Résumé
Parallelism ci done yi dafay gëna gaaw ci tàggat benn model ci ni ñu koy toppandoo ci GPU yu bari, GPU bu nekk di liggéey ci wàll wu wuute ci done yi. Mooy pexem workhorse biy may ekip yi ñu mëna yegg ba fukki-fukki wala junni-junni accelerator.
Parallelism ci done yi, jumtukaayu tabax la bu am njeexital ci kalite model bi, njëgu infrastructure bi, latency bi, ak wóor gi ci escale bi.
Plongeur bu xóot
Ci parallelism done, GPU bu nekk dafay yor benn kopi bu nuróo ci diisaayu model bi waaye dafay liggéey ci misaali tàggat yu ndaw yu wuute. Aparey bu nekk dafay xayma paas bi ci kanam ak ci ginaaw boppam, ba noppi defar ay gradient boppam. Laata ñuy yeesal poid yi, gradient yi dañu leen di moyenne ci GPU yépp di jëfandikoo ab jokkoo buy wàññi lépp, kon replica bu nekk dafay des ci sync ba noppi di doxalee ni dafa tàggat ci benn batch bu mag buñ boole. Loolu dafay yokk produit yi: 8 GPUs mën nañu sàqami lu tollu ci 8x done ci jéego bu nekk. Japp bi mooy GPU bu nekk dafa wara méngoo ak model bi yépp, ay gradient, ak nekkinu optimisatër bi ci mémoire bi, kon parallelism done bu leer du jàppale sudee model bi dafa rëy lool ci benn aparey.
Gis-gis xarala
Li gëna am solo mooy wàññi lépp, muy boole gradient yi ci aparey yi ba noppi séddalewaat li ci génn. Ring all-reduce, bibliotek yu melni NCCL ak Horovod di jëfandikoo, dafay romb ay gradient yu wër benn ring bu logic suko defee jokkoo bu mat sëkk nekkul ci lim GPU. DistributedDataParallel bu PyTorch dafay jaxasoo jokkoo bi ak paas bi ci ginaaw, di génne sync gradient ci diisaay yu njëkk yi fekk diisaay yu ci topp yi ñu ngi nekk ci ordinatër, di nëbb lu bari ci latency reso bi.
Xam paralelismu done
Parallelism ci done yi dafay gëna gaaw ci tàggat benn model ci ni ñu koy toppandoo ci GPU yu bari, GPU bu nekk di liggéey ci wàll wu wuute ci done yi. Mooy pexem workhorse biy may ekip yi ñu mëna yegg ba fukki-fukki wala junni-junni accelerator. Parallelism ci done yi, jumtukaayu tabax la bu am njeexital ci kalite model bi, njëgu infrastructure bi, latency bi, ak wóor gi ci escale bi. Ngir tabax xam-xam bu xóot, jàppal Data Parallelism ni xeetu liggéey, du benn man-man: leeral njariñ yi nga bëgg, leeral xalaat yi, ba noppi tàqale li sistem bi mëna def ci anam wu wóor ak li ba leegi soxla àtteb kàngam.
Ci jëf, ekip yu am doole yiy jëfandikoo Data Parallelism dañuy gëna baaxal architecture, done, ak tànneefi infrastructure ci wàllu wóor ak njëg. Dañuy bind kritër yu leer ngir am ndam, natt leen ci done yu dëggu ak def liggéey, ba noppi ñu baamtu ci anamu ñàkka mëna seetlu, du ci benn yoon benchmark wins. Mooy barab bi xam-xam theorie bi di soppiku nekk kàttan buy yàgg ci produit yi, ci politik yi ak ci liggéey yi.
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw. Ci jamano jooju, Optimisation benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi. Xeetu jëf bi gëna dëgër mooy boole gaawaayu jàngat ak disipline nguur: doxal pilote, jàpp firnde, siiwal dogal yi, ak wéy di yeesal kaaraange gi ci anam wi ñuy doxalee, li jëfandikukat bi di xaar, ak sàrti sàrt yi di jëm kanam.
njeextalu pexe
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw.
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw. Ci jëfandikoo yu am kalite bu kawe, loolu dañu koy tekki ci sàrti liggéey yuñ mëna natt, ay peggu boroom, ak ay xew-xewu xoolaat yu bari suko defee ekip yi mëna yokk wóolu seen bopp ci barabu yokk lu jaxasoo.
