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Major changes: - Moved TinyGPT from Module 16 to examples/tinygpt (capstone demo) - Fixed Module 10 (optimizers) and Module 11 (training) bugs - All 16 modules now passing tests (100% health) - Added comprehensive testing with 'tito test --comprehensive' - Renamed example files for clarity (train_xor_network.py, etc.) - Created working TinyGPT example structure - Updated documentation to reflect 15 core modules + examples - Added KISS principle and testing framework documentation
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TinyTorch: Build Your Own ML Framework from First Principles
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</button>
|
||
`);
|
||
</script>
|
||
<button class="sidebar-toggle secondary-toggle btn btn-sm" title="Toggle secondary sidebar" data-bs-placement="bottom" data-bs-toggle="tooltip">
|
||
<span class="fa-solid fa-list"></span>
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</button>
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</div></div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
<div id="jb-print-docs-body" class="onlyprint">
|
||
<h1>The TinyTorch Vision</h1>
|
||
<!-- Table of contents -->
|
||
<div id="print-main-content">
|
||
<div id="jb-print-toc">
|
||
|
||
<div>
|
||
<h2> Contents </h2>
|
||
</div>
|
||
<nav aria-label="Page">
|
||
<ul class="visible nav section-nav flex-column">
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-problem-were-solving">The Problem We’re Solving</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#traditional-ml-education">Traditional ML Education:</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#the-tinytorch-difference">The TinyTorch Difference:</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#what-we-teach-systems-thinking">What We Teach: Systems Thinking</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#beyond-algorithms-system-level-understanding">Beyond Algorithms: System-Level Understanding</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#target-audience-future-ml-systems-engineers">Target Audience: Future ML Systems Engineers</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#perfect-for">Perfect For:</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#career-transformation">Career Transformation:</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pedagogical-philosophy-build-use-understand">Pedagogical Philosophy: Build → Use → Understand</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#build-first">1. Build First</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#use-immediately">2. Use Immediately</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#understand-systems">3. Understand Systems</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#reflect-on-trade-offs">4. Reflect on Trade-offs</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#unique-value-proposition">Unique Value Proposition</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#what-makes-tinytorch-different">What Makes TinyTorch Different:</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#learning-outcomes-becoming-an-ml-systems-engineer">Learning Outcomes: Becoming an ML Systems Engineer</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#technical-mastery">Technical Mastery</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#systems-understanding">Systems Understanding</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#professional-skills">Professional Skills</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#career-impact">Career Impact</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#success-stories-what-students-say">Success Stories: What Students Say</a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#ready-to-become-an-ml-systems-engineer">Ready to Become an ML Systems Engineer?</a></li>
|
||
</ul>
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
<div id="searchbox"></div>
|
||
<article class="bd-article">
|
||
|
||
<section id="the-tinytorch-vision">
|
||
<h1>The TinyTorch Vision<a class="headerlink" href="#the-tinytorch-vision" title="Link to this heading">#</a></h1>
|
||
<p><strong>Training ML Systems Engineers: From Computer Vision to Language Models</strong></p>
|
||
<hr class="docutils" />
|
||
<section id="the-problem-were-solving">
|
||
<h2>The Problem We’re Solving<a class="headerlink" href="#the-problem-were-solving" title="Link to this heading">#</a></h2>
|
||
<p>The ML field has a critical gap: <strong>most education teaches you to use frameworks, not build them.</strong></p>
|
||
<section id="traditional-ml-education">
|
||
<h3>Traditional ML Education:<a class="headerlink" href="#traditional-ml-education" title="Link to this heading">#</a></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="kn">import</span><span class="w"> </span><span class="nn">torch.nn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">nn</span>
|
||
