import{S as rT,i as aT,s as nT,w as b,x as v,y as E,q as g,o as u,B as T,F as lT,e as n,t,k as c,c as o,a as s,h as r,d as a,m as d,b as i,g as f,G as e,j as iT,H as cT,I as dT,J as mT,M as fT,v as hT,L as oT}from"../../chunks/vendor-hf-doc-builder.js";import{T as sT}from"../../chunks/Tip-hf-doc-builder.js";import{D as M}from"../../chunks/Docstring-hf-doc-builder.js";import{C as mc}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Pl}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as tT}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function pT(j){let h,L,_,k,w;const p=j[1].default,$=lT(p,j,j[2],null);return{c(){h=n("p"),L=t("Deprecated in "),_=t(j[0]),k=c(),$&&$.c(),this.h()},l(A){h=o(A,"P",{class:!0});var O=s(h);L=r(O,"Deprecated in "),_=r(O,j[0]),O.forEach(a),k=d(A),$&&$.l(A),this.h()},h(){i(h,"class","font-medium")},m(A,O){f(A,h,O),e(h,L),e(h,_),f(A,k,O),$&&$.m(A,O),w=!0},p(A,O){(!w||O&1)&&iT(_,A[0]),$&&$.p&&(!w||O&4)&&cT($,p,A,A[2],w?mT(p,A[2],O,null):dT(A[2]),null)},i(A){w||(g($,A),w=!0)},o(A){u($,A),w=!1},d(A){A&&a(h),A&&a(k),$&&$.d(A)}}}function gT(j){let h,L;return h=new sT({props:{warning:!0,$$slots:{default:[pT]},$$scope:{ctx:j}}}),{c(){b(h.$$.fragment)},l(_){v(h.$$.fragment,_)},m(_,k){E(h,_,k),L=!0},p(_,[k]){const w={};k&5&&(w.$$scope={dirty:k,ctx:_}),h.$set(w)},i(_){L||(g(h.$$.fragment,_),L=!0)},o(_){u(h.$$.fragment,_),L=!1},d(_){T(h,_)}}}function uT(j,h,L){let{$$slots:_={},$$scope:k}=h,{version:w}=h;return j.$$set=p=>{"version"in p&&L(0,w=p.version),"$$scope"in p&&L(2,k=p.$$scope)},[w,_,k]}class _T extends rT{constructor(h){super();aT(this,h,uT,gT,nT,{version:0})}}function bT(j){let h,L,_,k,w,p,$,A;return{c(){h=n("p"),L=t("Setting "),_=n("code"),k=t("WANDB_LOG_MODEL"),w=t(" as "),p=n("code"),$=t("bool"),A=t(" will be deprecated in version 5 of \u{1F917} Transformers.")},l(O){h=o(O,"P",{});var S=s(h);L=r(S,"Setting "),_=o(S,"CODE",{});var Z=s(_);k=r(Z,"WANDB_LOG_MODEL"),Z.forEach(a),w=r(S," as "),p=o(S,"CODE",{});var We=s(p);$=r(We,"bool"),We.forEach(a),A=r(S," will be deprecated in version 5 of \u{1F917} Transformers."),S.forEach(a)},m(O,S){f(O,h,S),e(h,L),e(h,_),e(_,k),e(h,w),e(h,p),e(p,$),e(h,A)},d(O){O&&a(h)}}}function vT(j){let h,L,_,k,w;return k=new mc({props:{code:"JTIzJTIwTm90ZSUzQSUyMFRoaXMlMjBleGFtcGxlJTIwc2tpcHMlMjBvdmVyJTIwc29tZSUyMHNldHVwJTIwc3RlcHMlMjBmb3IlMjBicmV2aXR5LiUwQWZyb20lMjBmbHl0ZWtpdCUyMGltcG9ydCUyMGN1cnJlbnRfY29udGV4dCUyQyUyMHRhc2slMEElMEElMEElNDB0YXNrJTBBZGVmJTIwdHJhaW5faGZfdHJhbnNmb3JtZXIoKSUzQSUwQSUyMCUyMCUyMCUyMGNwJTIwJTNEJTIwY3VycmVudF9jb250ZXh0KCkuY2hlY2twb2ludCUwQSUyMCUyMCUyMCUyMHRyYWluZXIlMjAlM0QlMjBUcmFpbmVyKC4uLiUyQyUyMGNhbGxiYWNrcyUzRCU1QkZseXRlQ2FsbGJhY2soKSU1RCklMEElMjAlMjAlMjAlMjBvdXRwdXQlMjAlM0QlMjB0cmFpbmVyLnRyYWluKHJlc3VtZV9mcm9tX2NoZWNrcG9pbnQlM0RjcC5yZXN0b3JlKCkp",highlighted:`<span class="hljs-comment"># Note: This example skips over some setup steps for brevity.</span>
<span class="hljs-keyword">from</span> flytekit <span class="hljs-keyword">import</span> current_context, task


<span class="hljs-meta">@task</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">train_hf_transformer</span>():
    cp = current_context().checkpoint
    trainer = Trainer(..., callbacks=[FlyteCallback()])
    output = trainer.train(resume_from_checkpoint=cp.restore())`}}),{c(){h=n("p"),L=t("Example:"),_=c(),b(k.$$.fragment)},l(p){h=o(p,"P",{});var $=s(h);L=r($,"Example:"),$.forEach(a),_=d(p),v(k.$$.fragment,p)},m(p,$){f(p,h,$),e(h,L),f(p,_,$),E(k,p,$),w=!0},p:oT,i(p){w||(g(k.$$.fragment,p),w=!0)},o(p){u(k.$$.fragment,p),w=!1},d(p){p&&a(h),p&&a(_),T(k,p)}}}function ET(j){let h,L,_,k,w;return k=new mc({props:{code:"Y2xhc3MlMjBQcmludGVyQ2FsbGJhY2soVHJhaW5lckNhbGxiYWNrKSUzQSUwQSUyMCUyMCUyMCUyMGRlZiUyMG9uX2xvZyhzZWxmJTJDJTIwYXJncyUyQyUyMHN0YXRlJTJDJTIwY29udHJvbCUyQyUyMGxvZ3MlM0ROb25lJTJDJTIwKiprd2FyZ3MpJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwXyUyMCUzRCUyMGxvZ3MucG9wKCUyMnRvdGFsX2Zsb3MlMjIlMkMlMjBOb25lKSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGlmJTIwc3RhdGUuaXNfbG9jYWxfcHJvY2Vzc196ZXJvJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwcHJpbnQobG9ncyk=",highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">PrinterCallback</span>(<span class="hljs-title class_ inherited__">TrainerCallback</span>):
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">on_log</span>(<span class="hljs-params">self, args, state, control, logs=<span class="hljs-literal">None</span>, **kwargs</span>):
        _ = logs.pop(<span class="hljs-string">&quot;total_flos&quot;</span>, <span class="hljs-literal">None</span>)
        <span class="hljs-keyword">if</span> state.is_local_process_zero:
            <span class="hljs-built_in">print</span>(logs)`}}),{c(){h=n("p"),L=t("Example:"),_=c(),b(k.$$.fragment)},l(p){h=o(p,"P",{});var $=s(h);L=r($,"Example:"),$.forEach(a),_=d(p),v(k.$$.fragment,p)},m(p,$){f(p,h,$),e(h,L),f(p,_,$),E(k,p,$),w=!0},p:oT,i(p){w||(g(k.$$.fragment,p),w=!0)},o(p){u(k.$$.fragment,p),w=!1},d(p){p&&a(h),p&&a(_),T(k,p)}}}function TT(j){let h,L,_,k,w,p,$,A;return{c(){h=n("p"),L=t(`In all this class, one step is to be understood as one update step. When using gradient accumulation, one update
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step requires going through `),p=n("em"),$=t("n"),A=t(" batches.")},l(O){h=o(O,"P",{});var S=s(h);L=r(S,`In all this class, one step is to be understood as one update step. When using gradient accumulation, one update
step may require several forward and backward passes: if you use `),_=o(S,"CODE",{});var Z=s(_);k=r(Z,"gradient_accumulation_steps=n"),Z.forEach(a),w=r(S,`, then one update
step requires going through `),p=o(S,"EM",{});var We=s(p);$=r(We,"n"),We.forEach(a),A=r(S," batches."),S.forEach(a)},m(O,S){f(O,h,S),e(h,L),e(h,_),e(_,k),e(h,w),e(h,p),e(p,$),e(h,A)},d(O){O&&a(h)}}}function kT(j){let h,L,_,k,w,p,$,A,O,S,Z,We,Ia,fc,hc,Rl,le,pc,Oa,gc,uc,xa,_c,bc,Na,vc,Ec,Gl,pt,Tc,Sa,kc,Cc,Bl,D,Fa,ja,$c,wc,yc,Ne,Ja,Mc,Ac,Ua,Lc,Dc,Wa,Ic,Oc,xc,Pa,Ra,Nc,Sc,Fc,gt,Ga,jc,Jc,ar,Uc,Wc,Pc,ut,Ba,Rc,Gc,nr,Bc,zc,Yc,_t,za,Hc,Vc,or,Xc,Zc,Qc,bt,Ya,Kc,qc,sr,ed,td,rd,vt,Ha,ad,nd,lr,od,sd,ld,Et,Va,id,cd,ir,dd,md,fd,Tt,Xa,hd,pd,cr,gd,ud,_d,kt,Za,bd,vd,dr,Ed,Td,kd,Ct,Qa,Cd,$d,mr,wd,yd,zl,G,Md,Ka,Ad,Ld,qa,Dd,Id,en,Od,xd,tn,Nd,Sd,rn,Fd,jd,Yl,Pe,$t,Bn,fr,Jd,zn,Ud,Hl,wt,Wd,an,Pd,Rd,Vl,Ee,hr,Gd,Re,Bd,nn,zd,Yd,pr,Hd,Vd,Xd,Q,gr,Zd,Yn,Qd,Kd,Hn,qd,em,Te,W,Vn,tm,rm,Xn,am,nm,Zn,om,sm,Qn,lm,im,Kn,cm,dm,qn,mm,fm,eo,hm,pm,gm,Se,to,um,_m,ro,bm,vm,ao,Em,Tm,km,K,no,Cm,$m,oo,wm,ym,so,Mm,Am,lo,Lm,Dm,io,Im,Om,xm,B,co,Nm,Sm,mo,Fm,jm,fo,Jm,Um,ho,Wm,Pm,po,Rm,Gm,go,Bm,zm,Ym,ur,Hm,_r,Vm,Xm,Xl,Ge,br,Zm,vr,Qm,on,Km,qm,Zl,Be,Er,ef,Tr,tf,sn,rf,af,Ql,ze,kr,nf,Cr,of,ln,sf,lf,Kl,ke,$r,cf,wr,df,cn,mf,ff,hf,P,pf,dn,gf,uf,uo,_f,bf,mn,vf,Ef,fn,Tf,kf,_o,Cf,$f,bo,wf,yf,ql,Ye,yr,Mf,He,Af,hn,Lf,Df,Mr,If,Of,ei,Ce,Ar,xf,Ve,Nf,pn,Sf,Ff,Lr,jf,Jf,Uf,q,Dr,Wf,Ir,Pf,vo,Rf,Gf,Bf,Or,zf,xr,Yf,Hf,Vf,Eo,Xf,Zf,$e,Nr,I,To,Qf,Kf,ko,qf,eh,Co,th,rh,$o,ah,nh,wo,oh,sh,yo,lh,ih,Mo,ch,dh,Ao,mh,fh,Lo,hh,ph,Do,gh,uh,Io,_h,bh,Oo,vh,Eh,Th,yt,kh,xo,F,No,Ch,$h,So,wh,yh,Fo,Mh,Ah,jo,Lh,Dh,Jo,Ih,Oh,Uo,xh,Nh,Wo,Sh,Fh,Po,jh,Jh,Ro,Uh,Wh,Ph,Go,ie,Bo,Rh,Gh,zo,Bh,zh,Yo,Yh,Hh,Ho,Vh,Xh,Zh,Vo,ee,Xo,Qh,Kh,Zo,qh,ep,Qo,tp,rp,Ko,ap,np,qo,op,sp,ti,we,Sr,lp,ye,ip,gn,cp,dp,Fr,mp,fp,es,hp,pp,gp,ce,jr,up,ts,_p,bp,rs,vp,Ep,Y,J,as,Tp,kp,ns,Cp,$p,os,wp,yp,ss,Mp,Ap,ls,Lp,Dp,is,Ip,Op,un,xp,Np,cs,Sp,Fp,jp,z,ds,Jp,Up,ms,Wp,Pp,fs,Rp,Gp,hs,Bp,zp,ps,Yp,Hp,gs,Vp,Xp,Zp,de,us,Qp,Kp,_s,qp,eg,bs,tg,rg,vs,ag,ng,og,te,Es,sg,lg,Ts,ig,cg,ks,dg,mg,Cs,fg,hg,$s,pg,gg,ug,re,ws,_g,bg,ys,vg,Eg,Ms,Tg,kg,As,Cg,$g,Ls,wg,yg,Mg,me,Ds,Ag,Lg,Is,Dg,Ig,Os,Og,xg,xs,Ng,Sg,ri,Xe,Jr,Fg,Ze,jg,_n,Jg,Ug,Ur,Wg,Pg,ai,Qe,Wr,Rg,Pr,Gg,bn,Bg,zg,ni,Me,Rr,Yg,Gr,Hg,Br,Vg,Xg,Zg,zr,Qg,Yr,Kg,qg,oi,ne,Hr,eu,Ke,tu,vn,ru,au,Vr,nu,ou,su,Ns,lu,iu,qe,fe,Ss,cu,du,Fs,mu,fu,js,hu,pu,Js,gu,uu,_u,he,Us,bu,vu,Ws,Eu,Tu,Ps,ku,Cu,Rs,$u,wu,yu,pe,Gs,Mu,Au,Bs,Lu,Du,zs,Iu,Ou,Ys,xu,Nu,si,Ae,Xr,Su,Fe,Fu,En,ju,Ju,Zr,Uu,Wu,Hs,Pu,Ru,ge,Qr,Gu,Vs,Bu,zu,Xs,Yu,Hu,Zs,ue,Qs,Vu,Xu,Ks,Zu,Qu,qs,Ku,qu,el,e_,t_,li,Le,Kr,r_,et,a_,Tn,n_,o_,qr,s_,l_,i_,Mt,ii,tt,At,tl,ea,c_,rl,d_,ci,C,ta,m_,al,f_,h_,ra,p_,nl,g_,u_,__,H,b_,ol,v_,E_,sl,T_,k_,ll,C_,$_,il,w_,y_,cl,M_,A_,L_,Lt,D_,Dt,aa,I_,dl,O_,x_,It,na,N_,ml,S_,F_,Ot,oa,j_,fl,J_,U_,xt,sa,W_,la,P_,kn,R_,G_,B_,Nt,ia,z_,hl,Y_,H_,St,ca,V_,pl,X_,Z_,Ft,da,Q_,gl,K_,q_,jt,ma,eb,ul,tb,rb,Jt,fa,ab,_l,nb,ob,Ut,ha,sb,bl,lb,ib,Wt,pa,cb,vl,db,mb,Pt,ga,fb,El,hb,pb,Rt,ua,gb,Tl,ub,di,Gt,_b,Cn,bb,vb,mi,_a,fi,Bt,Eb,kl,Tb,kb,hi,ba,pi,rt,zt,Cl,va,Cb,$l,$b,gi,V,Ea,wb,at,yb,$n,Mb,Ab,wn,Lb,Db,Ib,Yt,Ob,Ht,Ta,xb,ka,Nb,wl,Sb,Fb,jb,Vt,Ca,Jb,$a,Ub,yl,Wb,Pb,ui,nt,Xt,Ml,wa,Rb,Al,Gb,_i,ot,ya,Bb,st,zb,yn,Yb,Hb,Mn,Vb,Xb,bi;return p=new Pl({}),fr=new Pl({}),hr=new M({props:{name:"class transformers.integrations.CometCallback",anchor:"transformers.integrations.CometCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L826"}}),gr=new M({props:{name:"setup",anchor:"transformers.integrations.CometCallback.setup",parameters:[{name:"args",val:""},{name:"state",val:""},{name:"model",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L837"}}),br=new M({props:{name:"class transformers.DefaultFlowCallback",anchor:"transformers.DefaultFlowCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L424"}}),Er=new M({props:{name:"class transformers.PrinterCallback",anchor:"transformers.PrinterCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L524"}}),kr=new M({props:{name:"class transformers.ProgressCallback",anchor:"transformers.ProgressCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L474"}}),$r=new M({props:{name:"class transformers.EarlyStoppingCallback",anchor:"transformers.EarlyStoppingCallback",parameters:[{name:"early_stopping_patience",val:": int = 1"},{name:"early_stopping_threshold",val:": typing.Optional[float] = 0.0"}],parametersDescription:[{anchor:"transformers.EarlyStoppingCallback.early_stopping_patience",description:`<strong>early_stopping_patience</strong> (<code>int</code>) &#x2014;
