google-cloud/pr_98/en/_app/immutable/nodes/2.83bd6bd4.js

import{s as pe,o as ue,n as he}from"../chunks/scheduler.8b74b908.js";import{S as me,i as Te,g as l,s as o,r as k,A as $e,h as s,f as n,c as a,j as ge,u as w,x as f,k as de,y as be,a as r,v as I,d as U,t as D,w as H}from"../chunks/index.0ed2a570.js";import{T as ye}from"../chunks/Tip.07d3ac1e.js";import{H as A,E as ve}from"../chunks/EditOnGithub.d2d81eda.js";function Ce(q){let i,d='The listing below only contains the latest version of each one of the Hugging Face DLCs, the full listing of the available published containers in Google Cloud can be found either in the <a href="https://cloud.google.com/deep-learning-containers/docs/choosing-container#hugging-face" rel="nofollow">Google Cloud Deep Learning Containers Documentation</a>, in the <a href="https://console.cloud.google.com/artifacts/docker/deeplearning-platform-release/us/gcr.io" rel="nofollow">Google Cloud Artifact Registry</a> or via the <code>gcloud container images list --repository=&quot;us-docker.pkg.dev/deeplearning-platform-release/gcr.io&quot; | grep &quot;huggingface-&quot;</code> command.';return{c(){i=l("p"),i.innerHTML=d},l(c){i=s(c,"P",{"data-svelte-h":!0}),f(i)!=="svelte-lkacfy"&&(i.innerHTML=d)},m(c,E){r(c,i,E)},p:he,d(c){c&&n(i)}}}function xe(q){let i,d,c,E,p,R,u,te="Below you can find a listing of all the Deep Learning Containers (DLCs) available on Google Cloud. Containers are created for each supported combination of use-case (training, inference), accelerator type (CPU, GPU, TPU), and framework (PyTorch, TGI, TEI).",B,g,S,h,j,m,ne='Text Generation Inference (TGI) DLC is available for high-performance text generation of Large Language Models on both GPU and TPU (soon). The TGI DLC enables you to deploy <a href="https://huggingface.co/models?other=text-generation-inference&amp;sort=trending" rel="nofollow">any of the +140,000 text generation inference supported models from the Hugging Face Hub</a>, or any custom model as long as <a href="https://huggingface.co/docs/text-generation-inference/supported_models" rel="nofollow">its architecture is supported within TGI</a>.',F,T,re='<thead><tr><th>Container URI</th> <th>Path</th> <th>Accelerator</th></tr></thead> <tbody><tr><td>us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-generation-inference-cu121.2-2.ubuntu2204.py310</td> <td><a href="./containers/tgi/gpu/2.2.0/Dockerfile">text-generation-inference-gpu.2.2.0</a></td> <td>GPU</td></tr></tbody>',z,$,O,b,oe='Text Embeddings Inference (TEI) DLC is available for high-performance serving of embedding models on both GPU and GPU. The TEI DLC enables you to deploy <a href="https://huggingface.co/models?other=text-embeddings-inference&amp;sort=trending" rel="nofollow">any of the +10,000 embedding, re-ranking or sequence classification supported models from the Hugging Face Hub</a>, or any custom model as long as <a href="https://huggingface.co/docs/text-embeddings-inference/en/supported_models" rel="nofollow">its architecture is