import{s as De,a as Ge,n as Oe,o as Ve}from"../chunks/scheduler.9cda857a.js";import{S as We,i as Je,e as l,s as i,c as m,h as Ke,a as o,d as a,b as n,f as Re,g as c,j as r,k as Y,l as Qe,m as s,n as u,t as f,o as h,p as d}from"../chunks/index.003612b0.js";import{C as Xe}from"../chunks/CopyLLMTxtMenu.44d08a39.js";import{C as Ye}from"../chunks/CodeBlock.a90632f1.js";import{H as O,E as Ze}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.e5cfda8c.js";function et(_e){let p,V,D,W,b,J,x,K,v,ke=`<strong>Aim</strong> is an easy-to-use &amp; supercharged open-source experiment tracker. Aim logs your training runs and enables a beautiful UI to compare them and an API to query them programmatically.
ML engineers and researchers use Aim explorers to compare 1000s of training runs in a few clicks.`,Q,w,Te=`Check out the <a href="https://aimstack.readthedocs.io/en/latest/" rel="nofollow">Aim docs</a> to learn more about Aim.
If you have an idea for a new feature or have noticed a bug, feel free to <a href="https://github.com/aimhubio/aim/issues/new/choose" rel="nofollow">open a feature request or report a bug</a>.`,X,_,He="In the following sections, you’ll learn how to deploy Aim on the Hugging Face Hub Spaces and explore your training runs directly from the Hub.",Z,k,ee,T,Ce="You can deploy Aim on Spaces with a single click!",te,g,Me='<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/deploy-to-spaces-lg.svg"/>',ae,H,Le="Once you have created the Space, you’ll see the <code>Building</code> status, and once it becomes <code>Running,</code> your Space is ready to go!",se,$,Ae,ie,C,Se="Now, when you navigate to your Space’s <strong>App</strong> section, you can access the Aim UI.",ne,M,le,L,Pe=`Let’s use a quick example of a PyTorch CNN trained on MNIST to demonstrate end-to-end Aim on Spaces deployment.
The full example is in the <a href="https://github.com/aimhubio/aim/blob/main/examples/pytorch_track.py" rel="nofollow">Aim repo examples folder</a>.`,oe,A,re,S,je="The experiments tracked by Aim are stored in the <code>.aim</code> folder. <strong>To display the logs with the Aim UI in your Space, you need to compress the <code>.aim</code> folder to a <code>tar.gz</code> file and upload it to your Space using <code>git</code> or the Files and Versions sections of your Space.</strong>",pe,P,Ie="Here’s a bash command for that:",me,j,ce,I,qe=`That’s it! Now open the App section of your Space and the Aim UI is available with your logs.
Here is what to expect:`,ue,q,Fe='<img src="https://user-images.githubusercontent.com/23078323/232034340-0ba3ebbf-0374-4b14-ba80-1d36162fc994.png" alt="Aim UI on HF Hub Spaces"/>',fe,F,Ee='Filter your runs using Aim’s Pythonic search. You can write pythonic <a href="https://aimstack.readthedocs.io/en/latest/using/search.html" rel="nofollow">queries against</a> EVERYTHING you have tracked - metrics, hyperparams etc. Check out some <a href="https://huggingface.co/aimstack" rel="nofollow">examples</a> on HF Hub Spaces.',he,y,Ne='<p>Note that if your logs are in TensorBoard format, you can easily convert <a href="https://aimstack.readthedocs.io/en/latest/quick_start/convert_data.html#show-tensorboard-logs-in-aim">them to Aim with one command</a> and use the many advanced and high-performant training run comparison features available.</p>',de,E,ge,N,Ue='<li><a href="https://huggingface.co/docs/hub/spaces-sdks-docker" rel="nofollow">HF Docker spaces</a></li> <li><a href="https://huggingface.co/docs/hub/spaces-sdks-docker-examples" rel="nofollow">HF Docker space examples</a></li>',$e,U,ye,B,Be='If you have improvement suggestions or need support, please open an issue on <a href="https://github.com/aimhubio/aim" rel="nofollow">Aim GitHub repo</a>.',be,z,ze='The <a href="https://github.com/aimhubio/aim#-community" rel="nofollow">Aim community Discord</a> is also available for community discussions.',xe,R,ve,G,we;return b=new Xe({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),x=new O({props:{title:"Aim on Spaces",local:"aim-on-spaces",headingTag:"h1"}}),k=new O({props:{title:"Deploy Aim on Spaces",local:"deploy-aim-on-spaces",headingTag:"h2"}}),M=new O({props:{title:"Compare your experiments with Aim on Spaces",local:"compare-your-experiments-with-aim-on-spaces",headingTag:"h2"}}),A=new Ye({props:{code:"JTBB",highlighted:`<span class="hljs-keyword">from</span> aim <span class="hljs-keyword">import</span> Run
<span class="hljs-keyword">from</span> aim.pytorch <span class="hljs-keyword">import</span> track_gradients_dists, track_params_dists

<span class="hljs-comment"># Initialize a new Run</span>
aim_run = Run()
...
items = {<span class="hljs-string">&#x27;accuracy&#x27;</span>: acc, <span class="hljs-string">&#x27;loss&#x27;</span>: loss}
aim_run.track(items, epoch=epoch, context={<span class="hljs-string">&#x27;subset&#x27;</span>: <span class="hljs-string">&#x27;train&#x27;</span>})

<span class="hljs-comment"># Track weights and gradients distributions</span>
track_params_dists(model, aim_run)
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tt='{"title":"Aim on Spaces","local":"aim-on-spaces","sections":[{"title":"Deploy Aim on Spaces","local":"deploy-aim-on-spaces","sections":[],"depth":2},{"title":"Compare your experiments with Aim on Spaces","local":"compare-your-experiments-with-aim-on-spaces","sections":[],"depth":2},{"title":"More on HF Spaces","local":"more-on-hf-spaces","sections":[],"depth":2},{"title":"Feedback and Support","local":"feedback-and-support","sections":[],"depth":2}],"depth":1}';function at(_e){return Ve(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class rt extends We{constructor(p){super(),Je(this,p,at,et,De,{})}}export{rt as component};
