From 6751b6848968f44f7252e36ac31a35075569b388 Mon Sep 17 00:00:00 2001
From: Espiobest <59823894+Espiobest@users.noreply.github.com>
Date: Tue, 15 Jul 2025 13:43:41 -0400
Subject: [PATCH 1/9] update notebooks

---
 notebooks/.gitattributes            |   2 +-
 notebooks/Basic Visualization.ipynb |  28 +++++---
 notebooks/Efficiency Analysis.ipynb |  46 ++++++-------
 notebooks/SlurmGPU.ipynb            | 100 +++++-----------------------
 src/config/enum_constants.py        |  12 ++--
 5 files changed, 64 insertions(+), 124 deletions(-)

diff --git a/notebooks/.gitattributes b/notebooks/.gitattributes
index 886e7e0..76d9e2b 100644
--- a/notebooks/.gitattributes
+++ b/notebooks/.gitattributes
@@ -1,3 +1,3 @@
 *.ipynb filter=strip-notebook-output
 # keep the output of the following notebooks when committing
-SlurmGPU.ipynb !filter=strip-notebook-output
\ No newline at end of file
+SlurmGPU.ipynb !filter=strip-notebook-output
diff --git a/notebooks/Basic Visualization.ipynb b/notebooks/Basic Visualization.ipynb
index 8969b2d..fa7b03f 100644
--- a/notebooks/Basic Visualization.ipynb	
+++ b/notebooks/Basic Visualization.ipynb	
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cd98c9b0-1829-4d87-9b4b-c9df57cdfd92",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "76ea80a7",
+   "id": "1",
    "metadata": {},
    "source": [
     "Jupyter server should be run at the notebook directory, so the output of the following cell would be the project root:"
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1e89443e",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "562cad40",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "12f06be5",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,7 +57,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3f544cd2",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +69,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d828105f",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,7 +80,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e6a1ee77",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -91,7 +91,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "93b5192d-f5be-44db-9047-f2547c61fa4e",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +101,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "47d1dde8",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,8 +110,14 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "duckdb",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "name": "python",
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Efficiency Analysis.ipynb b/notebooks/Efficiency Analysis.ipynb
index b3bf86c..c2c4beb 100644
--- a/notebooks/Efficiency Analysis.ipynb	
+++ b/notebooks/Efficiency Analysis.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "d5c78d30",
+   "id": "0",
    "metadata": {},
    "source": [
     "# <a id='toc1_'></a>[Efficiency Analysis](#toc0_)\n",
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9710fb4",
+   "id": "1",
    "metadata": {},
    "source": [
     "**Table of contents**<a id='toc0_'></a>    \n",
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffa87b37",
+   "id": "2",
    "metadata": {},
    "source": [
     "## <a id='toc1_1_'></a>[Setup](#toc0_)"
@@ -46,7 +46,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "474cfe7a",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9fd2d9f",
+   "id": "4",
    "metadata": {},
    "source": [
     "Jupyter server should be run at the notebook directory, so the output of the following cell would be the project root:"
@@ -67,7 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f4cc3afe",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +78,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d4f486b7",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,7 +99,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "82339fb7",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3aaff640",
+   "id": "8",
    "metadata": {},
    "source": [
     "## <a id='toc1_2_'></a>[Example: Analyze workload efficiency of GPU users who set no VRAM constraints and used 0 GB of VRAM](#toc0_)\n"
@@ -124,7 +124,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9161500e",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -152,7 +152,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff63f8e3",
+   "id": "10",
    "metadata": {},
    "source": [
     "### <a id='toc1_2_1_'></a>[User efficiency metrics](#toc0_)"
@@ -161,7 +161,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cb5782f0",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bbca2c66",
+   "id": "12",
    "metadata": {},
    "source": [
     "#### <a id='toc1_2_1_1_'></a>[Find Inefficient Users based on alloc_vram_efficiency](#toc0_)"
@@ -179,7 +179,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e13aa509",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -247,7 +247,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d69de45",
+   "id": "14",
    "metadata": {},
    "source": [
     "#### <a id='toc1_2_1_2_'></a>[Find Inefficient Users based on vram_hours](#toc0_)"
@@ -256,7 +256,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2f65e9bd",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,7 +312,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80198235",
+   "id": "16",
    "metadata": {},
    "source": [
     "### <a id='toc1_2_2_'></a>[PI group metrics](#toc0_)"
@@ -321,7 +321,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "67dbee71",
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -330,7 +330,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25561aa6",
+   "id": "18",
    "metadata": {},
    "source": [
     "#### <a id='toc1_2_2_1_'></a>[Find inefficient PIs](#toc0_)"
@@ -339,7 +339,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e599334a",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -396,7 +396,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32ea5ef7",
+   "id": "20",
    "metadata": {},
    "source": [
     "## <a id='toc1_3_'></a>[Example: Analyze all jobs with no VRAM constraints](#toc0_)"
@@ -405,7 +405,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "54827579",
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -430,7 +430,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3177fbe3",
+   "id": "22",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/notebooks/SlurmGPU.ipynb b/notebooks/SlurmGPU.ipynb
index fa4acd5..b54b412 100644
--- a/notebooks/SlurmGPU.ipynb
+++ b/notebooks/SlurmGPU.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9d1e8bea-5de8-430c-be4a-5f9c268cdc45",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cad34b63-0a18-4f3c-9787-7223355a42b8",
+   "id": "1",
    "metadata": {},
    "source": [
     "First we take a look at average and median queue wait times for jobs, based on how much GPU VRam they request."
@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ac722fed-aeee-4dc5-8322-784842481ab1",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,20 +46,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "11480943-6fa5-4685-9c5a-5c1213e46387",
+   "id": "3",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def plot_queued(stat=\"Mean\"):\n",
     "    \"\"\"Plot Queue statistics\"\"\"\n",
@@ -86,20 +75,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2feec163-848c-4510-942e-b0150ed8e708",
+   "id": "4",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 684.972x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def do_relplot():\n",
     "    \"\"\"Plot relationship between timelimit and queue time\"\"\"\n",
@@ -122,27 +100,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "78b09a34-fb43-4be2-9585-21eac1bca739",
+   "id": "5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_queued(stat=\"Median\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bc5ce0eb-358d-4ae2-8b40-9be99da18535",
+   "id": "6",
    "metadata": {},
    "source": [
     "Next we examine VRAM usage levels for all jobs, jobs with no specific VRAM request, and for jobs that request the largest GPU possible (80G) of VRAM."
@@ -151,20 +118,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3f30cf45-a64f-496b-94e1-4dcfd744540a",
+   "id": "7",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 900x900 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from gpu_metrics import vram_cutoffs, vram_labels\n",
     "\n",
@@ -206,20 +162,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e7098f34-ebc5-44ad-8453-831fb3e4133f",
+   "id": "8",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 900x900 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "efficiency_plot(constrs=[\"requested_vram=0\"], title=\"Used GPU VRam by GPU Compute Hours (no VRAM constraint)\")"
    ]
@@ -227,20 +172,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2d91abc3-8f36-44fa-895b-e0e91b2e0bc9",
+   "id": "9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 900x900 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "efficiency_plot(\n",
     "    constrs=[\"requested_vram>=80\", \"partition!='superpod-a100'\"],\n",
@@ -251,7 +185,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "30120541-d6e4-4e3f-97ea-83d54a315f7e",
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/config/enum_constants.py b/src/config/enum_constants.py
index 50b1a54..5e09197 100644
--- a/src/config/enum_constants.py
+++ b/src/config/enum_constants.py
@@ -81,9 +81,9 @@ class PartitionEnum(Enum):
 
 
 class FilterTypeEnum(Enum):
-    DICTIONARY = "dictionary",
-    LIST = "list",
-    SET = "set",
-    TUPLE = "tuple",
-    SCALAR = "scalar",
-    PD_NA = "pd_na",
\ No newline at end of file
+    DICTIONARY = "dictionary"
+    LIST = "list"
+    SET = "set"
+    TUPLE = "tuple"
+    SCALAR = "scalar"
+    PD_NA = "pd_na"
\ No newline at end of file

From c6ae4334e5d68cf0983a9fccc9761f0bca3b4b3b Mon Sep 17 00:00:00 2001
From: Espiobest <59823894+Espiobest@users.noreply.github.com>
Date: Tue, 15 Jul 2025 13:53:32 -0400
Subject: [PATCH 2/9] fix gitattributes syntax

---
 notebooks/.gitattributes | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/notebooks/.gitattributes b/notebooks/.gitattributes
index 76d9e2b..66ff4ea 100644
--- a/notebooks/.gitattributes
+++ b/notebooks/.gitattributes
@@ -1,3 +1,3 @@
 *.ipynb filter=strip-notebook-output
 # keep the output of the following notebooks when committing
-SlurmGPU.ipynb !filter=strip-notebook-output
+SlurmGPU.ipynb -filter=strip-notebook-output

From da7ede1f461b37fcc132dc4ce6b3007715116750 Mon Sep 17 00:00:00 2001
From: Espiobest <59823894+Espiobest@users.noreply.github.com>
Date: Tue, 15 Jul 2025 14:03:43 -0400
Subject: [PATCH 3/9] update scripts and run notebooks

---
 notebooks/Efficiency Analysis.ipynb | 56 ++++++++++++-----------------
 notebooks/SlurmGPU.ipynb            | 46 ++++++++++++++++--------
 scripts/gpu_metrics.py              |  9 ++++-
 src/__init__.py                     |  2 +-
 src/analysis/vram_usage.py          | 23 +++++-------
 src/config/constants.py             |  2 +-
 src/config/enum_constants.py        |  3 +-
 src/preprocess/preprocess.py        |  2 +-
 8 files changed, 76 insertions(+), 67 deletions(-)

diff --git a/notebooks/Efficiency Analysis.ipynb b/notebooks/Efficiency Analysis.ipynb
index c2c4beb..d227eac 100644
--- a/notebooks/Efficiency Analysis.ipynb	
+++ b/notebooks/Efficiency Analysis.ipynb	
@@ -106,7 +106,7 @@
     "# Load the jobs DataFrame from DuckDB\n",
     "\n",
     "efficiency_analysis = vram_usage.EfficiencyAnalysis(\n",
-    "\tdb_path='../data/slurm_data.db',\n",
+    "    db_path=\"../data/slurm_data.db\",\n",
     ")\n",
     "\n",
     "display(efficiency_analysis.jobs_df.head(10))\n",
@@ -137,16 +137,16 @@
     "metrics_dict = efficiency_analysis.calculate_all_efficiency_metrics(filtered_jobs)\n",
     "\n",
     "\n",
-    "jobs_with_metrics = metrics_dict['jobs_with_efficiency_metrics']\n",
-    "users_with_metrics = metrics_dict['users_with_efficiency_metrics']\n",
-    "pi_accounts_with_metrics = metrics_dict['pi_accounts_with_efficiency_metrics']\n",
+    "jobs_with_metrics = metrics_dict[\"jobs_with_efficiency_metrics\"]\n",
+    "users_with_metrics = metrics_dict[\"users_with_efficiency_metrics\"]\n",
+    "pi_accounts_with_metrics = metrics_dict[\"pi_accounts_with_efficiency_metrics\"]\n",
     "\n",
     "# Set option to display all columns\n",
-    "pd.set_option('display.max_columns', None)\n",
+    "pd.set_option(\"display.max_columns\", None)\n",
     "# Display the DataFrame\n",
     "display(jobs_with_metrics.head(10))\n",
     "# To revert to default settings (optional)\n",
-    "pd.reset_option('display.max_columns')\n",
+    "pd.reset_option(\"display.max_columns\")\n",
     "print(f\"Jobs found: {len(jobs_with_metrics)}\")"
    ]
   },
@@ -196,16 +196,11 @@
     "display(inefficient_users.head(10))\n",
     "\n",
     "\n",
-    "\n",
     "# Plot top inefficient users by GPU hours, with efficiency as labels\n",
     "top_users = inefficient_users.head(10)\n",
     "\n",
     "plt.figure(figsize=(8, 5))\n",
-    "barplot = sns.barplot(\n",
-    "    y=top_users[\"User\"],\n",
-    "    x=top_users[\"user_job_hours\"],\n",
-    "    orient=\"h\"\n",
-    ")\n",
+    "barplot = sns.barplot(y=top_users[\"User\"], x=top_users[\"user_job_hours\"], orient=\"h\")\n",
     "plt.xlabel(\"Job Hours\")\n",
     "plt.ylabel(\"User\")\n",
     "plt.title(\"Top 10 Inefficient Users by Allocated VRAM Efficiency Contribution\")\n",
@@ -230,16 +225,7 @@
     "    # If bar is very close to right spine, nudge annotation left to avoid overlap\n",
     "    if xpos > xlim * 0.96:\n",
     "        xpos = xlim * 0.96\n",
-    "    ax.text(\n",
-    "        xpos,\n",
-    "        i,\n",
-    "        f\"Eff: {efficiency:.2f}\",\n",
-    "        va=\"center\",\n",
-    "        ha=\"left\",\n",
-    "        fontsize=10,\n",
-    "        color=\"black\",\n",
-    "        clip_on=True\n",
-    "    )\n",
+    "    ax.text(xpos, i, f\"Eff: {efficiency:.2f}\", va=\"center\", ha=\"left\", fontsize=10, color=\"black\", clip_on=True)\n",
     "\n",
     "plt.tight_layout()\n",
     "plt.show()"
@@ -272,11 +258,7 @@
     "\n",
     "# Plot top inefficient users by VRAM-hours, with VRAM-hours as labels\n",
     "plt.figure(figsize=(8, 8))\n",
-    "barplot = sns.barplot(\n",
-    "    y=top_users[\"User\"],\n",
-    "    x=top_users[\"vram_hours\"],\n",
-    "    orient=\"h\"\n",
-    ")\n",
+    "barplot = sns.barplot(y=top_users[\"User\"], x=top_users[\"vram_hours\"], orient=\"h\")\n",
     "plt.xlabel(\"VRAM-Hours\")\n",
     "plt.ylabel(\"User\")\n",
     "plt.title(\"Top 10 Inefficient Users by VRAM-Hours\")\n",
@@ -304,7 +286,7 @@
     "        ha=\"left\",\n",
     "        fontsize=10,\n",
     "        color=\"black\",\n",
-    "        clip_on=True\n",
+    "        clip_on=True,\n",
     "    )\n",
     "plt.tight_layout()\n",
     "plt.show()"
@@ -359,7 +341,7 @@
     "    y=top_pi_accounts[\"pi_account\"],\n",
     "    x=top_pi_accounts[\"pi_acc_vram_hours\"],\n",
     "    order=top_pi_accounts[\"pi_account\"].tolist(),  # Only show present values\n",
-    "    orient=\"h\"\n",
+    "    orient=\"h\",\n",
     ")\n",
     "plt.xlabel(\"VRAM-Hours\")\n",
     "plt.ylabel(\"PI Account\")\n",
@@ -388,7 +370,7 @@
     "        ha=\"left\",\n",
     "        fontsize=10,\n",
     "        color=\"black\",\n",
-    "        clip_on=True\n",
+    "        clip_on=True,\n",
     "    )\n",
     "plt.tight_layout()\n",
     "plt.show()"
@@ -413,17 +395,17 @@
     "\n",
     "filtered_jobs = efficiency_analysis.filter_jobs_for_analysis(\n",
     "    vram_constraint_filter={\"min\": 0, \"inclusive\": False},  # No VRAM constraints\n",
-    "    gpu_count_filter={\"min\": 1, \"inclusive\": True}  # At least one GPU allocated\n",
+    "    gpu_count_filter={\"min\": 1, \"inclusive\": True},  # At least one GPU allocated\n",
     ")\n",
     "\n",
     "jobs_with_metrics = efficiency_analysis.calculate_job_efficiency_metrics(filtered_jobs)\n",
     "\n",
     "# Set option to display all columns\n",
-    "pd.set_option('display.max_columns', None)\n",
+    "pd.set_option(\"display.max_columns\", None)\n",
     "# Display the DataFrame\n",
     "display(jobs_with_metrics.head(10))\n",
     "# To revert to default settings (optional)\n",
-    "pd.reset_option('display.max_columns')\n",
+    "pd.reset_option(\"display.max_columns\")\n",
     "print(f\"Jobs found: {len(jobs_with_metrics)}\")"
    ]
   },
@@ -450,8 +432,14 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "duckdb",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "name": "python",
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/SlurmGPU.ipynb b/notebooks/SlurmGPU.ipynb
index b54b412..195292f 100644
--- a/notebooks/SlurmGPU.ipynb
+++ b/notebooks/SlurmGPU.ipynb
@@ -6,9 +6,27 @@
    "id": "0",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "import sys\n",
+    "from pathlib import Path\n",
+    "\n",
+    "project_root = str(Path.cwd().resolve().parent)\n",
+    "print(f\"Project root: {project_root}\")\n",
+    "\n",
+    "if project_root not in sys.path:\n",
+    "    sys.path.append(project_root)\n",
+    "    print(f\"Added project root to sys.path: {project_root}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Import modules and load the dataframe with job information\n",
-    "from gpu_metrics import GPUMetrics\n",
+    "from scripts.gpu_metrics import GPUMetrics\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "\n",
@@ -20,13 +38,13 @@
     "sns.set_theme()\n",
     "sns.set_palette(\"muted\")\n",
     "# Filter out jobs less than 10 minutes\n",
-    "metrics = GPUMetrics(min_elapsed=600)\n",
+    "metrics = GPUMetrics(min_elapsed=600, local=True)\n",
     "df = metrics.df"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1",
+   "id": "2",
    "metadata": {},
    "source": [
     "First we take a look at average and median queue wait times for jobs, based on how much GPU VRam they request."
@@ -35,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +64,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +93,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,7 +118,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6",
+   "id": "7",
    "metadata": {},
    "source": [
     "Next we examine VRAM usage levels for all jobs, jobs with no specific VRAM request, and for jobs that request the largest GPU possible (80G) of VRAM."
@@ -118,11 +136,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from gpu_metrics import vram_cutoffs, vram_labels\n",
+    "from scripts.gpu_metrics import vram_cutoffs, vram_labels\n",
     "\n",
     "\n",
     "def efficiency_plot(constrs=None, title=\"Used GPU VRAM by GPU Compute Hours\"):\n",
@@ -133,7 +151,7 @@
     "    else:\n",
     "        where = \"\"\n",
     "\n",
-    "    where = \"where  + \"(\" and \".join(constrs)) if constrs else \"\"\n",
+    "    where = \"where \" + (\" and \".join(constrs)) if constrs else \"\"\n",
     "    filtered_df = duckdb.query(\n",
     "        \"select GPUs, GPUMemUsage, Elapsed, requested_vram, IsArray, Elapsed*GPUs/3600 as gpu_hours  from df \" + where\n",
     "    ).df()\n",
@@ -162,7 +180,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -172,7 +190,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9",
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -185,7 +203,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "10",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/scripts/gpu_metrics.py b/scripts/gpu_metrics.py
index 9f67c49..267610b 100644
--- a/scripts/gpu_metrics.py
+++ b/scripts/gpu_metrics.py
@@ -56,13 +56,20 @@ def get_requested_vram(constraints):
 class GPUMetrics:
     """A class for computing and plotting metrics about GPU jobs."""
 
