import{s as Ct,o as Ht,n as Tt}from"../chunks/scheduler.6062bdaf.js";import{S as Lt,i as Mt,g as i,s as t,r as u,A as Pt,h as o,f as r,c as a,j as $,u as h,x as g,k as v,y as n,a as s,v as l,d as m,t as _,w as b}from"../chunks/index.4bca734e.js";import{T as Dt}from"../chunks/Tip.b9ac1f03.js";import{D as y}from"../chunks/Docstring.81ba5e3c.js";import{C as At}from"../chunks/CodeBlock.fa01571c.js";import{E as jt}from"../chunks/ExampleCodeBlock.30e7542f.js";import{H as Ge,E as zt}from"../chunks/EditOnGithub.74ab2baa.js";function qt(be){let f,M='‘huggingface_hub’를 사용하여 추론 엔드포인트를 프로그래밍 방식으로 관리하는 방법을 알고 싶다면, <a href="../guides/inference_endpoints">관련 가이드</a>를 확인해 보세요.';return{c(){f=i("p"),f.innerHTML=M},l(I){f=o(I,"P",{"data-svelte-h":!0}),g(f)!=="svelte-k34n4y"&&(f.innerHTML=M)},m(I,x){s(I,f,x)},p:Tt,d(I){I&&r(f)}}}function Nt(be){let f,M="Example:",I,x,k;return x=new At({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMGdldF9pbmZlcmVuY2VfZW5kcG9pbnQlMEFlbmRwb2ludCUyMCUzRCUyMGdldF9pbmZlcmVuY2VfZW5kcG9pbnQoJTIybXktdGV4dC10by1pbWFnZSUyMiklMEFlbmRwb2ludCUwQSUwQWVuZHBvaW50LnN0YXR1cyUwQWVuZHBvaW50LnVybCUwQSUwQWVuZHBvaW50LmNsaWVudC50ZXh0X3RvX2ltYWdlKC4uLiklMEElMEFlbmRwb2ludC5wYXVzZSgpJTBBJTBBZW5kcG9pbnQucmVzdW1lKCklMEFlbmRwb2ludC53YWl0KCklMEFlbmRwb2ludC5jbGllbnQudGV4dF90b19pbWFnZSguLi4p",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> get_inference_endpoint
<span class="hljs-meta">&gt;&gt;&gt; </span>endpoint = get_inference_endpoint(<span class="hljs-string">&quot;my-text-to-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>endpoint
InferenceEndpoint(name=<span class="hljs-string">&#x27;my-text-to-image&#x27;</span>, ...)

<span class="hljs-comment"># Get status</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>endpoint.status
<span class="hljs-string">&#x27;running&#x27;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>endpoint.url
<span class="hljs-string">&#x27;https://my-text-to-image.region.vendor.endpoints.huggingface.cloud&#x27;</span>

<span class="hljs-comment"># Run inference</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>endpoint.client.text_to_image(...)

<span class="hljs-comment"># Pause endpoint to save $$$</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>endpoint.pause()

<span class="hljs-comment"># ...</span>
<span class="hljs-comment"># Resume and wait for deployment</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>endpoint.resume()
<span class="hljs-meta">&gt;&gt;&gt; </span>endpoint.wait()
<span class="hljs-meta">&gt;&gt;&gt; </span>endpoint.client.text_to_image(...)`,wrap:!1}}),{c(){f=i("p"),f.textContent=M,I=t(),u(x.$$.fragment)},l(E){f=o(E,"P",{"data-svelte-h":!0}),g(f)!=="svelte-11lpom8"&&(f.textContent=M),I=a(E),h(x.$$.fragment,E)},m(E,L){s(E,f,L),s(E,I,L),l(x,E,L),k=!0},p:Tt,i(E){k||(m(x.$$.fragment,E),k=!0)},o(E){_(x.$$.fragment,E),k=!1},d(E){E&&(r(f),r(I)),b(x,E)}}}function Vt(be){let f,M,I,x,k,E,L,Jn='Hugging Face가 관리하는 추론 엔드포인트는 우리가 모델을 쉽고 안전하게 배포할 수 있게 해주는 도구입니다. 이러한 추론 엔드포인트는 <a href="https://huggingface.co/models" rel="nofollow">Hub</a>에 있는 모델을 기반으로 설계되었습니다. 이 문서는 <code>huggingface_hub</code>와 추론 엔드포인트 통합에 관한 참조 페이지이며, 더욱 자세한 정보는 <a href="https://huggingface.co/docs/inference-endpoints/index" rel="nofollow">공식 문서</a>를 통해 확인할 수 있습니다.',Ze,q,Be,Z,Kn='추론 엔드포인트는 API로 쉽게 접근할 수 있습니다. 