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295 lines
63 KiB
HTML
295 lines
63 KiB
HTML
<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="utf-8" />
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<link rel="icon" crossorigin="anonymous" href="https://cdn.jsdelivr.net/npm/@marimo-team/frontend@0.19.6/dist/favicon.ico" />
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but at that point we cannot load these images from the server -->
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<meta name="theme-color" content="#000000" />
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<meta name="description" content="a marimo app" />
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function __resizeIframe(obj) {
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setHeight();
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<marimo-filename hidden>lab_01_ml_intro.py</marimo-filename>
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<marimo-server-token data-token="{{ server_token }}" hidden></marimo-server-token>
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<!-- /TODO -->
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<title>lab 01 ml intro</title>
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"notebook": {"cells": [{"code": "import marimo as mo\nimport sys\nimport os\nfrom pathlib import Path\nimport plotly.graph_objects as go\nimport numpy as np\n\n# --- PATH INJECTION ---\ntry:\n notebook_path = Path(os.path.abspath(__file__))\nexcept NameError:\n notebook_path = Path(os.getcwd()) / \"labs\" / \"vol1\" / \"lab_01_ml_intro.py\"\n\nproject_root = notebook_path.parents[2]\nquarto_path = str(project_root / \"book\" / \"quarto\")\nif quarto_path not in sys.path:\n sys.path.append(quarto_path)\nif str(project_root) not in sys.path:\n sys.path.append(str(project_root))\n\n# --- ENGINE \u0026 CORE IMPORTS ---\nfrom mlsys import Engine, Models, Systems, ureg, Q_\nfrom labs.core.style import COLORS, LAB_CSS, apply_plotly_theme\nfrom labs.core.components import Card, PredictionLock\n", "code_hash": "ee869f622e0c3c832c7b4ad9a37b1965", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "Hbol", "name": "__"}, {"code": "# --- VISUAL THEME ---\nmo.vstack([\n LAB_CSS,\n mo.md(\"# \ud83d\ude80 Lab 01: The Moment of AI\")\n])", "code_hash": "be4d587c7a23de2968f9520b5eccaa41", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "MJUe", "name": "__"}, {"code": "mo.md(\n r\"\"\"\n ### **Mission: Witnessing the Foundations**\n Before you claim a specialization, you must understand the physical and mathematical laws that govern the ML universe. Complete the tasks below to earn your certification.\n \"\"\"\n)", "code_hash": "f6229f97526e0f04cd72fc32f2ce095a", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "vblA", "name": "__"}, {"code": "mo.md(\"## Part 1: The Magnitude Gap (D\u00b7A\u00b7M Taxonomy)\")", "code_hash": "0c0ee2ad997c9966b8db9ebfad926b0f", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "bkHC", "name": "__"}, {"code": "# Task 1 Definitions\np1_val, p1_ui = PredictionLock(1, \"How many orders of magnitude separate an ESP32 from an H100 RAM?