-    def __init__(self, metricsfile="./modules/admin-resources/reporting/slurm_data.db", min_elapsed=600) -> None:
+    def __init__(
+        self,
+        metricsfile="./modules/admin-resources/reporting/slurm_data.db",
+        min_elapsed=600,
+        local=False,
+    ) -> None:
         """Initialize GPUMetrics with job data from a DuckDB database.
 
         Args:
             metricsfile (str, optional): Path to the DuckDB database file containing job data.
             min_elapsed (int, optional): Minimum elapsed time (in seconds) for jobs to be included.
         """
+        if local:
+            metricsfile = "../data/slurm_data_small.db"
         self.con = duckdb.connect(metricsfile)
         # TODO - handle array jobs properly
         df = self.con.query(
diff --git a/src/__init__.py b/src/__init__.py
index 5b8383e..12bffbd 100644
--- a/src/__init__.py
+++ b/src/__init__.py
@@ -1 +1 @@
-from .preprocess.preprocess import preprocess_data as preprocess_data
\ No newline at end of file
+from .preprocess.preprocess import preprocess_data as preprocess_data
diff --git a/src/analysis/vram_usage.py b/src/analysis/vram_usage.py
index f67c3f7..2711988 100644
--- a/src/analysis/vram_usage.py
+++ b/src/analysis/vram_usage.py
@@ -41,13 +41,14 @@ def load_jobs_dataframe_from_duckdb(
         processed_data = processed_data.sample(n=sample_size, random_state=random_state)
     return processed_data
 
+
 class EfficiencyAnalysis:
     """
     Class to encapsulate the efficiency analysis of jobs based on various metrics.
 
     It provides methods to load data, analyze workload efficiency, and evaluate CPU-GPU usage patterns.
     """
-                
+
     # Store the variable names as a class-level constant for maintainability
     _efficiency_metric_vars = [
         "jobs_with_efficiency_metrics",
@@ -225,7 +226,7 @@ def filter_jobs_for_analysis(
                     self.jobs_df["GPUMemUsage"],
                     gpu_mem_usage_filter,
                     {FilterTypeEnum.SCALAR, FilterTypeEnum.DICTIONARY},
-                    "gpu_mem_usage_filter"
+                    "gpu_mem_usage_filter",
                 )
             except ValueError as e:
                 raise ValueError("Invalid GPU memory usage filter.") from e
@@ -237,7 +238,7 @@ def filter_jobs_for_analysis(
                     self.jobs_df["allocated_vram"],
                     allocated_vram_filter,
                     set(FilterTypeEnum.__members__.values()).difference({FilterTypeEnum.PD_NA}),
-                    "allocated_vram_filter"
+                    "allocated_vram_filter",
                 )
             except ValueError as e:
                 raise ValueError("Invalid allocated VRAM filter.") from e
@@ -249,7 +250,7 @@ def filter_jobs_for_analysis(
                     self.jobs_df["GPUs"],
                     gpu_count_filter,
                     set(FilterTypeEnum.__members__.values()).difference({FilterTypeEnum.PD_NA}),
-                    "gpu_count_filter"
+                    "gpu_count_filter",
                 )
             except ValueError as e:
                 raise ValueError("Invalid GPU count filter.") from e
@@ -375,11 +376,9 @@ def calculate_user_efficiency_metrics(self) -> pd.DataFrame:
 
         self.users_with_efficiency_metrics = users_w_efficiency_metrics
         return self.users_with_efficiency_metrics
-    
+
     def find_inefficient_users_by_alloc_vram_efficiency(
-        self,
-        alloc_vram_efficiency_filter: int | float | dict | None,
-        min_jobs: int = 5
+        self, alloc_vram_efficiency_filter: int | float | dict | None, min_jobs: int = 5
     ) -> pd.DataFrame:
         """
         Identify users with low expected allocated VRAM efficiency across their jobs compared to others
@@ -425,9 +424,7 @@ def find_inefficient_users_by_alloc_vram_efficiency(
         return inefficient_users
 
     def find_inefficient_users_by_vram_hours(
-        self,
-        vram_hours_filter: int | float | dict = 200, 
-        min_jobs: int = 5
+        self, vram_hours_filter: int | float | dict = 200, min_jobs: int = 5
     ) -> pd.DataFrame:
         """
         Identify users with high VRAM-hours across their jobs compared to others.
@@ -567,9 +564,7 @@ def calculate_pi_account_efficiency_metrics(self) -> pd.DataFrame:
         return self.pi_accounts_with_efficiency_metrics
 
     def find_inefficient_pis_by_vram_hours(
-        self,
-        vram_hours_filter: int | float | dict = 200,
-        min_jobs: int = 5
+        self, vram_hours_filter: int | float | dict = 200, min_jobs: int = 5
     ) -> pd.DataFrame:
         """
         Identify inefficient PI accounts based on VRAM hours.
diff --git a/src/config/constants.py b/src/config/constants.py
index 4a6f73c..bc2e60d 100644
--- a/src/config/constants.py
+++ b/src/config/constants.py
@@ -22,7 +22,7 @@
 
 VRAM_CATEGORIES = [0, 8, 11, 12, 16, 23, 32, 40, 48, 80]
 
-DEFAULT_MIN_ELAPSED_SECONDS = 600 # 10 minutes, used for filtering jobs with short execution times
+DEFAULT_MIN_ELAPSED_SECONDS = 600  # 10 minutes, used for filtering jobs with short execution times
 
 # A map for categorical type construction, containing some values that exist in each type
 ATTRIBUTE_CATEGORIES = {
diff --git a/src/config/enum_constants.py b/src/config/enum_constants.py
index 5e09197..9ee4992 100644
--- a/src/config/enum_constants.py
+++ b/src/config/enum_constants.py
@@ -4,6 +4,7 @@
 
 from enum import Enum
 
+
 class InteractiveEnum(Enum):
     NON_INTERACTIVE = "non-interactive"
     SHELL = "shell"
@@ -86,4 +87,4 @@ class FilterTypeEnum(Enum):
     SET = "set"
     TUPLE = "tuple"
     SCALAR = "scalar"
-    PD_NA = "pd_na"
\ No newline at end of file
+    PD_NA = "pd_na"
diff --git a/src/preprocess/preprocess.py b/src/preprocess/preprocess.py
index c079f00..159cc2c 100644
--- a/src/preprocess/preprocess.py
+++ b/src/preprocess/preprocess.py
@@ -297,7 +297,7 @@ def preprocess_data(
     res.loc[:, "Queued"] = res["StartTime"] - res["SubmitTime"]
     res.loc[:, "vram_constraint"] = res.apply(
         lambda row: _get_vram_constraint(row["Constraints"], row["GPUs"], row["GPUMemUsage"]), axis=1
-    ).astype(pd.Int64Dtype()) # Use Int64Dtype to allow for nullable integers
+    ).astype(pd.Int64Dtype())  # Use Int64Dtype to allow for nullable integers
     res.loc[:, "allocated_vram"] = res.apply(
         lambda row: _get_approx_allocated_vram(row["GPUType"], row["NodeList"], row["GPUs"], row["GPUMemUsage"]),
         axis=1,

From 50abc719614ab88893797f77b725456b33fe17d2 Mon Sep 17 00:00:00 2001
From: Espiobest <59823894+Espiobest@users.noreply.github.com>
Date: Tue, 15 Jul 2025 14:09:58 -0400
Subject: [PATCH 4/9] remove filters

---
 notebooks/.gitattributes | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/notebooks/.gitattributes b/notebooks/.gitattributes
index 66ff4ea..5a4e934 100644
--- a/notebooks/.gitattributes
+++ b/notebooks/.gitattributes
@@ -1,3 +1,4 @@
-*.ipynb filter=strip-notebook-output
-# keep the output of the following notebooks when committing
-SlurmGPU.ipynb -filter=strip-notebook-output
+# *.ipynb filter=strip-notebook-output
+# # keep the output of the following notebooks when committing
+# SlurmGPU.ipynb -filter=strip-notebook-output
+# notebooks/SlurmGPU.ipynb -filter=strip-notebook-output
\ No newline at end of file

From da86150b3d6a26925b5b5706f54d8fc6c8f0509c Mon Sep 17 00:00:00 2001
From: Espiobest <59823894+Espiobest@users.noreply.github.com>
Date: Tue, 19 Aug 2025 13:04:09 -0400
Subject: [PATCH 5/9] add documentation changes

---
 docs/analysis/efficiency_analysis.md     |   4 +-
 docs/analysis/frequency_analysis.md      |   5 +
 docs/contact.md                          | 106 ++++++++++
 docs/data-and-metrics.md                 | 164 +++++++++++++++
 docs/demo.md                             | 134 ++++++++++++
 docs/faq.md                              | 231 +++++++++++++++++++++
 docs/getting-started.md                  | 253 +++++++++++++++++++++++
 docs/index.md                            |  28 ++-
 docs/mvp_scripts/cpu_metrics.md          |   2 +-
 docs/mvp_scripts/gpu_metrics.md          |   2 +-
 docs/mvp_scripts/zero_gpu_usage.md       |   2 +-
 docs/notebooks                           |   1 +
 docs/preprocess.md                       |  16 +-
 docs/visualization/columns.md            |   1 +
 docs/visualization/efficiency_metrics.md |   1 +
 docs/visualization/models.md             |   1 +
 docs/visualization/visualization.md      |   1 +
 mkdocs.yml                               |  73 ++++++-
 18 files changed, 999 insertions(+), 26 deletions(-)
 create mode 100644 docs/analysis/frequency_analysis.md
 create mode 100644 docs/contact.md
 create mode 100644 docs/data-and-metrics.md
 create mode 100644 docs/demo.md
 create mode 100644 docs/faq.md
 create mode 100644 docs/getting-started.md
 create mode 120000 docs/notebooks
 create mode 100644 docs/visualization/columns.md
 create mode 100644 docs/visualization/efficiency_metrics.md
 create mode 100644 docs/visualization/models.md
 create mode 100644 docs/visualization/visualization.md

