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hf-doc-build/doc-dev / transformers /pr_35339 /ko /_app /immutable /nodes /21.f2d08e84.js

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	import{s as vs,n as Qs,o as Vs}from"../chunks/scheduler.bdbef820.js";import{S as xs,i as Ns,g as p,s as e,r as M,A as Gs,h as r,f as l,c as n,j as Ys,u as i,x as m,k as As,y as Rs,a as t,v as y,d as J,t as c,w as o}from"../chunks/index.33f81d56.js";import{C as j}from"../chunks/CodeBlock.362b34a4.js";import{H as ws,E as Ss}from"../chunks/EditOnGithub.a9246e21.js";function Hs(Is){let T,H,R,F,U,z,h,fs='🤗 Transformers에서는 🤗 Transformers 모델을 학습시키는데 최적화된 <a href="/docs/transformers/pr_35339/ko/main_classes/trainer#transformers.Trainer">Trainer</a> 클래스를 제공하기 때문에, 사용자는 직접 훈련 루프를 작성할 필요 없이 더욱 간편하게 학습을 시킬 수 있습니다. 또한, <a href="/docs/transformers/pr_35339/ko/main_classes/trainer#transformers.Trainer">Trainer</a>는 하이퍼파라미터 탐색을 위한 API를 제공합니다. 이 문서에서 이 API를 활용하는 방법을 예시와 함께 보여드리겠습니다.',E,u,D,w,Cs=`<a href="/docs/transformers/pr_35339/ko/main_classes/trainer#transformers.Trainer">Trainer</a>는 현재 아래 4가지 하이퍼파라미터 탐색 백엔드를 지원합니다:
	<a href="https://optuna.org/" rel="nofollow">optuna</a>와 <a href="https://sigopt.com/" rel="nofollow">sigopt</a>, <a href="https://docs.ray.io/en/latest/tune/index.html" rel="nofollow">raytune</a>, <a href="https://wandb.ai/site/sweeps" rel="nofollow">wandb</a> 입니다.`,L,I,ds="하이퍼파라미터 탐색 백엔드로 사용하기 전에 아래의 명령어를 사용하여 라이브러리들을 설치하세요.",P,f,K,C,O,d,bs="하이퍼파라미터 탐색 공간을 정의하세요. 하이퍼파라미터 탐색 백엔드마다 서로 다른 형식이 필요합니다.",ss,b,_s='sigopt의 경우, 해당 <a href="https://docs.sigopt.com/ai-module-api-references/api_reference/objects/object_parameter" rel="nofollow">object_parameter</a> 문서를 참조하여 아래와 같이 작성하세요:',as,_,ls,g,gs='optuna의 경우, 해당 <a href="https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html#sphx-glr-tutorial-10-key-features-002-configurations-py" rel="nofollow">object_parameter</a> 문서를 참조하여 아래와 같이 작성하세요:',ts,$,es,q,$s='raytune의 경우, 해당 <a href="https://docs.ray.io/en/latest/tune/api/search_space.html" rel="nofollow">object_parameter</a> 문서를 참조하여 아래와 같이 작성하세요:',ns,W,ps,Z,qs='wandb의 경우, 해당 <a href="https://docs.wandb.ai/guides/sweeps/configuration" rel="nofollow">object_parameter</a> 문서를 참조하여 아래와 같이 작성하세요:',rs,B,Ms,X,Ws='<code>model_init</code> 함수를 정의하고 이를 <a href="/docs/transformers/pr_35339/ko/main_classes/trainer#transformers.Trainer">Trainer</a>에 전달하세요. 아래는 그 예시입니다.',is,k,ms,Y,Zs='아래와 같이 <code>model_init</code> 함수, 훈련 인수, 훈련 및 테스트 데이터셋, 그리고 평가 함수를 사용하여 <a href="/docs/transformers/pr_35339/ko/main_classes/trainer#transformers.Trainer">Trainer</a>를 생성하세요:',ys,A,Js,v,Bs="하이퍼파라미터 탐색을 호출하고, 최적의 시험 매개변수를 가져오세요. 백엔드는 <code>"optuna"</code>/<code>"sigopt"</code>/<code>"wandb"</code>/<code>"ray"</code> 중에서 선택할 수 있습니다. 방향은 <code>"minimize"</code> 또는 <code>"maximize"</code> 중 선택하며, 목표를 최소화할 것인지 최대화할 것인지를 결정합니다.",cs,Q,Xs="자신만의 compute_objective 함수를 정의할 수 있습니다. 만약 이 함수를 정의하지 않으면, 기본 compute_objective가 호출되고, f1과 같은 평가 지표의 합이 목푯값으로 반환됩니다.",os,V,Ts,x,js,N,ks="현재, DDP(Distributed Data Parallelism; 분산 데이터 병렬처리)를 위한 하이퍼파라미터 탐색은 optuna와 sigopt에서 가능합니다. 최상위 프로세스가 하이퍼파라미터 탐색 과정을 시작하고 그 결과를 다른 프로세스에 전달합니다.",Us,G,hs,S,us;return U=new ws({props:{title:"Trainer API를 사용한 하이퍼파라미터 탐색",local:"hyperparameter-search-using-trainer-api",headingTag:"h1"}}),u=new ws({props:{title:"하이퍼파라미터 탐색 백엔드",local:"hyperparameter-search-backend",headingTag:"h2"}}),f=new j({props:{code:"cGlwJTIwaW5zdGFsbCUyMG9wdHVuYSUyRnNpZ29wdCUyRndhbmRiJTJGcmF5JTVCdHVuZSU1RA==",highlighted:"pip install optuna/sigopt/wandb/ray[tune]",wrap:!1}}),C=new ws({props:{title:"예제에서 하이퍼파라미터 탐색을 활성화하는 방법",local:"how-to-enable-hyperparameter-search-in-example",headingTag:"h2"}}),_=new j({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">sigopt_hp_space</span>(<span class="hljs-params">trial</span>):
