01-introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "## Notebook Objectives\n",
    "* **Spin up a Dask cluster.** A cluster consists of a scheduler (that manages flow of work) and workers (that perform actual computations).\n",
    "* **Introduction to Dask Delayed API**, an interface for parallelizing Python operations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spin up a Dask Cluster\n",
    "\n",
    "Spin up a new cluster with the following code. You can specify the number of workers with `n_workers`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:58550</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>4</li>\n",
       "  <li><b>Cores: </b>12</li>\n",
       "  <li><b>Memory: </b>17.18 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:58550' processes=4 threads=12, memory=17.18 GB>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.distributed import Client\n",
    "\n",
    "client = Client(n_workers=4)\n",
    "\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the 4 workers have 12 cores overall and 17GB of memory to use (might vary on your machine!).\n",
    "\n",
    "The Daskboard link takes you to Dask's diagnostic dashboard that contains real-time information about the state of your cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Always remember to close the session with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to **Dask Delayed API**\n",
    "\n",
    "Dask Delayed is a low-level collection that can be used to parallelize most python operations.\n",
    "\n",
    "For example, consider the following functions for incrementing a number and adding two numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import sleep\n",
    "\n",
    "def inc(x):\n",
    "    sleep(1)\n",
    "    return x + 1\n",
    "\n",
    "def add(x, y):\n",
    "    sleep(1)\n",
    "    return x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.1 ms, sys: 1.12 ms, total: 2.22 ms\n",
      "Wall time: 3.01 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "a = 10\n",
    "b = 10\n",
    "\n",
    "a = inc(a)\n",
    "b = inc(b)\n",
    "\n",
    "c = add(a, b)\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be parallelized using Dask Delayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask import delayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 773 µs, sys: 612 µs, total: 1.39 ms\n",
      "Wall time: 909 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Delayed('add-9158607d-7eb8-4415-b777-ec3030d51285')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "x = delayed(inc)(10)\n",
    "y = delayed(inc)(10)\n",
    "\n",
    "z = delayed(add)(x, y)\n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lazy evaluation\n",
    "\n",
    "The above code does not produce any output because Dask's Delayed API that evaluates _lazily_. Lazy evaluation refers to the paradigm of generating the entire task graph but evaluating it only when necessary.\n",
    "\n",
    "To evaluate and get an output, you can use the `compute()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.87 ms, sys: 3.21 ms, total: 8.08 ms\n",
      "Wall time: 2.01 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "z.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task Graph\n",
    "\n",
    "As mentioned earlier, a task graph determines how the computation must be executed in parallel. To view the task graph, you can call `visualize()` on interfaces that are backed by the Delayed API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will take a deeper look at the Delayed API in the next course."
   ]
  },
  {
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}