slurm jupyter notebook

slurm-sacct-import.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9a7d3214-7b35-4347-b9b6-0bab6543f0f7",
   "metadata": {},
   "source": [
    "# Slurm `sacct` data into a Pandas `dataframe`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdf05b33-7e3e-4a55-bae7-3e57f57fde8d",
   "metadata": {},
   "source": [
    "To generate input data do something like this:\n",
    "\n",
    "`sacct -a -X -P --format=ALL --starttime=2020-01-01 > slurm_sacct_output.csv`\n",
    "\n",
    "I had considered using `SLURM_TIME_FORMAT=\"%s\"` but am converting times to datetime64 and timedelta on import (`pd.read_csv`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "343a9fac-3640-4dd2-9997-c451a5a5cfb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "002e40b9-5a01-47a4-b12a-35c7cd55c3b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "# Default SLURM_TIME_FORMAT: YYYY-MM-DDTHH:MM:SS\n",
    "default_slurm_time_format = \"%Y-%m-%dT%H:%M:%S\"\n",
    "\n",
    "def date_converter(value_str):\n",
    "    try:\n",
    "        dt = datetime.strptime(value_str, default_slurm_time_format)\n",
    "        return np.datetime64(dt)\n",
    "    except ValueError:\n",
    "        return np.nan\n",
    "\n",
    "\n",
    "def elapsed_to_timedelta(val):\n",
    "    if pd.isna(val) or val == \"\":\n",
    "        return pd.NaT\n",
    "\n",
    "    # Slurm elapsed time \"days-hours:minutes:seconds\".\n",
    "    # Convert to: \"days hours:minutes:seconds\" which pandas understands.\n",
    "    if \"-\" in val:\n",
    "        days, rest = val.split(\"-\", 1)\n",
    "        # Guard against stray whitespace\n",
    "        val = f\"{days} days {rest}\"\n",
    "\n",
    "    try:\n",
    "        # `errors='raise'` so we know if something truly fails\n",
    "        return pd.to_timedelta(val, errors='raise')\n",
    "    except Exception:\n",
    "        # Anything we cannot parse -> NaT (the datetime analogue of NaN)\n",
    "        return pd.NaT\n",
    "\n",
    "\n",
    "column_dtypes = {\n",
    "    \"SystemComment\" : str,\n",
    "}\n",
    "\n",
    "df_converters = {\n",
    "    \"Start\" : date_converter,\n",
    "    \"End\" : date_converter,\n",
    "    \"Elapsed\" : elapsed_to_timedelta,\n",
    "    \"CpuTime\" : elapsed_to_timedelta,\n",
    "    \"Eligible\" : date_converter,\n",
    "    \"Planned\" : elapsed_to_timedelta,\n",
    "    \"PlannedCPU\" : elapsed_to_timedelta,\n",
    "    \"Suspended\" : date_converter,\n",
    "    \"SystemCPU\" : elapsed_to_timedelta,\n",
    "    \"TotalCPU\" : elapsed_to_timedelta,\n",
    "    #\"Timelimit\" : elapsed_to_timedelta\n",
    "    #\"AllocTRES\" : kv_string_to_dict\n",
    "}\n",
    "\n",
    "from pathlib import Path\n",
    "home = str(Path.home())\n",
    "df = pd.read_csv(\n",
    "  f\"{home}/slurm_workdir/slurm_sacct_output.csv\",\n",
    "    delimiter=\"|\",\n",
    "    dtype=column_dtypes,\n",
    "    converters=df_converters\n",
    ")\n",
    "#df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a8ec45-2ac9-4d5d-9235-47a9855d13ff",
   "metadata": {},
   "source": [
    "Uncomment the below to see more rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "69a4fb49-9933-4773-b85b-a4e6fed53b03",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.set_option('display.max_rows', None)\n",
    "#pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9281bbb0-02d1-40e0-bf8d-12f22083b764",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "521340be-e0f9-41e5-b0be-b0f2259aece7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Duration\n",
    "df['duration'] = df['End'] - df['Start']\n",
    "\n",
    "# Wait time\n",
    "df['wait_time'] = df['Start'] - df['Eligible']\n",
    "\n",
    "# Normalize the state field - remove \"by uid\" from CANCELLED\n",
    "df['State_normalized'] = df['State'].apply(\n",
    "    lambda x: 'CANCELLED' if 'CANCELLED' in str(x) else x\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "516694a1-d0e3-4447-a571-1a21ba6b5e62",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_valid = df.dropna(subset=['Start', 'End', 'duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4488177b-19b8-48ba-881c-354392093552",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total jobs: 1417\n",
      "Valid jobs for plotting: 320\n",
      "\n",
      "Job counts by state:\n",
      "State_normalized\n",
      "COMPLETED        191\n",
      "FAILED            69\n",
      "CANCELLED         38\n",
      "TIMEOUT           16\n",
      "PREEMPTED          5\n",
      "OUT_OF_MEMORY      1\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(f\"Total jobs: {len(df)}\")\n",
    "print(f\"Valid jobs for plotting: {len(df_valid)}\")\n",
    "print()\n",
    "print(f\"Job counts by state:\")\n",
    "print(df_valid['State_normalized'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aebbb477-ed0d-413b-a415-b57bd415af55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<StringArray>\n",
       "['COMPLETED', 'FAILED', 'CANCELLED', 'PREEMPTED', 'TIMEOUT', 'OUT_OF_MEMORY']\n",
       "Length: 6, dtype: str"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_valid['State_normalized'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6aa091da-da10-4cd0-bf10-7e607d53facf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4ef341ca-c3f6-4433-aacf-aae7fb238fde",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot 1: Duration vs Start Time (colored by status)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "\n",
