5b_job_wait_and_run_time.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 5b: Job Wait- and Runtimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import psutil\n",
    "from pyspark.sql.types import IntegerType\n",
    "import pyspark.sql.functions as F\n",
    "from util.read_and_print_df import *\n",
    "from util.handle_nan import *\n",
    "from util.extract_json_attributes import *\n",
    "from util.plotting import *\n",
    "import matplotlib.pyplot as plt\n",
    "import builtins\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from datetime import date, datetime, time, timedelta\n",
    "import time, datetime, pytz\n",
    "\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['font.family'] = 'libertine'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <th>end_date</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>2021-12-26 17:11:18</td>\n",
       "      <td>2021-12-26 23:06:31</td>\n",
       "      <td>2021-12-31 23:06:50</td>\n",
       "      <td>r13n5</td>\n",
       "      <td>normal</td>\n",
       "      <td>1</td>\n",
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       "      <td>TIMEOUT</td>\n",
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       "      <td>2021-12-31 23:06:50</td>\n",
       "      <td>r14n27</td>\n",
       "      <td>normal</td>\n",
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       "      <td>TIMEOUT</td>\n",
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       "      <td>2021-12-26 23:06:43</td>\n",
       "      <td>2021-12-31 23:06:50</td>\n",
       "      <td>r15n12</td>\n",
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       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>{r15n12}</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2021-12-26 23:06:43</td>\n",
       "      <td>2021-12-31 23:06:50</td>\n",
       "      <td>r10n14</td>\n",
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       "      <td>TIMEOUT</td>\n",
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       "      <td>2021-12-26 23:06:43</td>\n",
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       "      <td>r10n30</td>\n",
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       "      <td>16</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>{r10n30}</td>\n",
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       "      <td>2022-11-01 13:56:17</td>\n",
       "      <td>2022-11-01 13:56:24</td>\n",
       "      <td>r13n10</td>\n",
       "      <td>shared</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>FAILED</td>\n",
       "      <td>{r13n10}</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1596960</th>\n",
       "      <td>2323561</td>\n",
       "      <td>2022-11-01 13:56:45</td>\n",
       "      <td>2022-11-01 13:56:45</td>\n",
       "      <td>2022-11-01 13:58:12</td>\n",
       "      <td>r28n1</td>\n",
       "      <td>gpu_titanrtx_shared</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>FAILED</td>\n",
       "      <td>{r28n1}</td>\n",
       "      <td>1</td>\n",
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       "      <td>2022-11-01 13:59:01</td>\n",
       "      <td>r14n20</td>\n",
       "      <td>normal</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>{r14n20}</td>\n",
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       "    <tr>\n",
       "      <th>1596962</th>\n",
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       "      <td>2022-11-01 13:58:19</td>\n",
       "      <td>2022-11-01 13:59:18</td>\n",
       "      <td>r14n21</td>\n",
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       "      <td>1</td>\n",
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       "      <td>COMPLETED</td>\n",
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       "<p>1596963 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "              id         submit_date          start_date            end_date  \\\n",
       "0              1 2021-12-26 17:11:18 2021-12-26 23:06:31 2021-12-31 23:06:50   \n",
       "1              2 2021-12-26 17:11:18 2021-12-26 23:06:43 2021-12-31 23:06:50   \n",
       "2              3 2021-12-26 17:11:18 2021-12-26 23:06:43 2021-12-31 23:06:50   \n",
       "3              4 2021-12-26 17:11:18 2021-12-26 23:06:43 2021-12-31 23:06:50   \n",
       "4              5 2021-12-26 17:11:18 2021-12-26 23:06:43 2021-12-31 23:06:50   \n",
       "...          ...                 ...                 ...                 ...   \n",
       "1596958  2323559 2022-11-01 13:55:57 2022-11-01 13:55:57 2022-11-01 13:58:54   \n",
       "1596959  2323560 2022-11-01 13:56:12 2022-11-01 13:56:17 2022-11-01 13:56:24   \n",
       "1596960  2323561 2022-11-01 13:56:45 2022-11-01 13:56:45 2022-11-01 13:58:12   \n",
       "1596961  2323557 2022-11-01 13:54:21 2022-11-01 13:58:19 2022-11-01 13:59:01   \n",
       "1596962  2323558 2022-11-01 13:54:26 2022-11-01 13:58:19 2022-11-01 13:59:18   \n",
       "\n",
       "           node            nodetypes  numnodes  numcores      state  \\\n",
       "0         r13n5               normal         1        16    TIMEOUT   \n",
       "1        r14n27               normal         1        16    TIMEOUT   \n",
       "2        r15n12               normal         1        16    TIMEOUT   \n",
       "3        r10n14               normal         1        16    TIMEOUT   \n",
       "4        r10n30               normal         1        16    TIMEOUT   \n",
       "...         ...                  ...       ...       ...        ...   \n",
       "1596958   r28n1  gpu_titanrtx_shared         1         6  CANCELLED   \n",
       "1596959  r13n10               shared         1         6     FAILED   \n",
       "1596960   r28n1  gpu_titanrtx_shared         1         6     FAILED   \n",
       "1596961  r14n20               normal         1        16  COMPLETED   \n",
       "1596962  r14n21               normal         1        16  COMPLETED   \n",
       "\n",
       "        split_nodes  is_ml  \n",
       "0           {r13n5}      0  \n",
       "1          {r14n27}      0  \n",
       "2          {r15n12}      0  \n",
       "3          {r10n14}      0  \n",
       "4          {r10n30}      0  \n",
       "...             ...    ...  \n",
       "1596958     {r28n1}      1  \n",
       "1596959    {r13n10}      0  \n",
       "1596960     {r28n1}      1  \n",
       "1596961    {r14n20}      0  \n",
       "1596962    {r14n21}      0  \n",
       "\n",
       "[1596963 rows x 11 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_slurm_pd = pd.read_parquet(path_job_dataset)\n",
    "mark_ml_df(df_slurm_pd)\n",
    "\n",
    "df_slurm_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2021-12-26 17:11:18</td>\n",
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       "      <td>2021-12-31 23:06:50</td>\n",
       "      <td>r13n5</td>\n",
       "      <td>normal</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>{r13n5}</td>\n",
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       "      <td>5.920278</td>\n",
       "      <td>120.005278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-12-26 17:11:18</td>\n",
       "      <td>2021-12-26 23:06:43</td>\n",
       "      <td>2021-12-31 23:06:50</td>\n",
       "      <td>r14n27</td>\n",
