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BentoML ak xeetu emballage
BentoML benn kaadaru Python la bu ubbeeku, muy boole ay xeeti jàngu masin yuñ tàggat ci ay yunit yuñ mëna jëfandikoo, ñu tuddee ko 'Bentos'.
Déglul tegtal biiIA-nettali · ~2 min
Ci xët wii2 simili jàng
Résumé
Dafay dindi bërëb bi nekk ci digganté ab model bu toog ci benn kaye ak ab sarwiisu defar bu mëna def ay wax luy waaja am ci benn API.
Plongeur bu xóot
Su gëstukat bi jeexalee ci tàggat benn model, dugal ko ci liggéey dafay faral di tekki bind kodu serwiis ak loxo, pinning dependency, tabax nataalu Docker, ak wiring API. BentoML dafay otomatise lii. Yaa ngi denc benn model ci magasin model bi ci dëkk bi, ba noppi nga joxe benn klaasu Serwiis bu am poñ API buñ defar ngir mëna jëfandikoo inference. Komànd 'bentoml build' dafay boole model bi, sa kodu Python, xeetu dependence yi, ak tabb runtime ci benn Bento bu nekk ci boppam, buñ joxe xeetu Bento. Foofu 'bentoml conteneriser' defar nataalu OCI Docker. BentoML dafay jàppale daanaka bépp kaadar (PyTorch, TensorFlow, scikit-jàng, XGBoost, Transformatëri kanam yuy laxasu, ONNX) ba noppi yokk ay micro-batching yuy méngoo, yuy boole laaj yiy dugg ci saasi ngir yokk GPU bi te doo soppi sa kode.
Doxal ci àdduna dëgg
Ekipu gis-gis njuuj njaaj dafay denc benn xeetu XGBoost ci bitik BentoML ba noppi tabax benn Bento buy wane / predict REST endpoint ngir serwiisu fayukaay yi woo ci jamono dëgg.
Benn ekipu platform ML dafay jëfandikoo 'bentoml contenerize' ngir soppi xeetu yëg-yëgu kanam buy laxasu ci nataalu Docker buy dugal ci seen cluster Kubernetes bi ci biir.
Benn startup dafay liggéey ci xeetu Llama buñ defar bu baax ak OpenLLM (ñu tabax ci kaw BentoML), di joxe ay token ci interfaasu jëfandikukat buy waxtaan ak batching buy méngoo ak GPU bi.
Benn kompiñi buy xool ci ordinatër dafay boole benn xeetu nataalu PyTorch ak gasoduk preprocessing bi ci benn Bento suko defee ñu mëna soppi gaal gi gëna jubal ci tàggat gaal gi ak model bi.
Ëlëgu BentoML ak xeetu emballage
BentoML dafa wéeru bu baax ci xeetu làkk bu yaatu ak sarwiisu IA buy defar, OpenLLM ak BentoCloud di joxe tontu ci token, autoscaling, ak xam-xam GPU. Xaarandi lëkkaloo bu gëna dëgër ak optimisatër yu melni vLLM ak TensorRT-LLM, jàppale bu gëna baax ci sistem IA yu bari, ak yoon yu gëna neexa joge ci Bento buñ defar ba ci GPU bu amul serwër. Bi ekip yi jogee ci benn model dem ci pipeline agent, BentoML mingi taxawal boppam ni couche de emballage ak de service biy boole composant yooyu.
Teg ko ci jëf
Lu tax mu am solo
- 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.
Moytul
- 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.
Jéego yi ci topp
- 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
Bulletin bu amul fayda
Jàngal xibaar AI bu bes
Ñatti jaar-jaari IA yuñ firndeel bis bu nekk ci ayu-bis bi, ñu bind leen ci Àngle bu leer. Gratis ba fàww, amul yëgle.
Benn email bis bu nekk ci ayu-bis bi. Benn klik nga dindi abonemaa bi. Dunu musa jaay wala séddoo sa adres.
Nattal sa bopp
Take the BentoML and Model Packaging quiz
Feedback ci saasi ci bépp tontu, ak sertifikaa buñ mëna séddoo ak ID buñ mëna saytu soo jàllee ci njàng mi.
Jàppale njàngum IA te doo fay. AI Understanding mbootaay la buy def te Yàlla tax — amul yëgle, amul paywall, musul am. Defal ab sarax
Laaj yi ñuy faral di laaj
Luy BentoML ak xeetu emballage?
BentoML benn kaadaru Python la bu ubbeeku, muy boole ay xeeti jàngu masin yuñ tàggat ci ay yunit yuñ mëna jëfandikoo, ñu tuddee ko 'Bentos'. Dafay dindi bërëb bi nekk ci digganté ab model bu toog ci benn kaye ak ab sarwiisu defar bu mëna def ay wax luy waaja am ci benn API.
Lan mooy jumtukaay biñ mëna jëfandikoo, bi BentoML di defar?
BentoML dafay boole ab model, kodam, ay dependansi, ak config runtime ci benn jumtukaay buñ soppali, bu nekk ci boppam bu tuddu Bento.
Lan mooy gëna suqaliku ci micro-batching bu BentoML?
Batching biy méngoo dafay boole ay laaj yuy dugg ci runtime ngir GPU yi nekk ci liggéey ba noppi yokk produit yi te duñu soppi kode bi.
In BentoML 1.1’s Runner-based architecture, what handles compute-heavy model execution separately from API logic?
BentoML documentation describes Runners as the legacy 1.1 computation units that could execute separately; newer BentoML versions streamline services.
Ban komand mooy soppi Bento biñ tabax ci nataalu OCI Docker?
'bentoml conteneriser' dafay defar nataalu OCI/Docker buñ miin bu bawoo ci Bento ngir génne ko.
Ci yii, ban ci ñoom moo waral BentoML di samp ay xeetu dependence ci Bento?
Pinning ak embedding environmaa bi dafay tax serwiis biñ defar mën nañu ko defaraat ba noppi di méngoo fépp fuñu ko mëna defee.
Weyal di jàng
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