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- Updated book generation to include 15_capstone with 5-star difficulty rating - Changed time estimate from '20-40 hours' to 'Capstone Project' for better visitor experience - Removed specific week references from project phases for more encouraging presentation - Maintained detailed project structure while making timeline more flexible - Ensures consistent 5-star rating for expert-level modules across the framework
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Tiny🔥Torch: Build your own Machine Learning framework from scratch.
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<h1>Tiny🔥Torch: Build your own Machine Learning framework from scratch.</h1>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-core-difference">💡 <strong>The Core Difference</strong></a></li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#learning-philosophy-build-use-master">🎓 <strong>Learning Philosophy: Build, Use, Master</strong></a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#example-activation-functions"><strong>Example: Activation Functions</strong></a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#course-journey-15-modules">📚 <strong>Course Journey: 15 Modules</strong></a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#complete-system-integration">🔗 <strong>Complete System Integration</strong></a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#capstone-optimize-your-framework"><strong>🚀 Capstone: Optimize Your Framework</strong></a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#choose-your-learning-path">🚀 <strong>Choose Your Learning Path</strong></a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#ready-to-start">🚀 <strong>Ready to Start?</strong></a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#quick-taste-try-module-1-right-now"><strong>Quick Taste: Try Module 1 Right Now</strong></a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#acknowledgments">🙏 <strong>Acknowledgments</strong></a></li>
|
||
</ul>
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
<div id="searchbox"></div>
|
||
<article class="bd-article" role="main">
|
||
|
||
<section class="tex2jax_ignore mathjax_ignore" id="tinytorch-build-your-own-machine-learning-framework-from-scratch">
|
||
<h1>Tiny🔥Torch: Build your own Machine Learning framework from scratch.<a class="headerlink" href="#tinytorch-build-your-own-machine-learning-framework-from-scratch" title="Permalink to this heading">#</a></h1>
|
||
<p><strong>Most ML education teaches you to <em>use</em> frameworks. TinyTorch teaches you to <em>build</em> them.</strong></p>
|
||
<p>TinyTorch is a minimalist educational framework designed for learning by doing. Instead of relying on PyTorch or TensorFlow, you implement everything from scratch—tensors, autograd, optimizers, even MLOps tooling. This hands-on approach builds the deep systems intuition that sets ML engineers apart from ML users.</p>
|
||
<div class="tip admonition">
|
||
<p class="admonition-title">🎯 What You’ll Build</p>
|
||
<p><strong>A complete ML framework from scratch</strong>: your own PyTorch style toolkit that can:</p>
|
||
<ul class="simple">
|
||
<li><p>✅ Train neural networks on CIFAR-10 (real dataset!)</p></li>
|
||
<li><p>✅ Implement automatic differentiation (the “magic” behind PyTorch)</p></li>
|
||
<li><p>✅ Deploy production systems with 75% model compression</p></li>
|
||
<li><p>✅ Handle complete ML pipeline from data to monitoring</p></li>
|
||
</ul>
|
||
<p><strong>Result:</strong> You become the expert others ask about “how PyTorch actually works.”</p>
|
||
</div>
|
||
<p><em>Everyone wants to be an astronaut.</em> 🧑🚀 <em>TinyTorch teaches you how to build the rocket ship.</em> 🚀</p>
|
||
<hr class="docutils" />
|
||
<section id="the-core-difference">
|
||
<h2>💡 <strong>The Core Difference</strong><a class="headerlink" href="#the-core-difference" title="Permalink to this heading">#</a></h2>
|
||
<p>Most ML courses focus on algorithms and theory. You learn <em>what</em> neural networks do and <em>why</em> they work, but you import everything:</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>Traditional ML Course: TinyTorch Approach:
|
||
├── import torch ├── class Tensor:
|
||
├── model = nn.Linear(10, 1) │ def __add__(self, other): ...
|
||
├── loss = nn.MSELoss() │ def backward(self): ...