       "      <td>normal</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>2021-12-26 17:11:18</td>\n",
       "      <td>2021-12-26 23:06:43</td>\n",
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       "      <td>r15n12</td>\n",
       "      <td>normal</td>\n",
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       "      <td>TIMEOUT</td>\n",
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       "      <td>2021-12-31 23:06:50</td>\n",
       "      <td>r10n14</td>\n",
       "      <td>normal</td>\n",
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       "      <td>TIMEOUT</td>\n",
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       "      <td>0</td>\n",
       "      <td>5.923611</td>\n",
       "      <td>120.001944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2021-12-26 17:11:18</td>\n",
       "      <td>2021-12-26 23:06:43</td>\n",
       "      <td>2021-12-31 23:06:50</td>\n",
       "      <td>r10n30</td>\n",
       "      <td>normal</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>{r10n30}</td>\n",
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       "      <td>2022-11-01 13:58:54</td>\n",
       "      <td>r28n1</td>\n",
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       "      <td>2022-11-01 13:56:12</td>\n",
       "      <td>2022-11-01 13:56:17</td>\n",
       "      <td>2022-11-01 13:56:24</td>\n",
       "      <td>r13n10</td>\n",
       "      <td>shared</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>FAILED</td>\n",
       "      <td>{r13n10}</td>\n",
       "      <td>0</td>\n",
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       "      <td>2022-11-01 13:56:45</td>\n",
       "      <td>2022-11-01 13:56:45</td>\n",
       "      <td>2022-11-01 13:58:12</td>\n",
       "      <td>r28n1</td>\n",
       "      <td>gpu_titanrtx_shared</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>FAILED</td>\n",
       "      <td>{r28n1}</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024167</td>\n",
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       "    <tr>\n",
       "      <th>1596961</th>\n",
       "      <td>2323557</td>\n",
       "      <td>2022-11-01 13:54:21</td>\n",
       "      <td>2022-11-01 13:58:19</td>\n",
       "      <td>2022-11-01 13:59:01</td>\n",
       "      <td>r14n20</td>\n",
       "      <td>normal</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>{r14n20}</td>\n",
       "      <td>0</td>\n",
       "      <td>0.066111</td>\n",
       "      <td>0.011667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596962</th>\n",
       "      <td>2323558</td>\n",
       "      <td>2022-11-01 13:54:26</td>\n",
       "      <td>2022-11-01 13:58:19</td>\n",
       "      <td>2022-11-01 13:59:18</td>\n",
       "      <td>r14n21</td>\n",
       "      <td>normal</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>{r14n21}</td>\n",
       "      <td>0</td>\n",
       "      <td>0.064722</td>\n",
       "      <td>0.016389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1596963 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id         submit_date          start_date            end_date  \\\n",
       "0              1 2021-12-26 17:11:18 2021-12-26 23:06:31 2021-12-31 23:06:50   \n",
       "1              2 2021-12-26 17:11:18 2021-12-26 23:06:43 2021-12-31 23:06:50   \n",
       "2              3 2021-12-26 17:11:18 2021-12-26 23:06:43 2021-12-31 23:06:50   \n",
       "3              4 2021-12-26 17:11:18 2021-12-26 23:06:43 2021-12-31 23:06:50   \n",
       "4              5 2021-12-26 17:11:18 2021-12-26 23:06:43 2021-12-31 23:06:50   \n",
       "...          ...                 ...                 ...                 ...   \n",
       "1596958  2323559 2022-11-01 13:55:57 2022-11-01 13:55:57 2022-11-01 13:58:54   \n",
       "1596959  2323560 2022-11-01 13:56:12 2022-11-01 13:56:17 2022-11-01 13:56:24   \n",
       "1596960  2323561 2022-11-01 13:56:45 2022-11-01 13:56:45 2022-11-01 13:58:12   \n",
       "1596961  2323557 2022-11-01 13:54:21 2022-11-01 13:58:19 2022-11-01 13:59:01   \n",
       "1596962  2323558 2022-11-01 13:54:26 2022-11-01 13:58:19 2022-11-01 13:59:18   \n",
       "\n",
       "           node            nodetypes  numnodes  numcores      state  \\\n",
       "0         r13n5               normal         1        16    TIMEOUT   \n",
       "1        r14n27               normal         1        16    TIMEOUT   \n",
       "2        r15n12               normal         1        16    TIMEOUT   \n",
       "3        r10n14               normal         1        16    TIMEOUT   \n",
       "4        r10n30               normal         1        16    TIMEOUT   \n",
       "...         ...                  ...       ...       ...        ...   \n",
       "1596958   r28n1  gpu_titanrtx_shared         1         6  CANCELLED   \n",
       "1596959  r13n10               shared         1         6     FAILED   \n",
       "1596960   r28n1  gpu_titanrtx_shared         1         6     FAILED   \n",
       "1596961  r14n20               normal         1        16  COMPLETED   \n",
       "1596962  r14n21               normal         1        16  COMPLETED   \n",
       "\n",
       "        split_nodes  is_ml  job_waiting_time  job_running_time  \n",
       "0           {r13n5}      0          5.920278        120.005278  \n",
       "1          {r14n27}      0          5.923611        120.001944  \n",
       "2          {r15n12}      0          5.923611        120.001944  \n",
       "3          {r10n14}      0          5.923611        120.001944  \n",
       "4          {r10n30}      0          5.923611        120.001944  \n",
       "...             ...    ...               ...               ...  \n",
       "1596958     {r28n1}      1          0.000000          0.049167  \n",
       "1596959    {r13n10}      0          0.001389          0.001944  \n",
       "1596960     {r28n1}      1          0.000000          0.024167  \n",
       "1596961    {r14n20}      0          0.066111          0.011667  \n",
       "1596962    {r14n21}      0          0.064722          0.016389  \n",
       "\n",
       "[1596963 rows x 13 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate wait time and run time\n",
    "\n",
    "df_slurm_pd[\"job_waiting_time\"] = df_slurm_pd[\"start_date\"] - df_slurm_pd[\"submit_date\"]\n",
    "df_slurm_pd[\"job_waiting_time\"] = df_slurm_pd[\"job_waiting_time\"].apply(lambda x:timedelta.total_seconds(x))\n",
    "df_slurm_pd[\"job_waiting_time\"] = df_slurm_pd[\"job_waiting_time\"]/60/60 # hour\n",
    "\n",
    "df_slurm_pd[\"job_running_time\"] = df_slurm_pd[\"end_date\"] - df_slurm_pd[\"start_date\"]\n",
    "df_slurm_pd[\"job_running_time\"] = df_slurm_pd[\"job_running_time\"].apply(lambda x:timedelta.total_seconds(x))\n",
    "df_slurm_pd[\"job_running_time\"] = df_slurm_pd[\"job_running_time\"]/60/60 # hour\n",
    "df_slurm_pd.loc[df_slurm_pd['job_running_time'] < 0, 'job_running_time'] += 1 # due to time change\n",
    "\n",
    "df_slurm_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select generic and ml jobs\n",
    "df_slurm_generic = df_slurm_pd[df_slurm_pd['is_ml'] == 0]\n",
    "df_slurm_generic_completed = df_slurm_generic[df_slurm_generic['state'] == 'COMPLETED']\n",
    "df_slurm_generic_failed = df_slurm_generic[df_slurm_generic['state'] == 'FAILED']\n",
