Modèle de potage
Dagg model dafay wàññi reso neuronal bi ci dindi ay poid wala ay structure yu mat te duñu def lu bari ci génnam.
Résumé
It cuts size, memory, and compute cost while aiming to keep accuracy nearly intact.
Plongeur bu xóot
Reseau neuronal yiñ tàggat dañuy faral di ëpp ay parametre: lëkkaloo yu bari dañuy yor ay poid yu tuuti yuy indi jafe-jafe ci wax luy waaja am. Dagg dafay xàmmee ba noppi dindi mbir yooyu, bàyyi xeetu garab gu gëna woyof. Dagg bu jaxasoowul dafay nul poid yi ci benn-benn, génne matris yu bari yuñ mëna comprimé bu baax waaye soxla hardware wala bibliothèque yu yam ngir gaaw. Dagg buñ yamale dafay dindi ay yunit yu mat - neuron, boppu bàyyi xel, chaine, wala diisaay - di joxe xeetu dense bu gëna ndaw buy daw ci hardware bu gëna gaaw. Benn ci rëset yiñ gëna xam mooy bouclage iteratif bi: tàggat, dagg parametre yi gëna néew solo ci yenn kritër (dafay faral di am magnitude poids), ba noppi defar ko bu baax ngir am njubte gi ñàkk, baamtu ba dayo bi wala gaawaay bi nga bëgga yegg. Dagg ñaari mbir ci anam wu natureel ak kantite ak distilaasioŋ ci biir tuyo yi ñuy dugal.
Gis-gis xarala
Njortu poñ yi ñooy wane li ñu wara dagg. Critère bi gëna yomba def mooy magnitude - poids absolu yu ndaw lañu jàpp ni ñoo gëna néew njariñ. Pexe yu gëna sell ñooy xayma njeexitalu diisaay bu nekk ci ñàkk gi, jëfandikoo gradient wala ñaareelu yoon (bu Hessian-based) sensitiwite, ni ci xeetu Chirurgien Yuur bu Gën. Ticket Lottery Hypothesis dafa wane ni reso yu dëgër yi dañu am ay subréseau yu néew, yuñ tàggat ci initialisation bu dëggu bi, mën nañu méngoo ak model bi yépp - loolu dafay tekki ni lu bari ci reso yi dañuy redondance ci ndoorte li.
njeextalu pexe
Njëgg ak budget
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw.
dogal yu gëna leer
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal.
Xool kalite
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi.
Ëlëgu modelu dagg
Dagg dañu koy gëna jëfandikoo ci modeli làkk yu mag, fu pexe yuñ yamale di dindi boppu attention yi, neuron yi, ba ci ay couche ngir méngale model yi ak GPU yu gëna ndaw ak aparey yu yam yi. Hardware ak kernel yiy jëfandikoo sparsity (lu melni NVIDIA's 2:4 sparsity buñ yamale) ñu ngi gëna màgg, moo tax dagg bu jaxasoowul dafay gëna gaaw. Xaarandil dagg bi ñuy boole ak kantite ak distilaasioŋ muy bokk ci gasoduk kompresioŋ otomatik buy yengu ci latency, energie, ak budget memory.
Doxal ci àdduna dëgg
Komprime ab modelu làkk bu yaatu ngir dox ci benn GPU konsomatër ci barabu benn cluster serwër.
Sewloo xeetu gis-gis bi suko defee mu mëna ànd ak memory telefon bu xarañ bi wala kamera biñ samp ci biir.
Di dindi boppu yiy gëna bàyyi xel ci Transformatër bu amul benn wàññeekaay buñu mëna natt ci kalite bi.
Wàññi energie inference ak latency ngir sarwis yu bari trafik ngir wàññi njëgu cloud 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.
Benchmark ci biir sargal ak done yu dëggu.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale.
Weyal di banneexu
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Gis bi ci topp
Modèlu IA
Laaj yi ñuy faral di laaj
What is Model Pruning?
Dagg model dafay wàññi reso neuronal bi ci dindi ay poid wala ay structure yu mat te duñu def lu bari ci génnam. Dafay wàññi dayo bi, mémoire bi, ak njëgu ordinatër bi, boole ci baña yàq njubte gi.
Lan mooy xalaat bu mag bi ci ginaaw modelu dagg garab?
Dagg dafay jëfandikoo parametrisation bu ëpp ci dagg poids wala unité yu néew doole ngir wàññi model bi boole ci baña yàq li gëna bari ci njubteem.
Lan mooy wuutale dagg buñ defar ak dagg buñ defarul?
Daggukaay buñ yamale dafay dindi bépp komposant, ba noppi génne ay model yu gëna ndaw yu gëna gaaw ci hardware buñ miin, fekk daggukaayu jaxasoowul dafay dindi poid yi benn-benn, di defar sparsity.
Lan moo waral dagg garab gu jaxasoo du gaawaale model bi ci hardware bu gëna bari?
Zero yu tasaaroo duñu tekki ci saasi ci xayma yu néew; hardware dense baa ngi leen di liggéey fileek jëfandikoo wuñu kernel wala accelerateur yu yam.
Lan mooy rëset bu ñuy faral di def ngir dagg garab?
Daggug iteratif dafay wuutale dindi parametre yu amul solo ak defar bu baax ngir am njub, ndànk-ndànk dem ba ci dayo wala gaawaay biñ bëgga am.
Luy xalaatu tiketu loteri bi wax?
Hypothèse bi dafa wane ni subréseau bu ndaw buñu tann bu baax ('ticket winning'), buñu tàggatee ci initialisation bu njëkk bi, mën na yegg ci njubteg reso dense bu mat sëkk.