diff --git a/docs/analysis/efficiency_analysis.md b/docs/analysis/efficiency_analysis.md
index 4f6cadd..6e409a5 100644
--- a/docs/analysis/efficiency_analysis.md
+++ b/docs/analysis/efficiency_analysis.md
@@ -1,4 +1,6 @@
 ---
 title: Efficiency Analysis
 ---
-::: src.analysis.vram_usage
\ No newline at end of file
+The ```efficiency_analysis.py``` script helps users to filter the Pandas dataframe they have obtained from the DuckDB database and then generate all the metrics necessary to analyze the data.
+
+::: src.analysis.efficiency_analysis
diff --git a/docs/analysis/frequency_analysis.md b/docs/analysis/frequency_analysis.md
new file mode 100644
index 0000000..8132a96
--- /dev/null
+++ b/docs/analysis/frequency_analysis.md
@@ -0,0 +1,5 @@
+---
+title: Frequency Analysis
+---
+
+::: src.analysis.frequency_analysis
diff --git a/docs/contact.md b/docs/contact.md
new file mode 100644
index 0000000..e1d297a
--- /dev/null
+++ b/docs/contact.md
@@ -0,0 +1,106 @@
+# Contact and Support
+
+If you encounter issues or need help using the DS4CG Unity Job Analytics project, here are the best ways to get support.
+
+## GitHub Issues
+
+For technical problems, bug reports, or feature requests, please create a GitHub issue:
+
+**🐛 Bug Reports**
+- Provide a clear description of the problem
+- Include steps to reproduce the issue
+- Share error messages and stack traces
+- Mention your environment (Python version, OS, etc.)
+
+**💡 Feature Requests**
+- Describe the desired functionality
+- Explain the use case and benefits
+- Suggest possible implementation approaches
+
+**📚 Documentation Issues**
+- Point out unclear or missing documentation
+- Suggest improvements or additions
+- Request examples for specific use cases
+
+[**Create a GitHub Issue →**](https://github.com/your-org/ds4cg-job-analytics/issues)
+
+## Response Time
+
+The development team will review and respond to GitHub issues periodically. Please allow:
+- **Critical bugs**: 1-2 business days
+- **General issues**: 3-5 business days  
+- **Feature requests**: 1-2 weeks
+- **Documentation updates**: 1 week
+
+## Community Guidelines
+
+When seeking help, please:
+
+✅ **Do:**
+
+- Search existing issues first
+- Provide minimal reproducible examples
+- Use clear, descriptive titles
+- Be respectful and patient
+- Share relevant context and details
+
+❌ **Don't:**
+
+- Post duplicate issues
+- Share sensitive data or credentials
+- Expect immediate responses
+- Use issues for general questions about Slurm or Unity
+
+## Unity Slack
+
+For urgent questions related to Unity cluster operations or data access, you can reach out via the Unity Slack workspace. However, for project-specific issues, GitHub issues are preferred.
+
+## Contributing
+
+Interested in contributing to the project? We welcome:
+
+- **Code contributions**: Bug fixes, new features, optimizations
+- **Documentation**: Improvements, examples, tutorials
+- **Testing**: Additional test cases, bug reports
+- **Feedback**: User experience insights, suggestions
+
+See our contributing guidelines in the repository for detailed information about:
+
+- Development setup
+- Code style requirements
+- Pull request process
+- Testing procedures
+
+## Academic Collaboration
+
+This project is part of the Data Science for the Common Good (DS4CG) program. For academic collaborations or research partnerships, consider reaching out through:
+
+- **DS4CG Program**: [DS4CG Website](https://ds.cs.umass.edu/programs/ds4cg)
+- **Unity HPC Team**: For cluster-related inquiries
+
+## Project Maintainers
+
+- **Project Lead**: Christopher Odoom
+- **Contributors**: DS4CG Summer 2025 Internship Team
+
+## Additional Resources
+
+Before reaching out for support, please check:
+
+1. **[FAQ](faq.md)** - Common questions and solutions
+2. **[Getting Started](getting-started.md)** - Setup and basic usage
+3. **[Demo](demo.md)** - Working examples and code samples
+4. **Jupyter Notebooks** - Interactive examples in `notebooks/` directory
+5. **API Documentation** - Detailed function/class documentation
+
+## Reporting Security Issues
+
+If you discover a security vulnerability, please **do not** create a public GitHub issue. Instead:
+
+1. Contact the project maintainers directly
+2. Provide a detailed description of the vulnerability
+3. Allow time for the issue to be addressed before public disclosure
+
+---
+
+**Remember**: The team volunteers their time to maintain this project. Clear, detailed, and respectful communication helps everyone get the help they need more efficiently. Thank you for using the DS4CG Unity Job Analytics project!
diff --git a/docs/data-and-metrics.md b/docs/data-and-metrics.md
new file mode 100644
index 0000000..6836ef2
--- /dev/null
+++ b/docs/data-and-metrics.md
@@ -0,0 +1,164 @@
+# Data and Efficiency Metrics
+
+This page provides comprehensive documentation about the data structure and efficiency metrics available in the DS4CG Unity Job Analytics project.
+
+## Data Structure
+
+The project works with job data from the Unity cluster's Slurm scheduler. After preprocessing, the data contains the following key attributes:
+
+### Job Identification
+- **JobID** – Unique identifier for each job.  
+- **ArrayID** – Array job identifier (`-1` for non-array jobs).  
+- **User** – Username of the job submitter.  
+- **Account** – Account/group associated with the job.  
+
+### Time Attributes
+- **StartTime** – When the job started execution (datetime).  
+- **SubmitTime** – When the job was submitted (datetime).  
+- **Elapsed** – Total runtime duration (timedelta).  
+- **TimeLimit** – Maximum allowed runtime (timedelta).  
+
+### Resource Allocation
+- **GPUs** – Number of GPUs allocated.  
+- **GPUType** – Type of GPU allocated (e.g., `"v100"`, `"a100"`, or `NA` for CPU-only jobs).  
+- **Nodes** – Number of nodes allocated.  
+- **CPUs** – Number of CPU cores allocated.  
+- **ReqMem** – Requested memory.  
+
+### Job Status
+- **Status** – Final job status (`"COMPLETED"`, `"FAILED"`, `"CANCELLED"`, etc.).  
+- **ExitCode** – Job exit code.  
+- **QOS** – Quality of Service level.  
+- **Partition** – Cluster partition used.  
+
+### Resource Usage
+- **CPUTime** – Total CPU time used.  
+- **CPUTimeRAW** – Raw CPU time measurement.  
+
+### Constraints and Configuration
+- **Constraints** – Hardware constraints specified.  
+- **Interactive** – Whether the job was interactive (`"interactive"` or `"non-interactive"`).  
+
+---
+
+## Efficiency and Resource Metrics
+
+### GPU and VRAM Metrics
+
+- **GPU Count** (`gpu_count`)  
+  Number of GPUs allocated to the job.
+
+- **Job Hours** (`job_hours`)  
+  $$
+  \text{job\_hours} = \frac{\text{Elapsed (seconds)}}{3600} \times \text{gpu\_count}
+  $$
+
+- **VRAM Constraint** (`vram_constraint`)  
+  VRAM requested via constraints, in GiB. Defaults are applied if not explicitly requested.
+
+- **Partition Constraint** (`partition_constraint`)  
+  VRAM derived from selecting a GPU partition, in GiB.
+
+- **Requested VRAM** (`requested_vram`)  
+  $$
+  \text{requested\_vram} =
+  \begin{cases}
+    \text{partition\_constraint}, & \text{if available} \\
+    \text{vram\_constraint}, & \text{otherwise}
+  \end{cases}
+  $$
+
+- **Used VRAM** (`used_vram_gib`)  
+  Sum of peak VRAM used on all allocated GPUs (GiB).
+
+- **Approximate Allocated VRAM** (`allocated_vram`)  
+  Estimated VRAM based on GPU model(s) and job node allocation.
+
+- **Total VRAM-Hours** (`vram_hours`)  
+  $$
+  \text{vram\_hours} = \text{allocated\_vram} \times \text{job\_hours}
+  $$
+
+- **Allocated VRAM Efficiency** (`alloc_vram_efficiency`)  
+  $$
+  \text{alloc\_vram\_efficiency} = \frac{\text{used\_vram\_gib}}{\text{allocated\_vram}}
+  $$
+
+- **VRAM Constraint Efficiency** (`vram_constraint_efficiency`)  
+  $$
+  \text{vram\_constraint\_efficiency} =
+  \frac{\text{used\_vram\_gib}}{\text{vram\_constraint}}
+  $$
+
+- **Allocated VRAM Efficiency Score** (`alloc_vram_efficiency_score`)  
+  $$
+  \text{alloc\_vram\_efficiency\_score} =
+  \ln(\text{alloc\_vram\_efficiency}) \times \text{vram\_hours}
+  $$
+  Penalizes long jobs with low VRAM efficiency.
+
+- **VRAM Constraint Efficiency Score** (`vram_constraint_efficiency_score`)  
+  $$
+  \text{vram\_constraint\_efficiency\_score} =
+  \ln(\text{vram\_constraint\_efficiency}) \times \text{vram\_hours}
+  $$
+
+### CPU Memory Metrics
+- **Used CPU Memory** (`used_cpu_mem_gib`) – Peak CPU RAM usage in GiB.  
+- **Allocated CPU Memory** (`allocated_cpu_mem_gib`) – Requested CPU RAM in GiB.  
+- **CPU Memory Efficiency** (`cpu_mem_efficiency`)  
+  $$
+  \text{cpu\_mem\_efficiency} = \frac{\text{used\_cpu\_mem\_gib}}{\text{allocated\_cpu\_mem\_gib}}
+  $$
+
+---
+
+## User-Level Metrics
+
+- **Job Count** (`job_count`) – Number of jobs submitted by the user.  
+- **Total Job Hours** (`user_job_hours`) – Sum of job hours for all jobs of the user.  
+- **Average Allocated VRAM Efficiency Score** (`avg_alloc_vram_efficiency_score`).  
+- **Average VRAM Constraint Efficiency Score** (`avg_vram_constraint_efficiency_score`).  
+
+- **Weighted Average Allocated VRAM Efficiency**  
+  $$
+  \text{expected\_value\_alloc\_vram\_efficiency} =
+  \frac{\sum (\text{alloc\_vram\_efficiency} \times \text{vram\_hours})}
+       {\sum \text{vram\_hours}}
+  $$
+
+- **Weighted Average VRAM Constraint Efficiency**  
+  $$
+  \text{expected\_value\_vram\_constraint\_efficiency} =
+  \frac{\sum (\text{vram\_constraint\_efficiency} \times \text{vram\_hours})}
+       {\sum \text{vram\_hours}}
+  $$
+
+- **Weighted Average GPU Count**  
+  $$
+  \text{expected\_value\_gpu\_count} =
+  \frac{\sum (\text{gpu\_count} \times \text{vram\_hours})}
+       {\sum \text{vram\_hours}}
+  $$
+
+- **Total VRAM-Hours** – Sum of allocated_vram × job_hours across all jobs of the user.
+
+---
+
+## Group-Level Metrics
+
+For a group of users (e.g., PI group):
+
+- **Job Count** – Total number of jobs across the group.  
+- **PI Group Job Hours** (`pi_acc_job_hours`).  
+- **PI Group VRAM Hours** (`pi_ac_vram_hours`).  
+- **User Count**.  
+- Group averages and weighted averages of efficiency metrics (similar formulas as above).
+
+---
+
+## Efficiency Categories
+- **High**: > 70%  
+- **Medium**: 30–70%  
+- **Low**: 10–30%  
+- **Very Low**: < 10%  
diff --git a/docs/demo.md b/docs/demo.md
new file mode 100644
index 0000000..86b8383
--- /dev/null
+++ b/docs/demo.md
@@ -0,0 +1,134 @@
+# Demo
+
+This page showcases the DS4CG Unity Job Analytics project in action with interactive examples and demonstrations.
+
+## Complete Workflow Notebooks
+
+Explore our comprehensive Jupyter notebooks that demonstrate the full capabilities:
+
+### 📊 [Frequency Analysis Demo](../notebooks/Frequency Analysis/)
+**Complete end-to-end workflow** showing:
+
+- Database connection and preprocessing
+- Efficiency analysis setup and filtering
+- Time series data preparation
+- Interactive visualizations
+- Best/worst user identification
+
+### 📈 [Basic Visualization](../notebooks/Basic%20Visualization/)
+**Column statistics and exploratory analysis** including:
+
+- Data loading and preprocessing
+- Column-level statistical visualizations
+- Distribution analysis
+- Data quality assessment
+
+### 🔍 [Efficiency Analysis](../notebooks/Efficiency%20Analysis/)
+**Advanced efficiency analysis techniques** covering:
+
+- Job filtering and metrics calculation
+- User and PI group analysis
+- Inefficiency identification
+- Performance comparison workflows
+
+### 🎯 [Clustering Analysis](../notebooks/clustering_analysis/)
+**User behavior clustering and pattern analysis**
+
+### 📊 [Frequency Analysis](../notebooks/Frequency%20Analysis/)
+**Time series frequency analysis and patterns**
+
+---
+
+## Quick Start Examples
+
+For quick reference, here are the key workflow patterns:
+
+### Database → Preprocessing → Analysis
+```python
+# See complete implementation in: VRAM Efficiency Analysis Demo notebook
+db = DatabaseConnection("../slurm_data_new.db")
+gpu_df = db.fetch_query("SELECT * FROM Jobs WHERE GPUs > 0")
+processed_df = preprocess_data(gpu_df, min_elapsed_seconds=0)
+```
+
+### Efficiency Analysis Workflow
+```python
+# See complete implementation in: Efficiency Analysis notebook
+efficiency_analyzer = EfficiencyAnalysis(jobs_df=processed_df)
+filtered_jobs = efficiency_analyzer.filter_jobs_for_analysis(...)
+job_metrics = efficiency_analyzer.calculate_job_efficiency_metrics(filtered_jobs)
+```
+
+### Interactive Visualizations
+```python
+# See complete implementation in: VRAM Efficiency Analysis Demo notebook
+time_series_visualizer = TimeSeriesVisualizer(time_series_data)
+fig = time_series_visualizer.plot_vram_efficiency_interactive(users=users_to_analyze)
+```
+
+---
+
+## Notebook Features
+
+### VRAM Efficiency Analysis Demo Features
+
+- Complete efficiency analysis setup
+- Time series data preparation and visualization
+- Interactive plot generation
+- Best/worst user identification
+- Custom date range analysis
+
+### Basic Visualization Features
+
+- Database connection and data loading
+- Column statistics generation
+- Individual column visualizations
+
+### Efficiency Analysis Features
+
+- Job filtering and metrics calculation
+- User efficiency analysis
+- PI group analysis
+
+---
+
+## Performance Tips from Notebooks
+
+Based on our notebook implementations:
+
+### For Large Datasets
+```python
+# From VRAM Efficiency Analysis Demo notebook
+filtered_jobs = efficiency_analyzer.filter_jobs_for_analysis(
+    gpu_count_filter=1,
+    allocated_vram_filter={"min": 0, "max": np.inf, "inclusive": False},
+    gpu_mem_usage_filter={"min": 0, "max": np.inf, "inclusive": False}
+)
+```
+
+### For Interactive Plots
+```python
+# From VRAM Efficiency Analysis Demo notebook
+fig = time_series_visualizer.plot_vram_efficiency_per_job_dot_interactive(
+    users=["user1", "user2"],
+    efficiency_metric="alloc_vram_efficiency",
+    max_points=500,  # Limit points for performance
+    exclude_fields=["Exit Code"]
+)
+```
+
+---
+
+## Running the Notebooks
+
+The notebooks are now integrated directly into this documentation! You can:
+
+1. **View in Documentation**: Click on any notebook link above to view it rendered in the documentation
+2. **Download and Run Locally**: 
+   ```bash
+   cd notebooks/
+   jupyter lab
+   ```
+3. **Interactive Execution**: The notebooks contain complete, tested implementations with real data and interactive outputs
+
+The integrated notebooks provide full access to working examples while keeping everything in one place!
diff --git a/docs/faq.md b/docs/faq.md
new file mode 100644
index 0000000..4a78315
--- /dev/null
+++ b/docs/faq.md
@@ -0,0 +1,231 @@
+# Frequently Asked Questions (FAQ)
+
+This page addresses common questions and technical issues encountered when using the DS4CG Unity Job Analytics project.
+
+## Installation and Setup
+
+### Q: When I try to install the requirements, I get dependency conflicts. How do I resolve this?
+