|
||
└── optimizer.step() ├── class Linear:
|
||
│ def forward(self, x):
|
||
│ return x @ self.weight + self.bias
|
||
├── def mse_loss(pred, target):
|
||
│ return ((pred - target) ** 2).mean()
|
||
├── class SGD:
|
||
│ def step(self):
|
||
└── param.data -= lr * param.grad
|
||
|
||
Go from "How does this work?" 🤷 to "I implemented every line!" 💪
|
||
</pre></div>
|
||
</div>
|
||
<p>TinyTorch focuses on implementation and systems thinking. You learn <em>how</em> to build working systems with progressive scaffolding, production ready practices, and comprehensive course infrastructure that bridges the gap between learning and building.</p>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="learning-philosophy-build-use-master">
|
||
<h2>🎓 <strong>Learning Philosophy: Build, Use, Master</strong><a class="headerlink" href="#learning-philosophy-build-use-master" title="Permalink to this heading">#</a></h2>
|
||
<p>Every component follows the same powerful learning cycle:</p>
|
||
<section id="example-activation-functions">
|
||
<h3><strong>Example: Activation Functions</strong><a class="headerlink" href="#example-activation-functions" title="Permalink to this heading">#</a></h3>
|
||
<p><strong>🔧 Build:</strong> Implement ReLU from scratch</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">relu</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
|
||
<span class="c1"># YOU implement this function</span>
|
||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span> <span class="c1"># Your solution</span>
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>🚀 Use:</strong> Immediately use your own code</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">tinytorch.core.activations</span> <span class="kn">import</span> <span class="n">ReLU</span> <span class="c1"># YOUR implementation!</span>
|
||
<span class="n">layer</span> <span class="o">=</span> <span class="n">ReLU</span><span class="p">()</span>
|
||
<span class="n">output</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">input_tensor</span><span class="p">)</span> <span class="c1"># Your code working!</span>
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>💡 Master:</strong> See it working in real networks</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Your ReLU is now part of a real neural network</span>
|
||
<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">([</span>
|
||
<span class="n">Dense</span><span class="p">(</span><span class="mi">784</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span>
|
||
<span class="n">ReLU</span><span class="p">(),</span> <span class="c1"># <-- Your implementation</span>
|
||
<span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
|
||
<span class="p">])</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>This pattern repeats for every component: tensors, layers, optimizers, even MLOps systems. You build it, use it immediately, then see how it fits into larger systems.</p>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="course-journey-15-modules">
|
||
<h2>📚 <strong>Course Journey: 15 Modules</strong><a class="headerlink" href="#course-journey-15-modules" title="Permalink to this heading">#</a></h2>
|
||
<div class="note admonition">
|
||
<p class="admonition-title">🏗️ Foundation</p>
|
||
<p><strong>1. Setup</strong> • <strong>2. Tensors</strong> • <strong>3. Activations</strong></p>
|
||
<p>Understanding workflow, multi-dimensional arrays, and the mathematical functions that enable learning.</p>
|
||
</div>
|
||
<div class="note admonition">
|
||
<p class="admonition-title">🧱 Building Blocks</p>
|
||
<p><strong>4. Layers</strong> • <strong>5. Networks</strong> • <strong>6. CNNs</strong></p>
|
||
<p>Dense layers, sequential architecture, and convolutional operations for computer vision.</p>
|
||
</div>
|
||
<div class="note admonition">
|
||
<p class="admonition-title">🎯 Training Systems</p>
|
||
<p><strong>7. DataLoader</strong> • <strong>8. Autograd</strong> • <strong>9. Optimizers</strong> • <strong>10. Training</strong></p>
|
||
<p>CIFAR-10 loading, automatic differentiation, SGD/Adam optimizers, and complete training orchestration.</p>
|
||
</div>
|
||
<div class="note admonition">
|
||
<p class="admonition-title">⚡ Production & Performance</p>
|
||
<p><strong>11. Compression</strong> • <strong>12. Kernels</strong> • <strong>13. Benchmarking</strong> • <strong>14. MLOps</strong></p>
|
||
<p>Model optimization, high-performance operations, systematic evaluation, and production monitoring.</p>
|
||
</div>
|
||
<div class="note admonition">