    "\n",
    "df_slurm_ml = df_slurm_pd[df_slurm_pd['is_ml'] == 1]\n",
    "df_slurm_ml_completed = df_slurm_ml[df_slurm_ml['state'] == 'COMPLETED']\n",
    "df_slurm_ml_failed = df_slurm_ml[df_slurm_ml['state'] == 'FAILED']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# job wait time cdf\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# fig size (9,6)\n",
    "fig.set_size_inches(9, 3)\n",
    "sns.ecdfplot(data=df_slurm_generic_completed['job_waiting_time'], ax=ax, color='steelblue', linestyle = 'solid', label = 'Generic Completed Jobs', linewidth=2)\n",
    "sns.ecdfplot(data=df_slurm_generic_failed['job_waiting_time'], ax=ax, color='steelblue', linestyle = 'dashed', label = 'Generic Failed Jobs', linewidth=2)\n",
    "sns.ecdfplot(data=df_slurm_ml_completed['job_waiting_time'], ax=ax, color='lightcoral', linestyle = 'solid', label = 'ML Completed Jobs', linewidth=2)\n",
    "sns.ecdfplot(data=df_slurm_ml_failed['job_waiting_time'], ax=ax, color='lightcoral', linestyle = 'dashed', label = 'ML Failed Jobs', linewidth=2)\n",
    "\n",
    "# set grid\n",
    "ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
    "ax.set_xscale('log')\n",
    "# ax.set_xlim([1, 10**2])\n",
    "plt.tick_params(axis='both', which='major', labelsize=18)\n",
    "plt.legend(loc='lower right', fontsize=14, bbox_to_anchor=(1.013, -0.035))\n",
    "plt.xlabel('Job Wait Time [hours]', fontsize=22)\n",
    "plt.ylabel('Fraction of Total Jobs', fontsize=22)\n",
    "# plt.title('CDF of Number of Cores for Different States and Generic/ML jobs')\n",
    "plt.savefig('plots/section_5/fig-job_wait_time.pdf', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# job run time cdf\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# fig size (9,6)\n",
    "fig.set_size_inches(9, 3)\n",
    "sns.ecdfplot(data=df_slurm_generic_completed['job_running_time'], ax=ax, color='steelblue', linestyle = 'solid', label = 'Generic Completed Jobs', linewidth=2)\n",
    "sns.ecdfplot(data=df_slurm_generic_failed['job_running_time'], ax=ax, color='steelblue', linestyle = 'dashed', label = 'Generic Failed Jobs', linewidth=2)\n",
    "sns.ecdfplot(data=df_slurm_ml_completed['job_running_time'], ax=ax, color='lightcoral', linestyle = 'solid', label = 'ML Completed Jobs', linewidth=2)\n",
    "sns.ecdfplot(data=df_slurm_ml_failed['job_running_time'], ax=ax, color='lightcoral', linestyle = 'dashed', label = 'ML Failed Jobs', linewidth=2)\n",
    "\n",
    "# set grid\n",
    "ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
    "ax.set_xscale('log')\n",
    "# ax.set_xlim([1, 10**2])\n",
    "plt.tick_params(axis='both', which='major', labelsize=18)\n",
    "plt.legend(loc='lower right', fontsize=14, bbox_to_anchor=(1.013, -0.035))\n",
    "plt.xlabel('Job Run Time [hours]', fontsize=22)\n",
    "plt.ylabel('Fraction of Total Jobs', fontsize=22)\n",
    "# plt.title('CDF of Number of Cores for Different States and generic/ML jobs')\n",
    "plt.savefig('plots/section_5/fig-job_run_time.pdf', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slurm_completed = df_slurm_pd[df_slurm_pd['state'] == 'COMPLETED']\n",
    "df_slurm_failed = df_slurm_pd[df_slurm_pd['state'] == 'FAILED']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Jobs in Hours\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.596963e+06</td>\n",
       "      <td>1.596963e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.930311e+00</td>\n",
       "      <td>1.057211e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.286146e+01</td>\n",
       "      <td>6.660395e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>8.333333e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.361111e-01</td>\n",
       "      <td>9.166667e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.142500e+00</td>\n",
       "      <td>1.363889e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.931478e+02</td>\n",
       "      <td>1.220478e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      1.596963e+06      1.596963e+06\n",
       "mean       3.930311e+00      1.057211e+00\n",
       "std        1.286146e+01      6.660395e+00\n",
       "min        0.000000e+00      0.000000e+00\n",
       "25%        1.000000e-02      8.333333e-04\n",
       "50%        1.361111e-01      9.166667e-03\n",
       "75%        2.142500e+00      1.363889e-01\n",
       "max        7.931478e+02      1.220478e+02"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Jobs In Minutes\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.596963e+06</td>\n",
       "      <td>1.596963e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.358187e+02</td>\n",
       "      <td>6.343267e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.716879e+02</td>\n",
       "      <td>3.996237e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000e-01</td>\n",
       "      <td>5.000000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.166667e+00</td>\n",
       "      <td>5.500000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.285500e+02</td>\n",
       "      <td>8.183333e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.758887e+04</td>\n",
       "      <td>7.322867e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      1.596963e+06      1.596963e+06\n",
       "mean       2.358187e+02      6.343267e+01\n",
       "std        7.716879e+02      3.996237e+02\n",
       "min        0.000000e+00      0.000000e+00\n",
       "25%        6.000000e-01      5.000000e-02\n",
       "50%        8.166667e+00      5.500000e-01\n",
       "75%        1.285500e+02      8.183333e+00\n",
       "max        4.758887e+04      7.322867e+03"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Jobs In Seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.596963e+06</td>\n",
       "      <td>1.596963e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.414912e+04</td>\n",
       "      <td>3.805960e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.630127e+04</td>\n",
       "      <td>2.397742e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.600000e+01</td>\n",
       "      <td>3.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.900000e+02</td>\n",
       "      <td>3.300000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.713000e+03</td>\n",
       "      <td>4.910000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.855332e+06</td>\n",
       "      <td>4.393720e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      1.596963e+06      1.596963e+06\n",
       "mean       1.414912e+04      3.805960e+03\n",
       "std        4.630127e+04      2.397742e+04\n",
       "min        0.000000e+00      0.000000e+00\n",
       "25%        3.600000e+01      3.000000e+00\n",
       "50%        4.900000e+02      3.300000e+01\n",
       "75%        7.713000e+03      4.910000e+02\n",
       "max        2.855332e+06      4.393720e+05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('All Jobs in Hours')\n",
    "display(df_slurm_pd[['job_waiting_time', 'job_running_time']].describe())\n",
    "print('All Jobs In Minutes')\n",
    "display((df_slurm_pd[['job_waiting_time', 'job_running_time']]*60).describe())\n",
    "print('All Jobs In Seconds')\n",
    "display((df_slurm_pd[['job_waiting_time', 'job_running_time']]*60*60).describe())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generic Jobs in Hours\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.406488e+06</td>\n",
