mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-05-24 04:55:49 -05:00
1165 lines
50 KiB
HTML
1165 lines
50 KiB
HTML
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<!DOCTYPE html>
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<html lang="en" data-content_root="./" >
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<head>
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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<title>Getting Started — Tiny🔥Torch</title>
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<script data-cfasync="false">
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|
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<script src="_static/subscribe-modal.js?v=42919b64"></script>
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<link rel="icon" href="_static/favicon.svg"/>
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<link rel="index" title="Index" href="genindex.html" />
|
||
<link rel="search" title="Search" href="search.html" />
|
||
<link rel="next" title="Getting Started with TinyTorch" href="getting-started.html" />
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Build Your Own ML Framework
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<a href="tiers/foundation.html" class="tier-card" style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb 100%); padding: 1.5rem; border-radius: 0.5rem; border-left: 5px solid #1976d2; text-decoration: none; display: block; transition: transform 0.2s ease, box-shadow 0.2s ease;">
|
||
<h3 style="margin: 0 0 0.75rem 0; color: #0d47a1; font-size: 1.15rem; font-weight: 600;">🏗 Foundation (Modules 01-07)</h3>
|
||
<p style="margin: 0 0 0.75rem 0; color: #1565c0; font-size: 0.95rem; line-height: 1.6;">Build the mathematical core that makes neural networks learn.</p>
|
||
<p style="margin: 0.75rem 0 0 0; color: #0d47a1; font-size: 0.85rem; font-style: italic;">
|
||
Unlocks: Perceptron (1957) • XOR Crisis (1969) • MLP (1986)
|
||
</p>
|
||
</a>
|
||
<a href="tiers/architecture.html" class="tier-card" style="background: linear-gradient(135deg, #f3e5f5 0%, #e1bee7 100%); padding: 1.5rem; border-radius: 0.5rem; border-left: 5px solid #7b1fa2; text-decoration: none; display: block; transition: transform 0.2s ease, box-shadow 0.2s ease;">
|
||
<h3 style="margin: 0 0 0.75rem 0; color: #4a148c; font-size: 1.15rem; font-weight: 600;">🏛️ Architecture (Modules 08-13)</h3>
|
||
<p style="margin: 0 0 0.75rem 0; color: #6a1b9a; font-size: 0.95rem; line-height: 1.6;">Build modern neural architectures—from computer vision to language models.</p>
|
||
<p style="margin: 0.75rem 0 0 0; color: #4a148c; font-size: 0.85rem; font-style: italic;">
|
||
Unlocks: CNN Revolution (1998) • Transformer Era (2017)
|
||
</p>
|
||
</a>
|
||
<a href="tiers/optimization.html" class="tier-card" style="background: linear-gradient(135deg, #fff3e0 0%, #ffe0b2 100%); padding: 1.5rem; border-radius: 0.5rem; border-left: 5px solid #f57c00; text-decoration: none; display: block; transition: transform 0.2s ease, box-shadow 0.2s ease;">
|
||
<h3 style="margin: 0 0 0.75rem 0; color: #e65100; font-size: 1.15rem; font-weight: 600;">⏱️ Optimization (Modules 14-19)</h3>
|
||
<p style="margin: 0 0 0.75rem 0; color: #ef6c00; font-size: 0.95rem; line-height: 1.6;">Transform research prototypes into production-ready systems.</p>
|
||
<p style="margin: 0.75rem 0 0 0; color: #e65100; font-size: 0.85rem; font-style: italic;">
|
||
Unlocks: MLPerf Torch Olympics (2018) • 8-16× compression • 12-40× speedup
|
||
</p>
|
||
</a>
|
||
<a href="tiers/olympics.html" class="tier-card" style="background: linear-gradient(135deg, #fce4ec 0%, #f8bbd0 100%); padding: 1.5rem; border-radius: 0.5rem; border-left: 5px solid #c2185b; text-decoration: none; display: block; transition: transform 0.2s ease, box-shadow 0.2s ease;">
|
||
<h3 style="margin: 0 0 0.75rem 0; color: #880e4f; font-size: 1.15rem; font-weight: 600;">🏅 Torch Olympics (Module 20)</h3>
|
||
<p style="margin: 0 0 0.75rem 0; color: #ad1457; font-size: 0.95rem; line-height: 1.6;">The ultimate test: Build a complete, competition-ready ML system.</p>
|
||
<p style="margin: 0.75rem 0 0 0; color: #880e4f; font-size: 0.85rem; font-style: italic;">
|
||
Capstone: Vision • Language • Speed • Compression tracks
|
||
</p>
|
||
</a>
|
||
</div>
|
||
<p><strong><a class="reference internal" href="chapters/00-introduction.html"><span class="doc std std-doc">Complete course structure</span></a></strong> • <strong><a class="reference internal" href="getting-started.html"><span class="doc std std-doc">Getting started guide</span></a></strong> • <strong><a class="reference internal" href="community.html"><span class="doc std std-doc">Join the community</span></a></strong></p>
|
||
</section>
|
||
<section id="recreate-ml-history">
