GUIDE teknik

Ray ngir IA buñ séddale

Ray benn kaadaru open-source la buy yombal escaleer Python ak IA ci ordinatër portable ba ci ay junni masin.

Déglul tegtal biiIA-nettali · ~2 min

Ci xët wii2 simili jàng
  1. Résumé
  2. Plongeur bu xóot
  3. Doxal ci àdduna dëgg
  4. Ëlëgu Ray ngir IA buñ séddale
  5. Teg ko ci jëf
  6. Weyal di banneexu
  7. Laaj yi ñuy faral di laaj

Résumé

Dafa am solo ndax dafay joxe anam wu yomb, boole ngir séddale tàggat, tuning, jëfandikoo done, ak liggéey te doo binndaat sa kode ngir ku nekk.

Plongeur bu xóot

Li gëna am solo ci xalaatu Ray mooy soppi fonction ak class yu Python ñu nekk ay unité yuñ séddale te du am benn coppite. Fonction buñu màrkee ni 'liggéey' bu sori dafay dox ci anam wu asynchrone ci bépp liggéeykat ci cluster bi; ab klaas buñu màrkee ni 'aktër' bu sori nekk na ab sarwis bu am doole buy dundu ci kaw ab liggéeykat. Ray dafay delloo futures yu woyof yi (royuwaayi mbir yi) ba noppi yor oraŋ bi, dem bi ak dikk bi ci done yi jaaraleko ci benn dencukaay buñ bokk, ak muñ ay njuumte. Ci kaw core bi dafa am bibliothèque yuñ tabax ci anam wu ñu ko jagleel: Ray Train ngir tàggat model buñ séddale, Ray Tune ngir seetlu hyperparameter, Ray Data ngir gasoduk done yuy jaar, RLlib ngir jàng buy dooleel, ak Ray Serve ngir model buñ mëna escaleer. Loolu dafay may benn cluster mu mëna yoriinu liggéeyu ML bi yépp.

Doxal ci àdduna dëgg

Dawal Ray Tune ngir seetee téemeeri boole hyperparametre yu paralel ci biir benn cluster GPU ngir gis tabb model bi gëna baax

Jëfandikoo Ray Train ngir séddale tàggat yaram ci xeetu jàng bu xóot ci GPU yu bari ak node yu bari te du am benn coppite ci kod bi

Tabax ab gasoduk inference ci ay done Ray ngir am ay milioŋ ciy dokimaa ci di leen jaay ci benn model ci biir benn cluster

Taxawal ay model yu bari ci ginnàaw benn poñ buy jeexal autoscaling ak Ray Serve ngir mëna jëflante ak dem bi ak dikk bi

Ëlëgu Ray ngir IA buñ séddale

Ray nekkaatna yaxu ndigg ci IA bu yaatu, ñu koy jëfandikoo bu baax ci taggat ak joxe xeetu lakk yu yaatu. Xaarandil màgg ci liggéey bu jëm ci LLM (Ray Serve ak vLLM), oraaru GPU bu wuute, boole bu gëna dëgër ak lak done ak Kubernetes jaaraleko ci KubeRay, ak autoscaling bu gëna baax ngir liggéey yu bari. Lu model yi di màgg, liggéeyu Ray ci defar tàggat yu bari, pipeline RLHF, ak inference ci ay junni accelerator mën na gëna yokk.

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

  1. Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo.
  2. Benchmark ci biir sargal ak done yu dëggu.
  3. Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi.
  4. 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 Ray for Distributed AI quiz

Feedback ci saasi ci bépp tontu, ak sertifikaa buñ mëna séddoo ak ID buñ mëna saytu soo jàllee ci njàng mi.

Tambalil quiz

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 Ray ngir IA buñ séddale?

Ray benn kaadaru open-source la buy yombal escaleer Python ak IA ci ordinatër portable ba ci ay junni masin. Dafa am solo ndax dafay joxe anam wu yomb, boole ngir séddale tàggat, tuning, jëfandikoo done, ak liggéey te doo binndaat sa kode ngir ku nekk.

Ban jafe-jafe la Ray njëkka saafara?

Ray dafay joxe anam wu yomb te boole ngir séddale ordinatër ci masin yu bari te doo binndaat sa kode ngir xeetu liggéey bu nekk.

Ci Ray, lan mooy wuutale 'liggéey' ak 'aktër'?

Liggéey yi ay fonction yu sori yu amul etaa lañu, ñuy dox ci paralel, waaye aktër yi ay mbir yu yàgg lañu yuy wéy di nekk, melni model buñ sarse.

Lan la Ray di delloo ci saasi soo tàmbali liggéey bu sori?

Ray delloosi ëlëg gu woyof ci saasi suko defee liggéey bi di dox asynchrone; woo ray.get() ngir am resultaa bi soo ko soxlaa.

Ban bibliotek Ray lañu jagleel seetug hyperparametre buñ séddale?

Ray Tune mingi yam ci doxal ay test yu bari ci paralel ci biir benn cluster.

Which object type do Ray’s docs explicitly say can be read from same-node object-store memory without a copy?

Ray documents zero-copy reads for NumPy arrays stored in the object store when workers on the same node access them. Other values may require deserialization, and data requested across nodes must be transferred.