supported within TEI</a>.',J,y,ae='<thead><tr><th>Container URI</th> <th>Path</th> <th>Accelerator</th></tr></thead> <tbody><tr><td>us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cu122.1-4.ubuntu2204</td> <td><a href="./containers/tei/gpu/1.4.0/Dockerfile">text-embeddings-inference-gpu.1.4.0</a></td> <td>GPU</td></tr> <tr><td>us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cpu.1-4</td> <td><a href="./containers/tei/cpu/1.4.0/Dockerfile">text-embeddings-inference-cpu.1.4.0</a></td> <td>CPU</td></tr></tbody>',K,v,N,C,ie="Pytorch Inference DLC is available for Pytorch via 🤗 Transformers, for serving models trained with 🤗 TRL, Sentence Transformers or 🧨 Diffusers, on both CPU and GPU.",Q,x,le='<thead><tr><th>Container URI</th> <th>Path</th> <th>Accelerator</th></tr></thead> <tbody><tr><td>us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-pytorch-inference-cu121.2-2.transformers.4-44.ubuntu2204.py311</td> <td><a href="./containers/pytorch/inference/gpu/2.2.2/transformers/4.44.0/py311/Dockerfile">huggingface-pytorch-inference-gpu.2.2.2.transformers.4.44.0.py311</a></td> <td>GPU</td></tr> <tr><td>us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-pytorch-inference-cpu.2-2.transformers.4-44.ubuntu2204.py311</td> <td><a href="./containers/pytorch/inference/cpu/2.2.2/transformers/4.44.0/py311/Dockerfile">huggingface-pytorch-inference-cpu.2.2.2.transformers.4.44.0.py311</a></td> <td>CPU</td></tr></tbody>',V,P,W,L,se="Pytorch Training DLC is available for PyTorch via 🤗 Transformers. It includes support for training with libraries such as 🤗 TRL, Sentence Transformers, or 🧨 Diffusers, on both GPUs and TPUs (soon).",X,_,ce='<thead><tr><th>Container URI</th> <th>Path</th> <th>Accelerator</th></tr></thead> <tbody><tr><td>us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-pytorch-training-cu121.2-3.transformers.4-42.ubuntu2204.py310</td> <td><a href="./containers/pytorch/training/gpu/2.3.0/transformers/4.42.3/py310/Dockerfile">huggingface-pytorch-training-gpu.2.3.0.transformers.4.42.3.py310</a></td> <td>GPU</td></tr></tbody>',Y,G,Z,M,ee;return p=new A({props:{title:"DLCs on Google Cloud",local:"dlcs-on-google-cloud",headingTag:"h1"}}),g=new ye({props:{$$slots:{default:[Ce]},$$scope:{ctx:q}}}),h=new A({props:{title:"Text Generation Inference (TGI)",local:"text-generation-inference-tgi",headingTag:"h2"}}),$=new A({props:{title:"Text Embeddings Inference (TEI)",local:"text-embeddings-inference-tei",headingTag:"h2"}}),v=new A({props:{title:"PyTorch Inference",local:"pytorch-inference",headingTag:"h2"}}),P=new A({props:{title:"PyTorch Training",local:"pytorch-training",headingTag:"h2"}}),G=new ve({props:{source:"https://github.com/huggingface/Google-Cloud-Containers/blob/main/docs/source/containers/available.mdx"}}),{c(){i=l("meta"),d=o(),c=l("p"),E=o(),k(p.$$.fragment),R=o(),u=l("p"),u.textContent=te,B=o(),k(g.$$.fragment),S=o(),k(h.$$.fragment),j=o(),m=l("p"),m.innerHTML=ne,F=o(),T=l("table"),T.innerHTML=re,z=o(),k($.