<span class="n">model</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">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>Questions students can’t answer:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Why does Adam use 3× more memory than SGD?</p></li>
|
||
<li><p>How does <code class="docutils literal notranslate"><span class="pre">loss.backward()</span></code> actually compute gradients?</p></li>
|
||
<li><p>When should you use gradient accumulation vs larger batch sizes?</p></li>
|
||
<li><p>Why do attention mechanisms limit context length?</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="the-tinytorch-difference">
|
||
<h3>The TinyTorch Difference:<a class="headerlink" href="#the-tinytorch-difference" title="Link to this heading">#</a></h3>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">Linear</span><span class="p">:</span>
|
||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_features</span><span class="p">,</span> <span class="n">out_features</span><span class="p">):</span>
|
||
<span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">in_features</span><span class="p">,</span> <span class="n">out_features</span><span class="p">))</span>
|
||
<span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">out_features</span><span class="p">))</span>
|
||
|
||
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||
<span class="k">return</span> <span class="n">x</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="c1"># YOU implemented @</span>
|
||
|
||
<span class="k">def</span><span class="w"> </span><span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">):</span>
|
||
<span class="c1"># YOU understand exactly how gradients flow</span>
|
||
<span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">grad</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">grad_output</span>
|
||
<span class="k">return</span> <span class="n">grad_output</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">T</span>
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>Questions students CAN answer:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Exactly how automatic differentiation works</p></li>
|
||
<li><p>Why certain optimizers use more memory</p></li>
|
||
<li><p>How to debug training instability</p></li>
|
||
<li><p>When to make performance vs accuracy trade-offs</p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="what-we-teach-systems-thinking">
|
||
<h2>What We Teach: Systems Thinking<a class="headerlink" href="#what-we-teach-systems-thinking" title="Link to this heading">#</a></h2>
|
||
<section id="beyond-algorithms-system-level-understanding">
|
||
<h3>Beyond Algorithms: System-Level Understanding<a class="headerlink" href="#beyond-algorithms-system-level-understanding" title="Link to this heading">#</a></h3>
|
||
<p><strong>Memory Management:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Why Adam needs 3× parameter memory (parameters + momentum + variance)</p></li>
|
||
<li><p>How attention matrices scale O(N²) with sequence length</p></li>
|
||
<li><p>When gradient accumulation saves memory vs compute trade-offs</p></li>
|
||
</ul>
|
||
<p><strong>Performance Analysis:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Why naive convolution is 100× slower than optimized versions</p></li>
|
||
<li><p>How cache misses destroy performance in matrix operations</p></li>
|
||
<li><p>When vectorization provides 10-100× speedups</p></li>
|
||
</ul>
|
||
<p><strong>Production Trade-offs:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>SGD vs Adam: convergence speed vs memory constraints</p></li>
|
||
<li><p>Gradient checkpointing: trading compute for memory</p></li>
|
||
<li><p>Mixed precision: 2× memory savings with accuracy considerations</p></li>
|
||
</ul>
|
||
<p><strong>Hardware Awareness:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>How memory bandwidth limits ML performance</p></li>
|
||
<li><p>Why GPU utilization matters more than peak FLOPS</p></li>
|
||
<li><p>When distributed training becomes necessary</p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="target-audience-future-ml-systems-engineers">
|
||
<h2>Target Audience: Future ML Systems Engineers<a class="headerlink" href="#target-audience-future-ml-systems-engineers" title="Link to this heading">#</a></h2>
|
||
<section id="perfect-for">
|
||
<h3>Perfect For:<a class="headerlink" href="#perfect-for" title="Link to this heading">#</a></h3>
|
||
<p><strong>Computer Science Students</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Going beyond “use PyTorch” to “understand PyTorch”</p></li>
|
||
<li><p>Building portfolio projects that demonstrate deep system knowledge</p></li>
|
||
<li><p>Preparing for ML engineering roles (not just data science)</p></li>
|
||
</ul>
|
||
<p><strong>Software Engineers → ML Engineers</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Leveraging existing programming skills for ML systems</p></li>
|
||
<li><p>Understanding performance, debugging, and optimization</p></li>
|
||
<li><p>Learning production ML patterns and infrastructure</p></li>
|
||
</ul>
|
||
