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_admonition/gpu.html

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<h4>Changelog (<a href="https://github.com/QuantEcon/lecture-python-programming.myst/commits/main/lectures/_admonition/gpu.md">full history</a>)</h4>
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<ul class="changelog-list">
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<a href="https://github.com/QuantEcon/lecture-python-programming.myst/commit/158090f" class="changelog-hash">158090f</a>
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<span class="changelog-author">Matt McKay</span>
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<span class="changelog-time">8 minutes ago</span>
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<span class="changelog-message">Update GPU admonition to match jstac's text (#448)</span>
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</li>
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<a href="https://github.com/QuantEcon/lecture-python-programming.myst/commit/c4c03c8" class="changelog-hash">c4c03c8</a>
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<span class="changelog-author">Matt McKay</span>
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<span class="changelog-time">7 minutes ago</span>
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<span class="changelog-time">1 hour ago</span>
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<span class="changelog-message">Add centralized GPU admonition for JAX lectures (#447)</span>
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</li>
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<div class="warning admonition">
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<p class="admonition-title">GPU</p>
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<p>This lecture is designed to run on a GPU. To use Google Colab’s free GPUs, click the play icon top right, select Colab, and set the runtime to include a GPU. For local GPU setup, see the <a class="reference external" href="https://github.com/google/jax">JAX installation guide</a>.</p>
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<p>This lecture was built using a machine with access to a GPU.</p>
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<p><a class="reference external" href="https://colab.research.google.com/">Google Colab</a> has a free tier with GPUs
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that you can access as follows:</p>
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<ol class="arabic simple">
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<li><p>Click on the “play” icon top right</p></li>
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<li><p>Select Colab</p></li>
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<li><p>Set the runtime environment to include a GPU</p></li>
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</ol>
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</div>
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_notebooks/about_py.ipynb

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"cells": [
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"\n",
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},
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{
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"cell_type": "markdown",
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"id": "2703b37a",
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"# About These Lectures\n",
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"## Overview\n",
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},
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{
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"cell_type": "markdown",
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"id": "606bebc4",
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"### Can’t I Just Use LLMs?\n",
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"### Isn’t MATLAB Better?\n",
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},
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{
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"cell_type": "markdown",
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"id": "766a6ff1",
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"metadata": {},
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"source": [
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"## Introducing Python\n",
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},
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{
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"cell_type": "markdown",
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"id": "8ac9dfc5",
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"### Common Uses\n",
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},
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{
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"cell_type": "markdown",
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"### Relative Popularity\n",
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},
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{
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"cell_type": "markdown",
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"### Features\n",
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},
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{
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"cell_type": "markdown",
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"### Syntax and Design\n",
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},
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"hide-output": false
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},
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},
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{
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"cell_type": "markdown",
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"This Java code opens an imaginary file called `data.csv` and computes the mean\n",
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{
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"cell_type": "code",
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"### The AI Connection\n",
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},
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{
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"cell_type": "markdown",
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"## Scientific Programming with Python\n",
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},
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"cell_type": "markdown",
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"### NumPy\n",
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"This array is very small so it’s fine to work with pure Python.\n",
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"Now let’s transform this array by applying functions to it."
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"cell_type": "code",
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"hide-output": false
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},
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"cell_type": "markdown",
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"Now we can easily take the inner product of `b` and `c`."
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{
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"cell_type": "code",
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"We can also do many other tasks, like\n",
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"cell_type": "markdown",
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"### NumPy Alternatives\n",
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},
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"### SciPy\n",
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"### Graphics\n",
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},
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"### Networks and Graphs\n",
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"### Other Scientific Libraries\n",
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}
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],
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"metadata": {
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"date": 1764540625.83234,
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"date": 1764546466.6258578,
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"filename": "about_py.md",
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"kernelspec": {
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"display_name": "Python",

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