Use with <code>metric_for_best_model</code> to stop training when the specified metric worsens for
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specified metric must improve to satisfy early stopping conditions. \``,name:"early_stopping_threshold(float,"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L535"}}),yr=new M({props:{name:"class transformers.integrations.TensorBoardCallback",anchor:"transformers.integrations.TensorBoardCallback",parameters:[{name:"tb_writer",val:" = None"}],parametersDescription:[{anchor:"transformers.integrations.TensorBoardCallback.tb_writer",description:`<strong>tb_writer</strong> (<code>SummaryWriter</code>, <em>optional</em>) &#x2014;
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variable (strongly recommended). See full setup instructions in the
<a href="https://docs.neptune.ai/setup/installation" rel="nofollow">docs</a>.`,name:"api_token"},{anchor:"transformers.integrations.NeptuneCallback.project",description:`<strong>project</strong> (<code>str</code>, <em>optional</em>) &#x2014; Name of an existing Neptune project, in the form &#x201C;workspace-name/project-name&#x201D;.
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When set to True, the training logs are saved as a Flyte Deck.`,name:"save_log_history"},{anchor:"transformers.integrations.FlyteCallback.sync_checkpoints",description:`<strong>sync_checkpoints</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
When set to True, checkpoints are synced with Flyte and can be used to resume training in the case of an
interruption.`,name:"sync_checkpoints"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L1540"}}),Mt=new tT({props:{anchor:"transformers.integrations.FlyteCallback.example",$$slots:{default:[vT]},$$scope:{ctx:j}}}),ea=new Pl({}),ta=new M({props:{name:"class transformers.TrainerCallback",anchor:"transformers.TrainerCallback",parameters:[],parametersDescription:[{anchor:"transformers.TrainerCallback.args",description:`<strong>args</strong> (<a href="/docs/transformers/main/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>) &#x2014;
The training arguments used to instantiate the <a href="/docs/transformers/main/en/main_classes/trainer#transformers.Trainer">Trainer</a>.`,name:"args"},{anchor:"transformers.TrainerCallback.state",description:`<strong>state</strong> (<a href="/docs/transformers/main/en/main_classes/callback#transformers.TrainerState">TrainerState</a>) &#x2014;
The current state of the <a href="/docs/transformers/main/en/main_classes/trainer#transformers.Trainer">Trainer</a>.`,name:"state"},{anchor:"transformers.TrainerCallback.control",description:`<strong>control</strong> (<a href="/docs/transformers/main/en/main_classes/callback#transformers.TrainerControl">TrainerControl</a>) &#x2014;
The object that is returned to the <a href="/docs/transformers/main/en/main_classes/trainer#transformers.Trainer">Trainer</a> and can be used to make some decisions.`,name:"control"},{anchor:"transformers.TrainerCallback.model",description:`<strong>model</strong> (<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <code>torch.nn.Module</code>) &#x2014;
The model being trained.`,name:"model"},{anchor:"transformers.TrainerCallback.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) &#x2014;
The tokenizer used for encoding the data.`,name:"tokenizer"},{anchor:"transformers.TrainerCallback.optimizer",description:`<strong>optimizer</strong> (<code>torch.optim.Optimizer</code>) &#x2014;
The optimizer used for the training steps.`,name:"optimizer"},{anchor:"transformers.TrainerCallback.lr_scheduler",description:`<strong>lr_scheduler</strong> (<code>torch.optim.lr_scheduler.LambdaLR</code>) &#x2014;
The scheduler used for setting the learning rate.`,name:"lr_scheduler"},{anchor:"transformers.TrainerCallback.train_dataloader",description:`<strong>train_dataloader</strong> (<code>torch.utils.data.DataLoader</code>, <em>optional</em>) &#x2014;
The current dataloader used for training.`,name:"train_dataloader"},{anchor:"transformers.TrainerCallback.eval_dataloader",description:`<strong>eval_dataloader</strong> (<code>torch.utils.data.DataLoader</code>, <em>optional</em>) &#x2014;
The current dataloader used for training.`,name:"eval_dataloader"},{anchor:"transformers.TrainerCallback.metrics",description:`<strong>metrics</strong> (<code>Dict[str, float]</code>) &#x2014;
The metrics computed by the last evaluation phase.</p>
<p>Those are only accessible in the event <code>on_evaluate</code>.`,name:"metrics"},{anchor:"transformers.TrainerCallback.logs",description:`<strong>logs</strong>  (<code>Dict[str, float]</code>) &#x2014;
The values to log.</p>
<p>Those are only accessible in the event <code>on_log</code>.`,name:"logs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L168"}}),Lt=new tT({props:{anchor:"transformers.TrainerCallback.example",$$slots:{default:[ET]},$$scope:{ctx:j}}}),aa=new M({props:{name:"on_epoch_begin",anchor:"transformers.TrainerCallback.on_epoch_begin",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L236"}}),na=new M({props:{name:"on_epoch_end",anchor:"transformers.TrainerCallback.on_epoch_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L242"}}),oa=new M({props:{name:"on_evaluate",anchor:"transformers.TrainerCallback.on_evaluate",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L268"}}),sa=new M({props:{name:"on_init_end",anchor:"transformers.TrainerCallback.on_init_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L218"}}),ia=new M({props:{name:"on_log",anchor:"transformers.TrainerCallback.on_log",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L286"}}),ca=new M({props:{name:"on_predict",anchor:"transformers.TrainerCallback.on_predict",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"metrics",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L274"}}),da=new M({props:{name:"on_prediction_step",anchor:"transformers.TrainerCallback.on_prediction_step",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L292"}}),ma=new M({props:{name:"on_save",anchor:"transformers.TrainerCallback.on_save",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L280"}}),fa=new M({props:{name:"on_step_begin",anchor:"transformers.TrainerCallback.on_step_begin",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L248"}}),ha=new M({props:{name:"on_step_end",anchor:"transformers.TrainerCallback.on_step_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L261"}}),pa=new M({props:{name:"on_substep_end",anchor:"transformers.TrainerCallback.on_substep_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L255"}}),ga=new M({props:{name:"on_train_begin",anchor:"transformers.TrainerCallback.on_train_begin",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L224"}}),ua=new M({props:{name:"on_train_end",anchor:"transformers.TrainerCallback.on_train_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L230"}}),_a=new mc({props:{code:"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",highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">MyCallback</span>(<span class="hljs-title class_ inherited__">TrainerCallback</span>):
    <span class="hljs-string">&quot;A callback that prints a message at the beginning of training&quot;</span>

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">on_train_begin</span>(<span class="hljs-params">self, args, state, control, **kwargs</span>):
        <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Starting training&quot;</span>)


trainer = Trainer(
    model,
    args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    callbacks=[MyCallback],  <span class="hljs-comment"># We can either pass the callback class this way or an instance of it (MyCallback())</span>
)`}}),ba=new mc({props:{code:"dHJhaW5lciUyMCUzRCUyMFRyYWluZXIoLi4uKSUwQXRyYWluZXIuYWRkX2NhbGxiYWNrKE15Q2FsbGJhY2spJTBBJTIzJTIwQWx0ZXJuYXRpdmVseSUyQyUyMHdlJTIwY2FuJTIwcGFzcyUyMGFuJTIwaW5zdGFuY2UlMjBvZiUyMHRoZSUyMGNhbGxiYWNrJTIwY2xhc3MlMEF0cmFpbmVyLmFkZF9jYWxsYmFjayhNeUNhbGxiYWNrKCkp",highlighted:`trainer = Trainer(...)
