import{s as Lt,o as Mt,n as Pt}from"../chunks/scheduler.0cf4ef2e.js";import{S as jt,i as At,e as i,s as t,c as f,h as zt,a as o,d as r,b as a,f as $,g as u,j as g,k as I,l as n,m as s,n as h,t as l,o as m,p as _}from"../chunks/index.abc433bf.js";import{C as qt,H as Re,E as Dt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.c5e6f579.js";import{D as v}from"../chunks/Docstring.d25cce65.js";import{C as Nt}from"../chunks/CodeBlock.0951eb7c.js";import{E as Vt}from"../chunks/ExampleCodeBlock.51f0b6db.js";function St(Fe){let E,F="Example:",L,x,y;return x=new Nt({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(){E=i("p"),E.textContent=F,L=t(),f(x.$$.fragment)},l(b){E=o(b,"P",{"data-svelte-h":!0}),g(E)!=="svelte-11lpom8"&&(E.textContent=F),L=a(b),u(x.$$.fragment,b)},m(b,H){s(b,E,H),s(b,L,H),h(x,b,H),y=!0},p:Pt,i(b){y||(l(x.$$.fragment,b),y=!0)},o(b){m(x.$$.fragment,b),y=!1},d(b){b&&(r(E),r(L)),_(x,b)}}}function Ut(Fe){let E,F,L,x,y,b,H,Ze,Z,et='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.',Be,q,nt='<p>‘huggingface_hub’를 사용하여 추론 엔드포인트를 프로그래밍 방식으로 관리하는 방법을 알고 싶다면, <a href="../guides/inference_endpoints">관련 가이드</a>를 확인해 보세요.</p>',Qe,B,tt='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.',Ye,Q,Xe,Y,at='다음과 같은 추론 엔드포인트의 기능이 <a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi">HfApi</a>안에 구현되어 있습니다:',Je,X,rt='<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>',Ke,J,en,K,it='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.',nn,p,ee,En,be,ot="Contains information about a deployed Inference Endpoint.",In,D,$n,N,ne,vn,Ee,ct="Initialize object from raw dictionary.",xn,V,te,yn,Ie,st="Returns a client to make predictions on this Inference Endpoint.",kn,S,ae,wn,$e,pt="Returns a client to make predictions on this Inference Endpoint.",Tn,k,re,Cn,ve,gt="Delete the Inference Endpoint.",Hn,xe,dt=`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>.`,Ln,ye,ft='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>.',Mn,U,ie,Pn,ke,ut="Fetch latest information about the Inference Endpoint.",jn,w,oe,An,we,ht="Pause the Inference Endpoint.",zn,Te,lt=`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 from 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.`,qn,Ce,mt=`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.`,Dn,M,ce,Nn,He,_t="Resume the Inference Endpoint.",Vn,Le,bt=`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.`,Sn,T,se,Un,Me,Et="Scale Inference Endpoint to zero.",On,Pe,It=`An Inference Endpoint scaled to zero will not be charged. It will be resumed on the next request to it, with a
cold start delay. This is different from 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>.`,Wn,je,$t=`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.`,Rn,C,pe,Gn,Ae,vt="Update the Inference Endpoint.",Fn,ze,xt=`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.`,Zn,qe,yt=`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.`,Bn,P,ge,Qn,De,kt="Wait for the Inference Endpoint to be deployed.",Yn,Ne,wt=`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.`,tn,de,an,j,fe,Xn,Ve,Tt="An enumeration.",rn,ue,on,A,he,Jn,Se,Ct="An enumeration.",cn,le,sn,z,me,Kn,Ue,Ht="Generic exception when dealing with Inference Endpoints.",pn,_e,gn,Ge,dn;return y=new qt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),H=new Re({props:{title:"추론 엔드포인트",local:"inference-endpoints",headingTag:"h1"}}),Q=new Re({props:{title:"매소드",local:"methods",headingTag:"h2"}}),J=new Re({props:{title:"InferenceEndpoint",local:"huggingface_hub.InferenceEndpoint ][ huggingface_hub.InferenceEndpoint",headingTag:"h2"}}),ee=new v({props:{name:"class huggingface_hub.InferenceEndpoint",anchor:"huggingface_hub.InferenceEndpoint",parameters:[{name:"namespace",val:": str"},{name:"raw",val:": 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"}}),D=new Vt({props:{anchor:"huggingface_hub.InferenceEndpoint.example",$$slots:{default:[St]},$$scope:{ctx:Fe}}}),ne=new v({props:{name:"from_raw",anchor:"huggingface_hub.InferenceEndpoint.from_raw",parameters:[{name:"raw",val:": 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"}}),te=new v({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>
`}}),ae=new v({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#L162",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>
`}}),re=new v({props:{name:"delete",anchor:"huggingface_hub.InferenceEndpoint.delete",parameters:[],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/_inference_endpoints.py#L385"}}),ie=new v({props:{name:"fetch",anchor:"huggingface_hub.InferenceEndpoint.fetch",parameters:[],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/_inference_endpoints.py#L237",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 v({props:{name:"pause",anchor:"huggingface_hub.InferenceEndpoint.pause",parameters:[],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/_inference_endpoints.py#L328",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 v({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#L346",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 v({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#L367",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 v({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[dict] = None"},{name:"secrets",val:": typing.Optional[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#L248",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>
`}}),ge=new v({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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