이 엔드포인트들은 <a href="https://api.endpoints.huggingface.cloud/" rel="nofollow">Swagger</a>를 통해 문서화되어 있고, <a href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint">InferenceEndpoint</a> 클래스는 이 API를 사용해 만든 간단한 래퍼입니다.',Qe,B,Ye,Q,et='다음과 같은 추론 엔드포인트의 기능이 <a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi">HfApi</a>안에 구현되어 있습니다:',Xe,Y,nt='<li><a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi.get_inference_endpoint">get_inference_endpoint()</a>와 <a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi.list_inference_endpoints">list_inference_endpoints()</a>를 사용해 엔드포인트 정보를 조회할 수 있습니다.</li> <li><a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi.create_inference_endpoint">create_inference_endpoint()</a>, <a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi.update_inference_endpoint">update_inference_endpoint()</a>, <a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi.delete_inference_endpoint">delete_inference_endpoint()</a>로 엔드포인트를 배포하고 관리할 수 있습니다.</li> <li><a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi.pause_inference_endpoint">pause_inference_endpoint()</a>와 <a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi.resume_inference_endpoint">resume_inference_endpoint()</a>로 엔드포인트를 잠시 멈추거나 다시 시작할 수 있습니다.</li> <li><a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi.scale_to_zero_inference_endpoint">scale_to_zero_inference_endpoint()</a>로 엔드포인트의 복제본을 0개로 설정할 수 있습니다.</li>',Je,X,Ke,J,tt='기본 데이터 클래스는 <a href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint">InferenceEndpoint</a>입니다. 여기에는 구성 및 현재 상태를 가지고 있는 배포된 <code>InferenceEndpoint</code>에 대한 정보가 포함되어 있습니다. 배포 후에는 <a href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint.client">InferenceEndpoint.client</a>와 <a href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint.async_client">InferenceEndpoint.async_client</a>를 사용해 엔드포인트에서 추론 작업을 할 수 있고, 이때 <a href="/docs/huggingface_hub/main/ko/package_reference/inference_client#huggingface_hub.InferenceClient">InferenceClient</a>와 <a href="/docs/huggingface_hub/main/ko/package_reference/inference_client#huggingface_hub.AsyncInferenceClient">AsyncInferenceClient</a> 객체를 반환합니다.',en,p,K,_n,Ee,at="Contains information about a deployed Inference Endpoint.",bn,N,En,V,ee,In,Ie,rt="Initialize object from raw dictionary.",$n,S,ne,vn,$e,it="Returns a client to make predictions on this Inference Endpoint.",xn,U,te,yn,ve,ot="Returns a client to make predictions on this Inference Endpoint.",kn,w,ae,wn,xe,ct="Delete the Inference Endpoint.",Tn,ye,st=`This operation is not reversible. If you don’t want to be charged for an Inference Endpoint, it is preferable
to pause it with <a href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint.pause">InferenceEndpoint.pause()</a> or scale it to zero with <a href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint.scale_to_zero">InferenceEndpoint.scale_to_zero()</a>.`,Cn,ke,pt='This is an alias for <a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi.delete_inference_endpoint">HfApi.delete_inference_endpoint()</a>.',Hn,W,re,Ln,we,gt="Fetch latest information about the Inference Endpoint.",Mn,T,ie,Pn,Te,dt="Pause the Inference Endpoint.",Dn,Ce,ft=`A paused Inference Endpoint will not be charged. It can be resumed at any time using <a href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint.resume">InferenceEndpoint.resume()</a>.
