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<h1 id="development-guide">Development Guide</h1>
<p>This guide outlines how to set up the development environment for this project, prioritizing <strong>reproducible builds</strong>, <strong>environment parity</strong>, and <strong>cross-hardware compatibility</strong>.</p>
<h2 id="reproducibility-uv">Reproducibility &amp;uv</h2>
<p>This project uses <a href="https://github.com/astral-sh/uv">uv</a> to manage dependencies and virtual environments. The <code>uv.lock</code> file is the absolute source of truth for package versions and must always be committed.</p>
<h3 id="source-of-truth">Source of Truth</h3>
<ul>
<li><strong>Never modify <code>uv.lock</code> manually.</strong></li>
<li>To add a dependency, run <code>uv add &lt;package&gt;</code>.</li>
<li>To update dependencies, run <code>uv lock --upgrade</code>.</li>
<li>To sync your environment with the lockfile, run <code>uv sync --frozen</code>.</li>
</ul>
<h2 id="git-lfs-critical">Git LFS (Critical)</h2>
<p><strong>All developers must have Git LFS installed locally.</strong> This repository tracks model weights (<code>.pt</code>, <code>.safetensors</code>, etc.), recordings (<code>.mp4</code>), and datasets using Git LFS.</p>
<ul>
<li><strong>Setup:</strong> Run <code>git lfs install</code> after cloning this repository. If you are using the <code>.devcontainer</code> or <code>flake.nix</code>, LFS is typically available automatically.</li>
<li>If you clone without LFS installed, run <code>git lfs pull</code> after installation to fetch the actual data files instead of the small pointer files.</li>
</ul>
<h2 id="devcontainer-setup-recommended">Devcontainer Setup (Recommended)</h2>
<p>The devcontainer provides an identical experience to local development but with all system dependencies pre-configured. It automatically detects your hardware (GPU vs CPU) and syncs the appropriate dependencies.</p>
<h3 id="prerequisites">Prerequisites</h3>
<ul>
<li>Docker Desktop or Docker Engine.</li>
<li><a href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html">NVIDIA Container Toolkit</a> (for GPU support).</li>
</ul>
<h3 id="setup-for-vs-code">Setup for VS Code</h3>
<ol>
<li>Install the <a href="https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers">Dev Containers</a> extension.</li>
<li>Open the project and click <strong>Reopen in Container</strong>.</li>
<li>On first launch, the <code>post-create.sh</code> script will:</li>
<li>Detect if an NVIDIA GPU is available via <code>nvidia-smi</code>.</li>
<li>Run <code>uv sync --frozen --extra cuda</code> if a GPU is found.</li>
<li>Run <code>uv sync --frozen</code> otherwise.</li>
<li>The environment is stored in a <strong>named volume</strong> for <code>.venv</code> to ensure persistence and performance.</li>
</ol>
<h3 id="setup-for-jetbrains-ides">Setup for JetBrains IDEs</h3>
<ol>
<li>The IDE will detect the <code>.devcontainer/devcontainer.json</code> file.</li>
<li>The environment is pre-configured to point to <code>/workspaces/project/.venv</code>.</li>
<li>The hardware-aware sync will run automatically during container creation.</li>
</ol>
<h2 id="local-development-alternative">Local Development (Alternative)</h2>
<p>If you prefer not to use Docker:
1. Install <a href="https://docs.astral.sh/uv/getting-started/installation/">uv</a>.
2. Run <code>uv sync --frozen</code> (CPU) or <code>uv sync --frozen --extra cuda</code> (GPU).</p>
<h2 id="hardware-acceleration-jax">Hardware Acceleration (JAX)</h2>
<p>Verify your setup by running the JAX initialization test:</p>
<p><div class="highlight"><pre><span></span><code>uv<span class="w"> </span>run<span class="w"> </span>pytest<span class="w"> </span>tests/test_jax_init.py
</code></pre></div>
In the devcontainer, this will succeed on both CPU and GPU. A <code>GpuDevice</code> is expected if a GPU is detected and the <code>cuda</code> extra was installed.</p>
<h2 id="logging-monitoring">Logging &amp; Monitoring</h2>
<p>This project uses a unified logging system through the <code>experiment_logger</code> package. </p>
<ul>
<li><strong>Usage in Code</strong>: To use the logger in your scripts, refer to the <a href="../src/experiment_logger/README.md">package README</a> for the API reference.</li>
<li><strong>WandB/TensorBoard Setup</strong>: For information on how to configure tracking for experiments, see the <a href="../api/tracking/">Tracking &amp; Monitoring API Guide</a>.</li>
</ul>
<p>The logger automatically detects if it is running in an interactive terminal or a non-interactive environment (like an HPC Slurm job), adjusting progress bars and fallback modes accordingly.</p>


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