	<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> [
	<span class="hljs-meta">... </span> {<span class="hljs-string">"bounds"</span>: {<span class="hljs-string">"min"</span>: <span class="hljs-number">1e-6</span>, <span class="hljs-string">"max"</span>: <span class="hljs-number">1e-4</span>}, <span class="hljs-string">"name"</span>: <span class="hljs-string">"learning_rate"</span>, <span class="hljs-string">"type"</span>: <span class="hljs-string">"double"</span>},
	<span class="hljs-meta">... </span> {
	<span class="hljs-meta">... </span> <span class="hljs-string">"categorical_values"</span>: [<span class="hljs-string">"16"</span>, <span class="hljs-string">"32"</span>, <span class="hljs-string">"64"</span>, <span class="hljs-string">"128"</span>],
	<span class="hljs-meta">... </span> <span class="hljs-string">"name"</span>: <span class="hljs-string">"per_device_train_batch_size"</span>,
	<span class="hljs-meta">... </span> <span class="hljs-string">"type"</span>: <span class="hljs-string">"categorical"</span>,
	<span class="hljs-meta">... </span> },
	<span class="hljs-meta">... </span> ]`,wrap:!1}}),$=new j({props:{code:"ZGVmJTIwb3B0dW5hX2hwX3NwYWNlKHRyaWFsKSUzQSUwQSUyMCUyMCUyMCUyMHJldHVybiUyMCU3QiUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMmxlYXJuaW5nX3JhdGUlMjIlM0ElMjB0cmlhbC5zdWdnZXN0X2Zsb2F0KCUyMmxlYXJuaW5nX3JhdGUlMjIlMkMlMjAxZS02JTJDJTIwMWUtNCUyQyUyMGxvZyUzRFRydWUpJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIycGVyX2RldmljZV90cmFpbl9iYXRjaF9zaXplJTIyJTNBJTIwdHJpYWwuc3VnZ2VzdF9jYXRlZ29yaWNhbCglMjJwZXJfZGV2aWNlX3RyYWluX2JhdGNoX3NpemUlMjIlMkMlMjAlNUIxNiUyQyUyMDMyJTJDJTIwNjQlMkMlMjAxMjglNUQpJTJDJTBBJTIwJTIwJTIwJTIwJTdE",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">optuna_hp_space</span>(<span class="hljs-params">trial</span>):
	<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> {
	<span class="hljs-meta">... </span> <span class="hljs-string">"learning_rate"</span>: trial.suggest_float(<span class="hljs-string">"learning_rate"</span>, <span class="hljs-number">1e-6</span>, <span class="hljs-number">1e-4</span>, log=<span class="hljs-literal">True</span>),
	<span class="hljs-meta">... </span> <span class="hljs-string">"per_device_train_batch_size"</span>: trial.suggest_categorical(<span class="hljs-string">"per_device_train_batch_size"</span>, [<span class="hljs-number">16</span>, <span class="hljs-number">32</span>, <span class="hljs-number">64</span>, <span class="hljs-number">128</span>]),
	<span class="hljs-meta">... </span> }`,wrap:!1}}),W=new j({props:{code:"ZGVmJTIwcmF5X2hwX3NwYWNlKHRyaWFsKSUzQSUwQSUyMCUyMCUyMCUyMHJldHVybiUyMCU3QiUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMmxlYXJuaW5nX3JhdGUlMjIlM0ElMjB0dW5lLmxvZ3VuaWZvcm0oMWUtNiUyQyUyMDFlLTQpJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIycGVyX2RldmljZV90cmFpbl9iYXRjaF9zaXplJTIyJTNBJTIwdHVuZS5jaG9pY2UoJTVCMTYlMkMlMjAzMiUyQyUyMDY0JTJDJTIwMTI4JTVEKSUyQyUwQSUyMCUyMCUyMCUyMCU3RA==",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">ray_hp_space</span>(<span class="hljs-params">trial</span>):
	<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> {
	<span class="hljs-meta">... </span> <span class="hljs-string">"learning_rate"</span>: tune.loguniform(<span class="hljs-number">1e-6</span>, <span class="hljs-number">1e-4</span>),
	<span class="hljs-meta">... </span> <span class="hljs-string">"per_device_train_batch_size"</span>: tune.choice([<span class="hljs-number">16</span>, <span class="hljs-number">32</span>, <span class="hljs-number">64</span>, <span class="hljs-number">128</span>]),
	<span class="hljs-meta">... </span> }`,wrap:!1}}),B=new j({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">wandb_hp_space</span>(<span class="hljs-params">trial</span>):
	<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> {