|
||
<p class="admonition-title">🎓 Capstone Project</p>
|
||
<p><strong>15. Framework Optimization</strong></p>
|
||
<p>Choose your focus: performance engineering, algorithm extensions, systems optimization, framework analysis, or developer tools.</p>
|
||
</div>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="complete-system-integration">
|
||
<h2>🔗 <strong>Complete System Integration</strong><a class="headerlink" href="#complete-system-integration" title="Permalink to this heading">#</a></h2>
|
||
<p><strong>This isn’t 14 separate exercises.</strong> Every component you build integrates into one fully functional ML framework:</p>
|
||
<div class="important admonition">
|
||
<p class="admonition-title">🎯 How It All Connects</p>
|
||
<p><strong>Module 2: Your Tensor class</strong> → <strong>Module 3: Powers your activation functions</strong> → <strong>Module 4: Enables your layers</strong> → <strong>Module 5: Forms your networks</strong> → <strong>Module 8: Drives your autograd system</strong> → <strong>Module 9: Optimizes with your SGD/Adam</strong> → <strong>Module 10: Trains on real CIFAR-10 data</strong></p>
|
||
<p><strong>Result:</strong> A complete, working ML framework that you built from scratch, capable of training real neural networks on real datasets.</p>
|
||
</div>
|
||
<section id="capstone-optimize-your-framework">
|
||
<h3><strong>🚀 Capstone: Optimize Your Framework</strong><a class="headerlink" href="#capstone-optimize-your-framework" title="Permalink to this heading">#</a></h3>
|
||
<p>After completing the 14 core modules, you have a <strong>complete ML framework</strong>. Now make it better through systems engineering:</p>
|
||
<p><strong>Choose Your Focus:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>⚡ <strong>Performance Optimization</strong>: GPU kernels, vectorization, memory-efficient operations</p></li>
|
||
<li><p>🧠 <strong>Algorithm Extensions</strong>: Transformer layers, BatchNorm, Dropout, advanced optimizers</p></li>
|
||
<li><p>🔧 <strong>Systems Engineering</strong>: Multi-GPU training, distributed computing, memory profiling</p></li>
|
||
<li><p>📊 <strong>Benchmarking Deep Dive</strong>: Compare your framework to PyTorch, identify bottlenecks</p></li>
|
||
<li><p>🛠️ <strong>Developer Experience</strong>: Better debugging tools, visualization, error messages</p></li>
|
||
</ul>
|
||
<p><strong>The Challenge:</strong> Use <strong>only your TinyTorch implementation</strong> as the base. No copying from PyTorch. This proves you understand the engineering trade-offs and can optimize real ML systems.</p>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="choose-your-learning-path">
|
||
<h2>🚀 <strong>Choose Your Learning Path</strong><a class="headerlink" href="#choose-your-learning-path" title="Permalink to this heading">#</a></h2>
|
||
<div class="important admonition">
|
||
<p class="admonition-title">Three Ways to Engage with TinyTorch</p>
|
||
<p class="rubric"><strong>🔬 <a class="reference internal" href="usage-paths/quick-exploration.html"><span class="doc std std-doc">Quick Exploration</span></a></strong> <em>(5 minutes)</em></p>
|
||
<p><em>“I want to see what this is about”</em></p>
|
||
<ul class="simple">
|
||
<li><p>Click and run code immediately in your browser (Binder)</p></li>
|
||
<li><p>No installation or setup required</p></li>
|
||
<li><p>Implement ReLU, tensors, neural networks interactively</p></li>
|
||
<li><p>Perfect for getting a feel for the course</p></li>
|
||
</ul>
|
||
<p class="rubric"><strong>🏗️ <a class="reference internal" href="usage-paths/serious-development.html"><span class="doc std std-doc">Serious Development</span></a></strong> <em>(8+ weeks)</em></p>
|
||
<p><em>“I want to build this myself”</em></p>
|
||
<ul class="simple">
|
||
<li><p>Fork the repo and work locally with full development environment</p></li>
|
||
<li><p>Build complete ML framework from scratch with <code class="docutils literal notranslate"><span class="pre">tito</span></code> CLI</p></li>
|
||
<li><p>14 progressive assignments from setup to production MLOps</p></li>
|
||
<li><p>Professional development workflow with automated testing</p></li>
|
||
</ul>
|
||
<p class="rubric"><strong>👨🏫 <a class="reference internal" href="usage-paths/classroom-use.html"><span class="doc std std-doc">Classroom Use</span></a></strong> <em>(Instructors)</em></p>
|
||
<p><em>“I want to teach this course”</em></p>
|
||
<ul class="simple">
|
||
<li><p>Complete course infrastructure with NBGrader integration</p></li>
|
||