+**A:** This usually happens due to conflicting package versions. Try these steps:
+
+1. Create a fresh virtual environment:
+```bash
+python -m venv fresh_env
+source fresh_env/bin/activate  # Linux/Mac
+# or
+fresh_env\Scripts\activate     # Windows
+```
+
+2. Update pip and install requirements:
+```bash
+pip install --upgrade pip
+pip install -r requirements.txt
+pip install -r dev-requirements.txt
+```
+
+3. If conflicts persist, try installing packages individually:
+```bash
+pip install pandas plotly matplotlib duckdb pydantic
+```
+
+### Q: I'm getting a "Python version not supported" error. What Python version should I use?
+
+**A:** The project requires Python 3.10 or higher. Check your Python version with:
+```bash
+python --version
+```
+
+If you have an older version, install Python 3.10+ from [python.org](https://python.org) or use a version manager like pyenv.
+
+## Performance and Memory Issues
+
+### Q: When I run my code on my computer, it crashes or runs very slowly.
+
+**A:** This happens because the job data can be quite large and memory-intensive. Consider these solutions:
+
+1. **Run on Unity cluster**: The data is designed to be processed on Unity where more computational resources are available.
+
+2. **Use data sampling**: Limit the dataset size for testing:
+```python
+# Sample 10% of the data for testing
+sample_df = full_df.sample(frac=0.1, random_state=42)
+```
+
+3. **Limit visualization points**:
+```python
+# Limit interactive plots to avoid memory issues
+fig = visualizer.plot_vram_efficiency_per_job_dot_interactive(
+    users=users,
+    efficiency_metric="alloc_vram_efficiency",
+    max_points=500  # Reduce from default 1000
+)
+```
+
+4. **Process in chunks**:
+```python
+chunk_size = 5000
+for chunk in pd.read_sql(query, connection, chunksize=chunk_size):
+    process_chunk(chunk)
+```
+
+### Q: The interactive plots are not loading or are very slow. What can I do?
+
+**A:** Interactive Plotly visualizations can be resource-intensive. Try these optimizations:
+
+1. **Reduce data points**: Use the `max_points` parameter
+2. **Exclude unnecessary fields**: Use `exclude_fields` to reduce hover text complexity
+3. **Use static plots for large datasets**: Switch to matplotlib versions for better performance
+4. **Filter users**: Analyze fewer users at once
+
+## Database and Data Issues
+
+### Q: I'm getting a "database not found" error. Where should the database file be located?
+
+**A:** The Slurm database files are typically located on the Unity cluster. Common locations:
+- `slurm_data.db`
+- `slurm_data_new.db` 
+- Check the `data/` directory in your project folder
+
+If working locally, ensure you've copied the database file from Unity.
+
+### Q: My analysis shows no data or empty results. What's wrong?
+
+**A:** This usually happens due to filtering issues. Check these common causes:
+
+1. **User filtering**: Ensure the users you're analyzing actually exist in the dataset:
+```python
+print("Available users:", df["User"].unique())
+```
+
+2. **Date range**: Check if your data covers the expected time period:
+```python
+print("Date range:", df["StartTime"].min(), "to", df["StartTime"].max())
+```
+
+3. **Preprocessing filters**: The preprocessing might be removing your data:
+```python
+# Check data before and after preprocessing
+print("Before preprocessing:", len(raw_df))
+processed_df = preprocess_jobs(raw_df, min_elapsed_seconds=60)
+print("After preprocessing:", len(processed_df))
+```
+
+### Q: I'm getting "KeyError" when trying to access certain columns. What's happening?
+
+**A:** This usually means the column doesn't exist in your dataset. Common issues:
+
+1. **Case sensitivity**: Column names are case-sensitive (`"User"` vs `"user"`)
+2. **Column not in dataset**: Check available columns:
+```python
+print("Available columns:", df.columns.tolist())
+```
+3. **Preprocessing changes**: Some columns might be renamed or removed during preprocessing
+
+## Visualization Issues
+
+### Q: The plots are not displaying or showing empty charts.
+
+**A:** Several possible causes:
+
+1. **Empty filtered data**: Check if your user/time filters are too restrictive
+2. **Zero values**: Try setting `remove_zero_values=False`
+3. **Jupyter notebook issues**: Ensure you have the right backend:
+```python
+%matplotlib inline
+import plotly.io as pio
+pio.renderers.default = "notebook"
+```
+
+### Q: The legend in my plots is cut off or overlapping.
+
+**A:** Adjust the figure layout:
+```python
+# For matplotlib plots
+plt.tight_layout()
+plt.subplots_adjust(right=0.8)  # Make room for legend
+
+# For Plotly plots
+fig.update_layout(
+    width=1200,  # Increase width
+    margin=dict(r=200)  # Add right margin for legend
+)
+```
+
+## Analysis and Metrics
+
+### Q: What's the difference between "alloc_vram_efficiency" and "avail_vram_efficiency"?
+
+**A:** 
+- **alloc_vram_efficiency**: Measures efficiency against allocated memory per GPU
+- **avail_vram_efficiency**: Measures efficiency against total available memory per GPU
+
+Use allocated efficiency for analyzing how well users utilize their requested resources, and available efficiency for understanding overall cluster utilization.
+
+### Q: Why are some efficiency values over 100%?
+
+**A:** This can happen when:
+1. Memory usage (`MaxRSS`) exceeds the baseline calculation
+2. Shared memory or system overhead affects measurements
+3. Multiple processes share GPU memory
+
+Values slightly over 100% are normal; significantly higher values may indicate measurement issues.
+
+### Q: How do I interpret the efficiency categories (Excellent, Good, Fair, etc.)?
+
+**A:** The categories are defined as:
+- **Excellent**: >80% - Very efficient resource usage
+- **Good**: 60-80% - Acceptable efficiency 
+- **Fair**: 40-60% - Room for improvement
+- **Poor**: 20-40% - Significant waste of resources
+- **Very Poor**: <20% - Major inefficiency
+
+## Development and Contributing
+
+### Q: How do I run the tests?
+
+**A:** Run the test suite using pytest:
+```bash
+# Run all tests
+pytest
+
+# Run specific test file
+pytest tests/test_efficiency_analysis.py
+
+# Run with coverage
+pytest --cov=src tests/
+```
+
+### Q: I want to add a new visualization. How do I structure the code?
+
+**A:** Follow these guidelines:
+
+1. Add visualization classes to `src/visualization/`
+2. Inherit from `DataVisualizer` base class
+3. Use Pydantic models for parameter validation
+4. Add both static (matplotlib) and interactive (Plotly) versions when possible
+5. Include comprehensive docstrings and type hints
+
+### Q: How do I contribute documentation changes?
+
+**A:** 
+1. Edit the markdown files in the `docs/` directory
+2. Test locally with: `mkdocs serve`
+3. Submit a pull request with your changes
+
+## Getting Help
+
+### Q: I found a bug or want to request a feature. What should I do?
+
+**A:** Please create a GitHub issue with:
+1. Clear description of the problem/feature request
+2. Steps to reproduce (for bugs)
+3. Expected vs actual behavior
+4. Your environment details (Python version, OS, etc.)
+
+### Q: The documentation doesn't cover my use case. Where can I get help?
+
+**A:** 
+1. Check the [Demo](demo.md) page for examples
+2. Look at the Jupyter notebooks in the `notebooks/` directory
+3. Create a GitHub issue for documentation improvements
+4. Reach out via Unity Slack for urgent questions
diff --git a/docs/getting-started.md b/docs/getting-started.md
new file mode 100644
index 0000000..bf2153c
--- /dev/null
+++ b/docs/getting-started.md
@@ -0,0 +1,253 @@
+# Getting Started
+
+This guide will help you set up and start using the DS4CG Unity Job Analytics project.
+
+## Getting the Libraries
+
+To get started with the project, clone the repository. Since the data is stored on the Unity cluster, we recommend cloning directly on Unity for best performance:
+
+```bash
+git clone https://github.com/Unity-HPC/ds4cg-job-analytics.git
+cd ds4cg-job-analytics
+```
+
+## Dependencies
+
+This project is compatible with **Python 3.10+**. We recommend first installing Python and then setting up a virtual environment for the project.
+
+### Setting Up Virtual Environment
+
+To set up a virtual environment, run the following commands:
+
+```bash
+# Create virtual environment
+python -m venv duckdb
+
+# Activate virtual environment
+# On Linux/Mac:
+source duckdb/bin/activate
+# On Windows:
+duckdb\Scripts\activate
+
+# Install required libraries
+pip install -r requirements.txt
+pip install -r dev-requirements.txt
+```
+
+### Required Libraries
+
+The main dependencies include:
+
+- pandas for data manipulation
+- plotly and matplotlib for visualization
+- duckdb for database operations
+- pydantic for data validation
+- mkdocs for documentation
+
+## Data Retrieval and Preprocessing
+
+The project provides streamlined functions to connect to the database and preprocess data:
+
+### Database Connection
+
+```python
+from src.database.database_connection import DatabaseConnection
+
+# Connect to the Slurm database
+db = DatabaseConnection("path/to/slurm_data.db")
+
+# Query GPU jobs
+gpu_df = db.fetch_query("SELECT * FROM Jobs WHERE GPUs > 0")
+```
+
+### Data Preprocessing
+
+The preprocessing pipeline handles data cleaning, type conversion, and filtering:
+
+```python
+from src.preprocess.preprocess import preprocess_data
+
+# Preprocess raw job data
+processed_df = preprocess_data(
+    gpu_df,
+    min_elapsed_seconds=600,
+    include_failed_cancelled_jobs=False,
+    include_cpu_only_jobs=True
+)
+```
+
+For detailed preprocessing criteria, see the [Data and Efficiency Metrics](data-and-metrics.md) section.
+
+## Getting Efficiency Metrics
+
+The analysis workflow follows a specific order as demonstrated in our notebooks. Here's the complete process:
+
+### Step 1: Initialize the Efficiency Analyzer
+
+```python
+from src.analysis.efficiency_analysis import EfficiencyAnalysis
+
+# Initialize efficiency analyzer
+efficiency_analyzer = EfficiencyAnalysis(jobs_df=processed_df)
+```
+
+### Step 2: Filter Jobs for Analysis
+
+```python
+import numpy as np
+
+# Filter jobs based on specific criteria
+filtered_jobs = efficiency_analyzer.filter_jobs_for_analysis(
+    gpu_count_filter=1,
+    vram_constraint_filter=None,
+    allocated_vram_filter={"min": 0, "max": np.inf, "inclusive": False},
+    gpu_mem_usage_filter={"min": 0, "max": np.inf, "inclusive": False}
+)
+```
+
+### Step 3: Calculate Metrics
+
+```python
+# Calculate job-level efficiency metrics
+job_metrics = efficiency_analyzer.calculate_job_efficiency_metrics(filtered_jobs=filtered_jobs)
+
+# Calculate user-level efficiency metrics  
+user_metrics = efficiency_analyzer.calculate_user_efficiency_metrics()
+
+# Find inefficient users
+inefficient_users = efficiency_analyzer.find_inefficient_users_by_alloc_vram_efficiency(
+    alloc_vram_efficiency_filter={"min": 0, "max": 0.3, "inclusive": False}, 
+    min_jobs=5
+)
+```
+
+### Step 4: Prepare Time Series Data
+
+```python
+from src.analysis.frequency_analysis import FrequencyAnalysis
+
+# Initialize frequency analyzer
+frequency_analyzer = FrequencyAnalysis(job_metrics)
+
+# Prepare time series data for visualization
+time_series_data = frequency_analyzer.prepare_time_series_data(
+    users=inefficient_users["User"].tolist(),
+    time_unit="Months",
+    metric="alloc_vram_efficiency_score",
+    remove_zero_values=False
+)
+```
+
+**📚 Complete Example**: See [Frequency Analysis Demo](../notebooks/Frequency Analysis/) for a full walkthrough.
+
+For detailed information about available metrics, see [Efficiency Metrics](visualization/efficiency_metrics.md).
+
+## Visualizing Job Analysis
+
+The project offers both static and interactive visualization capabilities with a specific workflow:
+
+### Step 1: Initialize Time Series Visualizer
+
+```python
+from src.visualization.time_series import TimeSeriesVisualizer
+
+# Create time series visualizer with your time series data
+visualizer = TimeSeriesVisualizer(time_series_data)
+```
+
+### Step 2: Static Time Series Plots
+
+```python
+# Static VRAM efficiency plot
+visualizer.plot_vram_efficiency(
+    users=["user1", "user2"],
+    annotation_style="none",
+    show_secondary_y=False
+)
+
+# Static VRAM hours plot
+visualizer.plot_vram_hours(
+    users=["user1", "user2"],
+    show_secondary_y=False
+)
+```
+
+### Step 3: Interactive Visualizations
+
+```python
+# Interactive VRAM efficiency plot
+fig = visualizer.plot_vram_efficiency_interactive(
+    users=["user1", "user2"],
+    max_points=100,
+    job_count_trace=True
+)
+
+# Interactive per-job dot plot
+fig = visualizer.plot_vram_efficiency_per_job_dot_interactive(
+    users=["user1", "user2"],
+    efficiency_metric="alloc_vram_efficiency",
+    vram_metric="job_hours",
+    max_points=500,
+    exclude_fields=["Exit Code"]
+)
+```
+
+### Step 4: Per-Job Analysis
+
+```python
+# Initialize with job-level data for individual job analysis
+job_visualizer = TimeSeriesVisualizer(job_metrics)
+
+# Static per-job dot plot
+job_visualizer.plot_vram_efficiency_per_job_dot(
+    users=["user1"],
+    efficiency_metric="alloc_vram_efficiency",
+    vram_metric="job_hours"
+)
+```
+
+### Column Statistics
+
+```python
+from src.visualization.columns import ColumnStatsVisualizer
+
+# Visualize column statistics
+col_visualizer = ColumnStatsVisualizer(processed_df)
+col_visualizer.visualize_all_columns()
+```
+
+**📚 Complete Examples**:
+
+- [Basic Visualization](../notebooks/Basic%20Visualization/) - Column statistics and basic plots
+- [Efficiency Analysis](../notebooks/Efficiency%20Analysis/) - Advanced efficiency analysis workflows
+
+For more visualization options, see [Visualization](visualization/visualization.md).
+
+## Typical Analysis Workflow Order
+
+Based on our notebooks, here's the recommended order for conducting analysis:
+
+1. **Data Setup** → Load database → Preprocess data
+2. **Initialize Analyzers** → EfficiencyAnalysis → FrequencyAnalysis  
+3. **Filter & Calculate** → Filter jobs → Calculate metrics
+4. **Identify Users** → Find inefficient/efficient users
+5. **Prepare Visualizations** → Time series data → Initialize visualizers
+6. **Generate Plots** → Static plots → Interactive plots → Per-job analysis
+
+## Optional Scripts (MVP Scripts)
+
+The project includes several standalone scripts for quick analysis:
+
+- **CPU Metrics**: Analyze CPU usage patterns
+- **GPU Metrics**: Analyze GPU utilization and efficiency
+- **Zero GPU Usage**: Identify jobs with zero GPU usage
+
+See [MVP Scripts](mvp_scripts/cpu_metrics.md) for detailed usage instructions.
+
+---
+
+**Next Steps:**
+
+- Follow the complete workflows in our [Demo Notebooks](demo.md)
+- Explore the [Data and Efficiency Metrics](data-and-metrics.md) page for detailed metric definitions
+- Visit [FAQ](faq.md) if you encounter any issues
diff --git a/docs/index.md b/docs/index.md
index 65b16f7..3939f37 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,15 +1,37 @@
 # DS4CG Unity Job Analytics
 