trainer.add_callback(MyCallback)
<span class="hljs-comment"># Alternatively, we can pass an instance of the callback class</span>
trainer.add_callback(MyCallback())`}}),va=new Pl({}),Ea=new M({props:{name:"class transformers.TrainerState",anchor:"transformers.TrainerState",parameters:[{name:"epoch",val:": typing.Optional[float] = None"},{name:"global_step",val:": int = 0"},{name:"max_steps",val:": int = 0"},{name:"logging_steps",val:": int = 500"},{name:"eval_steps",val:": int = 500"},{name:"save_steps",val:": int = 500"},{name:"num_train_epochs",val:": int = 0"},{name:"total_flos",val:": float = 0"},{name:"log_history",val:": typing.List[typing.Dict[str, float]] = None"},{name:"best_metric",val:": typing.Optional[float] = None"},{name:"best_model_checkpoint",val:": typing.Optional[str] = None"},{name:"is_local_process_zero",val:": bool = True"},{name:"is_world_process_zero",val:": bool = True"},{name:"is_hyper_param_search",val:": bool = False"},{name:"trial_name",val:": str = None"},{name:"trial_params",val:": typing.Dict[str, typing.Union[str, float, int, bool]] = None"}],parametersDescription:[{anchor:"transformers.TrainerState.epoch",description:`<strong>epoch</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Only set during training, will represent the epoch the training is at (the decimal part being the
percentage of the current epoch completed).`,name:"epoch"},{anchor:"transformers.TrainerState.global_step",description:`<strong>global_step</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
During training, represents the number of update steps completed.`,name:"global_step"},{anchor:"transformers.TrainerState.max_steps",description:`<strong>max_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
The number of update steps to do during the current training.`,name:"max_steps"},{anchor:"transformers.TrainerState.logging_steps",description:`<strong>logging_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 500) &#x2014;
Log every X updates steps`,name:"logging_steps"},{anchor:"transformers.TrainerState.eval_steps",description:`<strong>eval_steps</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Run an evaluation every X steps.`,name:"eval_steps"},{anchor:"transformers.TrainerState.save_steps",description:`<strong>save_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 500) &#x2014;
Save checkpoint every X updates steps.`,name:"save_steps"},{anchor:"transformers.TrainerState.total_flos",description:`<strong>total_flos</strong> (<code>float</code>, <em>optional</em>, defaults to 0) &#x2014;
The total number of floating operations done by the model since the beginning of training (stored as floats
to avoid overflow).`,name:"total_flos"},{anchor:"transformers.TrainerState.log_history",description:`<strong>log_history</strong> (<code>List[Dict[str, float]]</code>, <em>optional</em>) &#x2014;
The list of logs done since the beginning of training.`,name:"log_history"},{anchor:"transformers.TrainerState.best_metric",description:`<strong>best_metric</strong> (<code>float</code>, <em>optional</em>) &#x2014;
When tracking the best model, the value of the best metric encountered so far.`,name:"best_metric"},{anchor:"transformers.TrainerState.best_model_checkpoint",description:`<strong>best_model_checkpoint</strong> (<code>str</code>, <em>optional</em>) &#x2014;
When tracking the best model, the value of the name of the checkpoint for the best model encountered so
far.`,name:"best_model_checkpoint"},{anchor:"transformers.TrainerState.is_local_process_zero",description:`<strong>is_local_process_zero</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on
several machines) main process.`,name:"is_local_process_zero"},{anchor:"transformers.TrainerState.is_world_process_zero",description:`<strong>is_world_process_zero</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be <code>True</code> for one process).`,name:"is_world_process_zero"},{anchor:"transformers.TrainerState.is_hyper_param_search",description:`<strong>is_hyper_param_search</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether we are in the process of a hyper parameter search using Trainer.hyperparameter_search. This will
impact the way data will be logged in TensorBoard.`,name:"is_hyper_param_search"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L35"}}),Yt=new sT({props:{$$slots:{default:[TT]},$$scope:{ctx:j}}}),Ta=new M({props:{name:"load_from_json",anchor:"transformers.TrainerState.load_from_json",parameters:[{name:"json_path",val:": str"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L110"}}),Ca=new M({props:{name:"save_to_json",anchor:"transformers.TrainerState.save_to_json",parameters:[{name:"json_path",val:": str"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L104"}}),wa=new Pl({}),ya=new M({props:{name:"class transformers.TrainerControl",anchor:"transformers.TrainerControl",parameters:[{name:"should_training_stop",val:": bool = False"},{name:"should_epoch_stop",val:": bool = False"},{name:"should_save",val:": bool = False"},{name:"should_evaluate",val:": bool = False"},{name:"should_log",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TrainerControl.should_training_stop",description:`<strong>should_training_stop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the training should be interrupted.</p>
<p>If <code>True</code>, this variable will not be set back to <code>False</code>. The training will just stop.`,name:"should_training_stop"},{anchor:"transformers.TrainerControl.should_epoch_stop",description:`<strong>should_epoch_stop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the current epoch should be interrupted.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next epoch.`,name:"should_epoch_stop"},{anchor:"transformers.TrainerControl.should_save",description:`<strong>should_save</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the model should be saved at this step.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next step.`,name:"should_save"},{anchor:"transformers.TrainerControl.should_evaluate",description:`<strong>should_evaluate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the model should be evaluated at this step.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next step.`,name:"should_evaluate"},{anchor:"transformers.TrainerControl.should_log",description:`<strong>should_log</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the logs should be reported at this step.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next step.`,name:"should_log"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L119"}}),{c(){h=n("meta"),L=c(),_=n("h1"),k=n("a"),w=n("span"),b(p.$$.fragment),$=c(),A=n("span"),O=t("Callbacks"),S=c(),Z=n("p"),We=t(`Callbacks are objects that can customize the behavior of the training loop in the PyTorch
`),Ia=n("a"),fc=t("Trainer"),hc=t(` (this feature is not yet implemented in TensorFlow) that can inspect the training loop
state (for progress reporting, logging on TensorBoard or other ML platforms\u2026) and take decisions (like early
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cannot change anything in the training loop. For customizations that require changes in the training loop, you should
subclass `),xa=n("a"),_c=t("Trainer"),bc=t(" and override the methods you need (see "),Na=n("a"),vc=t("trainer"),Ec=t(" for examples)."),Gl=c(),pt=n("p"),Tc=t("By default a "),Sa=n("a"),kc=t("Trainer"),Cc=t(" will use the following callbacks:"),Bl=c(),D=n("ul"),Fa=n("li"),ja=n("a"),$c=t("DefaultFlowCallback"),wc=t(" which handles the default behavior for logging, saving and evaluation."),yc=c(),Ne=n("li"),Ja=n("a"),Mc=t("PrinterCallback"),Ac=t(" or "),Ua=n("a"),Lc=t("ProgressCallback"),Dc=t(` to display progress and print the
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installed.`),ld=c(),Et=n("li"),Va=n("a"),id=t("CodeCarbonCallback"),cd=t(" if "),ir=n("a"),dd=t("codecarbon"),md=t(` is
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`),qa=n("a"),Dd=t("TrainingArguments"),Id=t(" used to instantiate the "),en=n("a"),Od=t("Trainer"),xd=t(`, can access that
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`),eo=n("code"),hm=t("DISABLED"),pm=t("."),gm=c(),Se=n("li"),to=n("strong"),um=t("COMET_PROJECT_NAME"),_m=t(" ("),ro=n("code"),bm=t("str"),vm=t(", "),ao=n("em"),Em=t("optional"),Tm=t(`):
Comet project name for experiments.`),km=c(),K=n("li"),no=n("strong"),Cm=t("COMET_OFFLINE_DIRECTORY"),$m=t(" ("),oo=n("code"),wm=t("str"),ym=t(", "),so=n("em"),Mm=t("optional"),Am=t(`):
Folder to use for saving offline experiments when `),lo=n("code"),Lm=t("COMET_MODE"),Dm=t(" is "),io=n("code"),Im=t("OFFLINE"),Om=t("."),xm=c(),B=n("li"),co=n("strong"),Nm=t("COMET_LOG_ASSETS"),Sm=t(" ("),mo=n("code"),Fm=t("str"),jm=t(", "),fo=n("em"),Jm=t("optional"),Um=t(", defaults to "),ho=n("code"),Wm=t("TRUE"),Pm=t(`):
Whether or not to log training assets (tf event logs, checkpoints, etc), to Comet. Can be `),po=n("code"),Rm=t("TRUE"),Gm=t(`, or
`),go=n("code"),Bm=t("FALSE"),zm=t("."),Ym=c(),ur=n("p"),Hm=t(`For a number of configurable items in the environment, see