       "      <td>1.406488e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.213892e+00</td>\n",
       "      <td>8.339245e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.339807e+01</td>\n",
       "      <td>6.070081e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.777778e-02</td>\n",
       "      <td>5.555556e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.941667e-01</td>\n",
       "      <td>6.666667e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.380278e+00</td>\n",
       "      <td>8.527778e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.931478e+02</td>\n",
       "      <td>1.220478e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      1.406488e+06      1.406488e+06\n",
       "mean       4.213892e+00      8.339245e-01\n",
       "std        1.339807e+01      6.070081e+00\n",
       "min        0.000000e+00      0.000000e+00\n",
       "25%        1.777778e-02      5.555556e-04\n",
       "50%        1.941667e-01      6.666667e-03\n",
       "75%        2.380278e+00      8.527778e-02\n",
       "max        7.931478e+02      1.220478e+02"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generic Jobs in Minutes\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.406488e+06</td>\n",
       "      <td>1.406488e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.528335e+02</td>\n",
       "      <td>5.003547e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.038842e+02</td>\n",
       "      <td>3.642049e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.066667e+00</td>\n",
       "      <td>3.333333e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.165000e+01</td>\n",
       "      <td>4.000000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.428167e+02</td>\n",
       "      <td>5.116667e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.758887e+04</td>\n",
       "      <td>7.322867e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      1.406488e+06      1.406488e+06\n",
       "mean       2.528335e+02      5.003547e+01\n",
       "std        8.038842e+02      3.642049e+02\n",
       "min        0.000000e+00      0.000000e+00\n",
       "25%        1.066667e+00      3.333333e-02\n",
       "50%        1.165000e+01      4.000000e-01\n",
       "75%        1.428167e+02      5.116667e+00\n",
       "max        4.758887e+04      7.322867e+03"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generic Jobs in Seconds\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.406488e+06</td>\n",
       "      <td>1.406488e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.517001e+04</td>\n",
       "      <td>3.002128e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.823305e+04</td>\n",
       "      <td>2.185229e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.990000e+02</td>\n",
       "      <td>2.400000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.569000e+03</td>\n",
       "      <td>3.070000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.855332e+06</td>\n",
       "      <td>4.393720e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      1.406488e+06      1.406488e+06\n",
       "mean       1.517001e+04      3.002128e+03\n",
       "std        4.823305e+04      2.185229e+04\n",
       "min        0.000000e+00      0.000000e+00\n",
       "25%        6.400000e+01      2.000000e+00\n",
       "50%        6.990000e+02      2.400000e+01\n",
       "75%        8.569000e+03      3.070000e+02\n",
       "max        2.855332e+06      4.393720e+05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Generic Jobs in Hours')\n",
    "display(df_slurm_generic[['job_waiting_time', 'job_running_time']].describe())\n",
    "print('Generic Jobs in Minutes')\n",
    "display((df_slurm_generic[['job_waiting_time', 'job_running_time']] * 60).describe())\n",
    "print('Generic Jobs in Seconds')\n",
    "display((df_slurm_generic[['job_waiting_time', 'job_running_time']] * 60 * 60).describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ML Jobs in Hours\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>190475.000000</td>\n",
       "      <td>190475.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.836319</td>\n",
       "      <td>2.705983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.509299</td>\n",
       "      <td>9.836977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.007778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.003056</td>\n",
       "      <td>0.108056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.482500</td>\n",
       "      <td>1.005556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>331.370278</td>\n",
       "      <td>120.008333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count     190475.000000     190475.000000\n",
       "mean           1.836319          2.705983\n",
       "std            7.509299          9.836977\n",
       "min            0.000000          0.000000\n",
       "25%            0.000278          0.007778\n",
       "50%            0.003056          0.108056\n",
       "75%            0.482500          1.005556\n",
       "max          331.370278        120.008333"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ML Jobs in Minutes\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>190475.000000</td>\n",
       "      <td>190475.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>110.179167</td>\n",
       "      <td>162.359006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>450.557941</td>\n",
       "      <td>590.218607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.183333</td>\n",
       "      <td>6.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>28.950000</td>\n",
       "      <td>60.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19882.216667</td>\n",
       "      <td>7200.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count     190475.000000     190475.000000\n",
       "mean         110.179167        162.359006\n",
       "std          450.557941        590.218607\n",
       "min            0.000000          0.000000\n",
       "25%            0.016667          0.466667\n",
       "50%            0.183333          6.483333\n",
       "75%           28.950000         60.333333\n",
       "max        19882.216667       7200.500000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ML Jobs in Seconds\n"
     ]
    },
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.904750e+05</td>\n",
       "      <td>190475.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.610750e+03</td>\n",
       "      <td>9741.540370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.703348e+04</td>\n",
       "      <td>35413.116432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>389.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.737000e+03</td>\n",
       "      <td>3620.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.192933e+06</td>\n",
       "      <td>432030.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      1.904750e+05     190475.000000\n",
       "mean       6.610750e+03       9741.540370\n",
       "std        2.703348e+04      35413.116432\n",
       "min        0.000000e+00          0.000000\n",
       "25%        1.000000e+00         28.000000\n",
       "50%        1.100000e+01        389.000000\n",
