<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>jupyter kernelspec on AgilePhD</title>
    <link>http://blog.agilephd.com/tags/jupyter-kernelspec/</link>
    <description>Recent content in jupyter kernelspec on AgilePhD</description>
    <generator>Hugo -- gohugo.io</generator>
    <language>en-us</language>
    <lastBuildDate>Sat, 10 May 2025 00:00:00 +0000</lastBuildDate><atom:link href="http://blog.agilephd.com/tags/jupyter-kernelspec/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Creating a Custom Jupyter KernelSpec</title>
      <link>http://blog.agilephd.com/posts/jupyter_kernelspec_issues/</link>
      <pubDate>Sat, 10 May 2025 00:00:00 +0000</pubDate>
      
      <guid>http://blog.agilephd.com/posts/jupyter_kernelspec_issues/</guid>
      <description>If you&amp;rsquo;re working with Jupyter notebooks and want to keep your machine learning environments clean and consistent, using a custom Jupyter KernelSpec is a great solution. In this post, I&amp;rsquo;ll walk you through how to create a Jupyter kernel specifically configured for xgboost using pipenv.
Using pipenv adds the benefit of managing dependencies with a Pipfile and Pipfile.lock, giving you a more reproducible setup across systems and collaborators.
Why Use a Custom KernelSpec with Pipenv?</description>
    </item>
    
  </channel>
</rss>