This is different than scaling the Inference Endpoint to zero with <a href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint.scale_to_zero">InferenceEndpoint.scale_to_zero()</a>, which
would be automatically restarted when a request is made to it.`,An,He,ut=`This is an alias for <a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi.pause_inference_endpoint">HfApi.pause_inference_endpoint()</a>. The current object is mutated in place with the
latest data from the server.`,jn,P,oe,zn,Le,ht="Resume the Inference Endpoint.",qn,Me,lt=`This is an alias for <a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi.resume_inference_endpoint">HfApi.resume_inference_endpoint()</a>. The current object is mutated in place with the
latest data from the server.`,Nn,C,ce,Vn,Pe,mt="Scale Inference Endpoint to zero.",Sn,De,_t=`An Inference Endpoint scaled to zero will not be charged. It will be resume on the next request to it, with a
cold start delay. This is different than pausing the Inference Endpoint with <a href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint.pause">InferenceEndpoint.pause()</a>, which
would require a manual resume with <a href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint.resume">InferenceEndpoint.resume()</a>.`,Un,Ae,bt=`This is an alias for <a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi.scale_to_zero_inference_endpoint">HfApi.scale_to_zero_inference_endpoint()</a>. The current object is mutated in place with the
latest data from the server.`,Wn,H,se,Rn,je,Et="Update the Inference Endpoint.",On,ze,It=`This method allows the update of either the compute configuration, the deployed model, or both. All arguments are
optional but at least one must be provided.`,Gn,qe,$t=`This is an alias for <a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi.update_inference_endpoint">HfApi.update_inference_endpoint()</a>. The current object is mutated in place with the
latest data from the server.`,Fn,D,pe,Zn,Ne,vt="Wait for the Inference Endpoint to be deployed.",Bn,Ve,xt=`Information from the server will be fetched every 1s. If the Inference Endpoint is not deployed after <code>timeout</code>
seconds, a <code>InferenceEndpointTimeoutError</code> will be raised. The <a href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint">InferenceEndpoint</a> will be mutated in place with the latest
data.`,nn,ge,tn,A,de,Qn,Se,yt="An enumeration.",an,fe,rn,j,ue,Yn,Ue,kt="An enumeration.",on,he,cn,z,le,Xn,We,wt="Generic exception when dealing with Inference Endpoints.",sn,me,pn,Fe,gn;return k=new Ge({props:{title:"추론 엔드포인트",local:"inference-endpoints",headingTag:"h1"}}),q=new Dt({props:{$$slots:{default:[qt]},$$scope:{ctx:be}}}),B=new Ge({props:{title:"매소드",local:"methods",headingTag:"h2"}}),X=new Ge({props:{title:"InferenceEndpoint",local:"huggingface_hub.InferenceEndpoint ][ huggingface_hub.InferenceEndpoint",headingTag:"h2"}}),K=new y({props:{name:"class huggingface_hub.InferenceEndpoint",anchor:"huggingface_hub.InferenceEndpoint",parameters:[{name:"namespace",val:": str"},{name:"raw",val:": typing.Dict"},{name:"_token",val:": typing.Union[str, bool, NoneType]"},{name:"_api",val:": HfApi"}],parametersDescription:[{anchor:"huggingface_hub.InferenceEndpoint.name",description:`<strong>name</strong> (<code>str</code>) &#x2014;
The unique name of the Inference Endpoint.`,name:"name"},{anchor:"huggingface_hub.InferenceEndpoint.namespace",description:`<strong>namespace</strong> (<code>str</code>) &#x2014;
The namespace where the Inference Endpoint is located.`,name:"namespace"},{anchor:"huggingface_hub.InferenceEndpoint.repository",description:`<strong>repository</strong> (<code>str</code>) &#x2014;
The name of the model repository deployed on this Inference Endpoint.`,name:"repository"},{anchor:"huggingface_hub.InferenceEndpoint.status",description:`<strong>status</strong> (<a href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpointStatus">InferenceEndpointStatus</a>) &#x2014;
The current status of the Inference Endpoint.`,name:"status"},{anchor:"huggingface_hub.InferenceEndpoint.url",description:`<strong>url</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The URL of the Inference Endpoint, if available. Only a deployed Inference Endpoint will have a URL.`,name:"url"},{anchor:"huggingface_hub.InferenceEndpoint.framework",description:`<strong>framework</strong> (<code>str</code>) &#x2014;
The machine learning framework used for the model.`,name:"framework"},{anchor:"huggingface_hub.InferenceEndpoint.revision",description:`<strong>revision</strong> (<code>str</code>) &#x2014;
The specific model revision deployed on the Inference Endpoint.`,name:"revision"},{anchor:"huggingface_hub.InferenceEndpoint.task",description:`<strong>task</strong> (<code>str</code>) &#x2014;
The task associated with the deployed model.`,name:"task"},{anchor:"huggingface_hub.InferenceEndpoint.created_at",description:`<strong>created_at</strong> (<code>datetime.datetime</code>) &#x2014;
The timestamp when the Inference Endpoint was created.`,name:"created_at"},{anchor:"huggingface_hub.InferenceEndpoint.updated_at",description:`<strong>updated_at</strong> (<code>datetime.datetime</code>) &#x2014;