       "75%        1.737000e+03       3620.000000\n",
       "max        1.192933e+06     432030.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('ML Jobs in Hours')\n",
    "display(df_slurm_ml[['job_waiting_time', 'job_running_time']].describe())\n",
    "print('ML Jobs in Minutes')\n",
    "display((df_slurm_ml[['job_waiting_time', 'job_running_time']] * 60).describe())\n",
    "print('ML Jobs in Seconds')\n",
    "display((df_slurm_ml[['job_waiting_time', 'job_running_time']] * 60 * 60).describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Completed Jobs in Hours\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.197035e+06</td>\n",
       "      <td>1.197035e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.578085e+00</td>\n",
       "      <td>8.026803e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.200615e+01</td>\n",
       "      <td>4.737323e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.500000e-02</td>\n",
       "      <td>8.333333e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.322222e-01</td>\n",
       "      <td>1.222222e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.850556e+00</td>\n",
       "      <td>1.413889e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.717825e+02</td>\n",
       "      <td>1.199972e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      1.197035e+06      1.197035e+06\n",
       "mean       3.578085e+00      8.026803e-01\n",
       "std        1.200615e+01      4.737323e+00\n",
       "min        0.000000e+00      0.000000e+00\n",
       "25%        1.500000e-02      8.333333e-04\n",
       "50%        1.322222e-01      1.222222e-02\n",
       "75%        1.850556e+00      1.413889e-01\n",
       "max        4.717825e+02      1.199972e+02"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Completed Jobs in Minutes\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.197035e+06</td>\n",
       "      <td>1.197035e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.146851e+02</td>\n",
       "      <td>4.816082e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.203688e+02</td>\n",
       "      <td>2.842394e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>5.000000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.933333e+00</td>\n",
       "      <td>7.333333e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.110333e+02</td>\n",
       "      <td>8.483333e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.830695e+04</td>\n",
       "      <td>7.199833e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      1.197035e+06      1.197035e+06\n",
       "mean       2.146851e+02      4.816082e+01\n",
       "std        7.203688e+02      2.842394e+02\n",
       "min        0.000000e+00      0.000000e+00\n",
       "25%        9.000000e-01      5.000000e-02\n",
       "50%        7.933333e+00      7.333333e-01\n",
       "75%        1.110333e+02      8.483333e+00\n",
       "max        2.830695e+04      7.199833e+03"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Completed Jobs in Seconds\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.197035e+06</td>\n",
       "      <td>1.197035e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.288111e+04</td>\n",
       "      <td>2.889649e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.322213e+04</td>\n",
       "      <td>1.705436e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.400000e+01</td>\n",
       "      <td>3.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.760000e+02</td>\n",
       "      <td>4.400000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.662000e+03</td>\n",
       "      <td>5.090000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.698417e+06</td>\n",
       "      <td>4.319900e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      1.197035e+06      1.197035e+06\n",
       "mean       1.288111e+04      2.889649e+03\n",
       "std        4.322213e+04      1.705436e+04\n",
       "min        0.000000e+00      0.000000e+00\n",
       "25%        5.400000e+01      3.000000e+00\n",
       "50%        4.760000e+02      4.400000e+01\n",
       "75%        6.662000e+03      5.090000e+02\n",
       "max        1.698417e+06      4.319900e+05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('All Completed Jobs in Hours')\n",
    "display(df_slurm_completed[['job_waiting_time', 'job_running_time']].describe())\n",
    "print('All Completed Jobs in Minutes')\n",
    "display((df_slurm_completed[['job_waiting_time', 'job_running_time']] * 60).describe())\n",
    "print('All Completed Jobs in Seconds')\n",
    "display((df_slurm_completed[['job_waiting_time', 'job_running_time']] * 60 * 60).describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Failed Jobs in Hours\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>245301.000000</td>\n",
       "      <td>245301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.269988</td>\n",
       "      <td>0.335831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.233293</td>\n",
       "      <td>3.651462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.004722</td>\n",
       "      <td>0.000556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.784722</td>\n",
       "      <td>0.001389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.563333</td>\n",
       "      <td>0.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>430.804444</td>\n",
       "      <td>119.835833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count     245301.000000     245301.000000\n",
       "mean           6.269988          0.335831\n",
       "std           13.233293          3.651462\n",
       "min            0.000000          0.000000\n",
       "25%            0.004722          0.000556\n",
       "50%            0.784722          0.001389\n",
       "75%            8.563333          0.009167\n",
       "max          430.804444        119.835833"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Failed Jobs in Minutes\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>245301.000000</td>\n",
       "      <td>245301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>376.199298</td>\n",
       "      <td>20.149850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>793.997589</td>\n",
       "      <td>219.087731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.283333</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>47.083333</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>513.800000</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25848.266667</td>\n",
       "      <td>7190.150000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count     245301.000000     245301.000000\n",
       "mean         376.199298         20.149850\n",
       "std          793.997589        219.087731\n",
       "min            0.000000          0.000000\n",
       "25%            0.283333          0.033333\n",
       "50%           47.083333          0.083333\n",
       "75%          513.800000          0.550000\n",
       "max        25848.266667       7190.150000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Failed Jobs in Seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.453010e+05</td>\n",
       "      <td>245301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.257196e+04</td>\n",
       "      <td>1208.990983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.763986e+04</td>\n",