	<span class="hljs-meta">... </span> <span class="hljs-string">"method"</span>: <span class="hljs-string">"random"</span>,
	<span class="hljs-meta">... </span> <span class="hljs-string">"metric"</span>: {<span class="hljs-string">"name"</span>: <span class="hljs-string">"objective"</span>, <span class="hljs-string">"goal"</span>: <span class="hljs-string">"minimize"</span>},
	<span class="hljs-meta">... </span> <span class="hljs-string">"parameters"</span>: {
	<span class="hljs-meta">... </span> <span class="hljs-string">"learning_rate"</span>: {<span class="hljs-string">"distribution"</span>: <span class="hljs-string">"uniform"</span>, <span class="hljs-string">"min"</span>: <span class="hljs-number">1e-6</span>, <span class="hljs-string">"max"</span>: <span class="hljs-number">1e-4</span>},
	<span class="hljs-meta">... </span> <span class="hljs-string">"per_device_train_batch_size"</span>: {<span class="hljs-string">"values"</span>: [<span class="hljs-number">16</span>, <span class="hljs-number">32</span>, <span class="hljs-number">64</span>, <span class="hljs-number">128</span>]},
	<span class="hljs-meta">... </span> },
	<span class="hljs-meta">... </span> }`,wrap:!1}}),k=new j({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">model_init</span>(<span class="hljs-params">trial</span>):
	<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> AutoModelForSequenceClassification.from_pretrained(
	<span class="hljs-meta">... </span> model_args.model_name_or_path,
	<span class="hljs-meta">... </span> from_tf=<span class="hljs-built_in">bool</span>(<span class="hljs-string">".ckpt"</span> <span class="hljs-keyword">in</span> model_args.model_name_or_path),
	<span class="hljs-meta">... </span> config=config,
	<span class="hljs-meta">... </span> cache_dir=model_args.cache_dir,
	<span class="hljs-meta">... </span> revision=model_args.model_revision,
	<span class="hljs-meta">... </span> token=<span class="hljs-literal">True</span> <span class="hljs-keyword">if</span> model_args.use_auth_token <span class="hljs-keyword">else</span> <span class="hljs-literal">None</span>,
	<span class="hljs-meta">... </span> )`,wrap:!1}}),A=new j({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span>trainer = Trainer(
	<span class="hljs-meta">... </span> model=<span class="hljs-literal">None</span>,
	<span class="hljs-meta">... </span> args=training_args,
	<span class="hljs-meta">... </span> train_dataset=small_train_dataset,
	<span class="hljs-meta">... </span> eval_dataset=small_eval_dataset,
	<span class="hljs-meta">... </span> compute_metrics=compute_metrics,
	<span class="hljs-meta">... </span> processing_class=tokenizer,
	<span class="hljs-meta">... </span> model_init=model_init,
	<span class="hljs-meta">... </span> data_collator=data_collator,
	<span class="hljs-meta">... </span>)`,wrap:!1}}),V=new j({props:{code:"YmVzdF90cmlhbCUyMCUzRCUyMHRyYWluZXIuaHlwZXJwYXJhbWV0ZXJfc2VhcmNoKCUwQSUyMCUyMCUyMCUyMGRpcmVjdGlvbiUzRCUyMm1heGltaXplJTIyJTJDJTBBJTIwJTIwJTIwJTIwYmFja2VuZCUzRCUyMm9wdHVuYSUyMiUyQyUwQSUyMCUyMCUyMCUyMGhwX3NwYWNlJTNEb3B0dW5hX2hwX3NwYWNlJTJDJTBBJTIwJTIwJTIwJTIwbl90cmlhbHMlM0QyMCUyQyUwQSUyMCUyMCUyMCUyMGNvbXB1dGVfb2JqZWN0aXZlJTNEY29tcHV0ZV9vYmplY3RpdmUlMkMlMEEp",highlighted:`<span class="hljs-meta">>>> </span>best_trial = trainer.hyperparameter_search(
	<span class="hljs-meta">... </span> direction=<span class="hljs-string">"maximize"</span>,
	<span class="hljs-meta">... </span> backend=<span class="hljs-string">"optuna"</span>,
	<span class="hljs-meta">... </span> hp_space=optuna_hp_space,
	<span class="hljs-meta">... </span> n_trials=<span class="hljs-number">20</span>,
	<span class="hljs-meta">... </span> compute_objective=compute_objective,
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