import{s as Ns,n as Rs,o as xs}from"../chunks/scheduler.25b97de1.js";import{S as Fs,i as Ss,g as p,s as l,r as i,A as zs,h as r,f as e,c as n,j as As,u as o,x as m,k as Gs,y as Es,a as t,v as c,d as y,t as M,w as J}from"../chunks/index.d9030fc9.js";import{C as T}from"../chunks/CodeBlock.e6cd0d95.js";import{H as Cs,E as Ds}from"../chunks/EditOnGithub.91d95064.js";function Ls(bs){let h,z,F,E,u,D,j,gs='🤗 Transformers provides a <a href="/docs/transformers/pr_30510/en/main_classes/trainer#transformers.Trainer">Trainer</a> class optimized for training 🤗 Transformers models, making it easier to start training without manually writing your own training loop. The <a href="/docs/transformers/pr_30510/en/main_classes/trainer#transformers.Trainer">Trainer</a> provides API for hyperparameter search. This doc shows how to enable it in example.',L,w,P,U,_s=`<a href="/docs/transformers/pr_30510/en/main_classes/trainer#transformers.Trainer">Trainer</a> supports four hyperparameter search backends currently:
<a href="https://optuna.org/" rel="nofollow">optuna</a>, <a href="https://sigopt.com/" rel="nofollow">sigopt</a>, <a href="https://docs.ray.io/en/latest/tune/index.html" rel="nofollow">raytune</a> and <a href="https://wandb.ai/site/sweeps" rel="nofollow">wandb</a>.`,K,d,$s="you should install them before using them as the hyperparameter search backend",O,f,ss,I,as,C,qs="Define the hyperparameter search space, different backends need different format.",es,b,vs='For sigopt, see sigopt <a href="https://docs.sigopt.com/ai-module-api-references/api_reference/objects/object_parameter" rel="nofollow">object_parameter</a>, it’s like following:',ts,g,ls,_,Xs='For optuna, see optuna <a href="https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html#sphx-glr-tutorial-10-key-features-002-configurations-py" rel="nofollow">object_parameter</a>, it’s like following:',ns,$,ps,q,Ws='Optuna provides multi-objective HPO. You can pass <code>direction</code> in <code>hyperparameter_search</code> and define your own compute_objective to return multiple objective values. The Pareto Front (<code>List[BestRun]</code>) will be returned in hyperparameter_search, you should refer to the test case <code>TrainerHyperParameterMultiObjectOptunaIntegrationTest</code> in <a href="https://github.com/huggingface/transformers/blob/main/tests/trainer/test_trainer.py" rel="nofollow">test_trainer</a>. It’s like following',rs,v,is,X,Ys='For raytune, see raytune <a href="https://docs.ray.io/en/latest/tune/api/search_space.html" rel="nofollow">object_parameter</a>, it’s like following:',os,W,ms,Y,ks='For wandb, see wandb <a href="https://docs.wandb.ai/guides/sweeps/configuration" rel="nofollow">object_parameter</a>, it’s like following:',cs,k,ys,B,Bs='Define a <code>model_init</code> function and pass it to the <a href="/docs/transformers/pr_30510/en/main_classes/trainer#transformers.Trainer">Trainer</a>, as an example:',Ms,Z,Js,V,Zs='Create a <a href="/docs/transformers/pr_30510/en/main_classes/trainer#transformers.Trainer">Trainer</a> with your <code>model_init</code> function, training arguments, training and test datasets, and evaluation function:',hs,H,Ts,Q,Vs="Call hyperparameter search, get the best trial parameters, backend could be <code>&quot;optuna&quot;</code>/<code>&quot;sigopt&quot;</code>/<code>&quot;wandb&quot;</code>/<code>&quot;ray&quot;</code>. direction can be<code>&quot;minimize&quot;</code> or <code>&quot;maximize&quot;</code>, which indicates whether to optimize greater or lower objective.",us,A,Hs="You could define your own compute_objective function, if