`),_r=n("a"),Vm=t("here"),Xm=t("."),Xl=c(),Ge=n("div"),b(br.$$.fragment),Zm=c(),vr=n("p"),Qm=t("A "),on=n("a"),Km=t("TrainerCallback"),qm=t(" that handles the default flow of the training loop for logs, evaluation and checkpoints."),Zl=c(),Be=n("div"),b(Er.$$.fragment),ef=c(),Tr=n("p"),tf=t("A bare "),sn=n("a"),rf=t("TrainerCallback"),af=t(" that just prints the logs."),Ql=c(),ze=n("div"),b(kr.$$.fragment),nf=c(),Cr=n("p"),of=t("A "),ln=n("a"),sf=t("TrainerCallback"),lf=t(" that displays the progress of training or evaluation."),Kl=c(),ke=n("div"),b($r.$$.fragment),cf=c(),wr=n("p"),df=t("A "),cn=n("a"),mf=t("TrainerCallback"),ff=t(" that handles early stopping."),hf=c(),P=n("p"),pf=t("This callback depends on "),dn=n("a"),gf=t("TrainingArguments"),uf=t(" argument "),uo=n("em"),_f=t("load_best_model_at_end"),bf=t(` functionality to set best_metric
in `),mn=n("a"),vf=t("TrainerState"),Ef=t(". Note that if the "),fn=n("a"),Tf=t("TrainingArguments"),kf=t(" argument "),_o=n("em"),Cf=t("save_steps"),$f=t(" differs from "),bo=n("em"),wf=t("eval_steps"),yf=t(`, the
early stopping will not occur until the next save step.`),ql=c(),Ye=n("div"),b(yr.$$.fragment),Mf=c(),He=n("p"),Af=t("A "),hn=n("a"),Lf=t("TrainerCallback"),Df=t(" that sends the logs to "),Mr=n("a"),If=t("TensorBoard"),Of=t("."),ei=c(),Ce=n("div"),b(Ar.$$.fragment),xf=c(),Ve=n("p"),Nf=t("A "),pn=n("a"),Sf=t("TrainerCallback"),Ff=t(" that logs metrics, media, model checkpoints to "),Lr=n("a"),jf=t("Weight and Biases"),Jf=t("."),Uf=c(),q=n("div"),b(Dr.$$.fragment),Wf=c(),Ir=n("p"),Pf=t("Setup the optional Weights & Biases ("),vo=n("em"),Rf=t("wandb"),Gf=t(") integration."),Bf=c(),Or=n("p"),zf=t(`One can subclass and override this method to customize the setup if needed. Find more information
`),xr=n("a"),Yf=t("here"),Hf=t(`. You can also override the following environment
variables:`),Vf=c(),Eo=n("p"),Xf=t("Environment:"),Zf=c(),$e=n("ul"),Nr=n("li"),I=n("p"),To=n("strong"),Qf=t("WANDB_LOG_MODEL"),Kf=t(" ("),ko=n("code"),qf=t("str"),eh=t(", "),Co=n("em"),th=t("optional"),rh=t(", defaults to "),$o=n("code"),ah=t('"false"'),nh=t(`):
Whether to log model and checkpoints during training. Can be `),wo=n("code"),oh=t('"end"'),sh=t(", "),yo=n("code"),lh=t('"checkpoint"'),ih=t(" or "),Mo=n("code"),ch=t('"false"'),dh=t(`. If set
to `),Ao=n("code"),mh=t('"end"'),fh=t(", the model will be uploaded at the end of training. If set to "),Lo=n("code"),hh=t('"checkpoint"'),ph=t(`, the checkpoint
will be uploaded every `),Do=n("code"),gh=t("args.save_steps"),uh=t(" . If set to "),Io=n("code"),_h=t('"false"'),bh=t(`, the model will not be uploaded. Use along
with `),Oo=n("code"),vh=t("load_best_model_at_end()"),Eh=t(" to upload best model."),Th=c(),b(yt.$$.fragment),kh=c(),xo=n("li"),F=n("p"),No=n("strong"),Ch=t("WANDB_WATCH"),$h=t(" ("),So=n("code"),wh=t("str"),yh=t(", "),Fo=n("em"),Mh=t("optional"),Ah=t(" defaults to "),jo=n("code"),Lh=t('"false"'),Dh=t(`):
Can be `),Jo=n("code"),Ih=t('"gradients"'),Oh=t(", "),Uo=n("code"),xh=t('"all"'),Nh=t(", "),Wo=n("code"),Sh=t('"parameters"'),Fh=t(", or "),Po=n("code"),jh=t('"false"'),Jh=t(". Set to "),Ro=n("code"),Uh=t('"all"'),Wh=t(` to log gradients and
parameters.`),Ph=c(),Go=n("li"),ie=n("p"),Bo=n("strong"),Rh=t("WANDB_PROJECT"),Gh=t(" ("),zo=n("code"),Bh=t("str"),zh=t(", "),Yo=n("em"),Yh=t("optional"),Hh=t(", defaults to "),Ho=n("code"),Vh=t('"huggingface"'),Xh=t(`):
Set this to a custom string to store results in a different project.`),Zh=c(),Vo=n("li"),ee=n("p"),Xo=n("strong"),Qh=t("WANDB_DISABLED"),Kh=t(" ("),Zo=n("code"),qh=t("bool"),ep=t(", "),Qo=n("em"),tp=t("optional"),rp=t(", defaults to "),Ko=n("code"),ap=t("False"),np=t(`):
Whether to disable wandb entirely. Set `),qo=n("code"),op=t("WANDB_DISABLED=true"),sp=t(" to disable."),ti=c(),we=n("div"),b(Sr.$$.fragment),lp=c(),ye=n("p"),ip=t("A "),gn=n("a"),cp=t("TrainerCallback"),dp=t(" that sends the logs to "),Fr=n("a"),mp=t("MLflow"),fp=t(`. Can be disabled by setting
environment variable `),es=n("code"),hp=t("DISABLE_MLFLOW_INTEGRATION = TRUE"),pp=t("."),gp=c(),ce=n("div"),b(jr.$$.fragment),up=c(),ts=n("p"),_p=t("Setup the optional MLflow integration."),bp=c(),rs=n("p"),vp=t("Environment:"),Ep=c(),Y=n("ul"),J=n("li"),as=n("strong"),Tp=t("HF_MLFLOW_LOG_ARTIFACTS"),kp=t(" ("),ns=n("code"),Cp=t("str"),$p=t(", "),os=n("em"),wp=t("optional"),yp=t(`):
Whether to use MLflow `),ss=n("code"),Mp=t(".log_artifact()"),Ap=t(` facility to log artifacts. This only makes sense if logging to a
remote server, e.g. s3 or GCS. If set to `),ls=n("code"),Lp=t("True"),Dp=t(" or "),is=n("em"),Ip=t("1"),Op=t(`, will copy each saved checkpoint on each save in
`),un=n("a"),xp=t("TrainingArguments"),Np=t("\u2019s "),cs=n("code"),Sp=t("output_dir"),Fp=t(` to the local or remote artifact storage. Using it without a remote
storage will just copy the files to your artifact location.`),jp=c(),z=n("li"),ds=n("strong"),Jp=t("MLFLOW_EXPERIMENT_NAME"),Up=t(" ("),ms=n("code"),Wp=t("str"),Pp=t(", "),fs=n("em"),Rp=t("optional"),Gp=t(", defaults to "),hs=n("code"),Bp=t("None"),zp=t(`):
Whether to use an MLflow experiment_name under which to launch the run. Default to `),ps=n("code"),Yp=t("None"),Hp=t(` which will point
to the `),gs=n("code"),Vp=t("Default"),Xp=t(` experiment in MLflow. Otherwise, it is a case sensitive name of the experiment to be
activated. If an experiment with this name does not exist, a new experiment with this name is created.`),Zp=c(),de=n("li"),us=n("strong"),Qp=t("MLFLOW_TAGS"),Kp=t(" ("),_s=n("code"),qp=t("str"),eg=t(", "),bs=n("em"),tg=t("optional"),rg=t(`):
A string dump of a dictionary of key/value pair to be added to the MLflow run as tags. Example:
`),vs=n("code"),ag=t(`os.environ['MLFLOW_TAGS']='{"release.candidate": "RC1", "release.version": "2.2.0"}'`),ng=t("."),og=c(),te=n("li"),Es=n("strong"),sg=t("MLFLOW_NESTED_RUN"),lg=t(" ("),Ts=n("code"),ig=t("str"),cg=t(", "),ks=n("em"),dg=t("optional"),mg=t(`):
Whether to use MLflow nested runs. If set to `),Cs=n("code"),fg=t("True"),hg=t(" or "),$s=n("em"),pg=t("1"),gg=t(`, will create a nested run inside the current
run.`),ug=c(),re=n("li"),ws=n("strong"),_g=t("MLFLOW_RUN_ID"),bg=t(" ("),ys=n("code"),vg=t("str"),Eg=t(", "),Ms=n("em"),Tg=t("optional"),kg=t(`):
Allow to reattach to an existing run which can be usefull when resuming training from a checkpoint. When
`),As=n("code"),Cg=t("MLFLOW_RUN_ID"),$g=t(" environment variable is set, "),Ls=n("code"),wg=t("start_run"),yg=t(` attempts to resume a run with the specified run ID
and other parameters are ignored.`),Mg=c(),me=n("li"),Ds=n("strong"),Ag=t("MLFLOW_FLATTEN_PARAMS"),Lg=t(" ("),Is=n("code"),Dg=t("str"),Ig=t(", "),Os=n("em"),Og=t("optional"),xg=t(", defaults to "),xs=n("code"),Ng=t("False"),Sg=t(`):
Whether to flatten the parameters dictionary before logging.`),ri=c(),Xe=n("div"),b(Jr.$$.fragment),Fg=c(),Ze=n("p"),jg=t("A "),_n=n("a"),Jg=t("TrainerCallback"),Ug=t(" that sends the logs to "),Ur=n("a"),Wg=t("AzureML"),Pg=t("."),ai=c(),Qe=n("div"),b(Wr.$$.fragment),Rg=c(),Pr=n("p"),Gg=t("A "),bn=n("a"),Bg=t("TrainerCallback"),zg=t(" that tracks the CO2 emission of training."),ni=c(),Me=n("div"),b(Rr.$$.fragment),Yg=c(),Gr=n("p"),Hg=t("TrainerCallback that sends the logs to "),Br=n("a"),Vg=t("Neptune"),Xg=t("."),Zg=c(),zr=n("p"),Qg=t("For instructions and examples, see the "),Yr=n("a"),Kg=t(`Transformers integration
guide`),qg=t(" in the Neptune documentation."),oi=c(),ne=n("div"),b(Hr.$$.fragment),eu=c(),Ke=n("p"),tu=t("A "),vn=n("a"),ru=t("TrainerCallback"),au=t(" that sends the logs to "),Vr=n("a"),nu=t("ClearML"),ou=t("."),su=c(),Ns=n("p"),lu=t("Environment:"),iu=c(),qe=n("ul"),fe=n("li"),Ss=n("strong"),cu=t("CLEARML_PROJECT"),du=t(" ("),Fs=n("code"),mu=t("str"),fu=t(", "),js=n("em"),hu=t("optional"),pu=t(", defaults to "),Js=n("code"),gu=t("HuggingFace Transformers"),uu=t(`):
ClearML project name.`),_u=c(),he=n("li"),Us=n("strong"),bu=t("CLEARML_TASK"),vu=t(" ("),Ws=n("code"),Eu=t("str"),Tu=t(", "),Ps=n("em"),ku=t("optional"),Cu=t(", defaults to "),Rs=n("code"),$u=t("Trainer"),wu=t(`):
ClearML task name.`),yu=c(),pe=n("li"),Gs=n("strong"),Mu=t("CLEARML_LOG_MODEL"),Au=t(" ("),Bs=n("code"),Lu=t("bool"),Du=t(", "),zs=n("em"),Iu=t("optional"),Ou=t(", defaults to "),Ys=n("code"),xu=t("False"),Nu=t(`):
Whether to log models as artifacts during training.`),si=c(),Ae=n("div"),b(Xr.$$.fragment),Su=c(),Fe=n("p"),Fu=t("A "),En=n("a"),ju=t("TrainerCallback"),Ju=t(" that logs to "),Zr=n("a"),Uu=t("DagsHub"),Wu=t(". Extends "),Hs=n("code"),Pu=t("MLflowCallback"),Ru=c(),ge=n("div"),b(Qr.$$.fragment),Gu=c(),Vs=n("p"),Bu=t("Setup the DagsHub\u2019s Logging integration."),zu=c(),Xs=n("p"),Yu=t("Environment:"),Hu=c(),Zs=n("ul"),ue=n("li"),Qs=n("strong"),Vu=t("HF_DAGSHUB_LOG_ARTIFACTS"),Xu=t(" ("),Ks=n("code"),Zu=t("str"),Qu=t(", "),qs=n("em"),Ku=t("optional"),qu=t(`):
Whether to save the data and model artifacts for the experiment. Default to `),el=n("code"),e_=t("False"),t_=t("."),li=c(),Le=n("div"),b(Kr.$$.fragment),r_=c(),et=n("p"),a_=t("A "),Tn=n("a"),n_=t("TrainerCallback"),o_=t(" that sends the logs to "),qr=n("a"),s_=t("Flyte"),l_=t(`.