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal.
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal. Ci jëfandikoo yu am kalite bu kawe, loolu dañu koy tekki ci sàrti liggéey yuñ mëna natt, ay peggu boroom, ak ay xew-xewu xoolaat yu bari suko defee ekip yi mëna yokk wóolu seen bopp ci barabu yokk lu jaxasoo.
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi.
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi. Ci jëfandikoo yu am kalite bu kawe, loolu dañu koy tekki ci sàrti liggéey yuñ mëna natt, ay peggu boroom, ak ay xew-xewu xoolaat yu bari suko defee ekip yi mëna yokk wóolu seen bopp ci barabu yokk lu jaxasoo.
Doxal ci àdduna dëgg
Taggat benn ResNet buy xaaj nataal ci 8 GPU ci benn serwër buy jëfandikoo PyTorch DistributedDataParallel, GPU bu nekk di jëfandikoo 32 ci 256 nataal.
Eskalaasioŋ BERT bi ñuy njëkka tàggat ci téemeeri GPU ak Horovod, jëfandikoo ring all-reduce ngir méngale gradient yi jéego bu nekk.
Defar ab xeetu xalaat ci cluster bu bari node fu node bu nekk di doxal ay shards yu wuute ci diggante jëfandikukat yi.
Jëfandikoo TensorFlow's MirroredStrategy ngir tasaare tàggat ci xeetu gis-gis ci GPU yu bari ci benn station de travail ak coppite kode yu néew.
Modèlu jëfandikoo
Paralelismu done ci jëf
Taggat benn ResNet buy xaaj nataal ci 8 GPU ci benn serwër buy jëfandikoo PyTorch DistributedDataParallel, GPU bu nekk di jëfandikoo 32 ci 256 nataal.
Taggat benn ResNet nataal buy xaaj 8 GPUs ci benn serwër buy jëfandikoo PyTorch DistributedDataParallel, GPU bu nekk di jëfandikoo 32 ci 256 nataal yu bari. waxtu.
Paralelismu done ci jëf
Eskalaasioŋ BERT bi ñuy njëkka tàggat ci téemeeri GPU ak Horovod, jëfandikoo ring all-reduce ngir méngale gradient yi jéego bu nekk.
Scaling BERT pretraining ci téemeeri GPUs ak Horovod, jëfandikoo ring all-reduce ngir synchronize gradients jéego bu nekk. Ekip yi dañuy faral di am njariñ yu gëna baax suñu joxee threshold yu baax ci kanam, tëye yoonu escalation nit ngir jafe-jafe yi, ak topp njariñu produit yi ak njuumte yi.
Paralelismu done ci jëf
Defar ab xeetu xalaat ci cluster bu bari node fu node bu nekk di doxal ay shards yu wuute ci diggante jëfandikukat yi.
Fine-tuning ab modelu recommandation ci cluster bu bari node fu node bu nekk di doxal ay shards interaction jëfandikukat yu wuute. Ekip yi dañuy faral di am njariñ yu gëna baax suñu joxe ay threshold yu baax ci kanam, tëye yoonu escalation nit ngir jafe-jafe yi, ba noppi topp njariñu produit ak njëgu njuumte ci diir bi.
Paralelismu done ci jëf
Jëfandikoo TensorFlow's MirroredStrategy ngir tasaare tàggat ci xeetu gis-gis ci GPU yu bari ci benn station de travail ak coppite kode yu néew.
Jëfandikoo TensorFlow's MirroredStrategy ngir tasaare tàggat yaram ci xeetu gis-gis ci GPUs yu bari ci benn station de travail ak coppite kode yu néew. Ekip yi dañuy faral di am njariñ yu gëna baax suñu joxee threshold yu baax ci kanam, tëye yoonu escalation nit ngir jafe-jafe yi, ak topp njuumte yi ci diir bi.
Risk yi ak balustrade yi
Optimize benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi.
Njëg li ñuy fay ci infrastructure yi ak ci toppatoo dañuy faral di suufeel.
Bu sistem yi di gëna xawa jafee xam, jafe-jafe yi am ci wàllu kaaraange ak seetlu mën nañu gëna bari.
Roadmap ngir samp gi
Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo.
Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.
Benchmark ci biir sargal ak done yu dëggu.
Benchmark ci biir sargal ak done yu dëggu. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.