Iyini i-AI Scalability?

Uma uke wabuka imodeli yedemo echoboza umthwalo omncane wokuhlola bese imisa lapho abasebenzisi bangempela bevela khona, uhlangabezane nesikhohlakali: ukukala. I-AI ihahela idatha, ikhompuyutha, inkumbulo, umkhawulokudonsa-futhi okuxakile, ukunakwa. Ngakho-ke yini i-AI Scalability, ngempela, futhi uyithola kanjani ngaphandle kokuphinda ubhale yonke into isonto ngalinye?

Izindatshana ongathanda ukuzifunda ngemva kwalesi:

🔗 Kuyini ukuchema kwe-AI kuchazwe kalula
Funda ukuthi ukuchema okufihliwe kuzishintsha kanjani izinqumo ze-AI kanye nemiphumela yamamodeli.

🔗 Igayidi yabaqalayo: kuyini ubuhlakani bokwenziwa
Uhlolojikelele lwe-AI, imiqondo ewumongo, izinhlobo, nezinhlelo zokusebenza zansuku zonke.

🔗 Yini echazekayo i-AI nokuthi kungani ibalulekile
Thola ukuthi i-AI echazekayo ikwandisa kanjani ukubeka izinto obala, ukwethembana, nokuthobela imithetho.

🔗 Iyini i-AI ebikezelayo nokuthi isebenza kanjani
Qonda i-AI eqagelayo, izimo zokusetshenziswa okuvamile, izinzuzo, kanye nemikhawulo.

Iyini i-AI Scalability? 📈

I-AI Scalability yikhono lesistimu ye-AI yokusingatha idatha eyengeziwe, izicelo, abasebenzisi, namacala okusebenzisa kuyilapho kugcinwa ukusebenza, ukwethembeka, kanye nezindleko ngaphakathi kwemikhawulo eyamukelekayo. Hhayi nje amaseva amakhudlwana-izakhiwo ezihlakaniphile ezigcina ukubambezeleka kuphansi, ukuphuma kuphezulu, kanye nekhwalithi engashintshi njengoba ijika likhuphuka. Cabanga ngengqalasizinda enwebekayo, amamodeli athuthukisiwe, nokubonakala okukutshela ukuthi yini eshile.

Yini eyenza i-AI Scalability enhle ✅

Lapho i-AI Scalability yenziwa kahle, uthola:

• Ukubambezeleka okubikezelwayo ngaphansi komthwalo onama-spiky noma oqhubekayo 🙂

• Ukukhiqiza okukhula cishe ngokulingana nezingxenyekazi zekhompuyutha ezingeziwe noma izifaniso

• Ukusebenza kahle kwezindleko okungaveli ibhaluni ngesicelo ngasinye

• Ukuzinza kwekhwalithi njengoba okokufaka kuyahlukahluka futhi nenani liyakhuphuka

• Ukusebenza okuzolile sibonga ukukala okuzenzakalelayo, ukulandelela, nama-SLO ahlakaniphile

Ngaphansi kwe-hood lokhu kuvame ukuhlanganisa ukukala okuvundlile, ukunqwabelana, ukulondoloza isikhashana, ukulinganisa, ukukhonza okuqinile, nezinqubomgomo zokukhishwa okucatshangelwayo ezihlanganiswe nesabelomali samaphutha [5].

I-AI Scalability vs ukusebenza vs umthamo 🧠

• Ukusebenza ukuthi isicelo esisodwa siqeda ngokushesha kangakanani sisodwa.

• Amandla ukuthi zingaki lezo zicelo ongazisingatha ngesikhathi esisodwa.

• I-AI Scalability ukuthi ukungeza izinsiza noma ukusebenzisa amasu ahlakaniphile kwandisa umthamo futhi kugcina ukusebenza kufana-ngaphandle kokuqhumisa ibhili lakho noma ipheyija yakho.

Umehluko omncane, imiphumela emikhulu.

Kungani isikali sisebenza ku-AI nhlobo: umbono wemithetho yokukala 📚

Ukuqonda okusetshenziswa kakhulu ku-ML yesimanje ukuthi ukulahlekelwa kuba ngcono ngezindlela ezingabikezelwa njengoba ukala usayizi wemodeli, idatha, futhi ubale -ngaphakathi kwesizathu. Kukhona futhi ibhalansi yekhompyutha elungile phakathi kosayizi wemodeli namathokheni okuqeqesha; ukukala kokubili ndawonye amabhithi ukukala eyodwa kuphela. Empeleni, le mibono yazisa amabhajethi okuqeqesha, ukuhlela amasethi edatha, kanye nokunikeza ukuhwebelana [4].