-Welcome to the documentation for the DS4CG Unity Job Analytics project.
+Welcome to the documentation for the Unity Job Analytics project.
 
 This documentation exists to:
+
 - Help new users and contributors understand the purpose and structure of the project.
 - Provide clear instructions for setup, usage, and contribution.
 - Serve as a reference for the available scripts, modules, and data analysis tools.
 - Document best practices and project standards for maintainability and collaboration.
 
-**Project Purpose:**
+## Project Purpose
+
+The Unity Job Analytics project provides tools and documentation for analyzing job data from the Unity cluster. It aims to help researchers and administrators gain insights into job performance, resource utilization, and efficiency, and to support reproducible, collaborative data science workflows.
+
+## Project Background
+
+The DS4CG Unity Job Analytics project was initiated as part of the DS4CG 2025 summer internship program in collaboration with the Unity HPC cluster at UMass. The goal is to provide robust tools and documentation for analyzing job data, improving resource utilization, and supporting research and operations on the Unity cluster.
+
+## Team & Contributors
+
+- Project Lead: Christopher Odoom
+- Contributors: DS4CG Summer 2025 Internship Team
+
+## Acknowledgments
+This project is supported by the Unity HPC team at UMass and the Data Science for the Common Good (DS4CG) program. Special thanks to all contributors and users who help improve the project.
+
+## Further Information
+
+- [Unity Documentation](https://docs.unity.rc.umass.edu/)
+- [DS4CG Program](https://ds.cs.umass.edu/programs/ds4cg)
+
+For questions or support, please reach out via the Unity Slack or contact the project lead.
 
-The DS4CG Unity Job Analytics project provides tools and documentation for analyzing job data from the Unity cluster. It aims to help researchers and administrators gain insights into job performance, resource utilization, and efficiency, and to support reproducible, collaborative data science workflows.
+---
 
 Use the navigation on the left to explore detailed guides, module documentation, and contributor resources.
diff --git a/docs/mvp_scripts/cpu_metrics.md b/docs/mvp_scripts/cpu_metrics.md
index 7ad4eb6..5debd45 100644
--- a/docs/mvp_scripts/cpu_metrics.md
+++ b/docs/mvp_scripts/cpu_metrics.md
@@ -1,3 +1,3 @@
 # CPU Metrics
 
-::: scripts.cpu_metrics
+::: mvp_scripts.cpu_metrics
diff --git a/docs/mvp_scripts/gpu_metrics.md b/docs/mvp_scripts/gpu_metrics.md
index 127b654..6c52b64 100644
--- a/docs/mvp_scripts/gpu_metrics.md
+++ b/docs/mvp_scripts/gpu_metrics.md
@@ -1,3 +1,3 @@
 # GPU Metrics
 
-::: scripts.gpu_metrics
\ No newline at end of file
+::: mvp_scripts.gpu_metrics
\ No newline at end of file
diff --git a/docs/mvp_scripts/zero_gpu_usage.md b/docs/mvp_scripts/zero_gpu_usage.md
index aa1ef43..b622038 100644
--- a/docs/mvp_scripts/zero_gpu_usage.md
+++ b/docs/mvp_scripts/zero_gpu_usage.md
@@ -7,4 +7,4 @@ email bodies with user-specific resource usage. This script will only run on Uni
 of the ```pi_bpachev_umass_edu``` group. It is included as an example of the sort of tool that 
 might be useful to the Unity team as a final deliverable of this project.
 
-::: scripts.zero_gpu_usage_list
+::: mvp_scripts.zero_gpu_usage_list
diff --git a/docs/notebooks b/docs/notebooks
new file mode 120000
index 0000000..9097c22
--- /dev/null
+++ b/docs/notebooks
@@ -0,0 +1 @@
+C:/Users/ayush/desktop/coding/DS4CG/ds4cg-job-analytics/notebooks
\ No newline at end of file
diff --git a/docs/preprocess.md b/docs/preprocess.md
index 318abb1..89a35d9 100644
--- a/docs/preprocess.md
+++ b/docs/preprocess.md
@@ -1,32 +1,32 @@
 # Preprocess Data
 
-## Preprocessing Criteria for Job Data
-### Attributes Omitted
+# Preprocessing Criteria for Job Data
+## Attributes Omitted
 - **UUID**
 - **Nodes**: NodesList have more specific information
 - **Preempted**: Status have more valid information
 - **EndTime**: Can be calculated from StartTime and Elapsed
 
-### Options for Including or Omitting Jobs
+## Options for Including/Omitting Jobs
 - **Keeping CPU jobs:**
-    - If `GPUType` is null, the value will be filled with `["cpu"]`
+    - If `GPUType` is null, the value will be filled with `NA`
     - If `GPUs` is null or is 0, the value will be 0.
 - **Keeping jobs where the status is "Failed" or "Cancelled"**
 
-### Records Omitted If:
+## Records Omitted If:
 - `Elapsed` is less than the minimum threshold
 - `account` is root
 - `partition` is building
 - `QOS` is updates
 
-### Null Attribute Defaults
+## Null Attribute Defaults
 - `ArrayID`: set to -1
 - `Interactive`: set to `"non-interactive"`
 - `Constraints`: set to an empty numpy array
 - `GPUs`: set to 0 (when CPU jobs are kept)
-- `GPUType`: set to an numpy array ["cpu"] (when CPU jobs are kept)
+- `GPUType`: set to NA
 
-### Attribute Types
+## Attribute Types
 - `StartTime`, `SubmitTime`: **datetime**
 - `TimeLimit`, `Elapsed`: **timedelta**
 - `Interactive`, `Status`, `ExitCode`, `QOS`, `Partition`, `Account`: **Categorical**
diff --git a/docs/visualization/columns.md b/docs/visualization/columns.md
new file mode 100644
index 0000000..da188ce
--- /dev/null
+++ b/docs/visualization/columns.md
@@ -0,0 +1 @@
+::: src.visualization.columns
\ No newline at end of file
diff --git a/docs/visualization/efficiency_metrics.md b/docs/visualization/efficiency_metrics.md
new file mode 100644
index 0000000..9be47d3
--- /dev/null
+++ b/docs/visualization/efficiency_metrics.md
@@ -0,0 +1 @@
+::: src.visualization.efficiency_metrics
\ No newline at end of file
diff --git a/docs/visualization/models.md b/docs/visualization/models.md
new file mode 100644
index 0000000..9b66416
--- /dev/null
+++ b/docs/visualization/models.md
@@ -0,0 +1 @@
+::: src.visualization.models
\ No newline at end of file
diff --git a/docs/visualization/visualization.md b/docs/visualization/visualization.md
new file mode 100644
index 0000000..32a64a3
--- /dev/null
+++ b/docs/visualization/visualization.md
@@ -0,0 +1 @@
+::: src.visualization.visualization
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
index 498a469..c2d36d4 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -2,9 +2,31 @@ site_name: "Unity Job Analytics"
 
 theme:
   name: "material"
+  features:
+    - navigation.tabs          # Enables horizontal top nav tabs
+    - navigation.tabs.sticky   # Keeps tabs visible when scrolling
+    - navigation.sections      # Groups subsections under tabs
+    - toc.integrate            # Puts table of contents in main pane
+  palette:
+    # Palette toggle for light mode
+    - scheme: default
+      toggle:
+        icon: material/brightness-7
+        name: Switch to dark mode
+
+    # Palette toggle for dark mode
+    - scheme: slate
+      toggle:
+        icon: material/brightness-4
+        name: Switch to light mode
 
 plugins:
   - search
+  - mkdocs-jupyter:
+      execute: true
+      include_source: true
+      allow_errors: false
+      ignore_h1_titles: true
   - mkdocstrings:
         handlers:
           python:
@@ -21,16 +43,45 @@ plugins:
               # render Parameters/Returns as a table
               docstring_section_style: list
 
+# Configure markdown extensions
+markdown_extensions:
+  - attr_list
+  - md_in_html
+  - pymdownx.arithmatex:
+      generic: true
+
+extra_javascript:
+  - javascripts/mathjax.js
+  - https://unpkg.com/mathjax@3/es5/tex-mml-chtml.js
+
+# Suppress warnings for external links to notebooks
+validation:
+  omitted_files: warn
+  absolute_links: warn
+  unrecognized_links: warn
+
 nav:
   - Home: 'index.md'
-  - About: 'about.md'
-  - Data:
-    - 'preprocess.md'
-  - Analysis:
-    - 'analysis/efficiency_analysis.md'
-    - 'analysis/visualization.md'
-  - MVP Scripts:
-    - 'mvp_scripts/cpu_metrics.md'
-    - 'mvp_scripts/gpu_metrics.md'
-    - 'mvp_scripts/zero_gpu_usage.md'
-
+  - Getting Started: 'getting-started.md'
+  - Data and Efficiency Metrics: 'data-and-metrics.md'
+  - Demo: 'demo.md'
+  - Notebooks:
+    - Basic Visualization: '../notebooks/Basic Visualization.ipynb'
+    - Efficiency Analysis: '../notebooks/Efficiency Analysis.ipynb'
+#    - Clustering Analysis: 'notebooks/clustering_analysis.ipynb'
+    - Frequency Analysis: '../notebooks/Frequency Analysis.ipynb'
+  - Technical Documentation:
+    - Data Processing: 'preprocess.md'
+    - Analysis:
+      - 'analysis/efficiency_analysis.md'
+    - Visualization:
+      - 'visualization/visualization.md'
+      - 'visualization/columns.md'
+      - 'visualization/efficiency_metrics.md'
+      - 'visualization/models.md'
+    - MVP Scripts:
+      - 'mvp_scripts/cpu_metrics.md'
+      - 'mvp_scripts/gpu_metrics.md'
+      - 'mvp_scripts/zero_gpu_usage.md'
+  - FAQ: 'faq.md'
+  - Contact: 'contact.md'
\ No newline at end of file

From 0b820d21910210d3e4cb5d1f86887a050c9317b8 Mon Sep 17 00:00:00 2001
From: Espiobest <59823894+Espiobest@users.noreply.github.com>
Date: Tue, 19 Aug 2025 13:23:17 -0400
Subject: [PATCH 6/9] Update documentation: streamline structure and improve
 content

- Simplified mkdocs.yml configuration and navigation
- Updated README.md with better project documentation
- Streamlined docs/index.md for clearer project overview
- Updated docs/preprocess.md with current preprocessing criteria
- Added .gitignore entries for Quarto report files
---
 .gitignore         |  6 +++-
 README.md          | 10 ++++--
 docs/index.md      | 28 ++---------------
 docs/preprocess.md | 16 +++++-----
 mkdocs.yml         | 77 +++++++++-------------------------------------
 5 files changed, 39 insertions(+), 98 deletions(-)

diff --git a/.gitignore b/.gitignore
index 9fb0f31..774f1ec 100644
--- a/.gitignore
+++ b/.gitignore
@@ -41,4 +41,8 @@ data/
 *.patch
 *.diff
 /docs/build
-/site
\ No newline at end of file
+/site
+
+# Quarto Reports
+.quarto 
+*.html
\ No newline at end of file
diff --git a/README.md b/README.md
index 78d573f..f0171e9 100644
--- a/README.md
+++ b/README.md
@@ -15,11 +15,16 @@ The following guidelines may prove helpful in maximizing the utility of this rep
 ## Getting started on Unity
 
 You'll need to first install a few dependencies, which include DuckDB, Pandas, and some plotting libraries. More details for running the project will need be added here later.
+
 ### Version Control
 To provide the path of the git configuration file of this project to git, run:
 
     git config --local include.path ../.gitconfig
 
+To ensure consistent LF line endings across all platforms, run the following command when developing on Windows machines:
+
+    git config --local core.autocrlf input
+
 ### Jupyter notebooks
 
 You can run Jupyter notebooks on Unity through the OpenOnDemand portal. To make your environment 
@@ -161,6 +166,7 @@ contains tools to add a number of useful derived columns for plotting and analys
 | UUID   | VARCHAR | Unique identifier | 
 | JobID  | INTEGER | Slurm job ID |
 | ArrayID | INTEGER | Position in job array |
+|ArrayJobID| INTEGER | Slurm job ID within array|
 | JobName |  VARCHAR | Name of job |
 | IsArray |  BOOLEAN | Indicator if job is part of an array |
 | Interactive |  VARCHAR | Indicator if job was interactive
@@ -179,10 +185,10 @@ contains tools to add a number of useful derived columns for plotting and analys
 | Partition |  VARCHAR | Job partition |
 | Nodes |  VARCHAR | Job nodes as compact string |
 | NodeList |  VARCHAR[] | List of job nodes |
-| CPUs |  SMALLINT | Number of CPUs |
+| CPUs |  SMALLINT | Number of CPU cores |
 | Memory |  INTEGER | Job allocated memory (bytes) |
 | GPUs |  SMALLINT | Number of GPUs requested |
-| GPUType |  VARCHAR[] | List of GPU types |
+| GPUType |  DICT | Dictionary with keys as type of GPU (str) and the values as number of GPUs corresponding to that type (int) |
 | GPUMemUsage |  FLOAT | GPU memory usage (bytes) |
 | GPUComputeUsage |  FLOAT | GPU compute usage (pct) |
 | CPUMemUsage |  FLOAT | GPU memory usage (bytes) |
diff --git a/docs/index.md b/docs/index.md
index 3939f37..65b16f7 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,37 +1,15 @@
 # DS4CG Unity Job Analytics
 
-Welcome to the documentation for the Unity Job Analytics project.
+Welcome to the documentation for the DS4CG Unity Job Analytics project.
 
 This documentation exists to:
-
 - Help new users and contributors understand the purpose and structure of the project.
 - Provide clear instructions for setup, usage, and contribution.
 - Serve as a reference for the available scripts, modules, and data analysis tools.
 - Document best practices and project standards for maintainability and collaboration.
 
