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   "source": [
    "# Simple Hello World example for PyWren\n",
    "\n",
    "## Setup Logging (optional)\n",
    "Only activate the below lines if you want to see all debug messages from PyWren."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "import logging\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.INFO)\n",
    "%env PYWREN_LOGLEVEL=INFO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hello World Examples"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "This is a simple Hello World example, showing how to take a function and run it with pywren. First we import the necessary libraries. "
   ]
  },
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   "source": [
    "import pywren\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
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   "source": [
    "Pywren is designed to run any existing python functions you have, in parallel, at scale, on the cloud. So first, we create an example python function. The function must take a single argument:"
   ]
  },
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   "source": [
    "def my_function(x):\n",
    "    return x + 7\n",
    "my_function(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
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   },
   "source": [
    "To start using `pywren`, we first create an executor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "run_control": {
     "frozen": false,
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   "source": [
    "wrenexec = pywren.default_executor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
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   },
   "source": [
    "We can call `my_function(3)` remotely via `call_async`: "
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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    "run_control": {
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   "outputs": [],
   "source": [
    "future = wrenexec.call_async(my_function, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
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    }
   },
   "source": [
    "Future is a placeholder for the returned value from applying `my_function` to the number `3`. We can call `result` on it and get the result. Note that this will block until the remote job has completed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "run_control": {
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   "outputs": [],
   "source": [
    "future.result()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
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   "source": [
    "You can apply `my_function` to a list of arguments, and each will be executed remotely at the same time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "run_control": {
     "frozen": false,
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   },
   "outputs": [],
   "source": [
    "futures = wrenexec.map(my_function, range(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "The pywren `get_all_results` function will wait until all of the futures are done and return their results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "run_control": {
     "frozen": false,
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   },
   "outputs": [],
   "source": [
    "pywren.get_all_results(futures)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it, we are now familiar how to make use of PyWren for parallelly executing a Python function across many AWS Lambda functions."
   ]
  }
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