<p><strong>ML Practitioners</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Moving from model users to model builders</p></li>
|
||
<li><p>Debugging training issues at the systems level</p></li>
|
||
<li><p>Optimizing models for production deployment</p></li>
|
||
</ul>
|
||
<p><strong>Researchers & Advanced Users</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Implementing custom operations and architectures</p></li>
|
||
<li><p>Understanding framework limitations and workarounds</p></li>
|
||
<li><p>Building specialized ML systems for unique domains</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="career-transformation">
|
||
<h3>Career Transformation:<a class="headerlink" href="#career-transformation" title="Link to this heading">#</a></h3>
|
||
<p><strong>Before TinyTorch:</strong> “I can train models with PyTorch”
|
||
<strong>After TinyTorch:</strong> “I can build and optimize ML systems”</p>
|
||
<p>You become the person your team asks:</p>
|
||
<ul class="simple">
|
||
<li><p><em>“Why is our training bottlenecked?”</em></p></li>
|
||
<li><p><em>“Can we fit this model in memory?”</em></p></li>
|
||
<li><p><em>“How do we implement this research paper?”</em></p></li>
|
||
<li><p><em>“What’s the best architecture for our constraints?”</em></p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="pedagogical-philosophy-build-use-understand">
|
||
<h2>Pedagogical Philosophy: Build → Use → Understand<a class="headerlink" href="#pedagogical-philosophy-build-use-understand" title="Link to this heading">#</a></h2>
|
||
<section id="build-first">
|
||
<h3>1. Build First<a class="headerlink" href="#build-first" title="Link to this heading">#</a></h3>
|
||
<p>Every component implemented from scratch:</p>
|
||
<ul class="simple">
|
||
<li><p>Tensors with broadcasting and memory management</p></li>
|
||
<li><p>Automatic differentiation with computational graphs</p></li>
|
||
<li><p>Optimizers with state management and memory profiling</p></li>
|
||
<li><p>Complete training loops with checkpointing and monitoring</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="use-immediately">
|
||
<h3>2. Use Immediately<a class="headerlink" href="#use-immediately" title="Link to this heading">#</a></h3>
|
||
<p>No toy examples - real applications:</p>
|
||
<ul class="simple">
|
||
<li><p>Train CNNs on CIFAR-10 (90%+ accuracy achievable)</p></li>
|
||
<li><p>Implement transformer attention mechanisms</p></li>
|
||
<li><p>Deploy production systems with MLOps monitoring</p></li>
|
||
<li><p>Profile and optimize for performance bottlenecks</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="understand-systems">
|
||
<h3>3. Understand Systems<a class="headerlink" href="#understand-systems" title="Link to this heading">#</a></h3>
|
||
<p>Connect implementations to production reality:</p>
|
||
<ul class="simple">
|
||
<li><p>How your tensor maps to PyTorch’s memory model</p></li>
|
||
<li><p>Why your optimizer choices affect GPU utilization</p></li>
|
||
<li><p>How your autograd compares to production frameworks</p></li>
|
||
<li><p>When your implementations would need modification at scale</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="reflect-on-trade-offs">
|
||
<h3>4. Reflect on Trade-offs<a class="headerlink" href="#reflect-on-trade-offs" title="Link to this heading">#</a></h3>
|
||
<p>ML Systems Thinking sections in every module:</p>
|
||
<ul class="simple">
|
||
<li><p>Memory vs compute trade-offs in different architectures</p></li>
|
||
<li><p>Accuracy vs efficiency considerations for deployment</p></li>
|
||
<li><p>Debugging strategies for common production issues</p></li>
|
||
<li><p>Framework design principles and their implications</p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="unique-value-proposition">
|
||
<h2>Unique Value Proposition<a class="headerlink" href="#unique-value-proposition" title="Link to this heading">#</a></h2>
|
||
<section id="what-makes-tinytorch-different">
|
||
<h3>What Makes TinyTorch Different:<a class="headerlink" href="#what-makes-tinytorch-different" title="Link to this heading">#</a></h3>
|
||
<p><strong>Systems-First Approach</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Not just “how does attention work” but “why does attention scale O(N²) and how do production systems handle this?”</p></li>
|
||
<li><p>Not just “implement SGD” but “when do you choose SGD vs Adam in production?”</p></li>
|
||
</ul>
|
||
<p><strong>Production Relevance</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Memory profiling, performance optimization, deployment patterns</p></li>
|
||
<li><p>Real datasets, realistic scale, professional development workflow</p></li>
|
||
<li><p>Connection to industry practices and framework design decisions</p></li>
|
||
</ul>
|
||
<p><strong>Framework Generalization</strong></p>
|
||
<ul class="simple">
|
||
<li><p>16 modules that build ONE cohesive ML framework supporting vision AND language</p></li>
|
||
<li><p>95% component reuse from computer vision to language models</p></li>
|
||
<li><p>Professional package structure with CLI tools and testing</p></li>