\")", "code_hash": "5dfa9fc192f32ba2d45349ac37b448c1", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "lEQa", "name": "__"}, {"code": "p1_ui", "code_hash": "f2a93472e853e599a885533bc9578f03", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "PKri", "name": "__"}, {"code": "mo.stop(p1_val.value == \"\")\ntier_slider = mo.ui.slider(start=0, stop=3, step=1, value=0, label=\"Scale Tier (Tiny -\u003E Cloud)\")\ntier_slider", "code_hash": "3ef4f412a57cccbd39d5b6624950bc56", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "Xref", "name": "__"}, {"code": "_tiers = [Systems.Tiny, Systems.Mobile, Systems.Edge, Systems.Cloud]\n_sel = _tiers[tier_slider.value]\n\n_fig = go.Figure(go.Bar(\n x=['RAM (Log GB)', 'Compute (Log TFLOPS)', 'Power (Log W)'],\n y=[\n np.log10(max(_sel.ram.m_as('GB'), 1e-6)),\n np.log10(max(_sel.peak_flops.m_as('TFLOPs/s'), 1e-6)),\n np.log10(max(_sel.power_budget.m_as('watt'), 1e-6))\n ],\n marker_color=COLORS['BlueLine']\n))\n_fig.update_layout(yaxis=dict(range=[-6, 6], title=\"Log10 Scale\"), height=300)\n\nmo.vstack([\n mo.md(\"#### Analyzing the 10^9 Scaling Gap\"),\n mo.hstack([\n Card(f\"Tier: {_sel.name}\", mo.as_html(apply_plotly_theme(_fig))),\n Card(\"Dimension Data\", f\"- **RAM:** {_sel.ram:~}\\n- **Peak:** {_sel.peak_flops:~}\\n- **TDP:** {_sel.power_budget:~}\")\n ], widths=[2, 1]),\n mo.ui.text_area(label=\"REFLECT: Why can't we 'just shrink' a Cloud model to TinyML?\")\n])", "code_hash": "bf12f3dd52d30857fa1ee0592a5afd9b", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "SFPL", "name": "__"}, {"code": "mo.md(\"---\")\nmo.md(\"## Part 2: The Bitter Lesson\")", "code_hash": "45ec3bdab81bf84e99d581dbed98487b", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "BYtC", "name": "__"}, {"code": "p2_val, p2_ui = PredictionLock(2, \"Which scales better with compute: Hand-coded Logic or Learned Patterns?\")", "code_hash": "22aba5c754fa547565e1fdd4d3d32a2e", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "RGSE", "name": "__"}, {"code": "p2_ui", "code_hash": "de1a3141361cb6939c3e93ffcd768036", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "Kclp", "name": "__"}, {"code": "mo.stop(p2_val.value == \"\")\ncompute_scale = mo.ui.slider(start=1, stop=1000, step=10, value=1, label=\"Compute Budget (x)\")\ncompute_scale", "code_hash": "5590abca0739dc5cf5925a74d96ab711", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "emfo", "name": "__"}, {"code": "_x = np.linspace(1, 1000, 100)\n_y_expert = 20 * np.log10(_x + 1) + 50 \n_y_learning = 15 * (_x**0.2) + 60\n\n_fig_b = go.Figure()\n_fig_b.add_trace(go.Scatter(x=_x, y=_y_expert, name=\"Rules\", line=dict(color=COLORS['Grey'])))\n_fig_b.add_trace(go.Scatter(x=_x, y=_y_learning, name=\"Learning\", line=dict(color=COLORS['RedLine'], width=4)))\n_curr_y = 15 * (compute_scale.value**0.2) + 60\n_fig_b.add_trace(go.Scatter(x=[compute_scale.value], y=[_curr_y], mode='markers', marker=dict(size=12, color=COLORS['BlueLine'])))\n_fig_b.update_layout(title=\"Accuracy vs. Compute\", height=300)\n\nmo.vstack([\n mo.md(\"#### The Bitter Lesson in Action\"),\n mo.hstack([\n Card(\"Scaling Simulator\", mo.as_html(apply_plotly_theme(_fig_b))),\n Card(\"Verification Gap\", \"Input Space: **10^362,000** configurations.\")\n ], widths=[2, 1]),\n mo.ui.text_area(label=\"REFLECT: Is human expertise a 'depreciating asset'?