Buckets:

HuggingFaceDocBuilder's picture
download
raw
8.85 kB
import{s as He,n as Pe,o as ye}from"../chunks/scheduler.f3b1e791.js";import{S as ke,i as Ae,e as a,s as c,c as o,h as Ee,a as x,d as l,b as n,f as Ue,g as r,j as p,k as be,l as je,m as i,n as u,t as d,o as m,p as h}from"../chunks/index.023a9934.js";import{C as ze,H as $,E as De}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.fd2107ec.js";function Se(pe){let s,X,q,F,f,G,_,K,v,$e=`A kernel can be compliant for a specific compute framework (e.g. CUDA) or
architecture (e.g. x86_64). For compliance with a compute framework and
architecture combination, all the build variants listed below must be
available. This list will be updated as new PyTorch versions are released.`,I,w,J,g,se="<li><code>torch211-cpu-aarch64-darwin</code></li> <li><code>torch212-cpu-aarch64-darwin</code></li> <li><code>torch213-cpu-aarch64-darwin</code></li>",N,T,Q,C,fe="<li><code>torch211-metal-aarch64-darwin</code></li> <li><code>torch212-metal-aarch64-darwin</code></li> <li><code>torch213-metal-aarch64-darwin</code></li>",V,L,W,M,_e="<li><code>torch211-cxx11-cpu-aarch64-linux</code></li> <li><code>torch212-cxx11-cpu-aarch64-linux</code></li> <li><code>torch213-cxx11-cpu-aarch64-linux</code></li>",Y,U,Z,b,ve="<li><code>torch211-cxx11-cu126-aarch64-linux</code></li> <li><code>torch211-cxx11-cu128-aarch64-linux</code></li> <li><code>torch211-cxx11-cu130-aarch64-linux</code></li> <li><code>torch212-cxx11-cu126-aarch64-linux</code></li> <li><code>torch212-cxx11-cu130-aarch64-linux</code></li> <li><code>torch212-cxx11-cu132-aarch64-linux</code></li> <li><code>torch213-cxx11-cu126-aarch64-linux</code></li> <li><code>torch213-cxx11-cu130-aarch64-linux</code></li> <li><code>torch213-cxx11-cu132-aarch64-linux</code></li>",ee,H,te,P,we="<li><code>torch211-cxx11-cpu-x86_64-linux</code></li> <li><code>torch212-cxx11-cpu-x86_64-linux</code></li> <li><code>torch213-cxx11-cpu-x86_64-linux</code></li>",le,y,ie,k,ge="<li><code>torch211-cxx11-cu126-x86_64-linux</code></li> <li><code>torch211-cxx11-cu128-x86_64-linux</code></li> <li><code>torch211-cxx11-cu130-x86_64-linux</code></li> <li><code>torch212-cxx11-cu126-x86_64-linux</code></li> <li><code>torch212-cxx11-cu130-x86_64-linux</code></li> <li><code>torch212-cxx11-cu132-x86_64-linux</code></li> <li><code>torch213-cxx11-cu126-x86_64-linux</code></li> <li><code>torch213-cxx11-cu130-x86_64-linux</code></li> <li><code>torch213-cxx11-cu132-x86_64-linux</code></li>",ce,A,ne,E,Te="<li><code>torch211-cxx11-rocm71-x86_64-linux</code></li> <li><code>torch211-cxx11-rocm72-x86_64-linux</code></li> <li><code>torch212-cxx11-rocm71-x86_64-linux</code></li> <li><code>torch212-cxx11-rocm72-x86_64-linux</code></li> <li><code>torch213-cxx11-rocm71-x86_64-linux</code></li> <li><code>torch213-cxx11-rocm72-x86_64-linux</code></li>",ae,j,xe,z,Ce="<li><code>torch211-cxx11-xpu20253-x86_64-linux</code></li> <li><code>torch212-cxx11-xpu20253-x86_64-linux</code></li> <li><code>torch213-cxx11-xpu20253-x86_64-linux</code></li>",oe,D,re,S,Le=`Kernels that are in pure Python (e.g. Triton kernels) only need to provide