not defined, the default compute_objective will be called, and the sum of eval metric like f1 is returned as objective value.",js,G,ws,N,Us,R,Qs="Currently, Hyperparameter search for DDP is enabled for optuna and sigopt. Only the rank-zero process will generate the search trial and pass the argument to other ranks.",ds,x,fs,S,Is;return u=new Cs({props:{title:"Hyperparameter Search using Trainer API",local:"hyperparameter-search-using-trainer-api",headingTag:"h1"}}),w=new Cs({props:{title:"Hyperparameter Search backend",local:"hyperparameter-search-backend",headingTag:"h2"}}),f=new T({props:{code:"cGlwJTIwaW5zdGFsbCUyMG9wdHVuYSUyRnNpZ29wdCUyRndhbmRiJTJGcmF5JTVCdHVuZSU1RCUyMA==",highlighted:"pip install optuna/sigopt/wandb/ray[tune] ",wrap:!1}}),I=new Cs({props:{title:"How to enable Hyperparameter search in example",local:"how-to-enable-hyperparameter-search-in-example",headingTag:"h2"}}),g=new T({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </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">&quot;bounds&quot;</span>: {<span class="hljs-string">&quot;min&quot;</span>: <span class="hljs-number">1e-6</span>, <span class="hljs-string">&quot;max&quot;</span>: <span class="hljs-number">1e-4</span>}, <span class="hljs-string">&quot;name&quot;</span>: <span class="hljs-string">&quot;learning_rate&quot;</span>, <span class="hljs-string">&quot;type&quot;</span>: <span class="hljs-string">&quot;double&quot;</span>},
<span class="hljs-meta">... </span>        {
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;categorical_values&quot;</span>: [<span class="hljs-string">&quot;16&quot;</span>, <span class="hljs-string">&quot;32&quot;</span>, <span class="hljs-string">&quot;64&quot;</span>, <span class="hljs-string">&quot;128&quot;</span>],
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;name&quot;</span>: <span class="hljs-string">&quot;per_device_train_batch_size&quot;</span>,
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;type&quot;</span>: <span class="hljs-string">&quot;categorical&quot;</span>,
<span class="hljs-meta">... </span>        },
<span class="hljs-meta">... </span>    ]`,wrap:!1}}),$=new T({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </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">&quot;learning_rate&quot;</span>: trial.suggest_float(<span class="hljs-string">&quot;learning_rate&quot;</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">&quot;per_device_train_batch_size&quot;</span>: trial.suggest_categorical(<span class="hljs-string">&quot;per_device_train_batch_size&quot;</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}}),v=new T({props:{code:"YmVzdF90cmlhbHMlMjAlM0QlMjB0cmFpbmVyLmh5cGVycGFyYW1ldGVyX3NlYXJjaCglMEElMjAlMjAlMjAlMjBkaXJlY3Rpb24lM0QlNUIlMjJtaW5pbWl6ZSUyMiUyQyUyMCUyMm1heGltaXplJTIyJTVEJTJDJTBBJTIwJTIwJTIwJTIwYmFja2VuZCUzRCUyMm9wdHVuYSUyMiUyQyUwQSUyMCUyMCUyMCUyMGhwX3NwYWNlJTNEb3B0dW5hX2hwX3NwYWNlJTJDJTBBJTIwJTIwJTIwJTIwbl90cmlhbHMlM0QyMCUyQyUwQSUyMCUyMCUyMCUyMGNvbXB1dGVfb2JqZWN0aXZlJTNEY29tcHV0ZV9vYmplY3RpdmUlMkMlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>best_trials = trainer.hyperparameter_search(
<span class="hljs-meta">... </span>    direction=[<span class="hljs-string">&quot;minimize&quot;</span>, <span class="hljs-string">&quot;maximize&quot;</span>],
<span class="hljs-meta">... </span>    backend=<span class="hljs-string">&quot;optuna&quot;</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,