\")\n])", "code_hash": "a94b43233fc436ff87cdf2956497f310", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "Hstk", "name": "__"}, {"code": "mo.md(\"---\")\nmo.md(\"## Part 3: Claim Mission\")", "code_hash": "bc489a7f875a2b239d9bcd6cd4754ce5", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "nWHF", "name": "__"}, {"code": "track_selector = mo.ui.radio(\n options={\n \"\u2601\ufe0f Cloud Titan\": \"CLOUD\",\n \"\ud83e\udd16 Edge Guardian\": \"EDGE\",\n \"\ud83d\udd76\ufe0f Mobile Nomad\": \"MOBILE\",\n \"\ud83d\udc42 Tiny Pioneer\": \"TINY\"\n },\n label=\"Select your Career Specialization Track\"\n)\ntrack_selector", "code_hash": "ff605dcbb67a9e66e52a05b8a3b7307e", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "iLit", "name": "__"}, {"code": "mo.stop(track_selector.value is None)\n\n_data = {\n \"CLOUD\": {\"title\": \"LLM Architect\", \"msg\": \"Maximize Llama-3-70B serving.\", \"color\": COLORS['BlueLine']},\n \"EDGE\": {\"title\": \"AV Lead\", \"msg\": \"Maintain 10ms safety loop.\", \"color\": COLORS['RedLine']},\n \"MOBILE\": {\"title\": \"AR Glasses Dev\", \"msg\": \"60FPS overlay under 2W.\", \"color\": COLORS['OrangeLine']},\n \"TINY\": {\"title\": \"Hearable Lead\", \"msg\": \"Speech isolation in \u003C10ms.\", \"color\": COLORS['GreenLine']}\n}\n\n_t = _data[track_selector.value]\n\nmo.vstack([\n mo.md(f\"### \ud83c\udf96\ufe0f MISSION GRANTED: {_t['title']}\"),\n mo.md(f\"\u003Cdiv style='padding:20px; border-left:8px solid {_t['color']}; background:#f8faff;'\u003E\u003Cstrong\u003EGoal:\u003C/strong\u003E {_t['msg']}\u003C/div\u003E\"),\n Card(\"Design Ledger\", \"\u2705 Initialized. Next: Lab 02.\")\n])", "code_hash": "a83251577e8a686537b3beb7fef0a385", "config": {"column": null, "disabled": false, "hide_code": false}, "id": "ZHCJ", "name": "__"}], "metadata": {"marimo_version": "0.19.6"}, "version": "1"},
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"session": {"cells": [{"code_hash": "ee869f622e0c3c832c7b4ad9a37b1965", "console": [], "id": "Hbol", "outputs": [{"data": {"text/plain": ""}, "type": "data"}]}, {"code_hash": "be4d587c7a23de2968f9520b5eccaa41", "console": [], "id": "MJUe", "outputs": [{"data": {"text/html": "\u003Cdiv style='display: flex;flex: 1;flex-direction: column;justify-content: flex-start;align-items: normal;flex-wrap: nowrap;gap: 0.5rem'\u003E\n\u003Cstyle\u003E\n .lab-card {\n background-color: white;\n border: 1px solid #dee2e6;\n border-radius: 8px;\n padding: 1.5rem;\n margin-bottom: 1rem;\n box-shadow: 0 2px 4px rgba(0,0,0,0.05);\n }\n .lab-card h3 {\n margin-top: 0;\n color: #2c3e50;\n border-bottom: 2px solid #006395;\n display: inline-block;\n padding-bottom: 4px;\n margin-bottom: 1.2rem;\n font-size: 1.1rem;\n font-weight: 700;\n text-transform: uppercase;\n letter-spacing: 0.05em;\n }\n .metric-value {\n font-family: 'SF Mono', 'Roboto Mono', 'Menlo', monospace;\n font-weight: 600;\n color: #006395;\n }\n .feasibility-banner {\n padding: 15px;\n color: white;\n border-radius: 8px;\n font-weight: bold;\n text-align: center;\n font-family: sans-serif;\n margin-bottom: 20px;\n box-shadow: 0 4px 6px rgba(0,0,0,0.1);\n font-size: 1.2em;\n }\n .prediction-box {\n background-color: #f8f9fa;\n border-left: 5px solid #CC5500;\n padding: 1rem;\n border-radius: 4px;\n margin-bottom: 1.5rem;\n }\n .stakeholder-quote {\n font-style: italic;\n color: #555;\n border-left: 3px solid #bdc3c7;\n padding-left: 15px;\n margin: 15px 0;\n }\n\u003C/style\u003E\n\u003Cspan class=\"markdown prose dark:prose-invert contents\"\u003E\u003Ch1 id=\"lab-01-the-moment-of-ai\"\u003E\ud83d\ude80 