NOTE: This callback only works within a Flyte task.`),i_=c(),b(Mt.$$.fragment),ii=c(),tt=n("h2"),At=n("a"),tl=n("span"),b(ea.$$.fragment),c_=c(),rl=n("span"),d_=t("TrainerCallback"),ci=c(),C=n("div"),b(ta.$$.fragment),m_=c(),al=n("p"),f_=t(`A class for objects that will inspect the state of the training loop at some events and take some decisions. At
each of those events the following arguments are available:`),h_=c(),ra=n("p"),p_=t("The "),nl=n("code"),g_=t("control"),u_=t(` object is the only one that can be changed by the callback, in which case the event that changes it
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You can unpack the ones you need in the signature of the event using them. As an example, see the code of the
simple `),cl=n("code"),M_=t("~transformer.PrinterCallback"),A_=t("."),L_=c(),b(Lt.$$.fragment),D_=c(),Dt=n("div"),b(aa.$$.fragment),I_=c(),dl=n("p"),O_=t("Event called at the beginning of an epoch."),x_=c(),It=n("div"),b(na.$$.fragment),N_=c(),ml=n("p"),S_=t("Event called at the end of an epoch."),F_=c(),Ot=n("div"),b(oa.$$.fragment),j_=c(),fl=n("p"),J_=t("Event called after an evaluation phase."),U_=c(),xt=n("div"),b(sa.$$.fragment),W_=c(),la=n("p"),P_=t("Event called at the end of the initialization of the "),kn=n("a"),R_=t("Trainer"),G_=t("."),B_=c(),Nt=n("div"),b(ia.$$.fragment),z_=c(),hl=n("p"),Y_=t("Event called after logging the last logs."),H_=c(),St=n("div"),b(ca.$$.fragment),V_=c(),pl=n("p"),X_=t("Event called after a successful prediction."),Z_=c(),Ft=n("div"),b(da.$$.fragment),Q_=c(),gl=n("p"),K_=t("Event called after a prediction step."),q_=c(),jt=n("div"),b(ma.$$.fragment),eb=c(),ul=n("p"),tb=t("Event called after a checkpoint save."),rb=c(),Jt=n("div"),b(fa.$$.fragment),ab=c(),_l=n("p"),nb=t(`Event called at the beginning of a training step. If using gradient accumulation, one training step might take
several inputs.`),ob=c(),Ut=n("div"),b(ha.$$.fragment),sb=c(),bl=n("p"),lb=t(`Event called at the end of a training step. If using gradient accumulation, one training step might take
several inputs.`),ib=c(),Wt=n("div"),b(pa.$$.fragment),cb=c(),vl=n("p"),db=t("Event called at the end of an substep during gradient accumulation."),mb=c(),Pt=n("div"),b(ga.$$.fragment),fb=c(),El=n("p"),hb=t("Event called at the beginning of training."),pb=c(),Rt=n("div"),b(ua.$$.fragment),gb=c(),Tl=n("p"),ub=t("Event called at the end of training."),di=c(),Gt=n("p"),_b=t("Here is an example of how to register a custom callback with the PyTorch "),Cn=n("a"),bb=t("Trainer"),vb=t(":"),mi=c(),b(_a.$$.fragment),fi=c(),Bt=n("p"),Eb=t("Another way to register a callback is to call "),kl=n("code"),Tb=t("trainer.add_callback()"),kb=t(" as follows:"),hi=c(),b(ba.$$.fragment),pi=c(),rt=n("h2"),zt=n("a"),Cl=n("span"),b(va.$$.fragment),Cb=c(),$l=n("span"),$b=t("TrainerState"),gi=c(),V=n("div"),b(Ea.$$.fragment),wb=c(),at=n("p"),yb=t("A class containing the "),$n=n("a"),Mb=t("Trainer"),Ab=t(` inner state that will be saved along the model and optimizer when checkpointing
and passed to the `),wn=n("a"),Lb=t("TrainerCallback"),Db=t("."),Ib=c(),b(Yt.$$.fragment),Ob=c(),Ht=n("div"),b(Ta.$$.fragment),xb=c(),ka=n("p"),Nb=t("Create an instance from the content of "),wl=n("code"),Sb=t("json_path"),Fb=t("."),jb=c(),Vt=n("div"),b(Ca.$$.fragment),Jb=c(),$a=n("p"),Ub=t("Save the content of this instance in JSON format inside "),yl=n("code"),Wb=t("json_path"),Pb=t("."),ui=c(),nt=n("h2"),Xt=n("a"),Ml=n("span"),b(wa.$$.fragment),Rb=c(),Al=n("span"),Gb=t("TrainerControl"),_i=c(),ot=n("div"),b(ya.$$.fragment),Bb=c(),st=n("p"),zb=t("A class that handles the "),yn=n("a"),Yb=t("Trainer"),Hb=t(" control flow. This class is used by the "),Mn=n("a"),Vb=t("TrainerCallback"),Xb=t(` to activate some
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or tensorboardX).`),Qb.forEach(a),Fc=d(N),gt=o(N,"LI",{});var Ol=s(gt);Ga=o(Ol,"A",{href:!0});var iv=s(Ga);jc=r(iv,"WandbCallback"),iv.forEach(a),Jc=r(Ol," if "),ar=o(Ol,"A",{href:!0,rel:!0});var cv=s(ar);Uc=r(cv,"wandb"),cv.forEach(a),Wc=r(Ol," is installed."),Ol.forEach(a),Pc=d(N),ut=o(N,"LI",{});var xl=s(ut);Ba=o(xl,"A",{href:!0});var dv=s(Ba);Rc=r(dv,"CometCallback"),dv.forEach(a),Gc=r(xl," if "),nr=o(xl,"A",{href:!0,rel:!0});var mv=s(nr);Bc=r(mv,"comet_ml"),mv.forEach(a),zc=r(xl," is installed."),xl.forEach(a),Yc=d(N),_t=o(N,"LI",{});var Nl=s(_t);za=o(Nl,"A",{href:!0});var fv=s(za);Hc=r(fv,"MLflowCallback"),fv.forEach(a),Vc=r(Nl," if "),or=o(Nl,"A",{href:!0,rel:!0});var hv=s(or);Xc=r(hv,"mlflow"),hv.forEach(a),Zc=r(Nl," is installed."),Nl.forEach(a),Qc=d(N),bt=o(N,"LI",{});var Sl=s(bt);Ya=o(Sl,"A",{href:!0});var pv=s(Ya);Kc=r(pv,"NeptuneCallback"),pv.forEach(a),qc=r(Sl," if "),sr=o(Sl,"A",{href:!0,rel:!0});var gv=s(sr);ed=r(gv,"neptune"),gv.forEach(a),td=r(Sl," is installed."),Sl.forEach(a),rd=d(N),vt=o(N,"LI",{});var Fl=s(vt);Ha=o(Fl,"A",{href:!0});var uv=s(Ha);ad=r(uv,"AzureMLCallback"),uv.forEach(a),nd=r(Fl," if "),lr=o(Fl,"A",{href:!0,rel:!0});var _v=s(lr);od=r(_v,"azureml-sdk"),_v.forEach(a),sd=r(Fl,` is
installed.`),Fl.forEach(a),ld=d(N),Et=o(N,"LI",{});var jl=s(Et);Va=o(jl,"A",{href:!0});var bv=s(Va);id=r(bv,"CodeCarbonCallback"),bv.forEach(a),cd=r(jl," if "),ir=o(jl,"A",{href:!0,rel:!0});var vv=s(ir);dd=r(vv,"codecarbon"),vv.forEach(a),md=r(jl,` is
installed.`),jl.forEach(a),fd=d(N),Tt=o(N,"LI",{});var Jl=s(Tt);Xa=o(Jl,"A",{href:!0});var Ev=s(Xa);hd=r(Ev,"ClearMLCallback"),Ev.forEach(a),pd=r(Jl," if "),cr=o(Jl,"A",{href:!0,rel:!0});var Tv=s(cr);gd=r(Tv,"clearml"),Tv.forEach(a),ud=r(Jl," is installed."),Jl.forEach(a),_d=d(N),kt=o(N,"LI",{});var Ul=s(kt);Za=o(Ul,"A",{href:!0});var kv=s(Za);bd=r(kv,"DagsHubCallback"),kv.forEach(a),vd=r(Ul," if "),dr=o(Ul,"A",{href:!0,rel:!0});var Cv=s(dr);Ed=r(Cv,"dagshub"),Cv.forEach(a),Td=r(Ul," is installed."),Ul.forEach(a),kd=d(N),Ct=o(N,"LI",{});var Wl=s(Ct);Qa=o(Wl,"A",{href:!0});var $v=s(Qa);Cd=r($v,"FlyteCallback"),$v.forEach(a),$d=r(Wl," if "),mr=o(Wl,"A",{href:!0,rel:!0});var wv=s(mr);wd=r(wv,"flyte"),wv.forEach(a),yd=r(Wl," is installed."),Wl.forEach(a),N.forEach(a),zl=d(l),G=o(l,"P",{});var _e=s(G);Md=r(_e,"The main class that implements callbacks is "),Ka=o(_e,"A",{href:!0});var yv=s(Ka);Ad=r(yv,"TrainerCallback"),yv.forEach(a),Ld=r(_e,`. It gets the
`),qa=o(_e,"A",{href:!0});var Mv=s(qa);Dd=r(Mv,"TrainingArguments"),Mv.forEach(a),Id=r(_e," used to instantiate the "),en=o(_e,"A",{href:!0});var Av=s(en);Od=r(Av,"Trainer"),Av.forEach(a),xd=r(_e,`, can access that
Trainer\u2019s internal state via `),tn=o(_e,"A",{href:!0});var Lv=s(tn);Nd=r(Lv,"TrainerState"),Lv.forEach(a),Sd=r(_e,`, and can take some actions on the training loop via
`),rn=o(_e,"A",{href:!0});var Dv=s(rn);Fd=r(Dv,"TrainerControl"),Dv.forEach(a),jd=r(_e,"."),_e.forEach(a),Yl=d(l),Pe=o(l,"H2",{class:!0});var Ti=s(Pe);$t=o(Ti,"A",{id:!0,class:!0,href:!0});var Iv=s($t);Bn=o(Iv,"SPAN",{});var Ov=s(Bn);v(fr.$$.fragment,Ov),Ov.forEach(a),Iv.forEach(a),Jd=d(Ti),zn=o(Ti,"SPAN",{});var xv=s(zn);Ud=r(xv,"Available Callbacks"),xv.forEach(a),Ti.forEach(a),Hl=d(l),wt=o(l,"P",{});var ki=s(wt);Wd=r(ki,"Here is the list of the available "),an=o(ki,"A",{href:!0});var Nv=s(an);Pd=r(Nv,"TrainerCallback"),Nv.forEach(a),Rd=r(ki," in the library:"),ki.forEach(a),Vl=d(l),Ee=o(l,"DIV",{class:!0});var An=s(Ee);v(hr.$$.fragment,An),Gd=d(An),Re=o(An,"P",{});var Ln=s(Re);Bd=r(Ln,"A "),nn=o(Ln,"A",{href:!0});var Sv=s(nn);zd=r(Sv,"TrainerCallback"),Sv.forEach(a),Yd=r(Ln," that sends the logs to "),pr=o(Ln,"A",{href:!0,rel:!0});var Fv=s(pr);Hd=r(Fv,"Comet ML"),Fv.forEach(a),Vd=r(Ln,"."),Ln.forEach(a),Xd=d(An),Q=o(An,"DIV",{class:!0});var je=s(Q);v(gr.$$.fragment,je),Zd=d(je),Yn=o(je,"P",{});var jv=s(Yn);Qd=r(jv,"Setup the optional Comet.ml integration."),jv.forEach(a),Kd=d(je),Hn=o(je,"P",{});var Jv=s(Hn);qd=r(Jv,"Environment:"),Jv.forEach(a),em=d(je),Te=o(je,"UL",{});var Qt=s(Te);W=o(Qt,"LI",{});var X=s(W);Vn=o(X,"STRONG",{});var Uv=s(Vn);tm=r(Uv,"COMET_MODE"),Uv.forEach(a),rm=r(X," ("),Xn=o(X,"CODE",{});var Wv=s(Xn);am=r(Wv,"str"),Wv.forEach(a),nm=r(X,", "),Zn=o(X,"EM",{});var Pv=s(Zn);om=r(Pv,"optional"),Pv.forEach(a),sm=r(X,", defaults to "),Qn=o(X,"CODE",{});var Rv=s(Qn);lm=r(Rv,"ONLINE"),Rv.forEach(a),im=r(X,`):
Whether to create an online, offline experiment or disable Comet logging. Can be `),Kn=o(X,"CODE",{});var Gv=s(Kn);cm=r(Gv,"OFFLINE"),Gv.forEach(a),dm=r(X,", "),qn=o(X,"CODE",{});var Bv=s(qn);mm=r(Bv,"ONLINE"),Bv.forEach(a),fm=r(X,`, or
`),eo=o(X,"CODE",{});var zv=s(eo);hm=r(zv,"DISABLED"),zv.forEach(a),pm=r(X,"."),X.forEach(a),gm=d(Qt),Se=o(Qt,"LI",{});var La=s(Se);to=o(La,"STRONG",{});var Yv=s(to);um=r(Yv,"COMET_PROJECT_NAME"),Yv.forEach(a),_m=r(La," ("),ro=o(La,"CODE",{});var Hv=s(ro);bm=r(Hv,"str"),Hv.forEach(a),vm=r(La,", "),ao=o(La,"EM",{});var Vv=s(ao);Em=r(Vv,"optional"),Vv.forEach(a),Tm=r(La,`):