Ukuhumusha okusheshayo: okukhulu kungaba ngcono, kodwa kuphela uma ukala okokufaka futhi ubala ngesilinganiso-kungenjalo kufana nokubeka amathayi kagandaganda ebhayisikilini. Kubukeka kushubile, akuyi ndawo.

Okuvundlile vs mpo: amalevu amabili okukala 🔩

• Ukukala okuqondile : amabhokisi amakhulu, ama-GPU e-beefier, inkumbulo eyengeziwe. Simple, ngezinye izikhathi pricey. Ilungele ukuqeqeshwa kwe-single-node, inference low-latency, noma lapho imodeli yakho yenqaba ukuhlakazeka kahle.

• Ukukala okuvundlile : amakhophi amaningi. Isebenza kahle kakhulu ngezikali ezizenzakalelayo ezengeza noma ezisusa ama-pod ngokusekelwe ku-CPU/GPU noma amamethrikhi ohlelo lokusebenza angokwezifiso. Ku-Kubernetes, i-HorizontalPodAutoscaler ikala ama-pods ukuphendula isidingo-ukulawulwa kwakho kwesixuku okuyisisekelo kokukhuphuka kwethrafikhi [1].

I-Anecdote (inhlanganisela): Ngesikhathi sokwethulwa kwephrofayili ephezulu, ivele inike amandla ukuhlanganisa kohlangothi lweseva nokuvumela isithwebuli esizenzakalelayo sisabele ekujuleni komugqa kuzinzile i-p95 ngaphandle koshintsho lweklayenti. Ukunqoba okungatheni kuseyiwo.

Isitaki esigcwele se-AI Scalability 🥞

1. Isendlalelo sedatha : izitolo zezinto ezisheshayo, izinkomba zevekhtha, nokungeniswa kokusakaza-bukhoma okungeke kucindezele abaqeqeshi bakho.

2. Isendlalelo sokuqeqesha : izinhlaka ezisabalalisiwe namashejuli aphatha ukufana kwedatha/imodeli, ukuhlola, ukuzama futhi.

3. Isendlalelo esinikezayo : izikhathi zokusebenza ezithuthukisiwe, ukuhlanganisa okuguquguqukayo , ukunakwa kwekhasi kwama-LLM, ukulondoloza isikhashana, ukusakaza amathokheni. I-Triton ne-vLLM zingamaqhawe avamile lapha [2][3].

4. I-Orchestration : I-Kubernetes yokunwebeka nge-HPA noma ama-autoscaler angokwezifiso [1].

5. Ukubonakala : ukulandelelwa, amamethrikhi, namalogi alandela uhambo lwabasebenzisi nokuziphatha okuyimodeli kumkhiqizo; ziklame eduze kwama-SLO akho [5].

6. Ukuphatha kanye nezindleko : ezomnotho ngesicelo ngasinye, isabelomali, noshintsho olubulalayo lwemisebenzi ebalekayo.

Ithebula lokuqhathanisa: amathuluzi namaphethini we-AI Scalability 🧰

Ukungalingani kancane ngenjongo-ngoba impilo yangempela injalo.

I-Deep dive 1: Ukunikeza amacebo anyakazisa inaliti 🚀

• Ukuhlanganiswa okunamandla kuhlanganisa izingcingo ezincane zokucabanga zibe amaqoqo amakhulu kuseva, okwandisa kakhulu ukusetshenziswa kwe-GPU ngaphandle kwezinguquko zeklayenti [2].

• Ukunakwa kwekhasi kugcina izingxoxo eziningi kakhulu enkumbulweni ngokupheja izinqolobane ze-KV, okuthuthukisa ukuphuma ngaphansi kwe-concurrency [3].

• Cela ukuhlanganisa nokulondoloza ukuze uthole imiyalo efanayo noma ukushumeka gwema umsebenzi oyimpinda.

• Ukukhipha amakhodi okuqagelayo nokusakaza amathokheni kunciphisa ukubambezeleka okucatshangwayo, ngisho noma iwashi lasodongeni linganyakazi kangako.