<span class="hljs-meta">... </span>)`,wrap:!1}}),W=new T({props:{code:"ZGVmJTIwcmF5X2hwX3NwYWNlKHRyaWFsKSUzQSUwQSUyMCUyMCUyMCUyMHJldHVybiUyMCU3QiUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMmxlYXJuaW5nX3JhdGUlMjIlM0ElMjB0dW5lLmxvZ3VuaWZvcm0oMWUtNiUyQyUyMDFlLTQpJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIycGVyX2RldmljZV90cmFpbl9iYXRjaF9zaXplJTIyJTNBJTIwdHVuZS5jaG9pY2UoJTVCMTYlMkMlMjAzMiUyQyUyMDY0JTJDJTIwMTI4JTVEKSUyQyUwQSUyMCUyMCUyMCUyMCU3RA==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </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">&quot;learning_rate&quot;</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">&quot;per_device_train_batch_size&quot;</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}}),k=new T({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </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">&quot;method&quot;</span>: <span class="hljs-string">&quot;random&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;metric&quot;</span>: {<span class="hljs-string">&quot;name&quot;</span>: <span class="hljs-string">&quot;objective&quot;</span>, <span class="hljs-string">&quot;goal&quot;</span>: <span class="hljs-string">&quot;minimize&quot;</span>},
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;parameters&quot;</span>: {
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;learning_rate&quot;</span>: {<span class="hljs-string">&quot;distribution&quot;</span>: <span class="hljs-string">&quot;uniform&quot;</span>, <span class="hljs-string">&quot;min&quot;</span>: <span class="hljs-number">1e-6</span>, <span class="hljs-string">&quot;max&quot;</span>: <span class="hljs-number">1e-4</span>},
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;per_device_train_batch_size&quot;</span>: {<span class="hljs-string">&quot;values&quot;</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}}),Z=new T({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </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">&quot;.ckpt&quot;</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}}),H=new T({props:{code:"dHJhaW5lciUyMCUzRCUyMFRyYWluZXIoJTBBJTIwJTIwJTIwJTIwbW9kZWwlM0ROb25lJTJDJTBBJTIwJTIwJTIwJTIwYXJncyUzRHRyYWluaW5nX2FyZ3MlMkMlMEElMjAlMjAlMjAlMjB0cmFpbl9kYXRhc2V0JTNEc21hbGxfdHJhaW5fZGF0YXNldCUyQyUwQSUyMCUyMCUyMCUyMGV2YWxfZGF0YXNldCUzRHNtYWxsX2V2YWxfZGF0YXNldCUyQyUwQSUyMCUyMCUyMCUyMGNvbXB1dGVfbWV0cmljcyUzRGNvbXB1dGVfbWV0cmljcyUyQyUwQSUyMCUyMCUyMCUyMHRva2VuaXplciUzRHRva2VuaXplciUyQyUwQSUyMCUyMCUyMCUyMG1vZGVsX2luaXQlM0Rtb2RlbF9pbml0JTJDJTBBJTIwJTIwJTIwJTIwZGF0YV9jb2xsYXRvciUzRGRhdGFfY29sbGF0b3IlMkMlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </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>    tokenizer=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}}),G=new T({props:{code:"YmVzdF90cmlhbCUyMCUzRCUyMHRyYWluZXIuaHlwZXJwYXJhbWV0ZXJfc2VhcmNoKCUwQSUyMCUyMCUyMCUyMGRpcmVjdGlvbiUzRCUyMm1heGltaXplJTIyJTJDJTBBJTIwJTIwJTIwJTIwYmFja2VuZCUzRCUyMm9wdHVuYSUyMiUyQyUwQSUyMCUyMCUyMCUyMGhwX3NwYWNlJTNEb3B0dW5hX2hwX3NwYWNlJTJDJTBBJTIwJTIwJTIwJTIwbl90cmlhbHMlM0QyMCUyQyUwQSUyMCUyMCUyMCUyMGNvbXB1dGVfb2JqZWN0aXZlJTNEY29tcHV0ZV9vYmplY3RpdmUlMkMlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>best_trial = trainer.hyperparameter_search(
<span class="hljs-meta">... </span>    direction=<span class="hljs-string">&quot;maximize&quot;</span>,
<span class="hljs-meta">... </span>    backend=<span class="hljs-string">&quot;optuna&quot;</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,
<span class="hljs-meta">... </span>)`,wrap:!1}}),N=new Cs({props:{title:"Hyperparameter search For DDP finetune",local:"hyperparameter-search-for-ddp-finetune",headingTag:"h2"}}),x=new 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