Comet project name for experiments.`),La.forEach(a),km=d(Qt),K=o(Qt,"LI",{});var De=s(K);no=o(De,"STRONG",{});var Xv=s(no);Cm=r(Xv,"COMET_OFFLINE_DIRECTORY"),Xv.forEach(a),$m=r(De," ("),oo=o(De,"CODE",{});var Zv=s(oo);wm=r(Zv,"str"),Zv.forEach(a),ym=r(De,", "),so=o(De,"EM",{});var Qv=s(so);Mm=r(Qv,"optional"),Qv.forEach(a),Am=r(De,`):
Folder to use for saving offline experiments when `),lo=o(De,"CODE",{});var Kv=s(lo);Lm=r(Kv,"COMET_MODE"),Kv.forEach(a),Dm=r(De," is "),io=o(De,"CODE",{});var qv=s(io);Im=r(qv,"OFFLINE"),qv.forEach(a),Om=r(De,"."),De.forEach(a),xm=d(Qt),B=o(Qt,"LI",{});var oe=s(B);co=o(oe,"STRONG",{});var e2=s(co);Nm=r(e2,"COMET_LOG_ASSETS"),e2.forEach(a),Sm=r(oe," ("),mo=o(oe,"CODE",{});var t2=s(mo);Fm=r(t2,"str"),t2.forEach(a),jm=r(oe,", "),fo=o(oe,"EM",{});var r2=s(fo);Jm=r(r2,"optional"),r2.forEach(a),Um=r(oe,", defaults to "),ho=o(oe,"CODE",{});var a2=s(ho);Wm=r(a2,"TRUE"),a2.forEach(a),Pm=r(oe,`):
Whether or not to log training assets (tf event logs, checkpoints, etc), to Comet. Can be `),po=o(oe,"CODE",{});var n2=s(po);Rm=r(n2,"TRUE"),n2.forEach(a),Gm=r(oe,`, or
`),go=o(oe,"CODE",{});var o2=s(go);Bm=r(o2,"FALSE"),o2.forEach(a),zm=r(oe,"."),oe.forEach(a),Qt.forEach(a),Ym=d(je),ur=o(je,"P",{});var Ci=s(ur);Hm=r(Ci,`For a number of configurable items in the environment, see
`),_r=o(Ci,"A",{href:!0,rel:!0});var s2=s(_r);Vm=r(s2,"here"),s2.forEach(a),Xm=r(Ci,"."),Ci.forEach(a),je.forEach(a),An.forEach(a),Xl=d(l),Ge=o(l,"DIV",{class:!0});var $i=s(Ge);v(br.$$.fragment,$i),Zm=d($i),vr=o($i,"P",{});var wi=s(vr);Qm=r(wi,"A "),on=o(wi,"A",{href:!0});var l2=s(on);Km=r(l2,"TrainerCallback"),l2.forEach(a),qm=r(wi," that handles the default flow of the training loop for logs, evaluation and checkpoints."),wi.forEach(a),$i.forEach(a),Zl=d(l),Be=o(l,"DIV",{class:!0});var yi=s(Be);v(Er.$$.fragment,yi),ef=d(yi),Tr=o(yi,"P",{});var Mi=s(Tr);tf=r(Mi,"A bare "),sn=o(Mi,"A",{href:!0});var i2=s(sn);rf=r(i2,"TrainerCallback"),i2.forEach(a),af=r(Mi," that just prints the logs."),Mi.forEach(a),yi.forEach(a),Ql=d(l),ze=o(l,"DIV",{class:!0});var Ai=s(ze);v(kr.$$.fragment,Ai),nf=d(Ai),Cr=o(Ai,"P",{});var Li=s(Cr);of=r(Li,"A "),ln=o(Li,"A",{href:!0});var c2=s(ln);sf=r(c2,"TrainerCallback"),c2.forEach(a),lf=r(Li," that displays the progress of training or evaluation."),Li.forEach(a),Ai.forEach(a),Kl=d(l),ke=o(l,"DIV",{class:!0});var Dn=s(ke);v($r.$$.fragment,Dn),cf=d(Dn),wr=o(Dn,"P",{});var Di=s(wr);df=r(Di,"A "),cn=o(Di,"A",{href:!0});var d2=s(cn);mf=r(d2,"TrainerCallback"),d2.forEach(a),ff=r(Di," that handles early stopping."),Di.forEach(a),hf=d(Dn),P=o(Dn,"P",{});var ae=s(P);pf=r(ae,"This callback depends on "),dn=o(ae,"A",{href:!0});var m2=s(dn);gf=r(m2,"TrainingArguments"),m2.forEach(a),uf=r(ae," argument "),uo=o(ae,"EM",{});var f2=s(uo);_f=r(f2,"load_best_model_at_end"),f2.forEach(a),bf=r(ae,` functionality to set best_metric
in `),mn=o(ae,"A",{href:!0});var h2=s(mn);vf=r(h2,"TrainerState"),h2.forEach(a),Ef=r(ae,". Note that if the "),fn=o(ae,"A",{href:!0});var p2=s(fn);Tf=r(p2,"TrainingArguments"),p2.forEach(a),kf=r(ae," argument "),_o=o(ae,"EM",{});var g2=s(_o);Cf=r(g2,"save_steps"),g2.forEach(a),$f=r(ae," differs from "),bo=o(ae,"EM",{});var u2=s(bo);wf=r(u2,"eval_steps"),u2.forEach(a),yf=r(ae,`, the
early stopping will not occur until the next save step.`),ae.forEach(a),Dn.forEach(a),ql=d(l),Ye=o(l,"DIV",{class:!0});var Ii=s(Ye);v(yr.$$.fragment,Ii),Mf=d(Ii),He=o(Ii,"P",{});var In=s(He);Af=r(In,"A "),hn=o(In,"A",{href:!0});var _2=s(hn);Lf=r(_2,"TrainerCallback"),_2.forEach(a),Df=r(In," that sends the logs to "),Mr=o(In,"A",{href:!0,rel:!0});var b2=s(Mr);If=r(b2,"TensorBoard"),b2.forEach(a),Of=r(In,"."),In.forEach(a),Ii.forEach(a),ei=d(l),Ce=o(l,"DIV",{class:!0});var On=s(Ce);v(Ar.$$.fragment,On),xf=d(On),Ve=o(On,"P",{});var xn=s(Ve);Nf=r(xn,"A "),pn=o(xn,"A",{href:!0});var v2=s(pn);Sf=r(v2,"TrainerCallback"),v2.forEach(a),Ff=r(xn," that logs metrics, media, model checkpoints to "),Lr=o(xn,"A",{href:!0,rel:!0});var E2=s(Lr);jf=r(E2,"Weight and Biases"),E2.forEach(a),Jf=r(xn,"."),xn.forEach(a),Uf=d(On),q=o(On,"DIV",{class:!0});var Je=s(q);v(Dr.$$.fragment,Je),Wf=d(Je),Ir=o(Je,"P",{});var Oi=s(Ir);Pf=r(Oi,"Setup the optional Weights & Biases ("),vo=o(Oi,"EM",{});var T2=s(vo);Rf=r(T2,"wandb"),T2.forEach(a),Gf=r(Oi,") integration."),Oi.forEach(a),Bf=d(Je),Or=o(Je,"P",{});var xi=s(Or);zf=r(xi,`One can subclass and override this method to customize the setup if needed. Find more information
`),xr=o(xi,"A",{href:!0,rel:!0});var k2=s(xr);Yf=r(k2,"here"),k2.forEach(a),Hf=r(xi,`. You can also override the following environment
variables:`),xi.forEach(a),Vf=d(Je),Eo=o(Je,"P",{});var C2=s(Eo);Xf=r(C2,"Environment:"),C2.forEach(a),Zf=d(Je),$e=o(Je,"UL",{});var Kt=s($e);Nr=o(Kt,"LI",{});var Ni=s(Nr);I=o(Ni,"P",{});var x=s(I);To=o(x,"STRONG",{});var $2=s(To);Qf=r($2,"WANDB_LOG_MODEL"),$2.forEach(a),Kf=r(x," ("),ko=o(x,"CODE",{});var w2=s(ko);qf=r(w2,"str"),w2.forEach(a),eh=r(x,", "),Co=o(x,"EM",{});var y2=s(Co);th=r(y2,"optional"),y2.forEach(a),rh=r(x,", defaults to "),$o=o(x,"CODE",{});var M2=s($o);ah=r(M2,'"false"'),M2.forEach(a),nh=r(x,`):
Whether to log model and checkpoints during training. Can be `),wo=o(x,"CODE",{});var A2=s(wo);oh=r(A2,'"end"'),A2.forEach(a),sh=r(x,", "),yo=o(x,"CODE",{});var L2=s(yo);lh=r(L2,'"checkpoint"'),L2.forEach(a),ih=r(x," or "),Mo=o(x,"CODE",{});var D2=s(Mo);ch=r(D2,'"false"'),D2.forEach(a),dh=r(x,`. If set
to `),Ao=o(x,"CODE",{});var I2=s(Ao);mh=r(I2,'"end"'),I2.forEach(a),fh=r(x,", the model will be uploaded at the end of training. If set to "),Lo=o(x,"CODE",{});var O2=s(Lo);hh=r(O2,'"checkpoint"'),O2.forEach(a),ph=r(x,`, the checkpoint
will be uploaded every `),Do=o(x,"CODE",{});var x2=s(Do);gh=r(x2,"args.save_steps"),x2.forEach(a),uh=r(x," . If set to "),Io=o(x,"CODE",{});var N2=s(Io);_h=r(N2,'"false"'),N2.forEach(a),bh=r(x,`, the model will not be uploaded. Use along
with `),Oo=o(x,"CODE",{});var S2=s(Oo);vh=r(S2,"load_best_model_at_end()"),S2.forEach(a),Eh=r(x," to upload best model."),x.forEach(a),Th=d(Ni),v(yt.$$.fragment,Ni),Ni.forEach(a),kh=d(Kt),xo=o(Kt,"LI",{});var F2=s(xo);F=o(F2,"P",{});var U=s(F);No=o(U,"STRONG",{});var j2=s(No);Ch=r(j2,"WANDB_WATCH"),j2.forEach(a),$h=r(U," ("),So=o(U,"CODE",{});var J2=s(So);wh=r(J2,"str"),J2.forEach(a),yh=r(U,", "),Fo=o(U,"EM",{});var U2=s(Fo);Mh=r(U2,"optional"),U2.forEach(a),Ah=r(U," defaults to "),jo=o(U,"CODE",{});var W2=s(jo);Lh=r(W2,'"false"'),W2.forEach(a),Dh=r(U,`):
Can be `),Jo=o(U,"CODE",{});var P2=s(Jo);Ih=r(P2,'"gradients"'),P2.forEach(a),Oh=r(U,", "),Uo=o(U,"CODE",{});var R2=s(Uo);xh=r(R2,'"all"'),R2.forEach(a),Nh=r(U,", "),Wo=o(U,"CODE",{});var G2=s(Wo);Sh=r(G2,'"parameters"'),G2.forEach(a),Fh=r(U,", or "),Po=o(U,"CODE",{});var B2=s(Po);jh=r(B2,'"false"'),B2.forEach(a),Jh=r(U,". Set to "),Ro=o(U,"CODE",{});var z2=s(Ro);Uh=r(z2,'"all"'),z2.forEach(a),Wh=r(U,` to log gradients and
parameters.`),U.forEach(a),F2.forEach(a),Ph=d(Kt),Go=o(Kt,"LI",{});var Y2=s(Go);ie=o(Y2,"P",{});var lt=s(ie);Bo=o(lt,"STRONG",{});var H2=s(Bo);Rh=r(H2,"WANDB_PROJECT"),H2.forEach(a),Gh=r(lt," ("),zo=o(lt,"CODE",{});var V2=s(zo);Bh=r(V2,"str"),V2.forEach(a),zh=r(lt,", "),Yo=o(lt,"EM",{});var X2=s(Yo);Yh=r(X2,"optional"),X2.forEach(a),Hh=r(lt,", defaults to "),Ho=o(lt,"CODE",{});var Z2=s(Ho);Vh=r(Z2,'"huggingface"'),Z2.forEach(a),Xh=r(lt,`):
Set this to a custom string to store results in a different project.`),lt.forEach(a),Y2.forEach(a),Zh=d(Kt),Vo=o(Kt,"LI",{});var Q2=s(Vo);ee=o(Q2,"P",{});var Ie=s(ee);Xo=o(Ie,"STRONG",{});var K2=s(Xo);Qh=r(K2,"WANDB_DISABLED"),K2.forEach(a),Kh=r(Ie," ("),Zo=o(Ie,"CODE",{});var q2=s(Zo);qh=r(q2,"bool"),q2.forEach(a),ep=r(Ie,", "),Qo=o(Ie,"EM",{});var eE=s(Qo);tp=r(eE,"optional"),eE.forEach(a),rp=r(Ie,", defaults to "),Ko=o(Ie,"CODE",{});var tE=s(Ko);ap=r(tE,"False"),tE.forEach(a),np=r(Ie,`):
Whether to disable wandb entirely. Set `),qo=o(Ie,"CODE",{});var rE=s(qo);op=r(rE,"WANDB_DISABLED=true"),rE.forEach(a),sp=r(Ie," to disable."),Ie.forEach(a),Q2.forEach(a),Kt.forEach(a),Je.forEach(a),On.forEach(a),ti=d(l),we=o(l,"DIV",{class:!0});var Nn=s(we);v(Sr.$$.fragment,Nn),lp=d(Nn),ye=o(Nn,"P",{});var qt=s(ye);ip=r(qt,"A "),gn=o(qt,"A",{href:!0});var aE=s(gn);cp=r(aE,"TrainerCallback"),aE.forEach(a),dp=r(qt," that sends the logs to "),Fr=o(qt,"A",{href:!0,rel:!0});var nE=s(Fr);mp=r(nE,"MLflow"),nE.forEach(a),fp=r(qt,`. Can be disabled by setting