one or more of the following variants:`,ue,O,Me="<li><code>torch-cpu</code></li> <li><code>torch-cuda</code></li> <li><code>torch-metal</code></li> <li><code>torch-rocm</code></li> <li><code>torch-xpu</code></li>",de,R,me,B,he;return f=new ze({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),_=new $({props:{title:"Build variants",local:"build-variants",headingTag:"h1"}}),w=new $({props:{title:"CPU aarch64-darwin",local:"cpu-aarch64-darwin",headingTag:"h2"}}),T=new $({props:{title:"Metal aarch64-darwin",local:"metal-aarch64-darwin",headingTag:"h2"}}),L=new $({props:{title:"CPU aarch64-linux",local:"cpu-aarch64-linux",headingTag:"h2"}}),U=new $({props:{title:"CUDA aarch64-linux",local:"cuda-aarch64-linux",headingTag:"h2"}}),H=new $({props:{title:"CPU x86_64-linux",local:"cpu-x8664-linux",headingTag:"h2"}}),y=new $({props:{title:"CUDA x86_64-linux",local:"cuda-x8664-linux",headingTag:"h2"}}),A=new $({props:{title:"ROCm x86_64-linux",local:"rocm-x8664-linux",headingTag:"h2"}}),j=new $({props:{title:"XPU x86_64-linux",local:"xpu-x8664-linux",headingTag:"h2"}}),D=new $({props:{title:"Python-only kernels",local:"python-only-kernels",headingTag:"h2"}}),R=new De({props:{source:"https://github.com/huggingface/kernels/blob/main/docs/source/builder/build-variants.md"}}),{c(){s=a("meta"),X=c(),q=a("p"),F=c(),o(f.$$.fragment),G=c(),o(_.$$.fragment),K=c(),v=a("p"),v.textContent=$e,I=c(),o(w.$$.fragment),J=c(),g=a("ul"),g.innerHTML=se,N=c(),o(T.$$.fragment),Q=c(),C=a("ul"),C.innerHTML=fe,V=c(),o(L.$$.fragment),W=c(),M=a("ul"),M.innerHTML=_e,Y=c(),o(U.$$.fragment),Z=c(),b=a("ul"),b.innerHTML=ve,ee=c(),o(H.$$.fragment),te=c(),P=a("ul"),P.innerHTML=we,le=c(),o(y.$$.fragment),ie=c(),k=a("ul"),k.innerHTML=ge,ce=c(),o(A.$$.fragment),ne=c(),E=a("ul"),E.innerHTML=Te,ae=c(),o(j.$$.fragment),xe=c(),z=a("ul"),z.innerHTML=Ce,oe=c(),o(D.$$.fragment),re=c(),S=a("p"),S.textContent=Le,ue=c(),O=a("ul"),O.innerHTML=Me,de=c(),o(R.$$.fragment),me=c(),B=a("p"),this.h()},l(e){const t=Ee("svelte-u9bgzb",document.head);s=x(t,"META",{name:!0,content:!0}),t.forEach(l),X=n(e),q=x(e,"P",{}),Ue(q).forEach(l),F=n(e),r(f.$$.fragment,e),G=n(e),r(_.$$.fragment,e),K=n(e),v=x(e,"P",{"data-svelte-h":!0}),p(v)!=="svelte-1jx18hk"&&(v.textContent=$e),I=n(e),r(w.$$.fragment,e),J=n(e),g=x(e,"UL",{"data-svelte-h":!0}),p(g)!=="svelte-l3xvz4"&&(g.innerHTML=se),N=n(e),r(T.$$.fragment,e),Q=n(e),C=x(e,"UL",{"data-svelte-h":!0}),p(C)!=="svelte-1e6ioz3"&&(C.innerHTML=fe),V=n(e),r(L.$$.fragment,e),W=n(e),M=x(e,"UL",{"data-svelte-h":!0}),p(M)!=="svelte-11q0p8j"&&(M.innerHTML=_e),Y=n(e),r(U.$$.fragment,e),Z=n(e),b=x(e,"UL",{"data-svelte-h":!0}),p(b)!=="svelte-gokuix"&&(b.innerHTML=ve),ee=n(e),r(H.$$.fragment,e),te=n(e),P=x(e,"UL",{"data-svelte-h":!0}),p(P)!=="svelte-152ithn"&&(P.innerHTML=we),le=n(e),r(y.$$.fragment,e),ie=n(e),k=x(e,"UL",{"data-svelte-h":!0}),p(k)!=="svelte-1kjk93v"&&(k.innerHTML=ge),ce=n(e),r(A.