|
||
<h1>Recreate ML History<a class="headerlink" href="#recreate-ml-history" title="Link to this heading">#</a></h1>
|
||
<p>Walk through ML history by rebuilding its greatest breakthroughs with YOUR TinyTorch implementations. Click each milestone to see what you’ll build and how it shaped modern AI.</p>
|
||
<div class="ml-timeline-container">
|
||
<div class="ml-timeline-line"></div>
|
||
|
||
<div class="ml-timeline-item left perceptron">
|
||
<div class="ml-timeline-dot"></div>
|
||
<div class="ml-timeline-content">
|
||
<div class="ml-timeline-year">1957</div>
|
||
<div class="ml-timeline-title">The Perceptron</div>
|
||
<div class="ml-timeline-desc">The first trainable neural network</div>
|
||
<div class="ml-timeline-tech">Input → Linear → Sigmoid → Output</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="ml-timeline-item right xor">
|
||
<div class="ml-timeline-dot"></div>
|
||
<div class="ml-timeline-content">
|
||
<div class="ml-timeline-year">1969</div>
|
||
<div class="ml-timeline-title">XOR Crisis Solved</div>
|
||
<div class="ml-timeline-desc">Hidden layers unlock non-linear learning</div>
|
||
<div class="ml-timeline-tech">Input → Linear → ReLU → Linear → Output</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="ml-timeline-item left mlp">
|
||
<div class="ml-timeline-dot"></div>
|
||
<div class="ml-timeline-content">
|
||
<div class="ml-timeline-year">1986</div>
|
||
<div class="ml-timeline-title">MLP Revival</div>
|
||
<div class="ml-timeline-desc">Backpropagation enables deep learning (95%+ MNIST)</div>
|
||
<div class="ml-timeline-tech">Images → Flatten → Linear → ... → Classes</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="ml-timeline-item right cnn">
|
||
<div class="ml-timeline-dot"></div>
|
||
<div class="ml-timeline-content">
|
||
<div class="ml-timeline-year">1998</div>
|
||
<div class="ml-timeline-title">CNN Revolution 🎯</div>
|
||
<div class="ml-timeline-desc">Spatial intelligence unlocks computer vision (75%+ CIFAR-10)</div>
|
||
<div class="ml-timeline-tech">Images → Conv → Pool → ... → Classes</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="ml-timeline-item left transformer">
|
||
<div class="ml-timeline-dot"></div>
|
||
<div class="ml-timeline-content">
|
||
<div class="ml-timeline-year">2017</div>
|
||
<div class="ml-timeline-title">Transformer Era</div>
|
||
<div class="ml-timeline-desc">Attention launches the LLM revolution</div>
|
||
<div class="ml-timeline-tech">Tokens → Attention → FFN → Output</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="ml-timeline-item right olympics">
|
||
<div class="ml-timeline-dot"></div>
|
||
<div class="ml-timeline-content">
|
||
<div class="ml-timeline-year">2018</div>
|
||
<div class="ml-timeline-title">MLPerf Benchmarks </div>
|
||
<div class="ml-timeline-desc">Production optimization (8-16× smaller, 12-40× faster)</div>
|
||
<div class="ml-timeline-tech">Profile → Compress → Accelerate</div>
|
||
</div>
|
||
</div>
|
||
</div><p><strong><a class="reference internal" href="chapters/milestones.html"><span class="doc std std-doc">View complete milestone details</span></a></strong> to see full technical requirements and learning objectives.</p>
|
||
</section>
|
||
<section id="why-build-instead-of-use">
|
||
<h1>Why Build Instead of Use?<a class="headerlink" href="#why-build-instead-of-use" title="Link to this heading">#</a></h1>
|
||
<p>Understanding the difference between using a framework and building one is the difference between being limited by tools and being empowered to create them.</p>
|
||
<div class="comparison-grid" style="display: grid; grid-template-columns: 1fr 1fr; gap: 2.5rem; margin: 3rem 0 2.5rem 0; max-width: 1100px;">
|
||
<div style="background: #fef2f2; padding: 2rem; border-radius: 0.5rem; border-left: 4px solid #ef4444;">
|
||
<h3 style="margin: 0 0 1.25rem 0; color: #991b1b; font-size: 1.15rem;">Traditional ML Education</h3>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
|
||
<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">784</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
|
||
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
|
||
<span class="c1"># When this breaks, you're stuck</span>
|
||
</pre></div>
|
||
</div>
|
||
<p style="margin: 1.25rem 0 0 0; line-height: 1.6;"><strong>Problem</strong>: OOM errors, NaN losses, slow training—you can't debug what you don't understand.</p>
|
||
</div>
|
||
<div style="background: #f0fdf4; padding: 2rem; border-radius: 0.5rem; border-left: 4px solid #22c55e;">
|
||
<h3 style="margin: 0 0 1.25rem 0; color: #166534; font-size: 1.15rem;">TinyTorch Approach</h3>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">tinytorch</span><span class="w"> </span><span class="kn">import</span> <span class="n">Linear</span> <span class="c1"># YOUR code</span>