       "      <td>13145.263869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.825000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.082800e+04</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.550896e+06</td>\n",
       "      <td>431409.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      2.453010e+05     245301.000000\n",
       "mean       2.257196e+04       1208.990983\n",
       "std        4.763986e+04      13145.263869\n",
       "min        0.000000e+00          0.000000\n",
       "25%        1.700000e+01          2.000000\n",
       "50%        2.825000e+03          5.000000\n",
       "75%        3.082800e+04         33.000000\n",
       "max        1.550896e+06     431409.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('All Failed Jobs in Hours')\n",
    "display(df_slurm_failed[['job_waiting_time', 'job_running_time']].describe())\n",
    "print('All Failed Jobs in Minutes')\n",
    "display((df_slurm_failed[['job_waiting_time', 'job_running_time']] * 60).describe())\n",
    "print('All Failed Jobs in Seconds')\n",
    "display((df_slurm_failed[['job_waiting_time', 'job_running_time']] * 60 * 60).describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed Generic Jobs in Hours\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200983.000000</td>\n",
       "      <td>200983.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.450514</td>\n",
       "      <td>0.278384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.155072</td>\n",
       "      <td>3.444145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.031389</td>\n",
       "      <td>0.000556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.951389</td>\n",
       "      <td>0.001111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.344167</td>\n",
       "      <td>0.005278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>430.804444</td>\n",
       "      <td>119.835833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count     200983.000000     200983.000000\n",
       "mean           7.450514          0.278384\n",
       "std           14.155072          3.444145\n",
       "min            0.000000          0.000000\n",
       "25%            0.031389          0.000556\n",
       "50%            1.951389          0.001111\n",
       "75%           13.344167          0.005278\n",
       "max          430.804444        119.835833"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed Generic Jobs in Minutes\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200983.000000</td>\n",
       "      <td>200983.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>447.030858</td>\n",
       "      <td>16.703047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>849.304295</td>\n",
       "      <td>206.648709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.883333</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>117.083333</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>800.650000</td>\n",
       "      <td>0.316667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25848.266667</td>\n",
       "      <td>7190.150000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count     200983.000000     200983.000000\n",
       "mean         447.030858         16.703047\n",
       "std          849.304295        206.648709\n",
       "min            0.000000          0.000000\n",
       "25%            1.883333          0.033333\n",
       "50%          117.083333          0.066667\n",
       "75%          800.650000          0.316667\n",
       "max        25848.266667       7190.150000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed Generic Jobs in Seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.009830e+05</td>\n",
       "      <td>200983.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.682185e+04</td>\n",
       "      <td>1002.182807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.095826e+04</td>\n",
       "      <td>12398.922552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.130000e+02</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.025000e+03</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.803900e+04</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.550896e+06</td>\n",
       "      <td>431409.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      2.009830e+05     200983.000000\n",
       "mean       2.682185e+04       1002.182807\n",
       "std        5.095826e+04      12398.922552\n",
       "min        0.000000e+00          0.000000\n",
       "25%        1.130000e+02          2.000000\n",
       "50%        7.025000e+03          4.000000\n",
       "75%        4.803900e+04         19.000000\n",
       "max        1.550896e+06     431409.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Failed Generic Jobs in Hours')\n",
    "display(df_slurm_generic_failed[['job_waiting_time', 'job_running_time']].describe())\n",
    "print('Failed Generic Jobs in Minutes')\n",
    "display((df_slurm_generic_failed[['job_waiting_time', 'job_running_time']] * 60).describe())\n",
    "print('Failed Generic Jobs in Seconds')\n",
    "display((df_slurm_generic_failed[['job_waiting_time', 'job_running_time']] * 60 * 60).describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed Generic Jobs in Hours\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.084458e+06</td>\n",
       "      <td>1.084458e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.688490e+00</td>\n",
       "      <td>6.242086e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.228995e+01</td>\n",
       "      <td>4.206577e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.777778e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.027778e-02</td>\n",
       "      <td>5.555556e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.516667e-01</td>\n",
       "      <td>1.027778e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.904167e+00</td>\n",
       "      <td>8.916667e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.717825e+02</td>\n",
       "      <td>1.199972e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      1.084458e+06      1.084458e+06\n",
       "mean       3.688490e+00      6.242086e-01\n",
       "std        1.228995e+01      4.206577e+00\n",
       "min        0.000000e+00      2.777778e-04\n",
       "25%        2.027778e-02      5.555556e-04\n",
       "50%        1.516667e-01      1.027778e-02\n",
       "75%        1.904167e+00      8.916667e-02\n",
       "max        4.717825e+02      1.199972e+02"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed Generic Jobs in Minutes\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.084458e+06</td>\n",
       "      <td>1.084458e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.213094e+02</td>\n",
       "      <td>3.745252e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.373972e+02</td>\n",
       "      <td>2.523946e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.666667e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.216667e+00</td>\n",
       "      <td>3.333333e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.100000e+00</td>\n",
       "      <td>6.166667e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.142500e+02</td>\n",
       "      <td>5.350000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.830695e+04</td>\n",
