import{s as Bt,n as qt,o as Kt}from"../chunks/scheduler.d6170356.js";import{S as Ot,i as te,g as i,s as l,r as f,A as ee,h as d,f as a,c as n,j as Et,u as o,x as m,k as N,y as ae,a as s,v as r,d as u,t as h,w as g}from"../chunks/index.fcd4cc08.js";import{C as jt}from"../chunks/CodeBlock.db16bf50.js";import{H as S,E as se}from"../chunks/index.9f2da87a.js";function le(Ut){let p,E,V,B,M,q,v,_t=`<a href="https://github.com/webdataset/webdataset" rel="nofollow">WebDataset</a> is a library for writing I/O pipelines for large datasets.
Its sequential I/O and sharding features make it especially useful for streaming large-scale datasets to a DataLoader.`,K,C,O,y,Jt=`A WebDataset file is a TAR archive containing a series of data files.
All successive data files with the same prefix are considered to be part of the same example (e.g., an image/audio file and its label or metadata):`,tt,c,Wt='<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/datasets-webdataset-format.png"/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/datasets-webdataset-format-dark.png"/>',et,$,Zt="Labels and metadata can be in a <code>.json</code> file, in a <code>.txt</code> (for a caption, a description), or in a <code>.cls</code> (for a class index).",at,x,Gt=`A large scale WebDataset is made of many files called shards, where each shard is a TAR archive.
Each shard is often ~1GB but the full dataset can be multiple terabytes!`,st,k,lt,T,Ht="WebDataset is designed for multimodal datasets, i.e. for image, audio and/or video datasets.",nt,j,Lt="Indeed, since media files tend to be quite big, WebDataset’s sequential I/O enables large reads and buffering, resulting in the best data loading speed.",it,U,Dt="Here is a non-exhaustive list of supported data formats:",dt,_,Rt="<li>image: jpeg, png, tiff</li> <li>audio: mp3, m4a, wav, flac</li> <li>video: mp4, mov, avi</li> <li>other: npy, npz</li>",mt,J,Xt='The full list evolves over time and depends on the implementation. For example, you can find which formats the <code>webdataset</code> package supports in the source code <a href="https://github.com/webdataset/webdataset/blob/main/webdataset/autodecode.py" rel="nofollow">here</a>.',pt,W,ft,Z,At=`Streaming TAR archives is fast because it reads contiguous chunks of data.
It can be orders of magnitude faster than reading separate data files one by one.`,ot,G,It="WebDataset streaming offers high-speed performance both when reading from disk and from cloud storage, which makes it an ideal format to feed to a DataLoader:",rt,b,zt='<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/datasets-webdataset-stream.png"/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/datasets-webdataset-stream-dark.png"/>',ut,H,Pt='For example here is how to stream the <a href="https://huggingface.co/datasets/timm/imagenet-12k-wds" rel="nofollow">timm/imagenet-12k-wds</a> dataset directly from Hugging Face:',ht,L,Qt='First you need to <a href="/docs/huggingface_hub/quick-start#login">Login with your Hugging Face account</a>, for example using:',gt,D,ct,R,Ft="And then you can stream the dataset with WebDataset:",bt,X,wt,A,Mt,I,Vt=`Generally, datasets in WebDataset formats are already shuffled and ready to feed to a DataLoader.
But you can still reshuffle the data with WebDataset’s approximate shuffling.`,vt,z,Yt="In addition to shuffling the list of shards, WebDataset uses a buffer to shuffle a dataset without any cost to speed:",Ct,w,Nt='<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/datasets-webdataset-shuffle.png"/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/datasets-webdataset-shuffle-dark.png"/>',yt,P,St="To shuffle a list of sharded files and randomly sample from the shuffle buffer:",$t,Q,xt,F,kt,Y,Tt;return M=new S({props:{title:"WebDataset",local:"webdataset",headingTag:"h1"}}),C=new S({props:{title:"The WebDataset format",local:"the-webdataset-format",headingTag:"h2"}}),k=new S({props:{title:"Multimodal support",local:"multimodal-support",headingTag:"h2"}}),W=new S({props:{title:"Streaming",local:"streaming",headingTag:"h2"}}),D=new jt({props:{code:"aHVnZ2luZ2ZhY2UtY2xpJTIwbG9naW4=",highlighted:'huggingface-<span class="hljs-keyword">cli</span> login',wrap:!1}}),X=new jt({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> webdataset <span class="hljs-keyword">as</span> wds
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> get_token
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> torch.utils.data <span class="hljs-keyword">import</span> DataLoader

<span class="hljs-meta">&gt;&gt;&gt; </span>hf_token = get_token()
<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://huggingface.co/datasets/timm/imagenet-12k-wds/resolve/main/imagenet12k-train-{{0000..1023}}.tar&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">f&quot;pipe:curl -s -L <span class="hljs-subst">{url}</span> -H &#x27;Authorization:Bearer <span class="hljs-subst">{hf_token}</span>&#x27;&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = wds.WebDataset(url).decode()
<span class="hljs-meta">&gt;&gt;&gt; </span>dataloader = DataLoader(dataset, batch_size=<span class="hljs-number">64</span>, num_workers=<span class="hljs-number">4</span>)`,wrap:!1}}),A=new S({props:{title:"Shuffle",local:"shuffle",headingTag:"h2"}}),Q=new jt({props:{code:"YnVmZmVyX3NpemUlMjAlM0QlMjAxMDAwJTBBZGF0YXNldCUyMCUzRCUyMCglMEElMjAlMjAlMjAlMjB3ZHMuV2ViRGF0YXNldCh1cmwlMkMlMjBzaGFyZHNodWZmbGUlM0RUcnVlKSUwQSUyMCUyMCUyMCUyMC5zaHVmZmxlKGJ1ZmZlcl9zaXplKSUwQSUyMCUyMCUyMCUyMC5kZWNvZGUoKSUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>buffer_size = <span class="hljs-number">1000</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = (
<span class="hljs-meta">... </span>    wds.WebDataset(url, shardshuffle=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span>    .shuffle(buffer_size)
<span class="hljs-meta">... </span>    .decode()
<span class="hljs-meta">... </span>)`,wrap:!1}}),F=new 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