-## Project Purpose
-
-The Unity Job Analytics project provides tools and documentation for analyzing job data from the Unity cluster. It aims to help researchers and administrators gain insights into job performance, resource utilization, and efficiency, and to support reproducible, collaborative data science workflows.
-
-## Project Background
-
-The DS4CG Unity Job Analytics project was initiated as part of the DS4CG 2025 summer internship program in collaboration with the Unity HPC cluster at UMass. The goal is to provide robust tools and documentation for analyzing job data, improving resource utilization, and supporting research and operations on the Unity cluster.
-
-## Team & Contributors
-
-- Project Lead: Christopher Odoom
-- Contributors: DS4CG Summer 2025 Internship Team
-
-## Acknowledgments
-This project is supported by the Unity HPC team at UMass and the Data Science for the Common Good (DS4CG) program. Special thanks to all contributors and users who help improve the project.
-
-## Further Information
-
-- [Unity Documentation](https://docs.unity.rc.umass.edu/)
-- [DS4CG Program](https://ds.cs.umass.edu/programs/ds4cg)
-
-For questions or support, please reach out via the Unity Slack or contact the project lead.
+**Project Purpose:**
 
----
+The DS4CG Unity Job Analytics project provides tools and documentation for analyzing job data from the Unity cluster. It aims to help researchers and administrators gain insights into job performance, resource utilization, and efficiency, and to support reproducible, collaborative data science workflows.
 
 Use the navigation on the left to explore detailed guides, module documentation, and contributor resources.
diff --git a/docs/preprocess.md b/docs/preprocess.md
index 89a35d9..318abb1 100644
--- a/docs/preprocess.md
+++ b/docs/preprocess.md
@@ -1,32 +1,32 @@
 # Preprocess Data
 
-# Preprocessing Criteria for Job Data
-## Attributes Omitted
+## Preprocessing Criteria for Job Data
+### Attributes Omitted
 - **UUID**
 - **Nodes**: NodesList have more specific information
 - **Preempted**: Status have more valid information
 - **EndTime**: Can be calculated from StartTime and Elapsed
 
-## Options for Including/Omitting Jobs
+### Options for Including or Omitting Jobs
 - **Keeping CPU jobs:**
-    - If `GPUType` is null, the value will be filled with `NA`
+    - If `GPUType` is null, the value will be filled with `["cpu"]`
     - If `GPUs` is null or is 0, the value will be 0.
 - **Keeping jobs where the status is "Failed" or "Cancelled"**
 
-## Records Omitted If:
+### Records Omitted If:
 - `Elapsed` is less than the minimum threshold
 - `account` is root
 - `partition` is building
 - `QOS` is updates
 
-## Null Attribute Defaults
+### Null Attribute Defaults
 - `ArrayID`: set to -1
 - `Interactive`: set to `"non-interactive"`
 - `Constraints`: set to an empty numpy array
 - `GPUs`: set to 0 (when CPU jobs are kept)
-- `GPUType`: set to NA
+- `GPUType`: set to an numpy array ["cpu"] (when CPU jobs are kept)
 
-## Attribute Types
+### Attribute Types
 - `StartTime`, `SubmitTime`: **datetime**
 - `TimeLimit`, `Elapsed`: **timedelta**
 - `Interactive`, `Status`, `ExitCode`, `QOS`, `Partition`, `Account`: **Categorical**
diff --git a/mkdocs.yml b/mkdocs.yml
index c2d36d4..a2e07ba 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -2,31 +2,9 @@ site_name: "Unity Job Analytics"
 
 theme:
   name: "material"
-  features:
-    - navigation.tabs          # Enables horizontal top nav tabs
-    - navigation.tabs.sticky   # Keeps tabs visible when scrolling
-    - navigation.sections      # Groups subsections under tabs
-    - toc.integrate            # Puts table of contents in main pane
-  palette:
-    # Palette toggle for light mode
-    - scheme: default
-      toggle:
-        icon: material/brightness-7
-        name: Switch to dark mode
-
-    # Palette toggle for dark mode
-    - scheme: slate
-      toggle:
-        icon: material/brightness-4
-        name: Switch to light mode
 
 plugins:
   - search
-  - mkdocs-jupyter:
-      execute: true
-      include_source: true
-      allow_errors: false
-      ignore_h1_titles: true
   - mkdocstrings:
         handlers:
           python:
@@ -43,45 +21,20 @@ plugins:
               # render Parameters/Returns as a table
               docstring_section_style: list
 
-# Configure markdown extensions
-markdown_extensions:
-  - attr_list
-  - md_in_html
-  - pymdownx.arithmatex:
-      generic: true
-
-extra_javascript:
-  - javascripts/mathjax.js
-  - https://unpkg.com/mathjax@3/es5/tex-mml-chtml.js
-
-# Suppress warnings for external links to notebooks
-validation:
-  omitted_files: warn
-  absolute_links: warn
-  unrecognized_links: warn
-
 nav:
   - Home: 'index.md'
-  - Getting Started: 'getting-started.md'
-  - Data and Efficiency Metrics: 'data-and-metrics.md'
-  - Demo: 'demo.md'
-  - Notebooks:
-    - Basic Visualization: '../notebooks/Basic Visualization.ipynb'
-    - Efficiency Analysis: '../notebooks/Efficiency Analysis.ipynb'
-#    - Clustering Analysis: 'notebooks/clustering_analysis.ipynb'
-    - Frequency Analysis: '../notebooks/Frequency Analysis.ipynb'
-  - Technical Documentation:
-    - Data Processing: 'preprocess.md'
-    - Analysis:
-      - 'analysis/efficiency_analysis.md'
-    - Visualization:
-      - 'visualization/visualization.md'
-      - 'visualization/columns.md'
-      - 'visualization/efficiency_metrics.md'
-      - 'visualization/models.md'
-    - MVP Scripts:
-      - 'mvp_scripts/cpu_metrics.md'
-      - 'mvp_scripts/gpu_metrics.md'
-      - 'mvp_scripts/zero_gpu_usage.md'
-  - FAQ: 'faq.md'
-  - Contact: 'contact.md'
\ No newline at end of file
+  - About: 'about.md'
+  - Data:
+    - 'preprocess.md'
+  - Analysis:
+    - 'analysis/efficiency_analysis.md'
+  - Visualization:
+    - 'visualization/visualization.md'
+    - 'visualization/columns.md'
+    - 'visualization/efficiency_metrics.md'
+    - 'visualization/models.md'
+  - MVP Scripts:
+    - 'mvp_scripts/cpu_metrics.md'
+    - 'mvp_scripts/gpu_metrics.md'
+    - 'mvp_scripts/zero_gpu_usage.md'
+

From c52c948f0f3dc5b1509a5dbc02a3c6f9c58d916e Mon Sep 17 00:00:00 2001
From: Espiobest <59823894+Espiobest@users.noreply.github.com>
Date: Tue, 19 Aug 2025 13:54:48 -0400
Subject: [PATCH 7/9] add documentationchanges

---
 .gitignore                          |  7 ++-
 docs/analysis/frequency_analysis.md |  3 +-
 docs/demo.md                        | 10 ++--
 mkdocs.yml                          | 79 +++++++++++++++++++++++------
 mvp_scripts/gpu_metrics.py          |  4 +-
 mvp_scripts/zero_gpu_usage_list.py  |  6 +--
 src/analysis/efficiency_analysis.py |  1 -
 src/preprocess/preprocess.py        |  2 +-
 8 files changed, 81 insertions(+), 31 deletions(-)

diff --git a/.gitignore b/.gitignore
index 774f1ec..26d5ac7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -43,6 +43,9 @@ data/
 /docs/build
 /site
 
-# Quarto Reports
+# Quarto Report files
 .quarto 
-*.html
\ No newline at end of file
+*.html
+*.pdf
+*.yml
+*.pkl
\ No newline at end of file
diff --git a/docs/analysis/frequency_analysis.md b/docs/analysis/frequency_analysis.md
index 8132a96..d817020 100644
--- a/docs/analysis/frequency_analysis.md
+++ b/docs/analysis/frequency_analysis.md
@@ -2,4 +2,5 @@
 title: Frequency Analysis
 ---
 
-::: src.analysis.frequency_analysis
+<!-- TODO (Ayush): Update when frequency analysis is merged-->
+<!-- ::: src.analysis.frequency_analysis -->
diff --git a/docs/demo.md b/docs/demo.md
index 86b8383..f5d9ffb 100644
--- a/docs/demo.md
+++ b/docs/demo.md
@@ -6,7 +6,7 @@ This page showcases the DS4CG Unity Job Analytics project in action with interac
 
 Explore our comprehensive Jupyter notebooks that demonstrate the full capabilities:
 
-### 📊 [Frequency Analysis Demo](../notebooks/Frequency Analysis/)
+### 📊 [Frequency Analysis Demo](notebooks/Frequency Analysis/)
 **Complete end-to-end workflow** showing:
 
 - Database connection and preprocessing
@@ -15,7 +15,7 @@ Explore our comprehensive Jupyter notebooks that demonstrate the full capabiliti
 - Interactive visualizations
 - Best/worst user identification
 
-### 📈 [Basic Visualization](../notebooks/Basic%20Visualization/)
+### 📈 [Basic Visualization](notebooks/Basic%20Visualization/)
 **Column statistics and exploratory analysis** including:
 
 - Data loading and preprocessing
@@ -23,7 +23,7 @@ Explore our comprehensive Jupyter notebooks that demonstrate the full capabiliti
 - Distribution analysis
 - Data quality assessment
 
-### 🔍 [Efficiency Analysis](../notebooks/Efficiency%20Analysis/)
+### 🔍 [Efficiency Analysis](notebooks/Efficiency%20Analysis/)
 **Advanced efficiency analysis techniques** covering:
 
 - Job filtering and metrics calculation
@@ -31,10 +31,10 @@ Explore our comprehensive Jupyter notebooks that demonstrate the full capabiliti
 - Inefficiency identification
 - Performance comparison workflows
 
-### 🎯 [Clustering Analysis](../notebooks/clustering_analysis/)
+### 🎯 [Clustering Analysis](notebooks/clustering_analysis/)
 **User behavior clustering and pattern analysis**
 
-### 📊 [Frequency Analysis](../notebooks/Frequency%20Analysis/)
+### 📊 [Frequency Analysis](notebooks/Frequency%20Analysis/)
 **Time series frequency analysis and patterns**
 
 ---
diff --git a/mkdocs.yml b/mkdocs.yml
index a2e07ba..74ccf10 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -2,9 +2,31 @@ site_name: "Unity Job Analytics"
 
 theme:
   name: "material"
+  features:
+    - navigation.tabs          # Enables horizontal top nav tabs
+    - navigation.tabs.sticky   # Keeps tabs visible when scrolling
+    - navigation.sections      # Groups subsections under tabs
+    - toc.integrate            # Puts table of contents in main pane
+  palette:
+    # Palette toggle for light mode
+    - scheme: default
+      toggle:
+        icon: material/brightness-7
+        name: Switch to dark mode
+
+    # Palette toggle for dark mode
+    - scheme: slate
+      toggle:
+        icon: material/brightness-4
+        name: Switch to light mode
 
 plugins:
   - search
+  - mkdocs-jupyter:
+      include_source: true
+      allow_errors: true 
+      ignore_h1_titles: true
+
   - mkdocstrings:
         handlers:
           python:
@@ -21,20 +43,47 @@ plugins:
               # render Parameters/Returns as a table
               docstring_section_style: list
 
+# Configure markdown extensions
+markdown_extensions:
+  - attr_list
+  - md_in_html
+  - pymdownx.arithmatex:
+      generic: true
+
+extra_javascript:
+  # - javascripts/mathjax.js
+  - https://unpkg.com/mathjax@3/es5/tex-mml-chtml.js
+
+# Suppress warnings for external links to notebooks
+validation:
+  omitted_files: warn
+  absolute_links: warn
+  unrecognized_links: warn
+
+docs_dir: docs
+
 nav:
   - Home: 'index.md'
-  - About: 'about.md'
-  - Data:
-    - 'preprocess.md'
-  - Analysis:
-    - 'analysis/efficiency_analysis.md'
-  - Visualization:
-    - 'visualization/visualization.md'
-    - 'visualization/columns.md'
-    - 'visualization/efficiency_metrics.md'
-    - 'visualization/models.md'
-  - MVP Scripts:
-    - 'mvp_scripts/cpu_metrics.md'
-    - 'mvp_scripts/gpu_metrics.md'
-    - 'mvp_scripts/zero_gpu_usage.md'
-
+  - Getting Started: 'getting-started.md'
+  - Data and Efficiency Metrics: 'data-and-metrics.md'
+  - Demo: 'demo.md'
+  - Notebooks:
+    - Efficiency Analysis: 'notebooks/Efficiency Analysis.ipynb'
+    - Visualization: 'notebooks/Basic Visualization.ipynb'
+    # - Frequency Analysis: 'notebooks/Frequency Analysis.ipynb'
+  - Technical Documentation:
+    - Data Processing: 'preprocess.md'
+    - Analysis:
+      - 'analysis/efficiency_analysis.md'
+      - 'analysis/frequency_analysis.md'
+    - Visualization:
+      - 'visualization/visualization.md'
+      - 'visualization/columns.md'
+      - 'visualization/efficiency_metrics.md'
+      - 'visualization/models.md'
+    - MVP Scripts:
+      - 'mvp_scripts/cpu_metrics.md'
+      - 'mvp_scripts/gpu_metrics.md'
+      - 'mvp_scripts/zero_gpu_usage.md'
+  - FAQ: 'faq.md'
+  - Contact: 'contact.md'
\ No newline at end of file
diff --git a/mvp_scripts/gpu_metrics.py b/mvp_scripts/gpu_metrics.py
index f9df3e5..9a65204 100644
--- a/mvp_scripts/gpu_metrics.py
+++ b/mvp_scripts/gpu_metrics.py
@@ -27,7 +27,7 @@
 vram_labels = [0] + vram_cutoffs[2:]
 
 
-def get_requested_vram(constraints):
+def get_requested_vram(constraints) -> int:
     """Get the minimum requested VRAM from job constraints.
 
     Args:
@@ -67,8 +67,6 @@ def __init__(
             metricsfile (str, optional): Path to the DuckDB database file containing job data.
             min_elapsed (int, optional): Minimum elapsed time (in seconds) for jobs to be included.
         """
-        if local:
-            metricsfile = "../data/slurm_data_small.db"
         self.con = duckdb.connect(metricsfile)
         # TODO - handle array jobs properly
         df = self.con.query(
diff --git a/mvp_scripts/zero_gpu_usage_list.py b/mvp_scripts/zero_gpu_usage_list.py
index 424b02d..f062cda 100644
--- a/mvp_scripts/zero_gpu_usage_list.py
+++ b/mvp_scripts/zero_gpu_usage_list.py
@@ -23,7 +23,7 @@
 HOURS = "{hours} unused GPU hours. The most recent jobs are the following:"
 
 
-def get_job_type_breakdown(interactive, jobs):
+def get_job_type_breakdown(interactive, jobs) -> str:
     """Generate a summary string describing the breakdown of interactive and batch jobs.
 
     Args:
@@ -45,7 +45,7 @@ def get_job_type_breakdown(interactive, jobs):
     )
 
 
-def pi_report(account, days_back=60):
+def pi_report(account, days_back=60) -> None:
     """Create an efficiency report for a given PI group, summarizing GPU usage and waste.
 
