notebook/intro_multiprocessing_via_ray.ipynb

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   "source": [
    "# Parallel Processing with ray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider the task to roll the dice a couple of times.  \n",
    "Each roll takes 0.1s, and if you roll 10 times, the total cost is 1s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 1.08 seconds\n",
      "Results: [1, 3, 3, 1, 2, 5, 5, 4, 2, 5]\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import random\n",
    "\n",
    "num_rolls = 10\n",
    "duration = 0.1\n",
    "\n",
    "def roll_dice():\n",
    "    time.sleep(duration)\n",
    "    return random.randint(1, 6)\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "results = [\n",
    "    roll_dice() for _ in range(num_rolls)\n",
    "]\n",
    "\n",
    "total_time = time.time() - start\n",
    "print(f\"Total time: {total_time:.2f} seconds\")\n",
    "print(f\"Results: {results}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: This shows that Python by default is running on only one CPU core.  \n",
    "Now let's try to parallelize this task using Ray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-25 16:09:30,310\tINFO worker.py:1612 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n"
     ]
    },
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       "    <tr>\n",
       "        <td style=\"text-align: left\"><b>Python version:</b></td>\n",
       "        <td style=\"text-align: left\"><b>3.8.17</b></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align: left\"><b>Ray version:</b></td>\n",
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       "RayContext(dashboard_url='127.0.0.1:8265', python_version='3.8.17', ray_version='2.6.1', ray_commit='d68bf04883af2e430fc3a50fd544bb7c84aff2e9', protocol_version=None)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ray\n",
    "ray.init()  \n",
    "# This starts a local scheduler and initializes the Ray system.\n",
    "# But there is no need to know more about it now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making the following modifications to the code above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 0.32 seconds\n",
      "Results: [2, 6, 3, 1, 2, 3, 2, 6, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "# 1. Attach the decorator to the function\n",
    "@ray.remote\n",
    "def roll_dice():\n",
    "    time.sleep(duration)\n",
    "    return random.randint(1, 6)\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "# 2. Call the function using .remote()\n",
    "results = [\n",
    "    roll_dice.remote() for _ in range(num_rolls)\n",
    "]\n",
    "\n",
    "# 3. Use ray.get() to retrieve the results\n",
    "results = ray.get(results)\n",
    "\n",
    "total_time = time.time() - start\n",
    "print(f\"Total time: {total_time:.2f} seconds\")\n",
    "print(f\"Results: {results}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- You can see that the computation time has been reduced.  \n",
    "- The benefits of decentralization can be felt very much as the number of function processes and executions increases.\n",
    "- Although this experiment was conducted on a single local machine, it is possible to execute tasks that are functions with **@ray.remote** on other machines."
   ]
  }
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