    "status_colors = {\n",
    "    'COMPLETED': 'green',\n",
    "    'FAILED': 'red',\n",
    "    'CANCELLED': 'orange',\n",
    "    'PREEMPTED': 'yellow',\n",
    "    'TIMEOUT': 'purple',\n",
    "    'OUT_OF_MEMORY': 'brown'\n",
    "}\n",
    "\n",
    "for status, color in status_colors.items():\n",
    "    mask = df_valid['State_normalized'] == status\n",
    "    if mask.any():\n",
    "        duration_minutes = df_valid[mask]['duration'].dt.total_seconds() / 60\n",
    "        ax.scatter(\n",
    "            df_valid[mask]['Start'],\n",
    "            duration_minutes,\n",
    "            s=df_valid[mask]['NNodes'] * 25,\n",
    "            alpha=0.6,\n",
    "            c=color,\n",
    "            label=f\"{status} (n={mask.sum()})\",\n",
    "            edgecolors='black',\n",
    "            linewidth=0.5\n",
    "        )\n",
    "\n",
    "ax.set_xlabel('Start Time', fontsize=12)\n",
    "ax.set_ylabel('Duration (minutes)', fontsize=12)\n",
    "ax.set_title('Job Duration Over Time (sized by nodes)', fontsize=14, fontweight='bold')\n",
    "ax.legend(loc='upper right', fontsize=10)\n",
    "ax.grid(True, alpha=0.3)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "# limit the y axis\n",
    "#max_min = df_valid['duration'].dt.total_seconds().max() / 60\n",
    "## 5 % margin, rounded to nearest 50\n",
    "#top     = np.ceil(max_min * 1.05 / 50) * 50  \n",
    "ax.set_ylim(bottom=0, top=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "22e4fa59-e530-40ea-b0f5-5f8a792f7469",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot 2: Wait Time vs Duration (colored by nodes)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df_plot = df_valid.dropna(subset=['wait_time'])\n",
    "\n",
    "if len(df_plot) > 0:\n",
    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
    "    \n",
    "    wait_minutes = df_plot['wait_time'].dt.total_seconds() / 60\n",
    "    duration_minutes = df_plot['duration'].dt.total_seconds() / 60\n",
    "    \n",
    "    scatter = ax.scatter(\n",
    "        wait_minutes,\n",
    "        duration_minutes,\n",
    "        s=df_plot['NNodes'] * 25,\n",
    "        alpha=0.6,\n",
    "        c=df_plot['NNodes'],\n",
    "        cmap='viridis',\n",
    "        edgecolors='black',\n",
    "        linewidth=0.5\n",
    "    )\n",
    "    cbar = plt.colorbar(scatter, ax=ax)\n",
    "    cbar.set_label('Number of Nodes', rotation=270, labelpad=20)\n",
    "    \n",
    "    ax.set_xlabel('Wait Time (hours)', fontsize=12)\n",
    "    ax.set_ylabel('Duration (hours)', fontsize=12)\n",
    "    ax.set_title('Wait Time vs Duration', fontsize=14, fontweight='bold')\n",
    "    ax.grid(True, alpha=0.3)\n",
    "    plt.tight_layout()\n",
    "    ax.set_ylim(bottom=0, top=600)\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\"No wait time data available\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f1553c3-9924-4a64-ab90-7233f5dbcbb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot 3: Node utilization over time\n",
    "df = df_valid.copy()\n",
    "df['Start'] = pd.to_datetime(df['Start'])\n",
    "df['End']   = pd.to_datetime(df['End'])\n",
    "\n",
    "TOTAL_NODES = 5            # <-- change to your cluster size\n",
    "RESOLUTION = '15min'\n",
    "\n",
    "# Build concurrent usage timeline\n",
    "t0 = df['Start'].min().floor(RESOLUTION)\n",
    "t1 = df['End'].max().ceil(RESOLUTION)\n",
    "timeline = pd.DataFrame(0, index=pd.date_range(t0, t1, freq=RESOLUTION), columns=['used_nodes'])\n",
    "\n",
    "for start, end, nnodes in df[['Start', 'End', 'NNodes']].itertuples(index=False):\n",
    "    timeline.loc[start:end, 'used_nodes'] += nnodes\n",
    "\n",
    "timeline['utilization'] = timeline['used_nodes'] / TOTAL_NODES\n",
    "\n",
    "# Plot utilization over time\n",
    "fig, ax = plt.subplots(figsize=(14, 5))\n",
    "ax.plot(timeline.index, timeline['utilization'],\n",
    "        color='steelblue', linewidth=1.5)\n",
    "ax.fill_between(timeline.index,\n",
    "                timeline['utilization'],\n",
    "                color='steelblue', alpha=0.3)\n",
    "\n",
    "ax.set_xlabel('Date / Time')\n",
    "ax.set_ylabel('Utilization (fraction of 5 nodes)')\n",
    "ax.set_title('Cluster utilization (15‑min resolution)')\n",
    "ax.set_ylim(0, 1)\n",
    "ax.grid(alpha=0.3)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4f7fb990-c016-4a6a-b525-8482e39d367d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot 4: Top accounts by node usage\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "top_accounts = df_valid.groupby('Account')['NNodes'].sum().nlargest(10).sort_values()\n",
    "\n",
    "ax.barh(range(len(top_accounts)), top_accounts.values, color='coral', alpha=0.7, edgecolor='black')\n",
    "ax.set_yticks(range(len(top_accounts)))\n",
    "ax.set_yticklabels(top_accounts.index, fontsize=10)\n",
    "ax.set_xlabel('Total Nodes Used', fontsize=12)\n",
    "ax.set_title('Top 10 Accounts by Node Usage', fontsize=14, fontweight='bold')\n",
    "ax.grid(True, alpha=0.3, axis='x')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}