environment variable `),es=o(qt,"CODE",{});var oE=s(es);hp=r(oE,"DISABLE_MLFLOW_INTEGRATION = TRUE"),oE.forEach(a),pp=r(qt,"."),qt.forEach(a),gp=d(Nn),ce=o(Nn,"DIV",{class:!0});var er=s(ce);v(jr.$$.fragment,er),up=d(er),ts=o(er,"P",{});var sE=s(ts);_p=r(sE,"Setup the optional MLflow integration."),sE.forEach(a),bp=d(er),rs=o(er,"P",{});var lE=s(rs);vp=r(lE,"Environment:"),lE.forEach(a),Ep=d(er),Y=o(er,"UL",{});var be=s(Y);J=o(be,"LI",{});var R=s(J);as=o(R,"STRONG",{});var iE=s(as);Tp=r(iE,"HF_MLFLOW_LOG_ARTIFACTS"),iE.forEach(a),kp=r(R," ("),ns=o(R,"CODE",{});var cE=s(ns);Cp=r(cE,"str"),cE.forEach(a),$p=r(R,", "),os=o(R,"EM",{});var dE=s(os);wp=r(dE,"optional"),dE.forEach(a),yp=r(R,`):
Whether to use MLflow `),ss=o(R,"CODE",{});var mE=s(ss);Mp=r(mE,".log_artifact()"),mE.forEach(a),Ap=r(R,` facility to log artifacts. This only makes sense if logging to a
remote server, e.g. s3 or GCS. If set to `),ls=o(R,"CODE",{});var fE=s(ls);Lp=r(fE,"True"),fE.forEach(a),Dp=r(R," or "),is=o(R,"EM",{});var hE=s(is);Ip=r(hE,"1"),hE.forEach(a),Op=r(R,`, will copy each saved checkpoint on each save in
`),un=o(R,"A",{href:!0});var pE=s(un);xp=r(pE,"TrainingArguments"),pE.forEach(a),Np=r(R,"\u2019s "),cs=o(R,"CODE",{});var gE=s(cs);Sp=r(gE,"output_dir"),gE.forEach(a),Fp=r(R,` to the local or remote artifact storage. Using it without a remote
storage will just copy the files to your artifact location.`),R.forEach(a),jp=d(be),z=o(be,"LI",{});var se=s(z);ds=o(se,"STRONG",{});var uE=s(ds);Jp=r(uE,"MLFLOW_EXPERIMENT_NAME"),uE.forEach(a),Up=r(se," ("),ms=o(se,"CODE",{});var _E=s(ms);Wp=r(_E,"str"),_E.forEach(a),Pp=r(se,", "),fs=o(se,"EM",{});var bE=s(fs);Rp=r(bE,"optional"),bE.forEach(a),Gp=r(se,", defaults to "),hs=o(se,"CODE",{});var vE=s(hs);Bp=r(vE,"None"),vE.forEach(a),zp=r(se,`):
Whether to use an MLflow experiment_name under which to launch the run. Default to `),ps=o(se,"CODE",{});var EE=s(ps);Yp=r(EE,"None"),EE.forEach(a),Hp=r(se,` which will point
to the `),gs=o(se,"CODE",{});var TE=s(gs);Vp=r(TE,"Default"),TE.forEach(a),Xp=r(se,` experiment in MLflow. Otherwise, it is a case sensitive name of the experiment to be
activated. If an experiment with this name does not exist, a new experiment with this name is created.`),se.forEach(a),Zp=d(be),de=o(be,"LI",{});var it=s(de);us=o(it,"STRONG",{});var kE=s(us);Qp=r(kE,"MLFLOW_TAGS"),kE.forEach(a),Kp=r(it," ("),_s=o(it,"CODE",{});var CE=s(_s);qp=r(CE,"str"),CE.forEach(a),eg=r(it,", "),bs=o(it,"EM",{});var $E=s(bs);tg=r($E,"optional"),$E.forEach(a),rg=r(it,`):
A string dump of a dictionary of key/value pair to be added to the MLflow run as tags. Example:
`),vs=o(it,"CODE",{});var wE=s(vs);ag=r(wE,`os.environ['MLFLOW_TAGS']='{"release.candidate": "RC1", "release.version": "2.2.0"}'`),wE.forEach(a),ng=r(it,"."),it.forEach(a),og=d(be),te=o(be,"LI",{});var Oe=s(te);Es=o(Oe,"STRONG",{});var yE=s(Es);sg=r(yE,"MLFLOW_NESTED_RUN"),yE.forEach(a),lg=r(Oe," ("),Ts=o(Oe,"CODE",{});var ME=s(Ts);ig=r(ME,"str"),ME.forEach(a),cg=r(Oe,", "),ks=o(Oe,"EM",{});var AE=s(ks);dg=r(AE,"optional"),AE.forEach(a),mg=r(Oe,`):
Whether to use MLflow nested runs. If set to `),Cs=o(Oe,"CODE",{});var LE=s(Cs);fg=r(LE,"True"),LE.forEach(a),hg=r(Oe," or "),$s=o(Oe,"EM",{});var DE=s($s);pg=r(DE,"1"),DE.forEach(a),gg=r(Oe,`, will create a nested run inside the current
run.`),Oe.forEach(a),ug=d(be),re=o(be,"LI",{});var xe=s(re);ws=o(xe,"STRONG",{});var IE=s(ws);_g=r(IE,"MLFLOW_RUN_ID"),IE.forEach(a),bg=r(xe," ("),ys=o(xe,"CODE",{});var OE=s(ys);vg=r(OE,"str"),OE.forEach(a),Eg=r(xe,", "),Ms=o(xe,"EM",{});var xE=s(Ms);Tg=r(xE,"optional"),xE.forEach(a),kg=r(xe,`):
Allow to reattach to an existing run which can be usefull when resuming training from a checkpoint. When
`),As=o(xe,"CODE",{});var NE=s(As);Cg=r(NE,"MLFLOW_RUN_ID"),NE.forEach(a),$g=r(xe," environment variable is set, "),Ls=o(xe,"CODE",{});var SE=s(Ls);wg=r(SE,"start_run"),SE.forEach(a),yg=r(xe,` attempts to resume a run with the specified run ID
and other parameters are ignored.`),xe.forEach(a),Mg=d(be),me=o(be,"LI",{});var ct=s(me);Ds=o(ct,"STRONG",{});var FE=s(Ds);Ag=r(FE,"MLFLOW_FLATTEN_PARAMS"),FE.forEach(a),Lg=r(ct," ("),Is=o(ct,"CODE",{});var jE=s(Is);Dg=r(jE,"str"),jE.forEach(a),Ig=r(ct,", "),Os=o(ct,"EM",{});var JE=s(Os);Og=r(JE,"optional"),JE.forEach(a),xg=r(ct,", defaults to "),xs=o(ct,"CODE",{});var UE=s(xs);Ng=r(UE,"False"),UE.forEach(a),Sg=r(ct,`):
Whether to flatten the parameters dictionary before logging.`),ct.forEach(a),be.forEach(a),er.forEach(a),Nn.forEach(a),ri=d(l),Xe=o(l,"DIV",{class:!0});var Si=s(Xe);v(Jr.$$.fragment,Si),Fg=d(Si),Ze=o(Si,"P",{});var Sn=s(Ze);jg=r(Sn,"A "),_n=o(Sn,"A",{href:!0});var WE=s(_n);Jg=r(WE,"TrainerCallback"),WE.forEach(a),Ug=r(Sn," that sends the logs to "),Ur=o(Sn,"A",{href:!0,rel:!0});var PE=s(Ur);Wg=r(PE,"AzureML"),PE.forEach(a),Pg=r(Sn,"."),Sn.forEach(a),Si.forEach(a),ai=d(l),Qe=o(l,"DIV",{class:!0});var Fi=s(Qe);v(Wr.$$.fragment,Fi),Rg=d(Fi),Pr=o(Fi,"P",{});var ji=s(Pr);Gg=r(ji,"A "),bn=o(ji,"A",{href:!0});var RE=s(bn);Bg=r(RE,"TrainerCallback"),RE.forEach(a),zg=r(ji," that tracks the CO2 emission of training."),ji.forEach(a),Fi.forEach(a),ni=d(l),Me=o(l,"DIV",{class:!0});var Fn=s(Me);v(Rr.$$.fragment,Fn),Yg=d(Fn),Gr=o(Fn,"P",{});var Ji=s(Gr);Hg=r(Ji,"TrainerCallback that sends the logs to "),Br=o(Ji,"A",{href:!0,rel:!0});var GE=s(Br);Vg=r(GE,"Neptune"),GE.forEach(a),Xg=r(Ji,"."),Ji.forEach(a),Zg=d(Fn),zr=o(Fn,"P",{});var Ui=s(zr);Qg=r(Ui,"For instructions and examples, see the "),Yr=o(Ui,"A",{href:!0,rel:!0});var BE=s(Yr);Kg=r(BE,`Transformers integration
guide`),BE.forEach(a),qg=r(Ui," in the Neptune documentation."),Ui.forEach(a),Fn.forEach(a),oi=d(l),ne=o(l,"DIV",{class:!0});var tr=s(ne);v(Hr.$$.fragment,tr),eu=d(tr),Ke=o(tr,"P",{});var jn=s(Ke);tu=r(jn,"A "),vn=o(jn,"A",{href:!0});var zE=s(vn);ru=r(zE,"TrainerCallback"),zE.forEach(a),au=r(jn," that sends the logs to "),Vr=o(jn,"A",{href:!0,rel:!0});var YE=s(Vr);nu=r(YE,"ClearML"),YE.forEach(a),ou=r(jn,"."),jn.forEach(a),su=d(tr),Ns=o(tr,"P",{});var HE=s(Ns);lu=r(HE,"Environment:"),HE.forEach(a),iu=d(tr),qe=o(tr,"UL",{});var Jn=s(qe);fe=o(Jn,"LI",{});var dt=s(fe);Ss=o(dt,"STRONG",{});var VE=s(Ss);cu=r(VE,"CLEARML_PROJECT"),VE.forEach(a),du=r(dt," ("),Fs=o(dt,"CODE",{});var XE=s(Fs);mu=r(XE,"str"),XE.forEach(a),fu=r(dt,", "),js=o(dt,"EM",{});var ZE=s(js);hu=r(ZE,"optional"),ZE.forEach(a),pu=r(dt,", defaults to "),Js=o(dt,"CODE",{});var QE=s(Js);gu=r(QE,"HuggingFace Transformers"),QE.forEach(a),uu=r(dt,`):
ClearML project name.`),dt.forEach(a),_u=d(Jn),he=o(Jn,"LI",{});var mt=s(he);Us=o(mt,"STRONG",{});var KE=s(Us);bu=r(KE,"CLEARML_TASK"),KE.forEach(a),vu=r(mt," ("),Ws=o(mt,"CODE",{});var qE=s(Ws);Eu=r(qE,"str"),qE.forEach(a),Tu=r(mt,", "),Ps=o(mt,"EM",{});var e1=s(Ps);ku=r(e1,"optional"),e1.forEach(a),Cu=r(mt,", defaults to "),Rs=o(mt,"CODE",{});var t1=s(Rs);$u=r(t1,"Trainer"),t1.forEach(a),wu=r(mt,`):
ClearML task name.`),mt.forEach(a),yu=d(Jn),pe=o(Jn,"LI",{});var ft=s(pe);Gs=o(ft,"STRONG",{});var r1=s(Gs);Mu=r(r1,"CLEARML_LOG_MODEL"),r1.forEach(a),Au=r(ft," ("),Bs=o(ft,"CODE",{});var a1=s(Bs);Lu=r(a1,"bool"),a1.forEach(a),Du=r(ft,", "),zs=o(ft,"EM",{});var n1=s(zs);Iu=r(n1,"optional"),n1.forEach(a),Ou=r(ft,", defaults to "),Ys=o(ft,"CODE",{});var o1=s(Ys);xu=r(o1,"False"),o1.forEach(a),Nu=r(ft,`):
Whether to log models as artifacts during training.`),ft.forEach(a),Jn.forEach(a),tr.forEach(a),si=d(l),Ae=o(l,"DIV",{class:!0});var Un=s(Ae);v(Xr.$$.fragment,Un),Su=d(Un),Fe=o(Un,"P",{});var Da=s(Fe);Fu=r(Da,"A "),En=o(Da,"A",{href:!0});var s1=s(En);ju=r(s1,"TrainerCallback"),s1.forEach(a),Ju=r(Da," that logs to "),Zr=o(Da,"A",{href:!0,rel:!0});var l1=s(Zr);Uu=r(l1,"DagsHub"),l1.forEach(a),Wu=r(Da,". Extends "),Hs=o(Da,"CODE",{});var i1=s(Hs);Pu=r(i1,"MLflowCallback"),i1.forEach(a),Da.forEach(a),Ru=d(Un),ge=o(Un,"DIV",{class:!0});var rr=s(ge);v(Qr.$$.fragment,rr),Gu=d(rr),Vs=o(rr,"P",{});var c1=s(Vs);Bu=r(c1,"Setup the DagsHub\u2019s Logging integration."),c1.forEach(a),zu=d(rr),Xs=o(rr,"P",{});var d1=s(Xs);Yu=r(d1,"Environment:"),d1.forEach(a),Hu=d(rr),Zs=o(rr,"UL",{});var m1=s(Zs);ue=o(m1,"LI",{});var ht=s(ue);Qs=o(ht,"STRONG",{});var f1=s(Qs);Vu=r(f1,"HF_DAGSHUB_LOG_ARTIFACTS"),f1.forEach(a),Xu=r(ht," ("),Ks=o(ht,"CODE",{});var h1=s(Ks);Zu=r(h1,"str"),h1.forEach(a),Qu=r(ht,", "),qs=o(ht,"EM",{});var p1=s(qs);Ku=r(p1,"optional"),p1.forEach(a),qu=r(ht,`):
Whether to save the data and model artifacts for the experiment. Default to `),el=o(ht,"CODE",{});var g1=s(el);e_=r(g1,"False"),g1.forEach(a),t_=r(ht,"."),ht.forEach(a),m1.forEach(a),rr.forEach(a),Un.forEach(a),li=d(l),Le=o(l,"DIV",{class:!0});var Wn=s(Le);v(Kr.$$.fragment,Wn),r_=d(Wn),et=o(Wn,"P",{});var Pn=s(et);a_=r(Pn,"A "),Tn=o(Pn,"A",{href:!0});var u1=s(Tn);n_=r(u1,"TrainerCallback"),u1.forEach(a),o_=r(Pn," that sends the logs to "),qr=o(Pn,"A",{href:!0,rel:!0});var _1=s(qr);s_=r(_1,"Flyte"),_1.forEach(a),l_=r(Pn,`.