The timestamp of the last update of the Inference Endpoint.`,name:"updated_at"},{anchor:"huggingface_hub.InferenceEndpoint.type",description:`<strong>type</strong> (<a href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpointType">InferenceEndpointType</a>) &#x2014;
The type of the Inference Endpoint (public, protected, private).`,name:"type"},{anchor:"huggingface_hub.InferenceEndpoint.raw",description:`<strong>raw</strong> (<code>Dict</code>) &#x2014;
The raw dictionary data returned from the API.`,name:"raw"},{anchor:"huggingface_hub.InferenceEndpoint.token",description:`<strong>token</strong> (<code>str</code> or <code>bool</code>, <em>optional</em>) &#x2014;
Authentication token for the Inference Endpoint, if set when requesting the API. Will default to the
locally saved token if not provided. Pass <code>token=False</code> if you don&#x2019;t want to send your token to the server.`,name:"token"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/_inference_endpoints.py#L38"}}),N=new jt({props:{anchor:"huggingface_hub.InferenceEndpoint.example",$$slots:{default:[Nt]},$$scope:{ctx:be}}}),ee=new y({props:{name:"from_raw",anchor:"huggingface_hub.InferenceEndpoint.from_raw",parameters:[{name:"raw",val:": typing.Dict"},{name:"namespace",val:": str"},{name:"token",val:": typing.Union[str, bool, NoneType] = None"},{name:"api",val:": typing.Optional[ForwardRef('HfApi')] = None"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/_inference_endpoints.py#L121"}}),ne=new y({props:{name:"client",anchor:"huggingface_hub.InferenceEndpoint.client",parameters:[],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/_inference_endpoints.py#L140",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>an inference client pointing to the deployed endpoint.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/ko/package_reference/inference_client#huggingface_hub.InferenceClient"
>InferenceClient</a></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpointError"
>InferenceEndpointError</a> — If the Inference Endpoint is not yet deployed.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpointError"
>InferenceEndpointError</a></p>
`}}),te=new y({props:{name:"async_client",anchor:"huggingface_hub.InferenceEndpoint.async_client",parameters:[],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/_inference_endpoints.py#L157",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>an asyncio-compatible inference client pointing to the deployed endpoint.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/ko/package_reference/inference_client#huggingface_hub.AsyncInferenceClient"
>AsyncInferenceClient</a></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpointError"
>InferenceEndpointError</a> — If the Inference Endpoint is not yet deployed.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpointError"
>InferenceEndpointError</a></p>
`}}),ae=new y({props:{name:"delete",anchor:"huggingface_hub.InferenceEndpoint.delete",parameters:[],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/_inference_endpoints.py#L369"}}),re=new y({props:{name:"fetch",anchor:"huggingface_hub.InferenceEndpoint.fetch",parameters:[],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/_inference_endpoints.py#L221",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>the same Inference Endpoint, mutated in place with the latest data.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint"
>InferenceEndpoint</a></p>
`}}),ie=new y({props:{name:"pause",anchor:"huggingface_hub.InferenceEndpoint.pause",parameters:[],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/_inference_endpoints.py#L312",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>the same Inference Endpoint, mutated in place with the latest data.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint"
>InferenceEndpoint</a></p>
`}}),oe=new y({props:{name:"resume",anchor:"huggingface_hub.InferenceEndpoint.resume",parameters:[{name:"running_ok",val:": bool = True"}],parametersDescription:[{anchor:"huggingface_hub.InferenceEndpoint.resume.running_ok",description:`<strong>running_ok</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If <code>True</code>, the method will not raise an error if the Inference Endpoint is already running. Defaults to
<code>True</code>.`,name:"running_ok"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/_inference_endpoints.py#L330",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>the same Inference Endpoint, mutated in place with the latest data.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint"
>InferenceEndpoint</a></p>
`}}),ce=new y({props:{name:"scale_to_zero",anchor:"huggingface_hub.InferenceEndpoint.scale_to_zero",parameters:[],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/_inference_endpoints.py#L351",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>the same Inference Endpoint, mutated in place with the latest data.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint"
>InferenceEndpoint</a></p>
