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Jan 31, 2021</div></div></div></section><section id=single><div class=container><div class="row justify-content-center"><div class="col-sm-12 col-md-12 col-lg-9"><div class=pr-lg-4><article class="page-content p-2"><p>At the end of last year I splashed out on a shiny new Apple MacBookAir with the M1 processor as I was fed up with an old Intel-based MacBookPro that was quite honestly crippled by corporate anti-virus software.</p><p>Out the box this machine is amazing. It&rsquo;s ridiculously fast, and lasts for ever on battery. Seriously - I charge it every 2 days and manage a full day of coding, writing, emails, Teams, the lot. Did I also mention it&rsquo;s fast? I can have all the things running and it barely breaks a sweat, even with only 8GB of RAM.</p><p>The downside is that not all software works on the new ARM-64 architecture. Apple have a translation layer called Rosetta 2 (Rosetta 1 was their translation from PowerPC to Intel), and this works great most of the time for every day apps, but it doesn&rsquo;t always work for development tools and libraries, as the mix of translated and untranslated stuff just breaks down.</p><p>One library I needed to use that isn&rsquo;t supported is Scikit-Learn. Now I&rsquo;m no Python expert, and I don&rsquo;t really understand what Scikit-Learn does, I just know I need it to train some TinyML models to <a href=https://eloquentarduino.github.io/2020/08/better-word-classification-with-arduino-33-ble-sense-and-machine-learning/>recognize wake words on an Arduino Nano 33 sense board</a>. If I try a normal pip install scikit-learn, I get a whole wall of errors, both using Python 3.9 for the M1, and Python 3.8 under Rosetta.</p><p>So what to do?</p><p>It turns out the solution is to use <a href=https://github.com/conda-forge/miniforge>Miniforge</a>, a version of Conda that is comparable to Miniconda, but supports various CPU architectures. Whatever that means. As I said, I&rsquo;m no Python expert, but this tool essentially allows me to create virtual environments and install packages compiling them for the M1 chip! Any packages it doesn&rsquo;t support can then be installed from pip.</p><p>So how do I install all this?</p><p>Firstly - I need to install Miniforge. The install script is on the GitHub page, or you can download it by clicking <a href=https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh>this link</a>. It wanted to activate it in every terminal, which I didn&rsquo;t want so I turned that off by running:</p><div class=highlight><pre tabindex=0 style=color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-bash data-lang=bash><span style=display:flex><span>conda config --set auto_activate_base false
</span></span></code></pre></div><p>Next I went to the folder containing my Python code, and created a virtual environment:</p><div class=highlight><pre tabindex=0 style=color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-bash data-lang=bash><span style=display:flex><span>conda create -n .venv python
</span></span></code></pre></div><p>This is pretty much the same as creating a virtual environment with Python, just using a different tool. Like with Python, the virtual environment then needs to be activated:</p><div class=highlight><pre tabindex=0 style=color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-bash data-lang=bash><span style=display:flex><span>conda activate .venv
</span></span></code></pre></div><p>Finally, I can install Scikit-Learn:</p><div class=highlight><pre tabindex=0 style=color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-bash data-lang=bash><span style=display:flex><span>conda install scikit-learn
</span></span></code></pre></div><p>Done! For the particular thing I&rsquo;m working on, I needed a package that isn&rsquo;t available from Miniforge, so I just installed it with pip:</p><p>pip install micromlgen
Done! I could then run my Python script as normal, and it all worked nicely. And fast - my M1 ran the script in question in 2 seconds, 5 times faster than the 10 seconds my Surface Book took.</p></article></div></div><div class="col-sm-12 col-md-12 col-lg-3"><div class=sticky-sidebar><aside class=toc><h5>Table Of Contents</h5><div class=toc-content><nav id=TableOfContents></nav></div></aside><aside class=tags><h5>Tags</h5><ul class="tags-ul list-unstyled list-inline"><li class=list-inline-item><a href=https://jimbobbennett.dev/tags/ai target=_blank>ai</a></li><li class=list-inline-item><a href=https://jimbobbennett.dev/tags/python target=_blank>python</a></li><li class=list-inline-item><a href=https://jimbobbennett.dev/tags/scikit-learn target=_blank>scikit-learn</a></li><li class=list-inline-item><a href=https://jimbobbennett.dev/tags/apple target=_blank>apple</a></li><li class=list-inline-item><a href=https://jimbobbennett.dev/tags/applesilicon target=_blank>applesilicon</a></li></ul></aside></div></div></div><div class=row><div class="col-sm-12 col-md-12 col-lg-9 p-4"><div id=disqus_thread></div><script>var disqus_config=function(){this.page.url="https://jimbobbennett.dev/blogs/installing-scikit-learn-on-an-apple-m1/",this.page.identifier="abe90ed2079563efe6f72081647cda2e"};(function(){if(window.location.hostname=="localhost")return;var e=document,t=e.createElement("script");t.src="https://jimbobbennett.disqus.com/embed.js",t.setAttribute("data-timestamp",+new Date),(e.head||e.body).appendChild(t)})()</script><noscript>Please enable JavaScript to view the <a href=https://disqus.com/?ref_noscript>comments powered by Disqus.</a></noscript></div></div></div><button class="p-2 px-3" onclick=topFunction() id=topScroll>
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