|
||
<span class="n">model</span> <span class="o">=</span> <span class="n">Linear</span><span class="p">(</span><span class="mi">784</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="c1"># YOUR implementation</span>
|
||
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
|
||
<span class="c1"># You know exactly how this works</span>
|
||
</pre></div>
|
||
</div>
|
||
<p style="margin: 1.25rem 0 0 0; line-height: 1.6;"><strong>Advantage</strong>: You understand memory layouts, gradient flows, and performance bottlenecks because you implemented them.</p>
|
||
</div>
|
||
</div>
|
||
<p><strong>Systems Thinking</strong>: TinyTorch emphasizes understanding how components interact—memory hierarchies, computational complexity, and optimization trade-offs—not just isolated algorithms. Every module connects mathematical theory to systems understanding.</p>
|
||
<p><strong>See <a class="reference internal" href="chapters/00-introduction.html"><span class="doc std std-doc">Course Philosophy</span></a></strong> for the full origin story and pedagogical approach.</p>
|
||
</section>
|
||
<section id="the-build-use-reflect-approach">
|
||
<h1>The Build → Use → Reflect Approach<a class="headerlink" href="#the-build-use-reflect-approach" title="Link to this heading">#</a></h1>
|
||
<p>Every module follows a proven learning cycle that builds deep understanding:</p>
|
||
<pre class="mermaid">
|
||
graph LR
|
||
B[Build<br/>Implement from scratch] --> U[Use<br/>Real data, real problems]
|
||
U --> R[Reflect<br/>Systems thinking questions]
|
||
R --> B
|
||
|
||
style B fill:#FFC107,color:#000
|
||
style U fill:#4CAF50,color:#fff
|
||
style R fill:#2196F3,color:#fff
|
||
</pre><ol class="arabic simple">
|
||
<li><p><strong>Build</strong>: Implement each component yourself—tensors, autograd, optimizers, attention</p></li>
|
||
<li><p><strong>Use</strong>: Apply your implementations to real problems—MNIST, CIFAR-10, text generation</p></li>
|
||
<li><p><strong>Reflect</strong>: Answer systems thinking questions—memory usage, scaling behavior, trade-offs</p></li>
|
||
</ol>
|
||
<p>This approach develops not just coding ability, but systems engineering intuition essential for production ML.</p>
|
||
</section>
|
||
<section id="is-this-for-you">
|
||
<h1>Is This For You?<a class="headerlink" href="#is-this-for-you" title="Link to this heading">#</a></h1>
|
||
<p>Perfect if you want to <strong>debug ML systems</strong>, <strong>implement custom operations</strong>, or <strong>understand how PyTorch actually works</strong>.</p>
|
||
<p><strong>Prerequisites</strong>: Python + basic linear algebra. No prior ML experience required.</p>
|
||
<hr class="docutils" />
|
||
</section>
|
||
<section id="join-the-community">
|
||
<h1>🌍 Join the Community<a class="headerlink" href="#join-the-community" title="Link to this heading">#</a></h1>
|
||
<div style="background: linear-gradient(135deg, #1e293b 0%, #0f172a 100%); padding: 2rem; border-radius: 1rem; margin: 2rem 0; text-align: center;">
|
||
<p style="color: #f1f5f9; font-size: 1.25rem; margin: 0 0 0.5rem 0; font-weight: 600;">
|
||
See learners building ML systems worldwide
|
||
</p>
|
||
<p style="color: #94a3b8; margin: 0 0 1.5rem 0;">
|
||
Add yourself to the map • Share your progress • Connect with builders
|
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✉️ Subscribe
|
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|
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<hr class="docutils" />
|
||
<p><strong>Next Steps</strong>: <strong><a class="reference internal" href="#quickstart-guide"><span class="xref myst">Quick Start Guide</span></a></strong> (15 min) • <strong><a class="reference internal" href="chapters/00-introduction.html"><span class="doc std std-doc">Course Structure</span></a></strong> • <strong><a class="reference internal" href="faq.html"><span class="std std-doc">FAQ</span></a></strong></p>
|
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<div style="text-align: center; padding: 1.5rem 0; margin-top: 2rem; border-top: 1px solid #e2e8f0; color: #64748b; font-size: 0.9rem;">
|
||
<span style="color: #f97316;">🔥</span> <strong>TinyTorch</strong>
|
||
<span style="margin: 0 0.75rem;">•</span>
|
||
<a href="https://mlsysbook.ai" style="color: #64748b; text-decoration: none;">MLSysBook</a>
|
||
<span style="margin: 0 0.75rem;">•</span>
|
||
<a href="https://github.com/mlsysbook/TinyTorch" style="color: #64748b; text-decoration: none;">GitHub</a>
|
||
<span style="margin: 0 0.75rem;">•</span>
|
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<a href="https://tinytorch.ai/leaderboard" style="color: #64748b; text-decoration: none;">Leaderboard</a>
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