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Versions of oneCCL and PyTorch must match.`),pt.forEach(n),Kt=p(e),$(V.$$.fragment,e),zt=p(e),B=o(e,"H2",{class:!0});var vn=l(B);J=o(vn,"A",{id:!0,class:!0,href:!0});var Cl=l(J);kt=o(Cl,"SPAN",{});var kl=l(kt);$(pe.$$.fragment,kl),kl.forEach(n),Cl.forEach(n),io=p(vn),Dt=o(vn,"SPAN",{});var Dl=l(Dt);so=i(Dl,"Intel\xAE MPI library"),Dl.forEach(n),vn.forEach(n),Gt=i(e,`

Use this standards-based MPI implementation to deliver flexible, efficient, scalable cluster messaging on Intel\xAE architecture. This component is part of the Intel\xAE oneAPI HPC Toolkit.
`),tt=o(e,"P",{});var Al=l(tt);co=i(Al,"oneccl_bindings_for_pytorch is installed along with the MPI tool set. Need to source the environment before using it."),Al.forEach(n),Vt=p(e),nt=o(e,"P",{});var Il=l(nt);po=i(Il,"for Intel\xAE oneCCL >= 1.12.0"),Il.forEach(n),Jt=p(e),$(he.$$.fragment,e),Yt=p(e),rt=o(e,"P",{});var Rl=l(rt);ho=i(Rl,"for Intel\xAE oneCCL whose version < 1.12.0"),Rl.forEach(n),Qt=p(e),$(fe.$$.fragment,e),Zt=p(e),W=o(e,"H4",{class:!0});var gn=l(W);Y=o(gn,"A",{id:!0,class:!0,href:!0});var Ul=l(Y);At=o(Ul,"SPAN",{});var Ll=l(At);$(de.$$.fragment,Ll),Ll.forEach(n),Ul.forEach(n),fo=p(gn),It=o(gn,"SPAN",{});var Ml=l(It);_o=i(Ml,"IPEX installation:"),Ml.forEach(n),gn.forEach(n),en=p(e),Q=o(e,"P",{});var bn=l(Q);uo=i(bn,"IPEX provides performance optimizations for CPU training with both Float32 and BFloat16, you could refer "),ot=o(bn,"A",{href:!0});var Nl=l(ot);mo=i(Nl,"single CPU section"),Nl.forEach(n),vo=i(bn,"."),bn.forEach(n),tn=p(e),lt=o(e,"P",{});var Ol=l(lt);go=i(Ol,"The following \u201CUsage in Trainer\u201D takes mpirun in Intel\xAE MPI library as an example."),Ol.forEach(n),nn=p(e),j=o(e,"H2",{class:!0});var En=l(j);Z=o(En,"A",{id:!0,class:!0,href:!0});var Sl=l(Z);Rt=o(Sl,"SPAN",{});var ql=l(Rt);$(_e.$$.fragment,ql),ql.forEach(n),Sl.forEach(n),bo=p(En),Ut=o(En,"SPAN",{});var Hl=l(Ut);Eo=i(Hl,"Usage in Trainer"),Hl.forEach(n),En.forEach(n),rn=i(e,`

To enable multi CPU distributed training in the Trainer with the ccl backend, users should add **\`--ddp_backend ccl\`** in the command arguments.
`),ue=o(e,"P",{});var Do=l(ue);yo=i(Do,"Let\u2019s see an example with the "),me=o(Do,"A",{href:!0,rel:!0});var Bl=l(me);wo=i(Bl,"question-answering example"),Bl.forEach(n),Do.forEach(n),on=p(e),at=o(e,"P",{});var Wl=l(at);xo=i(Wl,"The following command enables training with 2 processes on one Xeon node, with one process running per one socket. The variables OMP_NUM_THREADS/CCL_WORKER_COUNT can be tuned for optimal performance."),Wl.forEach(n),ln=p(e),$(ve.$$.fragment,e),an=p(e),it=o(e,"P",{});var jl=l(it);$o=i(jl,"The following command enables training with a total of four processes on two Xeons (node0 and node1, taking node0 as the main process), ppn (processes per node) is set to 2, with one process running per one socket. 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