NOTE: This callback only works within a Flyte task.`),Pn.forEach(a),i_=d(Wn),v(Mt.$$.fragment,Wn),Wn.forEach(a),ii=d(l),tt=o(l,"H2",{class:!0});var Wi=s(tt);At=o(Wi,"A",{id:!0,class:!0,href:!0});var b1=s(At);tl=o(b1,"SPAN",{});var v1=s(tl);v(ea.$$.fragment,v1),v1.forEach(a),b1.forEach(a),c_=d(Wi),rl=o(Wi,"SPAN",{});var E1=s(rl);d_=r(E1,"TrainerCallback"),E1.forEach(a),Wi.forEach(a),ci=d(l),C=o(l,"DIV",{class:!0});var y=s(C);v(ta.$$.fragment,y),m_=d(y),al=o(y,"P",{});var T1=s(al);f_=r(T1,`A class for objects that will inspect the state of the training loop at some events and take some decisions. At
each of those events the following arguments are available:`),T1.forEach(a),h_=d(y),ra=o(y,"P",{});var Pi=s(ra);p_=r(Pi,"The "),nl=o(Pi,"CODE",{});var k1=s(nl);g_=r(k1,"control"),k1.forEach(a),u_=r(Pi,` object is the only one that can be changed by the callback, in which case the event that changes it
should return the modified version.`),Pi.forEach(a),__=d(y),H=o(y,"P",{});var ve=s(H);b_=r(ve,"The argument "),ol=o(ve,"CODE",{});var C1=s(ol);v_=r(C1,"args"),C1.forEach(a),E_=r(ve,", "),sl=o(ve,"CODE",{});var $1=s(sl);T_=r($1,"state"),$1.forEach(a),k_=r(ve," and "),ll=o(ve,"CODE",{});var w1=s(ll);C_=r(w1,"control"),w1.forEach(a),$_=r(ve," are positionals for all events, all the others are grouped in "),il=o(ve,"CODE",{});var y1=s(il);w_=r(y1,"kwargs"),y1.forEach(a),y_=r(ve,`.
You can unpack the ones you need in the signature of the event using them. As an example, see the code of the
simple `),cl=o(ve,"CODE",{});var M1=s(cl);M_=r(M1,"~transformer.PrinterCallback"),M1.forEach(a),A_=r(ve,"."),ve.forEach(a),L_=d(y),v(Lt.$$.fragment,y),D_=d(y),Dt=o(y,"DIV",{class:!0});var Ri=s(Dt);v(aa.$$.fragment,Ri),I_=d(Ri),dl=o(Ri,"P",{});var A1=s(dl);O_=r(A1,"Event called at the beginning of an epoch."),A1.forEach(a),Ri.forEach(a),x_=d(y),It=o(y,"DIV",{class:!0});var Gi=s(It);v(na.$$.fragment,Gi),N_=d(Gi),ml=o(Gi,"P",{});var L1=s(ml);S_=r(L1,"Event called at the end of an epoch."),L1.forEach(a),Gi.forEach(a),F_=d(y),Ot=o(y,"DIV",{class:!0});var Bi=s(Ot);v(oa.$$.fragment,Bi),j_=d(Bi),fl=o(Bi,"P",{});var D1=s(fl);J_=r(D1,"Event called after an evaluation phase."),D1.forEach(a),Bi.forEach(a),U_=d(y),xt=o(y,"DIV",{class:!0});var zi=s(xt);v(sa.$$.fragment,zi),W_=d(zi),la=o(zi,"P",{});var Yi=s(la);P_=r(Yi,"Event called at the end of the initialization of the "),kn=o(Yi,"A",{href:!0});var I1=s(kn);R_=r(I1,"Trainer"),I1.forEach(a),G_=r(Yi,"."),Yi.forEach(a),zi.forEach(a),B_=d(y),Nt=o(y,"DIV",{class:!0});var Hi=s(Nt);v(ia.$$.fragment,Hi),z_=d(Hi),hl=o(Hi,"P",{});var O1=s(hl);Y_=r(O1,"Event called after logging the last logs."),O1.forEach(a),Hi.forEach(a),H_=d(y),St=o(y,"DIV",{class:!0});var Vi=s(St);v(ca.$$.fragment,Vi),V_=d(Vi),pl=o(Vi,"P",{});var x1=s(pl);X_=r(x1,"Event called after a successful prediction."),x1.forEach(a),Vi.forEach(a),Z_=d(y),Ft=o(y,"DIV",{class:!0});var Xi=s(Ft);v(da.$$.fragment,Xi),Q_=d(Xi),gl=o(Xi,"P",{});var N1=s(gl);K_=r(N1,"Event called after a prediction step."),N1.forEach(a),Xi.forEach(a),q_=d(y),jt=o(y,"DIV",{class:!0});var Zi=s(jt);v(ma.$$.fragment,Zi),eb=d(Zi),ul=o(Zi,"P",{});var S1=s(ul);tb=r(S1,"Event called after a checkpoint save."),S1.forEach(a),Zi.forEach(a),rb=d(y),Jt=o(y,"DIV",{class:!0});var Qi=s(Jt);v(fa.$$.fragment,Qi),ab=d(Qi),_l=o(Qi,"P",{});var F1=s(_l);nb=r(F1,`Event called at the beginning of a training step. If using gradient accumulation, one training step might take
several inputs.`),F1.forEach(a),Qi.forEach(a),ob=d(y),Ut=o(y,"DIV",{class:!0});var Ki=s(Ut);v(ha.$$.fragment,Ki),sb=d(Ki),bl=o(Ki,"P",{});var j1=s(bl);lb=r(j1,`Event called at the end of a training step. If using gradient accumulation, one training step might take
several inputs.`),j1.forEach(a),Ki.forEach(a),ib=d(y),Wt=o(y,"DIV",{class:!0});var qi=s(Wt);v(pa.$$.fragment,qi),cb=d(qi),vl=o(qi,"P",{});var J1=s(vl);db=r(J1,"Event called at the end of an substep during gradient accumulation."),J1.forEach(a),qi.forEach(a),mb=d(y),Pt=o(y,"DIV",{class:!0});var ec=s(Pt);v(ga.$$.fragment,ec),fb=d(ec),El=o(ec,"P",{});var U1=s(El);hb=r(U1,"Event called at the beginning of training."),U1.forEach(a),ec.forEach(a),pb=d(y),Rt=o(y,"DIV",{class:!0});var tc=s(Rt);v(ua.$$.fragment,tc),gb=d(tc),Tl=o(tc,"P",{});var W1=s(Tl);ub=r(W1,"Event called at the end of training."),W1.forEach(a),tc.forEach(a),y.forEach(a),di=d(l),Gt=o(l,"P",{});var rc=s(Gt);_b=r(rc,"Here is an example of how to register a custom callback with the PyTorch "),Cn=o(rc,"A",{href:!0});var P1=s(Cn);bb=r(P1,"Trainer"),P1.forEach(a),vb=r(rc,":"),rc.forEach(a),mi=d(l),v(_a.$$.fragment,l),fi=d(l),Bt=o(l,"P",{});var ac=s(Bt);Eb=r(ac,"Another way to register a callback is to call "),kl=o(ac,"CODE",{});var R1=s(kl);Tb=r(R1,"trainer.add_callback()"),R1.forEach(a),kb=r(ac," as follows:"),ac.forEach(a),hi=d(l),v(ba.$$.fragment,l),pi=d(l),rt=o(l,"H2",{class:!0});var nc=s(rt);zt=o(nc,"A",{id:!0,class:!0,href:!0});var G1=s(zt);Cl=o(G1,"SPAN",{});var B1=s(Cl);v(va.$$.fragment,B1),B1.forEach(a),G1.forEach(a),Cb=d(nc),$l=o(nc,"SPAN",{});var z1=s($l);$b=r(z1,"TrainerState"),z1.forEach(a),nc.forEach(a),gi=d(l),V=o(l,"DIV",{class:!0});var Ue=s(V);v(Ea.$$.fragment,Ue),wb=d(Ue),at=o(Ue,"P",{});var Rn=s(at);yb=r(Rn,"A class containing the "),$n=o(Rn,"A",{href:!0});var Y1=s($n);Mb=r(Y1,"Trainer"),Y1.forEach(a),Ab=r(Rn,` inner state that will be saved along the model and optimizer when checkpointing
and passed to the `),wn=o(Rn,"A",{href:!0});var H1=s(wn);Lb=r(H1,"TrainerCallback"),H1.forEach(a),Db=r(Rn,"."),Rn.forEach(a),Ib=d(Ue),v(Yt.$$.fragment,Ue),Ob=d(Ue),Ht=o(Ue,"DIV",{class:!0});var oc=s(Ht);v(Ta.$$.fragment,oc),xb=d(oc),ka=o(oc,"P",{});var sc=s(ka);Nb=r(sc,"Create an instance from the content of "),wl=o(sc,"CODE",{});var V1=s(wl);Sb=r(V1,"json_path"),V1.forEach(a),Fb=r(sc,"."),sc.forEach(a),oc.forEach(a),jb=d(Ue),Vt=o(Ue,"DIV",{class:!0});var lc=s(Vt);v(Ca.$$.fragment,lc),Jb=d(lc),$a=o(lc,"P",{});var ic=s($a);Ub=r(ic,"Save the content of this instance in JSON format inside "),yl=o(ic,"CODE",{});var X1=s(yl);Wb=r(X1,"json_path"),X1.forEach(a),Pb=r(ic,"."),ic.forEach(a),lc.forEach(a),Ue.forEach(a),ui=d(l),nt=o(l,"H2",{class:!0});var cc=s(nt);Xt=o(cc,"A",{id:!0,class:!0,href:!0});var Z1=s(Xt);Ml=o(Z1,"SPAN",{});var Q1=s(Ml);v(wa.$$.fragment,Q1),Q1.forEach(a),Z1.forEach(a),Rb=d(cc),Al=o(cc,"SPAN",{});var K1=s(Al);Gb=r(K1,"TrainerControl"),K1.forEach(a),cc.forEach(a),_i=d(l),ot=o(l,"DIV",{class:!0});var dc=s(ot);v(ya.$$.fragment,dc),Bb=d(dc),st=o(dc,"P",{});var Gn=s(st);zb=r(Gn,"A class that handles the "),yn=o(Gn,"A",{href:!0});var q1=s(yn);Yb=r(q1,"Trainer"),q1.forEach(a),Hb=r(Gn," control flow. This class is used by the "),Mn=o(Gn,"A",{href:!0});var eT=s(Mn);Vb=r(eT,"TrainerCallback"),eT.forEach(a),Xb=r(Gn,` to activate some
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