I-Deep dive 2: Ukusebenza kahle kwezinga lemodeli - quantize, distill, thena 🧪

• Ukulinganisa kwamanani kunciphisa ukunemba kwepharamitha (isb, 8-bit/4-bit) ukuze kunciphe inkumbulo futhi kusheshise ukuqagela; hlala uhlola kabusha ikhwalithi yomsebenzi ngemva kwezinguquko.

• I-Distillation idlulisela ulwazi kusuka kuthisha omkhulu kuya kumfundi omncane othandwa yihadiwe yakho.

• Ukuthena okuhleliwe kunciphisa izisindo/amakhanda anikela kancane.

Masikhulume iqiniso, kufana nokwehlisa ipotimende lakho bese ugcizelela ukuthi zonke izicathulo zakho zisalingana. Ngandlela thize kuyenzeka, ikakhulukazi.

I-Deep 3: Idatha nokuqeqeshwa ukukala ngaphandle kwezinyembezi 🧵

• Sebenzisa ukuqeqeshwa okusabalalisiwe okufihla izingxenye eziqinile zokuhambisana ukuze ukwazi ukuthumela izivivinyo ngokushesha.

• Khumbula leyo mithetho yokukala : nikeza isabelomali kuwo wonke usayizi wemodeli namathokheni ngokucabangisisa; ukukala kokubili ndawonye kusebenza kahle ngekhompyutha [4].

• Ikharikhulamu nekhwalithi yedatha ngokuvamile ishintsha imiphumela ngaphezu kokuvuma abantu. Idatha engcono ngezinye izikhathi idlula idatha eyengeziwe-ngisho noma usuvele u-ode iqoqo elikhulu.

I-Deep 4: I-RAG njengesu lokukala lolwazi 🧭

Esikhundleni sokuqeqesha kabusha imodeli ukuze ihambisane namaqiniso aguqukayo, i-RAG yengeza isinyathelo sokubuyisa ekuqondeni. Ungagcina imodeli iqinile futhi ukale inkomba nama -retrievers njengoba ikhophasi yakho ikhula. Inhle-futhi ngokuvamile ishibhile kunokuqeqeshwa kabusha okugcwele kwezinhlelo zokusebenza ezinzima zolwazi.

Ukubonwa okuzikhokhelayo 🕵️♀️

Awukwazi ukukala lokho ongakuboni. Izinto ezimbili ezibalulekile:

• Amamethrikhi okuhlelwa komthamo kanye nokulinganisa okuzenzakalelayo: amaphesenti okubambezeleka, ukujula komugqa, inkumbulo ye-GPU, osayizi beqoqo, ukuphuma kwethokheni, amanani okushaywa kwenqolobane.

• Ithrekhi elandela isicelo esisodwa ngaphesheya kwesango → ukubuyisa → imodeli → ukucutshungulwa kwangemuva. Bopha lokho okulinganisayo kuma-SLO akho ukuze amadeshibhodi aphendule imibuzo ngaphansi komzuzu owodwa [5].

Uma amadeshibhodi ephendula imibuzo ngaphansi kweminithi, abantu bayayisebenzisa. Lapho bengakwenzi, kuhle, benza sengathi benza.

Izinyathelo zokuqapha ezinokwethenjelwa: Ama-SLO, isabelomali samaphutha, ukukhishwa okuhlakaniphile 🧯

• Chaza ama-SLO wokubambezeleka, ukutholakala, kanye nekhwalithi yomphumela, futhi usebenzise ibhajethi yamaphutha ukulinganisa ukwethembeka nesivinini sokukhishwa [5].

• Sebenzisa ngemuva kokuhlukaniswa kwethrafikhi, yenza ama-canaries, futhi wenze izivivinyo zethunzi ngaphambi kokunqamuka komhlaba. Ikusasa lakho lizokuthumelela ukudla okulula.

Ukulawulwa kwezindleko ngaphandle kwedrama 💸

Ukukala akuwona nje umsebenzi wobuchwepheshe; yimali. Phatha amahora we-GPU namathokheni njengezinsiza zesigaba sokuqala ngeyunithi yezomnotho (izindleko ngamathokheni angu-1k, ukushumeka ngakunye, ngombuzo wevekhtha ngayinye). Engeza ibhajethi kanye nesexwayiso; bungaza ukususa izinto.

Imephu yomgwaqo elula eya ku-AI Scalability 🗺️

1. Qala ngama-SLO ukuze uthole ukubambezeleka kwe-p95, ukutholakala, nokunemba komsebenzi; ama-wire metrics/ukulandelela ngosuku lokuqala [5].

