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<h1>Setup - TinyTorch System Configuration</h1>
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<h2> Contents </h2>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-big-picture-why-configuration-matters-in-ml-systems">The Big Picture: Why Configuration Matters in ML Systems</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#system-awareness">1. <strong>System Awareness</strong></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#reproducibility">2. <strong>Reproducibility</strong></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#professional-development">3. <strong>Professional Development</strong></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#ml-systems-context">4. <strong>ML Systems Context</strong></a></li>
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<section class="tex2jax_ignore mathjax_ignore" id="setup-tinytorch-system-configuration">
<h1>Setup - TinyTorch System Configuration<a class="headerlink" href="#setup-tinytorch-system-configuration" title="Link to this heading">#</a></h1>
<p>Welcome to TinyTorch! This setup module configures your personal TinyTorch installation and teaches you the NBGrader workflow.</p>
<div class="tip admonition">
<p class="admonition-title">🎯 Learning Goals</p>
<ul class="simple">
<li><p>Configure your personal TinyTorch installation with custom information</p></li>
<li><p>Learn to query system information using Python modules</p></li>
<li><p>Master the NBGrader workflow: implement → test → export</p></li>
<li><p>Create functions that become part of your tinytorch package</p></li>
<li><p>Understand solution blocks, hidden tests, and automated grading</p></li>
</ul>
</div>
<section id="the-big-picture-why-configuration-matters-in-ml-systems">
<h2>The Big Picture: Why Configuration Matters in ML Systems<a class="headerlink" href="#the-big-picture-why-configuration-matters-in-ml-systems" title="Link to this heading">#</a></h2>
<p>Configuration is the foundation of any production ML system. In this module, youll learn:</p>
<section id="system-awareness">
<h3>1. <strong>System Awareness</strong><a class="headerlink" href="#system-awareness" title="Link to this heading">#</a></h3>
<p>Real ML systems need to understand their environment:</p>
<ul class="simple">
<li><p><strong>Hardware constraints</strong>: Memory, CPU cores, GPU availability</p></li>
<li><p><strong>Software dependencies</strong>: Python version, library compatibility</p></li>
<li><p><strong>Platform differences</strong>: Linux servers, macOS development, Windows deployment</p></li>
</ul>
</section>
<section id="reproducibility">
<h3>2. <strong>Reproducibility</strong><a class="headerlink" href="#reproducibility" title="Link to this heading">#</a></h3>
<p>Configuration enables reproducible ML:</p>
<ul class="simple">
<li><p><strong>Environment documentation</strong>: Exactly what system was used</p></li>
<li><p><strong>Dependency management</strong>: Precise versions and requirements</p></li>
<li><p><strong>Debugging support</strong>: System info helps troubleshoot issues</p></li>
</ul>
</section>
<section id="professional-development">
<h3>3. <strong>Professional Development</strong><a class="headerlink" href="#professional-development" title="Link to this heading">#</a></h3>
<p>Proper configuration shows engineering maturity:</p>
<ul class="simple">
<li><p><strong>Attribution</strong>: Your work is properly credited</p></li>
<li><p><strong>Collaboration</strong>: Others can understand and extend your setup</p></li>
<li><p><strong>Maintenance</strong>: Systems can be updated and maintained</p></li>
</ul>
</section>
<section id="ml-systems-context">
<h3>4. <strong>ML Systems Context</strong><a class="headerlink" href="#ml-systems-context" title="Link to this heading">#</a></h3>
<p>This connects to broader ML engineering:</p>
<ul class="simple">
<li><p><strong>Model deployment</strong>: Different environments need different configs</p></li>
<li><p><strong>Monitoring</strong>: System metrics help track performance</p></li>
<li><p><strong>Scaling</strong>: Understanding hardware helps optimize training</p></li>
</ul>
<p>Lets build the foundation of your ML systems engineering skills!</p>
</section>
</section>
<section id="interactive-learning">
<h2>🚀 Interactive Learning<a class="headerlink" href="#interactive-learning" title="Link to this heading">#</a></h2>
<p>Choose your preferred way to engage with this module:</p>
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⚡ Open in Colab</div>
<p class="sd-card-text">Use Google Colab for GPU access and cloud compute power.</p>
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<a class="sd-stretched-link sd-hide-link-text reference external" href="https://colab.research.google.com/github/mlsysbook/TinyTorch/blob/main/modules/source/01_setup/setup_dev.ipynb"><span>https://colab.research.google.com/github/mlsysbook/TinyTorch/blob/main/modules/source/01_setup/setup_dev.ipynb</span></a></div>
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<p class="sd-card-text">Browse the Python source code and understand the implementation.</p>
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<p class="admonition-title">💾 Save Your Progress</p>
<p><strong>Binder sessions are temporary!</strong> Download your completed notebook when done, or switch to local development for persistent work.</p>
<p>Ready for serious development? → <a class="reference internal" href="../usage-paths/serious-development.html"><span class="std std-doc">🏗️ Local Setup Guide</span></a></p>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-big-picture-why-configuration-matters-in-ml-systems">The Big Picture: Why Configuration Matters in ML Systems</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#system-awareness">1. <strong>System Awareness</strong></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#reproducibility">2. <strong>Reproducibility</strong></a></li>
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