Lab 01: The Moment of AI\u003C/h1\u003E\u003C/span\u003E\u003C/div\u003E"}, "type": "data"}]}, {"code_hash": "f6229f97526e0f04cd72fc32f2ce095a", "console": [], "id": "vblA", "outputs": [{"data": {"text/markdown": "\u003Cspan class=\"markdown prose dark:prose-invert contents\"\u003E\u003Ch3 id=\"mission-witnessing-the-foundations\"\u003E\u003Cstrong\u003EMission: Witnessing the Foundations\u003C/strong\u003E\u003C/h3\u003E\n\u003Cspan class=\"paragraph\"\u003EBefore you claim a specialization, you must understand the physical and mathematical laws that govern the ML universe. Complete the tasks below to earn your certification.\u003C/span\u003E\u003C/span\u003E"}, "type": "data"}]}, {"code_hash": "0c0ee2ad997c9966b8db9ebfad926b0f", "console": [], "id": "bkHC", "outputs": [{"data": {"text/markdown": "\u003Cspan class=\"markdown prose dark:prose-invert contents\"\u003E\u003Ch2 id=\"part-1-the-magnitude-gap-dam-taxonomy\"\u003EPart 1: The Magnitude Gap (D\u00b7A\u00b7M Taxonomy)\u003C/h2\u003E\u003C/span\u003E"}, "type": "data"}]}, {"code_hash": "5dfa9fc192f32ba2d45349ac37b448c1", "console": [], "id": "lEQa", "outputs": [{"data": {"text/plain": ""}, "type": "data"}]}, {"code_hash": "f2a93472e853e599a885533bc9578f03", "console": [], "id": "PKri", "outputs": [{"data": {"text/html": "\u003Cdiv style='display: flex;flex: 1;flex-direction: column;justify-content: flex-start;align-items: normal;flex-wrap: nowrap;gap: 0.5rem'\u003E\u003Cmarimo-ui-element object-id='lEQa-0' random-id='c2775a30-f563-d4a1-a93e-a696df615b2f'\u003E\u003Cmarimo-text-area data-initial-value='\u0026quot;\u0026quot;' data-label='\u0026quot;\u0026lt;span class=\u0026#92;\u0026quot;markdown prose dark:prose-invert contents\u0026#92;\u0026quot;\u0026gt;\u0026lt;span class=\u0026#92;\u0026quot;paragraph\u0026#92;\u0026quot;\u0026gt;PREDICT: How many orders of magnitude separate an ESP32 from an H100 RAM?\u0026lt;/span\u0026gt;\u0026lt;/span\u0026gt;\u0026quot;' data-placeholder='\u0026quot;Enter your hypothesis to unlock the instruments...\u0026quot;' data-disabled='false' data-debounce='true' data-full-width='true'\u003E\u003C/marimo-text-area\u003E\u003C/marimo-ui-element\u003E\u003Cspan class=\"markdown prose dark:prose-invert contents\"\u003E\u003Cdiv class='prediction-box'\u003E\n \u003Ch4\u003E\u26a0\ufe0f Task 1 Locked\u003C/h4\u003E\n \u003Cspan class=\"paragraph\"\u003EPlease enter your prediction above to witness the physics.\u003C/span\u003E\n\u003C/div\u003E\u003C/span\u003E\u003C/div\u003E"}, "type": "data"}]}, {"code_hash": "3ef4f412a57cccbd39d5b6624950bc56", "console": [], "id": "Xref", "outputs": [{"data": {"text/plain": ""}, "type": "data"}]}, {"code_hash": "bf12f3dd52d30857fa1ee0592a5afd9b", "console": [], "id": "SFPL", "outputs": [{"ename": "ancestor-stopped", "evalue": "This cell wasn't run because an ancestor was stopped with `mo.stop`: ", "traceback": [], "type": "error"}]}, {"code_hash": "45ec3bdab81bf84e99d581dbed98487b", "console": [], "id": "BYtC", "outputs": [{"data": {"text/markdown": "\u003Cspan class=\"markdown prose dark:prose-invert contents\"\u003E\u003Ch2 id=\"part-2-the-bitter-lesson\"\u003EPart 2: The Bitter Lesson\u003C/h2\u003E\u003C/span\u003E"}, "type": "data"}]}, {"code_hash": "22aba5c754fa547565e1fdd4d3d32a2e", "console": [], "id": "RGSE", "outputs": [{"data": {"text/plain": ""}, "type": "data"}]}, {"code_hash": "de1a3141361cb6939c3e93ffcd768036", "console": [], "id": "Kclp", "outputs": [{"data": {"text/html": "\u003Cdiv