`}}),se=new y({props:{name:"update",anchor:"huggingface_hub.InferenceEndpoint.update",parameters:[{name:"accelerator",val:": typing.Optional[str] = None"},{name:"instance_size",val:": typing.Optional[str] = None"},{name:"instance_type",val:": typing.Optional[str] = None"},{name:"min_replica",val:": typing.Optional[int] = None"},{name:"max_replica",val:": typing.Optional[int] = None"},{name:"scale_to_zero_timeout",val:": typing.Optional[int] = None"},{name:"repository",val:": typing.Optional[str] = None"},{name:"framework",val:": typing.Optional[str] = None"},{name:"revision",val:": typing.Optional[str] = None"},{name:"task",val:": typing.Optional[str] = None"},{name:"custom_image",val:": typing.Optional[typing.Dict] = None"},{name:"secrets",val:": typing.Optional[typing.Dict[str, str]] = None"}],parametersDescription:[{anchor:"huggingface_hub.InferenceEndpoint.update.accelerator",description:`<strong>accelerator</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The hardware accelerator to be used for inference (e.g. <code>&quot;cpu&quot;</code>).`,name:"accelerator"},{anchor:"huggingface_hub.InferenceEndpoint.update.instance_size",description:`<strong>instance_size</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The size or type of the instance to be used for hosting the model (e.g. <code>&quot;x4&quot;</code>).`,name:"instance_size"},{anchor:"huggingface_hub.InferenceEndpoint.update.instance_type",description:`<strong>instance_type</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The cloud instance type where the Inference Endpoint will be deployed (e.g. <code>&quot;intel-icl&quot;</code>).`,name:"instance_type"},{anchor:"huggingface_hub.InferenceEndpoint.update.min_replica",description:`<strong>min_replica</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The minimum number of replicas (instances) to keep running for the Inference Endpoint.`,name:"min_replica"},{anchor:"huggingface_hub.InferenceEndpoint.update.max_replica",description:`<strong>max_replica</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The maximum number of replicas (instances) to scale to for the Inference Endpoint.`,name:"max_replica"},{anchor:"huggingface_hub.InferenceEndpoint.update.scale_to_zero_timeout",description:`<strong>scale_to_zero_timeout</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The duration in minutes before an inactive endpoint is scaled to zero.`,name:"scale_to_zero_timeout"},{anchor:"huggingface_hub.InferenceEndpoint.update.repository",description:`<strong>repository</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The name of the model repository associated with the Inference Endpoint (e.g. <code>&quot;gpt2&quot;</code>).`,name:"repository"},{anchor:"huggingface_hub.InferenceEndpoint.update.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The machine learning framework used for the model (e.g. <code>&quot;custom&quot;</code>).`,name:"framework"},{anchor:"huggingface_hub.InferenceEndpoint.update.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The specific model revision to deploy on the Inference Endpoint (e.g. <code>&quot;6c0e6080953db56375760c0471a8c5f2929baf11&quot;</code>).`,name:"revision"},{anchor:"huggingface_hub.InferenceEndpoint.update.task",description:`<strong>task</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The task on which to deploy the model (e.g. <code>&quot;text-classification&quot;</code>).`,name:"task"},{anchor:"huggingface_hub.InferenceEndpoint.update.custom_image",description:`<strong>custom_image</strong> (<code>Dict</code>, <em>optional</em>) &#x2014;
A custom Docker image to use for the Inference Endpoint. This is useful if you want to deploy an
Inference Endpoint running on the <code>text-generation-inference</code> (TGI) framework (see examples).`,name:"custom_image"},{anchor:"huggingface_hub.InferenceEndpoint.update.secrets",description:`<strong>secrets</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
Secret values to inject in the container environment.`,name:"secrets"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/_inference_endpoints.py#L232",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>the same Inference Endpoint, mutated in place with the latest data.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint"
>InferenceEndpoint</a></p>
`}}),pe=new y({props:{name:"wait",anchor:"huggingface_hub.InferenceEndpoint.wait",parameters:[{name:"timeout",val:": typing.Optional[int] = None"},{name:"refresh_every",val:": int = 5"}],parametersDescription:[{anchor:"huggingface_hub.InferenceEndpoint.wait.timeout",description:`<strong>timeout</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The maximum time to wait for the Inference Endpoint to be deployed, in seconds. If <code>None</code>, will wait
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<p>the same Inference Endpoint, mutated in place with the latest data.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpoint"
>InferenceEndpoint</a></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><a
  href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpointError"
>InferenceEndpointError</a> —
If the Inference Endpoint ended up in a failed state.</li>
<li><code>InferenceEndpointTimeoutError</code> —
If the Inference Endpoint is not deployed after <code>timeout</code> seconds.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/huggingface_hub/main/ko/package_reference/inference_endpoints#huggingface_hub.InferenceEndpointError"
>InferenceEndpointError</a> or <code>InferenceEndpointTimeoutError</code></p>
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