       "      <td>7.199833e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      1.084458e+06      1.084458e+06\n",
       "mean       2.213094e+02      3.745252e+01\n",
       "std        7.373972e+02      2.523946e+02\n",
       "min        0.000000e+00      1.666667e-02\n",
       "25%        1.216667e+00      3.333333e-02\n",
       "50%        9.100000e+00      6.166667e-01\n",
       "75%        1.142500e+02      5.350000e+00\n",
       "max        2.830695e+04      7.199833e+03"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed Generic Jobs in Seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.084458e+06</td>\n",
       "      <td>1.084458e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.327857e+04</td>\n",
       "      <td>2.247151e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.424383e+04</td>\n",
       "      <td>1.514368e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.300000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.460000e+02</td>\n",
       "      <td>3.700000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.855000e+03</td>\n",
       "      <td>3.210000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.698417e+06</td>\n",
       "      <td>4.319900e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      1.084458e+06      1.084458e+06\n",
       "mean       1.327857e+04      2.247151e+03\n",
       "std        4.424383e+04      1.514368e+04\n",
       "min        0.000000e+00      1.000000e+00\n",
       "25%        7.300000e+01      2.000000e+00\n",
       "50%        5.460000e+02      3.700000e+01\n",
       "75%        6.855000e+03      3.210000e+02\n",
       "max        1.698417e+06      4.319900e+05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Completed Generic Jobs in Hours\")\n",
    "display(df_slurm_generic_completed[['job_waiting_time', 'job_running_time']].describe())\n",
    "print(\"Completed Generic Jobs in Minutes\")\n",
    "display((df_slurm_generic_completed[['job_waiting_time', 'job_running_time']] * 60).describe())\n",
    "print(\"Completed Generic Jobs in Seconds\")\n",
    "display((df_slurm_generic_completed[['job_waiting_time', 'job_running_time']] * 60 * 60).describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed ML Jobs in Hours\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>112577.000000</td>\n",
       "      <td>112577.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.514551</td>\n",
       "      <td>2.521904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.745076</td>\n",
       "      <td>8.056529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.011944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.008611</td>\n",
       "      <td>0.210556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.213889</td>\n",
       "      <td>1.247500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>264.826389</td>\n",
       "      <td>119.790556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count     112577.000000     112577.000000\n",
       "mean           2.514551          2.521904\n",
       "std            8.745076          8.056529\n",
       "min            0.000000          0.000000\n",
       "25%            0.000278          0.011944\n",
       "50%            0.008611          0.210556\n",
       "75%            1.213889          1.247500\n",
       "max          264.826389        119.790556"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed ML Jobs in Minutes\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>112577.000000</td>\n",
       "      <td>112577.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>150.873088</td>\n",
       "      <td>151.314267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>524.704587</td>\n",
       "      <td>483.391738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.716667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.516667</td>\n",
       "      <td>12.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>72.833333</td>\n",
       "      <td>74.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15889.583333</td>\n",
       "      <td>7187.433333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count     112577.000000     112577.000000\n",
       "mean         150.873088        151.314267\n",
       "std          524.704587        483.391738\n",
       "min            0.000000          0.000000\n",
       "25%            0.016667          0.716667\n",
       "50%            0.516667         12.633333\n",
       "75%           72.833333         74.850000\n",
       "max        15889.583333       7187.433333"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed ML Jobs in Seconds\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>112577.000000</td>\n",
       "      <td>112577.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9052.385292</td>\n",
       "      <td>9078.856019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>31482.275228</td>\n",
       "      <td>29003.504305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>758.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4370.000000</td>\n",
       "      <td>4491.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>953375.000000</td>\n",
       "      <td>431246.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count     112577.000000     112577.000000\n",
       "mean        9052.385292       9078.856019\n",
       "std        31482.275228      29003.504305\n",
       "min            0.000000          0.000000\n",
       "25%            1.000000         43.000000\n",
       "50%           31.000000        758.000000\n",
       "75%         4370.000000       4491.000000\n",
       "max       953375.000000     431246.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Completed ML Jobs in Hours\")\n",
    "display(df_slurm_ml_completed[['job_waiting_time', 'job_running_time']].describe())\n",
    "print(\"Completed ML Jobs in Minutes\")\n",
    "display((df_slurm_ml_completed[['job_waiting_time', 'job_running_time']] * 60).describe())\n",
    "print(\"Completed ML Jobs in Seconds\")\n",
    "display((df_slurm_ml_completed[['job_waiting_time', 'job_running_time']] * 60 * 60).describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed ML Jobs in Hours\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>44318.000000</td>\n",
       "      <td>44318.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.916280</td>\n",
       "      <td>0.596353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.064354</td>\n",
       "      <td>4.463401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.002500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000556</td>\n",
       "      <td>0.006667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.044722</td>\n",
       "      <td>0.027222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>222.358333</td>\n",
       "      <td>114.615000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      44318.000000      44318.000000\n",
       "mean           0.916280          0.596353\n",
       "std            5.064354          4.463401\n",
       "min            0.000000          0.000000\n",
       "25%            0.000278          0.002500\n",
       "50%            0.000556          0.006667\n",
       "75%            0.044722          0.027222\n",