style='display: flex;flex: 1;flex-direction: column;justify-content: flex-start;align-items: normal;flex-wrap: nowrap;gap: 0.5rem'\u003E\u003Cmarimo-ui-element object-id='RGSE-0' random-id='57af9608-42f3-530a-d177-96d7f4d070f6'\u003E\u003Cmarimo-text-area data-initial-value='\u0026quot;\u0026quot;' data-label='\u0026quot;\u0026lt;span class=\u0026#92;\u0026quot;markdown prose dark:prose-invert contents\u0026#92;\u0026quot;\u0026gt;\u0026lt;span class=\u0026#92;\u0026quot;paragraph\u0026#92;\u0026quot;\u0026gt;PREDICT: Which scales better with compute: Hand-coded Logic or Learned Patterns?\u0026lt;/span\u0026gt;\u0026lt;/span\u0026gt;\u0026quot;' data-placeholder='\u0026quot;Enter your hypothesis to unlock the instruments...\u0026quot;' data-disabled='false' data-debounce='true' data-full-width='true'\u003E\u003C/marimo-text-area\u003E\u003C/marimo-ui-element\u003E\u003Cspan class=\"markdown prose dark:prose-invert contents\"\u003E\u003Cdiv class='prediction-box'\u003E\n \u003Ch4\u003E\u26a0\ufe0f Task 2 Locked\u003C/h4\u003E\n \u003Cspan class=\"paragraph\"\u003EPlease enter your prediction above to witness the physics.\u003C/span\u003E\n\u003C/div\u003E\u003C/span\u003E\u003C/div\u003E"}, "type": "data"}]}, {"code_hash": "5590abca0739dc5cf5925a74d96ab711", "console": [], "id": "emfo", "outputs": [{"data": {"text/plain": ""}, "type": "data"}]}, {"code_hash": "a94b43233fc436ff87cdf2956497f310", "console": [], "id": "Hstk", "outputs": [{"ename": "ancestor-stopped", "evalue": "This cell wasn't run because an ancestor was stopped with `mo.stop`: ", "traceback": [], "type": "error"}]}, {"code_hash": "bc489a7f875a2b239d9bcd6cd4754ce5", "console": [], "id": "nWHF", "outputs": [{"data": {"text/markdown": "\u003Cspan class=\"markdown prose dark:prose-invert contents\"\u003E\u003Ch2 id=\"part-3-claim-mission\"\u003EPart 3: Claim Mission\u003C/h2\u003E\u003C/span\u003E"}, "type": "data"}]}, {"code_hash": "ff605dcbb67a9e66e52a05b8a3b7307e", "console": [], "id": "iLit", "outputs": [{"data": {"text/html": "\u003Cmarimo-ui-element object-id='iLit-0' random-id='b0e964fd-e372-1a4e-cb40-24cafd94be84'\u003E\u003Cmarimo-radio data-initial-value='null' data-label='\u0026quot;\u0026lt;span class=\u0026#92;\u0026quot;markdown prose dark:prose-invert contents\u0026#92;\u0026quot;\u0026gt;\u0026lt;span class=\u0026#92;\u0026quot;paragraph\u0026#92;\u0026quot;\u0026gt;Select your Career Specialization Track\u0026lt;/span\u0026gt;\u0026lt;/span\u0026gt;\u0026quot;' data-options='[\u0026quot;\u2601\ufe0f Cloud Titan\u0026quot;,\u0026quot;\ud83e\udd16 Edge Guardian\u0026quot;,\u0026quot;\ud83d\udd76\ufe0f Mobile Nomad\u0026quot;,\u0026quot;\ud83d\udc42 Tiny Pioneer\u0026quot;]' data-inline='false' data-disabled='false'\u003E\u003C/marimo-radio\u003E\u003C/marimo-ui-element\u003E"}, "type": "data"}]}, {"code_hash": "a83251577e8a686537b3beb7fef0a385", "console": [], "id": "ZHCJ", "outputs": [{"data": {"text/plain": ""}, "type": "data"}]}], "metadata": {"marimo_version": "0.19.6"}, "version": "1"},
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};
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<marimo-code hidden="">
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