<li><p>Automated grading for comprehensive testing</p></li>
|
||
<li><p>Flexible pacing (8-16 weeks) with proven pedagogical structure</p></li>
|
||
<li><p>Turn-key solution for ML systems education</p></li>
|
||
</ul>
|
||
</div>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="ready-to-start">
|
||
<h2>🚀 <strong>Ready to Start?</strong><a class="headerlink" href="#ready-to-start" title="Permalink to this heading">#</a></h2>
|
||
<section id="quick-taste-try-module-1-right-now">
|
||
<h3><strong>Quick Taste: Try Module 1 Right Now</strong><a class="headerlink" href="#quick-taste-try-module-1-right-now" title="Permalink to this heading">#</a></h3>
|
||
<p>Want to see what TinyTorch feels like? <strong><a class="reference internal" href="chapters/01-setup.html"><span class="doc std std-doc">Launch the Setup chapter</span></a></strong> in Binder and implement your first TinyTorch function in 2 minutes!</p>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="acknowledgments">
|
||
<h2>🙏 <strong>Acknowledgments</strong><a class="headerlink" href="#acknowledgments" title="Permalink to this heading">#</a></h2>
|
||
<p>TinyTorch originated from CS249r: Tiny Machine Learning Systems at Harvard University. We’re inspired by projects like <a class="reference external" href="https://github.com/geohot/tinygrad">tinygrad</a> and <a class="reference external" href="https://github.com/karpathy/micrograd">micrograd</a> that demonstrate the power of minimal implementations.</p>
|
||
<p><strong>Complementary Learning</strong>: For comprehensive ML systems knowledge, we recommend <a class="reference external" href="https://mlsysbook.ai"><strong>Machine Learning Systems</strong></a> by <a class="reference external" href="https://profvjreddi.github.io/website/">Prof. Vijay Janapa Reddi</a>. While TinyTorch teaches you to <strong>build</strong> ML systems from scratch, that book provides the broader <strong>systems context</strong> and engineering principles for production AI.</p>
|
||
</section>
|
||
<div class="toctree-wrapper compound">
|
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</div>
|
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<div class="toctree-wrapper compound">
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<i class="fa-solid fa-list"></i> Contents
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<ul class="visible nav section-nav flex-column">
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-core-difference">💡 <strong>The Core Difference</strong></a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#learning-philosophy-build-use-master">🎓 <strong>Learning Philosophy: Build, Use, Master</strong></a><ul class="nav section-nav flex-column">
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#example-activation-functions"><strong>Example: Activation Functions</strong></a></li>
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</ul>
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</li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#course-journey-15-modules">📚 <strong>Course Journey: 15 Modules</strong></a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#complete-system-integration">🔗 <strong>Complete System Integration</strong></a><ul class="nav section-nav flex-column">
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#capstone-optimize-your-framework"><strong>🚀 Capstone: Optimize Your Framework</strong></a></li>
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</ul>
|
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</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#choose-your-learning-path">🚀 <strong>Choose Your Learning Path</strong></a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#quick-taste-try-module-1-right-now"><strong>Quick Taste: Try Module 1 Right Now</strong></a></li>
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</ul>
|
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</li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#acknowledgments">🙏 <strong>Acknowledgments</strong></a></li>
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By Prof. Vijay Janapa Reddi (Harvard University)
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||
|
||
<!-- Scripts loaded after <body> so the DOM is not blocked -->
|
||
<script src="_static/scripts/bootstrap.js?digest=5b4479735964841361fd"></script>
|
||
<script src="_static/scripts/pydata-sphinx-theme.js?digest=5b4479735964841361fd"></script>
|
||
|
||
<footer class="bd-footer">
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||
</footer>
|
||
</body>
|
||
</html> |