       "max          222.358333        114.615000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed ML Jobs in Minutes\n"
     ]
    },
    {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>44318.000000</td>\n",
       "      <td>44318.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.976778</td>\n",
       "      <td>35.781169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>303.861259</td>\n",
       "      <td>267.804077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.683333</td>\n",
       "      <td>1.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13341.500000</td>\n",
       "      <td>6876.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      44318.000000      44318.000000\n",
       "mean          54.976778         35.781169\n",
       "std          303.861259        267.804077\n",
       "min            0.000000          0.000000\n",
       "25%            0.016667          0.150000\n",
       "50%            0.033333          0.400000\n",
       "75%            2.683333          1.633333\n",
       "max        13341.500000       6876.900000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed ML Jobs in Seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_waiting_time</th>\n",
       "      <th>job_running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>44318.000000</td>\n",
       "      <td>44318.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3298.606706</td>\n",
       "      <td>2146.870120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18231.675552</td>\n",
       "      <td>16068.244625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>161.000000</td>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>800490.000000</td>\n",
       "      <td>412614.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_waiting_time  job_running_time\n",
       "count      44318.000000      44318.000000\n",
       "mean        3298.606706       2146.870120\n",
       "std        18231.675552      16068.244625\n",
       "min            0.000000          0.000000\n",
       "25%            1.000000          9.000000\n",
       "50%            2.000000         24.000000\n",
       "75%          161.000000         98.000000\n",
       "max       800490.000000     412614.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Failed ML Jobs in Hours')\n",
    "display(df_slurm_ml_failed[['job_waiting_time', 'job_running_time']].describe())\n",
    "print('Failed ML Jobs in Minutes')\n",
    "display((df_slurm_ml_failed[['job_waiting_time', 'job_running_time']] * 60).describe())\n",
    "print('Failed ML Jobs in Seconds')\n",
    "display((df_slurm_ml_failed[['job_waiting_time', 'job_running_time']] * 60 * 60).describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of Generic Jobs Wait Time less than 1 hour\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6466944616662211"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of ML Jobs Wait Time less than 1 hour\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7983304895655597"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of Generic Jobs with Running Time within 1 hour\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9072469868210749"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of ML Jobs with Running Time within 1 hour\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7415093844336527"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of Generic Jobs Completed within 1 hour\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.908012112963342"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of ML Jobs Completed within 1 hour\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7189212716629507"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of Generic Jobs Failing within 1 hour\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9777593129767195"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of ML Jobs Failing within 1 hour\n"
     ]
    },
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     "data": {
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       "0.9416264271853423"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of Generic Jobs Failing within 6 minutes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.883925506137335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of ML Jobs Failing within 6 minutes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8462927027392932"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime of All Jobs at 80th percentile in hours\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "job_running_time    0.303056\n",
       "Name: 0.8, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime of All Jobs at 80th percentile in minutes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "job_running_time    18.183333\n",
       "Name: 0.8, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Proportion of Generic Jobs Wait Time less than 1 hour')\n",
    "display(df_slurm_generic[df_slurm_generic['job_waiting_time'] <= 1].shape[0] / df_slurm_generic.shape[0])\n",
    "\n",
    "print('Proportion of ML Jobs Wait Time less than 1 hour')\n",
    "display(df_slurm_ml[df_slurm_ml['job_waiting_time'] <= 1].shape[0] / df_slurm_ml.shape[0])\n",
    "\n",
    "print('Proportion of Generic Jobs with Running Time within 1 hour')\n",
    "display(df_slurm_generic[df_slurm_generic['job_running_time'] <= 1].shape[0] / df_slurm_generic.shape[0])\n",
    "\n",
    "print('Proportion of ML Jobs with Running Time within 1 hour')\n",
    "display(df_slurm_ml[df_slurm_ml['job_running_time'] <= 1].shape[0] / df_slurm_ml.shape[0])\n",
    "\n",
    "print('Proportion of Generic Jobs Completed within 1 hour')\n",
    "display(df_slurm_generic_completed[df_slurm_generic_completed['job_running_time'] <= 1].shape[0] / df_slurm_generic_completed.shape[0])\n",
    "\n",
    "print('Proportion of ML Jobs Completed within 1 hour')\n",
    "display(df_slurm_ml_completed[df_slurm_ml_completed['job_running_time'] <= 1].shape[0] / df_slurm_ml_completed.shape[0])\n",
    "\n",
    "print('Proportion of Generic Jobs Failing within 1 hour')\n",
    "display(df_slurm_generic_failed[df_slurm_generic_failed['job_running_time'] <= 1].shape[0] / df_slurm_generic_failed.shape[0])\n",
    "\n",
    "print('Proportion of ML Jobs Failing within 1 hour')\n",
    "display(df_slurm_ml_failed[df_slurm_ml_failed['job_running_time'] <= 1].shape[0] / df_slurm_ml_failed.shape[0])\n",
    "\n",
    "print('Proportion of Generic Jobs Failing within 6 minutes')\n",
    "display(df_slurm_generic_failed[df_slurm_generic_failed['job_running_time'] <= 6/60].shape[0] / df_slurm_generic_failed.shape[0])\n",
    "\n",
    "print('Proportion of ML Jobs Failing within 6 minutes')\n",
    "display(df_slurm_ml_failed[df_slurm_ml_failed['job_running_time'] <= 6/60].shape[0] / df_slurm_ml_failed.shape[0])\n",
    "\n",
    "print('Runtime of All Jobs at 80th percentile in hours')\n",
    "display(df_slurm_pd[['job_running_time']].quantile(0.8))\n",
    "\n",
    "print('Runtime of All Jobs at 80th percentile in minutes')\n",
    "display(df_slurm_pd[['job_running_time']].quantile(0.8) * 60)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regarding median and average times, ML/GPU-enabled jobs wait shorter and run longer than generic CPU-only jobs."
   ]
  }
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