     Args:
@@ -74,7 +74,7 @@ def pi_report(account, days_back=60):
 
 def main(
     dbfile="./modules/admin-resources/reporting/slurm_data.db", userlist="./users.csv", sendEmail=False, days_back=60
-):
+) -> None:
     """Print out a list of users who habitually waste GPU hours, and optionally email them a report.
 
     Args:
diff --git a/src/analysis/efficiency_analysis.py b/src/analysis/efficiency_analysis.py
index fa57938..fd6f120 100644
--- a/src/analysis/efficiency_analysis.py
+++ b/src/analysis/efficiency_analysis.py
@@ -53,7 +53,6 @@ def load_preprocessed_jobs_dataframe_from_duckdb(
         raise RuntimeError(f"Failed to load jobs DataFrame: {e}") from e
 
 
-
 class EfficiencyAnalysis:
     """
     Class to encapsulate the efficiency analysis of jobs based on various metrics.
diff --git a/src/preprocess/preprocess.py b/src/preprocess/preprocess.py
index 9c69ed5..cd2011a 100644
--- a/src/preprocess/preprocess.py
+++ b/src/preprocess/preprocess.py
@@ -821,4 +821,4 @@ def preprocess_data(
     processing_error_logs.clear()
     error_indices.clear()
 
-    return data
+    return data
\ No newline at end of file

From 8bb745cd674f404e1a2f92b4f6e0b1a2ff1fcd36 Mon Sep 17 00:00:00 2001
From: Espiobest <59823894+Espiobest@users.noreply.github.com>
Date: Tue, 19 Aug 2025 14:11:44 -0400
Subject: [PATCH 8/9] revert gitignore

---
 .gitignore | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/.gitignore b/.gitignore
index 26d5ac7..5eb719f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -42,10 +42,3 @@ data/
 *.diff
 /docs/build
 /site
-
-# Quarto Report files
-.quarto 
-*.html
-*.pdf
-*.yml
-*.pkl
\ No newline at end of file

From 53162f89c7964f8189bcb10f891d870fbd20b4dd Mon Sep 17 00:00:00 2001
From: Espiobest <59823894+Espiobest@users.noreply.github.com>
Date: Thu, 21 Aug 2025 17:19:25 -0400
Subject: [PATCH 9/9] update documentation and add notebook copies

---
 .gitignore                               |   1 +
 docs/README.md                           |  48 ++
 docs/getting-started.md                  |   2 +-
 docs/index.md                            |  30 +-
 docs/notebooks                           |   1 -
 docs/notebooks/Basic Visualization.ipynb | 129 +++++
 docs/notebooks/Efficiency Analysis.ipynb | 614 +++++++++++++++++++++++
 7 files changed, 819 insertions(+), 6 deletions(-)
 create mode 100644 docs/README.md
 delete mode 120000 docs/notebooks
 create mode 100644 docs/notebooks/Basic Visualization.ipynb
 create mode 100644 docs/notebooks/Efficiency Analysis.ipynb

diff --git a/.gitignore b/.gitignore
index 5eb719f..bba95ec 100644
--- a/.gitignore
+++ b/.gitignore
@@ -42,3 +42,4 @@ data/
 *.diff
 /docs/build
 /site
+.quarto
\ No newline at end of file
diff --git a/docs/README.md b/docs/README.md
new file mode 100644
index 0000000..9d3cb51
--- /dev/null
+++ b/docs/README.md
@@ -0,0 +1,48 @@
+# DS4CG Job Analytics Documentation
+
+This directory contains the documentation for the DS4CG Job Analytics project.
+
+## Overview
+The documentation provides detailed information about the data pipeline, analysis scripts, reporting tools, and usage instructions for the DS4CG Job Analytics platform. It is intended for users, contributors, and administrators who want to understand or extend the analytics and reporting capabilities.
+
+## How to Build and View the Documentation
+
+The documentation is built using [MkDocs](https://www.mkdocs.org/) and [Quarto](https://quarto.org/) for interactive reports and notebooks.
+
+### MkDocs
+- To serve the documentation locally:
+  ```sh
+  mkdocs serve
+  ```
+  This will start a local server (usually at http://127.0.0.1:8000/) where you can browse the docs.
+
+- To build the static site:
+  ```sh
+  mkdocs build
+  ```
+  The output will be in the `site/` directory.
+
+### Quarto
+- Quarto is used for rendering interactive reports and notebooks (e.g., `.qmd` files).
+- To render a Quarto report:
+  ```sh
+  quarto render path/to/report.qmd
+  ```
+
+## Structure
+- `index.md`: Main landing page for the documentation site.
+- `about.md`: Project background and team information.
+- `preprocess.md`: Data preprocessing details.
+- `analysis/`, `visualization/`, `mvp_scripts/`: Subsections for specific topics and scripts.
+- `notebooks/`: Example notebooks and interactive analysis.
+
+## Requirements
+- Python 3.10+
+- MkDocs (`pip install mkdocs`)
+- Quarto (see https://quarto.org/docs/get-started/ for installation)
+
+## Contributing
+Contributions to the documentation are welcome! Edit or add Markdown files in this directory and submit a pull request.
+
+---
+For more details, see the main project README or contact the maintainers.
diff --git a/docs/getting-started.md b/docs/getting-started.md
index bf2153c..80e465e 100644
--- a/docs/getting-started.md
+++ b/docs/getting-started.md
@@ -250,4 +250,4 @@ See [MVP Scripts](mvp_scripts/cpu_metrics.md) for detailed usage instructions.
 
 - Follow the complete workflows in our [Demo Notebooks](demo.md)
 - Explore the [Data and Efficiency Metrics](data-and-metrics.md) page for detailed metric definitions
-- Visit [FAQ](faq.md) if you encounter any issues
+- Visit [FAQ](faq.md) if you encounter any issues
\ No newline at end of file
diff --git a/docs/index.md b/docs/index.md
index 65b16f7..f7e5e1c 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,15 +1,37 @@
 # DS4CG Unity Job Analytics
 
-Welcome to the documentation for the DS4CG Unity Job Analytics project.
+Welcome to the documentation for the Unity Job Analytics project.
 
 This documentation exists to:
+
 - Help new users and contributors understand the purpose and structure of the project.
 - Provide clear instructions for setup, usage, and contribution.
 - Serve as a reference for the available scripts, modules, and data analysis tools.
 - Document best practices and project standards for maintainability and collaboration.
 
-**Project Purpose:**
+## Project Purpose
+
+The Unity Job Analytics project provides tools and documentation for analyzing job data from the Unity cluster. It aims to help researchers and administrators gain insights into job performance, resource utilization, and efficiency, and to support reproducible, collaborative data science workflows.
+
+## Project Background
+
+The DS4CG Unity Job Analytics project was initiated as part of the DS4CG 2025 summer internship program in collaboration with the Unity HPC cluster at UMass. The goal is to provide robust tools and documentation for analyzing job data, improving resource utilization, and supporting research and operations on the Unity cluster.
+
+## Team & Contributors
+
+- Project Lead: Christopher Odoom
+- Contributors: DS4CG Summer 2025 Internship Team
+
+## Acknowledgments
+This project is supported by the Unity HPC team at UMass and the Data Science for the Common Good (DS4CG) program. Special thanks to all contributors and users who help improve the project.
+
+## Further Information
+
+- [Unity Documentation](https://docs.unity.rc.umass.edu/)
+- [DS4CG Program](https://ds.cs.umass.edu/programs/ds4cg)
+
+For questions or support, please reach out via the Unity Slack or contact the project lead.
 
-The DS4CG Unity Job Analytics project provides tools and documentation for analyzing job data from the Unity cluster. It aims to help researchers and administrators gain insights into job performance, resource utilization, and efficiency, and to support reproducible, collaborative data science workflows.
+---
 