$$.fragment,e),ne=n(e),E=x(e,"UL",{"data-svelte-h":!0}),p(E)!=="svelte-e9s29z"&&(E.innerHTML=Te),ae=n(e),r(j.$$.fragment,e),xe=n(e),z=x(e,"UL",{"data-svelte-h":!0}),p(z)!=="svelte-8pm6b4"&&(z.innerHTML=Ce),oe=n(e),r(D.$$.fragment,e),re=n(e),S=x(e,"P",{"data-svelte-h":!0}),p(S)!=="svelte-1vxjwd6"&&(S.textContent=Le),ue=n(e),O=x(e,"UL",{"data-svelte-h":!0}),p(O)!=="svelte-837bvb"&&(O.innerHTML=Me),de=n(e),r(R.$$.fragment,e),me=n(e),B=x(e,"P",{}),Ue(B).forEach(l),this.h()},h(){be(s,"name","hf:doc:metadata"),be(s,"content",Oe)},m(e,t){je(document.head,s),i(e,X,t),i(e,q,t),i(e,F,t),u(f,e,t),i(e,G,t),u(_,e,t),i(e,K,t),i(e,v,t),i(e,I,t),u(w,e,t),i(e,J,t),i(e,g,t),i(e,N,t),u(T,e,t),i(e,Q,t),i(e,C,t),i(e,V,t),u(L,e,t),i(e,W,t),i(e,M,t),i(e,Y,t),u(U,e,t),i(e,Z,t),i(e,b,t),i(e,ee,t),u(H,e,t),i(e,te,t),i(e,P,t),i(e,le,t),u(y,e,t),i(e,ie,t),i(e,k,t),i(e,ce,t),u(A,e,t),i(e,ne,t),i(e,E,t),i(e,ae,t),u(j,e,t),i(e,xe,t),i(e,z,t),i(e,oe,t),u(D,e,t),i(e,re,t),i(e,S,t),i(e,ue,t),i(e,O,t),i(e,de,t),u(R,e,t),i(e,me,t),i(e,B,t),he=!0},p:Pe,i(e){he||(d(f.$$.fragment,e),d(_.$$.fragment,e),d(w.$$.fragment,e),d(T.$$.fragment,e),d(L.$$.fragment,e),d(U.$$.fragment,e),d(H.$$.fragment,e),d(y.$$.fragment,e),d(A.$$.fragment,e),d(j.$$.fragment,e),d(D.$$.fragment,e),d(R.$$.fragment,e),he=!0)},o(e){m(f.$$.fragment,e),m(_.$$.fragment,e),m(w.$$.fragment,e),m(T.$$.fragment,e),m(L.$$.fragment,e),m(U.$$.fragment,e),m(H.$$.fragment,e),m(y.$$.fragment,e),m(A.$$.fragment,e),m(j.$$.fragment,e),m(D.$$.fragment,e),m(R.$$.fragment,e),he=!1},d(e){e&&(l(X),l(q),l(F),l(G),l(K),l(v),l(I),l(J),l(g),l(N),l(Q),l(C),l(V),l(W),l(M),l(Y),l(Z),l(b),l(ee),l(te),l(P),l(le),l(ie),l(k),l(ce),l(ne),l(E),l(ae),l(xe),l(z),l(oe),l(re),l(S),l(ue),l(O),l(de),l(me),l(B)),l(s),h(f,e),h(_,e),h(w,e),h(T,e),h(L,e),h(U,e),h(H,e),h(y,e),h(A,e),h(j,e),h(D,e),h(R,e)}}}const Oe='{"title":"Build variants","local":"build-variants","sections":[{"title":"CPU aarch64-darwin","local":"cpu-aarch64-darwin","sections":[],"depth":2},{"title":"Metal aarch64-darwin","local":"metal-aarch64-darwin","sections":[],"depth":2},{"title":"CPU aarch64-linux","local":"cpu-aarch64-linux","sections":[],"depth":2},{"title":"CUDA aarch64-linux","local":"cuda-aarch64-linux","sections":[],"depth":2},{"title":"CPU x86_64-linux","local":"cpu-x8664-linux","sections":[],"depth":2},{"title":"CUDA x86_64-linux","local":"cuda-x8664-linux","sections":[],"depth":2},{"title":"ROCm x86_64-linux","local":"rocm-x8664-linux","sections":[],"depth":2},{"title":"XPU x86_64-linux","local":"xpu-x8664-linux","sections":[],"depth":2},{"title":"Python-only kernels","local":"python-only-kernels","sections":[],"depth":2}],"depth":1}';function Re(pe){return ye(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Fe extends ke{constructor(s){super(),Ae(this,s,Re,Se,He,{})}}export{Fe as component};

Xet Storage Details

Size:
8.85 kB
·
Xet hash:
829bb1cc88235d25439a4e479f866d8bbd4acdbf18a7aab0f847589534df222a

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.