|
||
</ul>
|
||
<p><strong>Proven Pedagogy</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Build → Use → Understand cycle creates deep intuition</p></li>
|
||
<li><p>Immediate testing and feedback for every component</p></li>
|
||
<li><p>Progressive complexity with solid foundations</p></li>
|
||
<li><p>NBGrader integration for classroom deployment</p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="learning-outcomes-becoming-an-ml-systems-engineer">
|
||
<h2>Learning Outcomes: Becoming an ML Systems Engineer<a class="headerlink" href="#learning-outcomes-becoming-an-ml-systems-engineer" title="Link to this heading">#</a></h2>
|
||
<section id="technical-mastery">
|
||
<h3>Technical Mastery<a class="headerlink" href="#technical-mastery" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong>Implement any ML paper</strong> from first principles</p></li>
|
||
<li><p><strong>Debug training issues</strong> at the systems level</p></li>
|
||
<li><p><strong>Optimize models</strong> for production deployment</p></li>
|
||
<li><p><strong>Profile and improve</strong> ML system performance</p></li>
|
||
<li><p><strong>Design custom architectures</strong> for specialized domains</p></li>
|
||
<li><p><strong>Understand framework generalization</strong> across vision and language</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="systems-understanding">
|
||
<h3>Systems Understanding<a class="headerlink" href="#systems-understanding" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong>Memory management</strong> in ML frameworks</p></li>
|
||
<li><p><strong>Computational complexity</strong> vs real-world performance</p></li>
|
||
<li><p><strong>Hardware utilization</strong> patterns and optimization</p></li>
|
||
<li><p><strong>Distributed training</strong> challenges and solutions</p></li>
|
||
<li><p><strong>Production deployment</strong> considerations and trade-offs</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="professional-skills">
|
||
<h3>Professional Skills<a class="headerlink" href="#professional-skills" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong>Test-driven development</strong> for ML systems</p></li>
|
||
<li><p><strong>Performance profiling</strong> and optimization techniques</p></li>
|
||
<li><p><strong>Code organization</strong> and package development</p></li>
|
||
<li><p><strong>Documentation</strong> and API design</p></li>
|
||
<li><p><strong>MLOps</strong> and production monitoring</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="career-impact">
|
||
<h3>Career Impact<a class="headerlink" href="#career-impact" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong>Technical interviews</strong>: Demonstrate deep ML systems knowledge</p></li>
|
||
<li><p><strong>Job opportunities</strong>: Qualify for ML engineer (not just data scientist) roles</p></li>
|
||
<li><p><strong>Team leadership</strong>: Become the go-to person for ML systems questions</p></li>
|
||
<li><p><strong>Research ability</strong>: Implement cutting-edge papers independently</p></li>
|
||
<li><p><strong>Entrepreneurship</strong>: Build ML products with full-stack understanding</p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="success-stories-what-students-say">
|
||
<h2>Success Stories: What Students Say<a class="headerlink" href="#success-stories-what-students-say" title="Link to this heading">#</a></h2>
|
||
<p><em>“Finally understood what happens when I call <code class="docutils literal notranslate"><span class="pre">loss.backward()</span></code> - now I can debug gradient issues instead of just hoping they go away.”</em></p>
|
||
<p><em>“Built my own attention mechanism from scratch, then extended my vision framework to language models with 95% component reuse. When GPT-4 came out, I actually understood both the technical details AND the framework unification.”</em></p>
|
||
<p><em>“Got hired as an ML engineer specifically because I could explain how optimizers work at the memory level during the technical interview.”</em></p>
|
||
<p><em>“Used TinyTorch concepts to optimize our production training pipeline for both vision and language models - saved 40% on cloud costs by understanding memory bottlenecks across modalities.”</em></p>
|
||
<p><em>“Implemented a custom loss function for our research project in 30 minutes instead of spending days figuring out PyTorch internals.”</em></p>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="ready-to-become-an-ml-systems-engineer">
|
||
<h2>Ready to Become an ML Systems Engineer?<a class="headerlink" href="#ready-to-become-an-ml-systems-engineer" title="Link to this heading">#</a></h2>
|
||
<p><strong>TinyTorch transforms ML users into ML builders.</strong></p>
|
||
<p>Stop wondering how frameworks work. Start building them.</p>
|
||
<p><strong><a class="reference internal" href="#chapters/00-introduction.md"><span class="xref myst">Begin Your Journey →</span></a></strong></p>
|
||
<hr class="docutils" />
|
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