-Use the navigation on the left to explore detailed guides, module documentation, and contributor resources.
+Use the navigation on the left to explore detailed guides, module documentation, and contributor resources.
\ No newline at end of file
diff --git a/docs/notebooks b/docs/notebooks
deleted file mode 120000
index 9097c22..0000000
--- a/docs/notebooks
+++ /dev/null
@@ -1 +0,0 @@
-C:/Users/ayush/desktop/coding/DS4CG/ds4cg-job-analytics/notebooks
\ No newline at end of file
diff --git a/docs/notebooks/Basic Visualization.ipynb b/docs/notebooks/Basic Visualization.ipynb
new file mode 100644
index 0000000..e9485c6
--- /dev/null
+++ b/docs/notebooks/Basic Visualization.ipynb	
@@ -0,0 +1,129 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "Jupyter server should be run at the notebook directory, so the output of the following cell would be the project root:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project_root = str(Path.cwd().resolve().parent)\n",
+    "print(f\"Project root: {project_root}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if project_root not in sys.path:\n",
+    "    sys.path.insert(0, project_root)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "# Reload all modules imported with %aimport every time before executing the Python code typed.\n",
+    "%autoreload 1\n",
+    "%aimport src.visualization.columns, src.database.database_connection, \\\n",
+    "    src.visualization.models, src.preprocess.preprocess"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.visualization import ColumnVisualizer\n",
+    "from src.preprocess import preprocess_data\n",
+    "from src.database import DatabaseConnection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db_connection = DatabaseConnection(\"../data/slurm_data.db\")\n",
+    "jobs_df = db_connection.fetch_all_jobs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clean_jobs_df = preprocess_data(jobs_df, min_elapsed_seconds=600)\n",
+    "clean_jobs_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "visualizer = ColumnVisualizer(clean_jobs_df.sample(10000, random_state=42))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "visualizer.visualize(\n",
+    "    output_dir_path=Path(\"../data/visualizations\"),\n",
+    "    columns=None,\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "duckdb",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/Efficiency Analysis.ipynb b/docs/notebooks/Efficiency Analysis.ipynb
new file mode 100644
index 0000000..1d07964
--- /dev/null
+++ b/docs/notebooks/Efficiency Analysis.ipynb	
@@ -0,0 +1,614 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# <a id='toc1_'></a>[Efficiency Analysis](#toc0_)\n",
+    "This notebook demonstrates the use of `EfficiencyAnalysis` class in `src/analysis/efficiency_analysis.py` for analyzing the efficiency of jobs, users, and PI groups."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "**Table of contents**<a id='toc0_'></a>    \n",
+    "- [Efficiency Analysis](#toc1_)    \n",
+    "  - [Setup](#toc1_1_)    \n",
+    "  - [Example: Analyze workload efficiency of GPU users who set no VRAM constraints and used 0 GB of VRAM](#toc1_2_)    \n",
+    "    - [Job Efficiency Metrics](#toc1_2_1_)    \n",
+    "      - [Find most inefficient jobs with no VRAM constraints based on `vram_hours`](#toc1_2_1_1_)    \n",
+    "    - [User Efficiency Metrics](#toc1_2_2_)    \n",
+    "      - [Find Inefficient Users based on `expected_value_alloc_vram_efficiency`](#toc1_2_2_1_)    \n",
+    "      - [Find Inefficient Users based on `vram_hours`](#toc1_2_2_2_)    \n",
+    "    - [PI Group Efficiency Metrics](#toc1_2_3_)    \n",
+    "      - [Find Inefficient PIs based on `vram_hours`](#toc1_2_3_1_)    \n",
+    "  - [Example: Analyze all jobs with no VRAM constraints](#toc1_3_)    \n",
+    "    - [Job Efficiency Metrics](#toc1_3_1_)    \n",
+    "      - [Problem with duplicate JobIDs](#toc1_3_1_1_)    \n",
+    "      - [Top users with most number of jobs that have no VRAM constraints](#toc1_3_1_2_)    \n",
+    "      - [Find inefficient jobs with no VRAM Constraints based on `alloc_vram_efficiency_score`](#toc1_3_1_3_)    \n",
+    "\n",
+    "<!-- vscode-jupyter-toc-config\n",
+    "\tnumbering=false\n",
+    "\tanchor=true\n",
+    "\tflat=false\n",
+    "\tminLevel=1\n",
+    "\tmaxLevel=6\n",
+    "\t/vscode-jupyter-toc-config -->\n",
+    "<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "## <a id='toc1_1_'></a>[Setup](#toc0_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import required modules\n",
+    "import sys\n",
+    "from pathlib import Path\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4",
+   "metadata": {},
+   "source": [
+    "Jupyter server should be run at the notebook directory, so the output of the following cell would be the project root:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project_root = str(Path.cwd().resolve().parent)\n",
+    "print(f\"Project root: {project_root}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add project root to sys.path for module imports\n",
+    "if project_root not in sys.path:\n",
+    "    sys.path.insert(0, project_root)\n",
+    "\n",
+    "from src.analysis import efficiency_analysis as ea\n",
+    "from src.visualization import JobsWithMetricsVisualizer, UsersWithMetricsVisualizer\n",
+    "\n",
+    "# Automatically reload modules before executing code\n",
+    "# This is useful for development to see changes without restarting the kernel.\n",
+    "%load_ext autoreload\n",
+    "# Reload all modules imported with %aimport every time before executing the Python code typed.\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the jobs DataFrame from DuckDB\n",
+    "preprocessed_jobs_df = ea.load_preprocessed_jobs_dataframe_from_duckdb(\n",
+    "    db_path=\"../data/slurm_data.db\",\n",
+    "    table_name=\"Jobs\",\n",
+    ")\n",
+    "display(preprocessed_jobs_df.head(10))\n",
+    "print(preprocessed_jobs_df.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8",
+   "metadata": {},
+   "source": [
+    "## <a id='toc1_2_'></a>[Example: Analyze workload efficiency of GPU users who set no VRAM constraints and used 0 GB of VRAM](#toc0_)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "efficiency_analysis = ea.EfficiencyAnalysis(jobs_df=preprocessed_jobs_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtered_jobs = efficiency_analysis.filter_jobs_for_analysis(\n",
+    "    vram_constraint_filter=pd.NA,  # No VRAM constraints\n",
+    "    gpu_mem_usage_filter=0,  # Used 0 GB of VRAM\n",
+    ")\n",
+    "filtered_jobs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11",
+   "metadata": {},
+   "source": [
+    "Generate all metrics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics_dict = efficiency_analysis.calculate_all_efficiency_metrics(filtered_jobs)\n",
+    "\n",
+    "jobs_with_metrics = metrics_dict[\"jobs_with_efficiency_metrics\"]\n",
+    "users_with_metrics = metrics_dict[\"users_with_efficiency_metrics\"]\n",
+    "pi_accounts_with_metrics = metrics_dict[\"pi_accounts_with_efficiency_metrics\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13",
+   "metadata": {},
+   "source": [
+    "### <a id='toc1_2_1_'></a>[Job Efficiency Metrics](#toc0_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set option to display all columns\n",
+    "pd.set_option(\"display.max_columns\", None)\n",
+    "# Display the DataFrame\n",
+    "display(jobs_with_metrics.head(10))\n",
+    "# To revert to default settings (optional)\n",
+    "pd.reset_option(\"display.max_columns\")\n",
+    "\n",
+    "print(f\"Jobs found: {len(jobs_with_metrics)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15",
+   "metadata": {},
+   "source": [
+    "#### <a id='toc1_2_1_1_'></a>[Find most inefficient jobs with no VRAM constraints based on `vram_hours`](#toc0_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inefficient_jobs_vram_hours = efficiency_analysis.sort_and_filter_records_with_metrics(\n",
+    "    metrics_df_name_enum=ea.MetricsDataFrameNameEnum.JOBS,\n",
+    "    sorting_key=\"vram_hours\",\n",
+    "    ascending=False,  # Sort by vram_hours in descending order\n",
+    "    filter_criteria={\n",
+    "        \"vram_hours\": {\"min\": 80 * 24, \"inclusive\": True},  # VRAM-hours threshold for identifying inefficient jobs\n",
+    "    },\n",
+    ")\n",
+    "# Display top inefficient users by VRAM-hours\n",
+    "print(\"\\nTop inefficient Jobs by VRAM-hours:\")\n",
+    "display(inefficient_jobs_vram_hours.head(10))\n",
+    "\n",
+    "# Plot top inefficient jobs by VRAM-hours, with VRAM-hours as labels\n",
+    "jobs_with_metrics_visualizer = JobsWithMetricsVisualizer(inefficient_jobs_vram_hours.head(20))\n",
+    "jobs_with_metrics_visualizer.visualize(\n",
+    "    column=\"vram_hours\",\n",
+    "    bar_label_columns=[\"vram_hours\", \"job_hours\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17",
+   "metadata": {},
+   "source": [
+    "### <a id='toc1_2_2_'></a>[User Efficiency Metrics](#toc0_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "users_with_metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19",
+   "metadata": {},
+   "source": [
+    "#### <a id='toc1_2_2_1_'></a>[Find Inefficient Users based on `expected_value_alloc_vram_efficiency`](#toc0_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inefficient_users_alloc_vram_eff = efficiency_analysis.sort_and_filter_records_with_metrics(\n",
+    "    metrics_df_name_enum=ea.MetricsDataFrameNameEnum.USERS,\n",
+    "    sorting_key=\"expected_value_alloc_vram_efficiency\",\n",
+    "    ascending=True,  # we want to find users with low efficiency\n",
+    "    filter_criteria={\n",
+    "        \"expected_value_alloc_vram_efficiency\": {\"max\": 0.3, \"inclusive\": True},\n",
+    "        \"job_count\": {\"min\": 5, \"inclusive\": True},  # Minimum number of jobs to consider a user\n",
+    "    },\n",
+    ")\n",
+    "print(\"\\nTop inefficient users by allocated vram efficiency:\")\n",
+    "display(inefficient_users_alloc_vram_eff.head(20))\n",
+    "\n",
+    "# Plot top inefficient users by allocated vram efficiency, with allocated vram efficiency as labels\n",
+    "users_with_metrics_visualizer = UsersWithMetricsVisualizer(inefficient_users_alloc_vram_eff.head(20))\n",
+    "users_with_metrics_visualizer.visualize(\n",
+    "    column=\"expected_value_alloc_vram_efficiency\",\n",
+    "    bar_label_columns=[\"expected_value_alloc_vram_efficiency\", \"user_job_hours\"],\n",
+    "    figsize=(8, 10),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inefficient_users = efficiency_analysis.sort_and_filter_records_with_metrics(\n",
+    "    metrics_df_name_enum=ea.MetricsDataFrameNameEnum.USERS,\n",
+    "    sorting_key=\"expected_value_alloc_vram_efficiency\",\n",
+    "    ascending=True,  # we want to find users with low efficiency\n",
+    "    filter_criteria={\n",
+    "        \"expected_value_alloc_vram_efficiency\": {\"max\": 0.3, \"inclusive\": True},\n",
+    "        \"job_count\": {\"min\": 5, \"inclusive\": True},  # Minimum number of jobs to consider a user\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Display top inefficient users by job count\n",
+    "print(\"\\nTop inefficient users by allocated vram efficiency:\")\n",
+    "display(inefficient_users.head(10))\n",
+    "\n",
+    "\n",
+    "# Plot top inefficient users by GPU hours, with efficiency as labels\n",
+    "top_users = inefficient_users.head(10)\n",
+    "\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "barplot = sns.barplot(y=top_users[\"User\"], x=top_users[\"user_job_hours\"], orient=\"h\")\n",
+    "plt.xlabel(\"Job Hours\")\n",
+    "plt.ylabel(\"User\")\n",
+    "plt.title(\"Top 10 Inefficient Users by Allocated VRAM Efficiency Contribution\")\n",
+    "\n",
+    "# Annotate bars with expected_value_alloc_vram_efficiency, keeping text fully inside the plot's right spine\n",
+    "ax = barplot\n",
+    "xmax = top_users[\"user_job_hours\"].max()\n",
+    "# Add headroom for annotation space (20% extra)\n",
+    "xlim = xmax * 1.20 if xmax > 0 else 1\n",
+    "ax.set_xlim(0, xlim)\n",
+    "\n",
+    "# Calculate annotation x-position: place at 98% of xlim or just left of the right spine, whichever is smaller\n",
+    "for i, (job_hours, efficiency) in enumerate(\n",
+    "    zip(\n",
+    "        top_users[\"user_job_hours\"],\n",
+    "        top_users[\"expected_value_alloc_vram_efficiency\"],\n",
+    "        strict=True,\n",
+    "    )\n",
+    "):\n",
+    "    # Place annotation at min(job_hours + 2% of xlim, 98% of xlim)\n",
+    "    xpos = min(job_hours + xlim * 0.02, xlim * 0.98)\n",
+    "    # If bar is very close to right spine, nudge annotation left to avoid overlap\n",
+    "    if xpos > xlim * 0.96:\n",
+    "        xpos = xlim * 0.96\n",
+    "    ax.text(xpos, i, f\"Eff: {efficiency:.2f}\", va=\"center\", ha=\"left\", fontsize=10, color=\"black\", clip_on=True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22",
+   "metadata": {},
+   "source": [
+    "#### <a id='toc1_2_2_2_'></a>[Find Inefficient Users based on `vram_hours`](#toc0_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inefficient_users_vram_hours = efficiency_analysis.find_inefficient_users_by_vram_hours(\n",
+    "    vram_hours_filter={\"min\": 200, \"inclusive\": True},  # VRAM-hours threshold for identifying inefficient users\n",
+    "    min_jobs=5,  # Minimum number of jobs to consider a user\n",
+    ")\n",
+    "# Display top inefficient users by VRAM-hours\n",
+    "print(\"\\nTop inefficient users by VRAM-hours:\")\n",
+    "display(inefficient_users_vram_hours.head(20))\n",
+    "\n",
+    "\n",
+    "# Plot top inefficient users by VRAM-hours, with VRAM-hours as labels\n",
+    "users_with_metrics_visualizer = UsersWithMetricsVisualizer(inefficient_users_vram_hours.head(20))\n",
+    "users_with_metrics_visualizer.visualize(\n",
+    "    column=\"vram_hours\", bar_label_columns=[\"vram_hours\", \"user_job_hours\"], figsize=(8, 10)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24",
+   "metadata": {},
+   "source": [
+    "### <a id='toc1_2_3_'></a>[PI Group Efficiency Metrics](#toc0_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pi_accounts_with_metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26",
+   "metadata": {},
+   "source": [
+    "#### <a id='toc1_2_3_1_'></a>[Find Inefficient PIs based on `vram_hours`](#toc0_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inefficient_pis_vram_hours = efficiency_analysis.sort_and_filter_records_with_metrics(\n",
+    "    metrics_df_name_enum=ea.MetricsDataFrameNameEnum.PI_GROUPS,\n",
+    "    sorting_key=\"pi_acc_vram_hours\",\n",
+    "    ascending=False,\n",
+    "    filter_criteria={\n",
+    "        \"pi_acc_vram_hours\": {\"min\": 200, \"inclusive\": True},  # VRAM-hours threshold for identifying inefficient users\n",
+    "        \"job_count\": {\"min\": 5, \"inclusive\": True},  # Minimum number of jobs to consider a PI account\n",
+    "    },\n",
+    ")\n",
+    "# Display top inefficient users by VRAM-hours\n",
+    "print(\"\\nTop inefficient PI Groups by VRAM-hours:\")\n",
+    "display(inefficient_pis_vram_hours.head(20))\n",
+    "\n",
+    "top_pi_accounts = inefficient_pis_vram_hours.head(20)\n",
+    "\n",
+    "# Plot top inefficient users by VRAM-hours, with VRAM-hours as labels\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "barplot = sns.barplot(\n",
+    "    y=top_pi_accounts[\"pi_account\"],\n",
+    "    x=top_pi_accounts[\"pi_acc_vram_hours\"],\n",
+    "    order=top_pi_accounts[\"pi_account\"].tolist(),  # Only show present values\n",
+    "    orient=\"h\",\n",
+    ")\n",
+    "plt.xlabel(\"VRAM-Hours\")\n",
+    "plt.ylabel(\"PI Account\")\n",
+    "plt.title(\"Top Inefficient PI Accounts by VRAM-Hours\")\n",
+    "# Annotate bars with gpu_hours, keeping text fully inside the plot's right spine\n",
+    "ax = barplot\n",
+    "xmax = top_pi_accounts[\"pi_acc_vram_hours\"].max()\n",
+    "# Add headroom for annotation space (20% extra)\n",
+    "xlim = xmax * 1.6 if xmax > 0 else 1\n",
+    "ax.set_xlim(0, xlim)\n",
+    "# Calculate annotation x-position: place at 98% of xlim or just left of the right spine, whichever is smaller\n",
+    "for i, (vram_hours, pi_acc_job_hours) in enumerate(\n",
+    "    zip(\n",
+    "        top_pi_accounts[\"pi_acc_vram_hours\"],\n",
+    "        top_pi_accounts[\"pi_acc_job_hours\"],\n",
+    "        strict=True,\n",
+    "    )\n",
+    "):\n",
+    "    # Place annotation at min(vram_hours + 2% of xlim, 98% of xlim)\n",
+    "    xpos = min(vram_hours + xlim * 0.02, xlim * 0.98)\n",
+    "    ax.text(\n",
+    "        xpos,\n",
+    "        i,\n",
+    "        f\"VRAM-Hours: {vram_hours:.2f}\\n Job Hours: {pi_acc_job_hours:.2f}\",\n",
+    "        va=\"center\",\n",
+    "        ha=\"left\",\n",
+    "        fontsize=10,\n",
+    "        color=\"black\",\n",
+    "        clip_on=True,\n",
+    "    )\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28",
+   "metadata": {},
+   "source": [
+    "## <a id='toc1_3_'></a>[Example: Analyze all jobs with no VRAM constraints](#toc0_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Filter jobs where no VRAM constraint was set but a GPU was allocated\n",
+    "no_vram_constraint_efficiency_analysis = ea.EfficiencyAnalysis(jobs_df=preprocessed_jobs_df)\n",
+    "all_no_vram_constraint_jobs = no_vram_constraint_efficiency_analysis.filter_jobs_for_analysis(\n",
+    "    vram_constraint_filter={\"min\": 0, \"inclusive\": False},  # No VRAM constraints\n",
+    "    gpu_count_filter={\"min\": 1, \"inclusive\": True},  # At least one GPU allocated\n",
+    "    gpu_mem_usage_filter={\"min\": 0, \"inclusive\": False},  # Used more than 0 GiB of VRAM\n",
+    ")\n",
+    "\n",
+    "display(all_no_vram_constraint_jobs.head(10))\n",
+    "print(all_no_vram_constraint_jobs.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30",
+   "metadata": {},
+   "source": [
+    "### <a id='toc1_3_1_'></a>[Job Efficiency Metrics](#toc0_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "no_vram_constraint_jobs_with_metrics = no_vram_constraint_efficiency_analysis.calculate_job_efficiency_metrics(\n",
+    "    all_no_vram_constraint_jobs\n",
+    ")\n",
+    "\n",
+    "# Set option to display all columns\n",
+    "pd.set_option(\"display.max_columns\", None)\n",
+    "# Display the DataFrame\n",
+    "display(no_vram_constraint_jobs_with_metrics.head(10))\n",
+    "# To revert to default settings (optional)\n",
+    "pd.reset_option(\"display.max_columns\")\n",
+    "print(f\"Jobs found: {len(no_vram_constraint_jobs_with_metrics)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32",
+   "metadata": {},
+   "source": [
+    "#### <a id='toc1_3_1_1_'></a>[Problem with duplicate JobIDs](#toc0_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# select jobs with specific job id\n",
+    "pd.set_option(\"display.max_columns\", None)\n",
+    "# Display the DataFrame\n",
+    "display(no_vram_constraint_jobs_with_metrics[no_vram_constraint_jobs_with_metrics[\"JobID\"] == 24374463])\n",
+    "pd.reset_option(\"display.max_columns\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34",
+   "metadata": {},
+   "source": [
+    "#### <a id='toc1_3_1_2_'></a>[Top users with most number of jobs that have no VRAM constraints](#toc0_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot top users by number of jobs with no VRAM constraints\n",
+    "if not all_no_vram_constraint_jobs.empty:\n",
+    "    plt.figure(figsize=(10, 5))\n",
+    "    user_counts = all_no_vram_constraint_jobs[\"User\"].value_counts().head(20)\n",
+    "    sns.barplot(x=user_counts.values, y=user_counts.index, orient=\"h\")\n",
+    "    plt.xlabel(\"Number of Jobs\")\n",
+    "    plt.ylabel(\"User\")\n",
+    "    plt.title(\"Top 20 Users: Jobs with no VRAM Constraints\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "else:\n",
+    "    print(\"No jobs found without VRAM constraints.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36",
+   "metadata": {},
+   "source": [
+    "#### <a id='toc1_3_1_3_'></a>[Find inefficient jobs with no VRAM Constraints based on `alloc_vram_efficiency_score`](#toc0_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "low_alloc_vram_score_jobs = no_vram_constraint_efficiency_analysis.sort_and_filter_records_with_metrics(\n",
+    "    metrics_df_name_enum=ea.MetricsDataFrameNameEnum.JOBS,\n",
+    "    sorting_key=\"alloc_vram_efficiency_score\",\n",
+    "    ascending=True,  # Sort by alloc_vram_efficiency_score in ascending order\n",
+    "    filter_criteria={\n",
+    "        \"alloc_vram_efficiency_score\": {\"max\": -10, \"inclusive\": True},  # score threshold\n",
+    "    },\n",
+    ")\n",
+    "# Display top inefficient users by alloc_vram_efficiency_score\n",
+    "print(\"\\nTop inefficient Jobs by allocated VRAM efficiency score:\")\n",
+    "\n",
+    "display(low_alloc_vram_score_jobs.head(20))\n",
+    "\n",
+    "jobs_with_metrics_visualizer = JobsWithMetricsVisualizer(low_alloc_vram_score_jobs.head(20))\n",
+    "jobs_with_metrics_visualizer.visualize(\n",
+    "    column=\"alloc_vram_efficiency_score\",\n",
+    "    bar_label_columns=[\"alloc_vram_efficiency_score\", \"job_hours\"],\n",
+    "    figsize=(10, 12),\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}