$$.fragment),O=o(),b=l("p"),b.innerHTML=oe,J=o(),y=l("table"),y.innerHTML=ae,K=o(),k(v.$$.fragment),N=o(),C=l("p"),C.textContent=ie,Q=o(),x=l("table"),x.innerHTML=le,V=o(),k(P.$$.fragment),W=o(),L=l("p"),L.textContent=se,X=o(),_=l("table"),_.innerHTML=ce,Y=o(),k(G.$$.fragment),Z=o(),M=l("p"),this.h()},l(e){const t=$e("svelte-u9bgzb",document.head);i=s(t,"META",{name:!0,content:!0}),t.forEach(n),d=a(e),c=s(e,"P",{}),ge(c).forEach(n),E=a(e),w(p.$$.fragment,e),R=a(e),u=s(e,"P",{"data-svelte-h":!0}),f(u)!=="svelte-cddt1o"&&(u.textContent=te),B=a(e),w(g.$$.fragment,e),S=a(e),w(h.$$.fragment,e),j=a(e),m=s(e,"P",{"data-svelte-h":!0}),f(m)!=="svelte-1xju71k"&&(m.innerHTML=ne),F=a(e),T=s(e,"TABLE",{"data-svelte-h":!0}),f(T)!=="svelte-16qrj82"&&(T.innerHTML=re),z=a(e),w($.$$.fragment,e),O=a(e),b=s(e,"P",{"data-svelte-h":!0}),f(b)!=="svelte-13c4h5t"&&(b.innerHTML=oe),J=a(e),y=s(e,"TABLE",{"data-svelte-h":!0}),f(y)!=="svelte-18rzbr7"&&(y.innerHTML=ae),K=a(e),w(v.$$.fragment,e),N=a(e),C=s(e,"P",{"data-svelte-h":!0}),f(C)!=="svelte-122ovbm"&&(C.textContent=ie),Q=a(e),x=s(e,"TABLE",{"data-svelte-h":!0}),f(x)!=="svelte-p98suh"&&(x.innerHTML=le),V=a(e),w(P.$$.fragment,e),W=a(e),L=s(e,"P",{"data-svelte-h":!0}),f(L)!=="svelte-novjsm"&&(L.textContent=se),X=a(e),_=s(e,"TABLE",{"data-svelte-h":!0}),f(_)!=="svelte-15qyvwm"&&(_.innerHTML=ce),Y=a(e),w(G.$$.fragment,e),Z=a(e),M=s(e,"P",{}),ge(M).forEach(n),this.h()},h(){de(i,"name","hf:doc:metadata"),de(i,"content",Pe)},m(e,t){be(document.head,i),r(e,d,t),r(e,c,t),r(e,E,t),I(p,e,t),r(e,R,t),r(e,u,t),r(e,B,t),I(g,e,t),r(e,S,t),I(h,e,t),r(e,j,t),r(e,m,t),r(e,F,t),r(e,T,t),r(e,z,t),I($,e,t),r(e,O,t),r(e,b,t),r(e,J,t),r(e,y,t),r(e,K,t),I(v,e,t),r(e,N,t),r(e,C,t),r(e,Q,t),r(e,x,t),r(e,V,t),I(P,e,t),r(e,W,t),r(e,L,t),r(e,X,t),r(e,_,t),r(e,Y,t),I(G,e,t),r(e,Z,t),r(e,M,t),ee=!0},p(e,[t]){const fe={};t&2&&(fe.$$scope={dirty:t,ctx:e}),g.$set(fe)},i(e){ee||(U(p.$$.fragment,e),U(g.$$.fragment,e),U(h.$$.fragment,e),U($.$$.fragment,e),U(v.$$.fragment,e),U(P.$$.fragment,e),U(G.$$.fragment,e),ee=!0)},o(e){D(p.$$.fragment,e),D(g.$$.fragment,e),D(h.$$.fragment,e),D($.$$.fragment,e),D(v.$$.fragment,e),D(P.$$.fragment,e),D(G.$$.fragment,e),ee=!1},d(e){e&&(n(d),n(c),n(E),n(R),n(u),n(B),n(S),n(j),n(m),n(F),n(T),n(z),n(O),n(b),n(J),n(y),n(K),n(N),n(C),n(Q),n(x),n(V),n(W),n(L),n(X),n(_),n(Y),n(Z),n(M)),n(i),H(p,e),H(g,e),H(h,e),H($,e),H(v,e),H(P,e),H(G,e)}}}const Pe='{"title":"DLCs on Google Cloud","local":"dlcs-on-google-cloud","sections":[{"title":"Text Generation Inference (TGI)","local":"text-generation-inference-tgi","sections":[],"depth":2},{"title":"Text Embeddings Inference (TEI)","local":"text-embeddings-inference-tei","sections":[],"depth":2},{"title":"PyTorch Inference","local":"pytorch-inference","sections":[],"depth":2},{"title":"PyTorch Training","local":"pytorch-training","sections":[],"depth":2}],"depth":1}';function Le(q){return ue(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Ie extends me{constructor(i){super(),Te(this,i,Le,xe,pe,{})}}export{Ie as component};