2. Khetha isitaki sokuphakelayo esisekela ukuhlanganisa nokuhlanganisa okuqhubekayo: Triton, vLLM, noma okulinganayo [2][3].

3. Lungiselela imodeli : linganisela lapho isiza khona, nika amandla izikhwebu ezisheshayo, noma i-distill yemisebenzi ethile; qinisekisa ikhwalithi ngama-evals wangempela.

4. I-Architect for elasticity : I-Kubernetes HPA enamasiginali alungile, izindlela ezihlukene zokufunda/zokubhala, kanye ne-replicas engaqondakali [1].

5. Yamukela ukubuyisa lapho ubusha bubalulekile ukuze ukale inkomba yakho esikhundleni sokuziqeqesha kabusha masonto onke.

6. Vala iluphu ngezindleko : sungula iyunithi yezomnotho nokubuyekezwa kwamasonto onke.

Izindlela zokwehluleka ezivamile nokulungiswa okusheshayo 🧨

• I-GPU ekusetshenzisweni okungu-30% kuyilapho ukubambezeleka kukubi

  • Vula ukunqwabelana okuguquguqukayo , phakamisa ama-batch cap ngokucophelela, bese uhlola kabusha ukuhlangana kweseva [2].

• Okokufaka kuyagoqa ngokutshelwa okude

  • Sebenzisa ukukhonza okusekela ukunakwa kwekhasi futhi ushune ubuningi bokulandelana okuhambisanayo [3].

• I-Autoscaler flaps

  • Amamethrikhi abushelelezi anamafasitela; sikala ekujuleni komugqa noma amathokheni angokwezifiso ngesekhondi ngalinye esikhundleni se-CPU emsulwa [1].

• Izindleko ziqhuma ngemva kokwethulwa

  • Engeza amamethrikhi ezindleko zeleveli yesicelo, nika amandla ukulinganisa lapho kuphephile, imibuzo ephezulu yenqolobane, kanye nomkhawulo wokulinganisa izaphuli mthetho ezimbi kakhulu.

I-AI Scalability playbook: uhlu lokuhlola olusheshayo ✅

• Ama-SLO namabhajethi amaphutha akhona futhi ayabonakala

• Amamethrikhi: ukubambezeleka, i-tps, i-GPU mem, usayizi weqoqo, amathokheni/s, ukushaya kwenqolobane

• Ilandelela ukusuka ku-ingress kuye kumodeli kuya ku-post-proc

• Ukukhonza: i-batching, i-concurrency ishuniwe, izinqolobane ezifudumele

• Imodeli: i-quantized noma i-distilled lapho isiza khona

• I-Infra: I-HPA ilungiselelwe ngamasignali alungile

• Indlela yokuthola ulwazi olusha

• Iyunithi yezomnotho ibuyekezwa kaningi

Kude Kakhulu Kangizange Ngiyifunde kanye Namazwi Okugcina 🧩

I-AI Scalability ayisona isici esisodwa noma iswishi eyimfihlo. Ulimi lwephethini: ukukala okuvundlile ngama-autoscaler, ukuhlanganiswa kohlangothi lweseva ukuze kusetshenziswe, ukusebenza kahle kweleveli yemodeli, ukubuyisa ukuze kukhishwe ulwazi, nokubonakala okwenza ukukhishwa kudina. Fafaza ngama-SLO futhi ubize inhlanzeko ukuze ugcine wonke umuntu eqondile. Ngeke ukuthole kuphelele okokuqala ngqa-akekho okwenzayo-kodwa uma unempendulo efanele, isistimu yakho izokhula ngaphandle kwalowo muzwa womjuluko obandayo ngo-2 am 😅

Izithenjwa

[1] Kubernetes Docs - Horizontal Pod Autoscaling - Funda kabanzi
[2] I-NVIDIA Triton - I-Dynamic Batcher - Funda kabanzi
[3] I-vLLM Amadokhumenti - Ukunakwa Kwekhasi - Funda kabanzi
[4] Hoffmann et al. (2022) - Ukuqeqeshwa Okuhlanganisayo-Amamodeli Olimi Olukhulu Olukhulu - Funda kabanzi
[5] I-Google SRE Workbook - Implementing SLOs - Funda kabanzi

Thola i-AI yakamuva esitolo esisemthethweni somsizi we-AI

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