    "my_list.append(delayed(sum)([1, 2]))\n",
    "my_list.append(delayed(sum)([1, 2]))\n",
    "visualize(delayed()(my_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "my_array = np.zeros(3, dtype=object)\n",
    "my_array[0] = 1 \n",
    "my_array[1] = delayed(sum)([1, 2])\n",
    "my_array[2] = delayed(sum)([1, 2])\n",
    "\n",
    "visualize(delayed()(my_array))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I can compute it if I first convert it to a list..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize(delayed()(my_array.tolist()))"
   ]
  },
  {
   "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
}