KServe ati Awoṣe Nṣiṣẹ lori Kubernetes
KServe jẹ idiwọn kan, pẹpẹ Kubernetes-abinibi fun ṣiṣe awọn awoṣe ikẹkọ ẹrọ ni iwọn.
Akopọ
It gives teams a single, declarative way to deploy models with autoscaling, canary rollouts, and scale-to-zero, abstracting away most of the Kubernetes plumbing.
Jin Dive
Ti a mọ tẹlẹ bi KFServing ati bi lati inu iṣẹ akanṣe Kubeflow, KServe n ṣalaye orisun orisun InferenceService kan. O kọ faili YAML kukuru kan ti o tọka si awoṣe ti o fipamọ sinu ibi ipamọ ohun (S3, GCS, Azure Blob), ati KServe n mu iyoku mu. O ṣe atilẹyin atọka asọtẹlẹ mejeeji ati, ni ilọsiwaju, ṣiṣe iranṣẹ LLM ti ipilẹṣẹ. Awọn ọkọ oju omi KServe ti a ti kọ tẹlẹ 'awọn akoko ṣiṣe ṣiṣe' fun awọn ilana ti o wọpọ (SinsorFlow Serving, TorchServe, Triton, scikit-learn, XGBoost, Face Hugging) ati atilẹyin awọn apoti aṣa. Ti a ṣe si oke ti Iṣẹ Knative ati Layer Nẹtiwọki kan (Istio tabi iru), o pese adaṣe-iwakọ ibeere pẹlu iwọn-si-odo tootọ, nitorinaa awọn awoṣe aiṣiṣẹ ko jẹ iṣiro. O tun ṣe deede API asọtẹlẹ ni ayika Ilana Ifarahan Ṣii, nitorinaa awọn alabara sọrọ si gbogbo awoṣe ni ọna kanna laibikita ilana.
Imọ-imọ-ẹrọ
KServe's autoscaling gbarale lori Knative, eyiti o ṣe iwọn kika ajọra ti o da lori concurrency tabi awọn ibeere-fun-keji ati pe o le ju silẹ si awọn ẹda odo nigbati ijabọ ba duro, lẹhinna tutu-bẹrẹ lori ibeere. Iṣẹ Inference n ṣe iwe opo gigun ti epo ni kikun sinu asọtẹlẹ, oluyipada (ṣaaju/lẹhin-iṣiṣẹ), ati awọn paati alaye. Awọn awoṣe kojọpọ lati ibi ipamọ ohun kan nipasẹ 'awọn olupilẹṣẹ ibi ipamọ' ti o fa awọn ohun-ọṣọ sinu podu ni ibẹrẹ, ibi ipamọ awoṣe decoupling lati aworan eiyan iṣẹ.
Ipa Ilana
Iye owo ati isuna
Awọn ipinnu faaji ṣe awakọ iṣẹ ati idiyele iṣẹ fun awọn ọdun.
Awọn ipinnu diẹ sii
Ẹkọ imọ-ẹrọ ṣe iranlọwọ fun awọn ẹgbẹ lati yan akopọ to tọ, kii ṣe ọkan tuntun nikan.
Iṣakoso didara
Awọn yiyan imọ-ẹrọ to dara julọ dinku awọn iṣẹlẹ igbẹkẹle ni iṣelọpọ.
Ojo iwaju ti KServe ati Awoṣe Ṣiṣẹ lori Kubernetes
KServe n dagba ni iyara si ọna AI ti ipilẹṣẹ, nfi orin ti o ni idojukọ LLM kan pẹlu awọn ẹya bii ipa-ọna KV-cache-aware, caching model, ati pipọ ṣaju/pipaṣẹ ṣiṣiṣẹsin fun awọn awoṣe ede nla. Reti isọpọ jinlẹ pẹlu awọn ẹrọ ifọkasi bii vLLM, iṣẹ-ọna pupọ ti o dara julọ fun awọn awoṣe ti o tobi ju fun GPU kan, ati ipa-ọna ipele-ọna fun iwọntunwọnsi fifuye orisun-ami. Gẹgẹbi iṣẹ idawọle CNCF, o n di idiwọn ṣiṣi silẹ de facto fun fifi awọn awoṣe si ẹhin Kubernetes, idinku aafo laarin awọn ohun-ọṣọ iwadii ati awọn opin iṣelọpọ resilient.
Real-World imuse
Ile-ifowopamosi kan nfi awoṣe igbelewọn kirẹditi ṣiṣẹ nipa kikọ 10-ila InferenceService YAML ti n tọka si awoṣe ni S3, pẹlu mimu KServe mu autoscaling ati ingress.
Ẹgbẹ e-commerce kan nlo awọn iyipo canary KServe lati firanṣẹ 10 ida ọgọrun ti ijabọ si awoṣe iṣeduro tuntun, lẹhinna awọn ramps si 100 ogorun ni kete ti awọn metiriki wo ni ilera.
Laabu iwadii n ṣe iranṣẹ awọn dosinni ti awọn awoṣe ti a ko lo pẹlu iwọn-si-odo, nitorinaa awoṣe kọọkan n yi soke nikan nigbati ibeere kan ba de ti ko gba GPU lakoko ti o ṣiṣẹ.
Ẹgbẹ MLOps kan nlo paati transformer KServe kan lati mu iwọn aworan ṣiṣẹ ati isọdọtun ṣaaju ki asọtẹlẹ naa nṣiṣẹ awoṣe iran ti Triton ṣe iranṣẹ.
Awọn ewu & Awọn ọna iṣọ
Ṣiṣepe ala-ilẹ kan le tọju awọn ailagbara eto ti o gbooro.
Awọn ohun elo amayederun ati awọn idiyele itọju nigbagbogbo ni aibikita.
Aabo ati awọn ela akiyesi le dagba bi awọn eto ṣe di eka sii.
Ilana Ilana imuse
Ṣetumo lairi, didara, ati awọn ibi-afẹde idiyele ṣaaju imuse.
Aṣepari labẹ ẹru ojulowo ati awọn ipo data.
Abojuto ohun elo fun awọn aṣiṣe, fiseete, ati ipa olumulo.
Mura ipadasẹhin pada ati awọn ipa ọna esi iṣẹlẹ ṣaaju iwọn.
Tesiwaju Ṣiṣawari
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Itọsọna atẹle
Awọn atunṣe akiyesi fun Awọn awoṣe koodu
Awọn ibeere ti a beere nigbagbogbo
What is KServe and Model Serving on Kubernetes?
KServe jẹ idiwọn kan, pẹpẹ Kubernetes-abinibi fun ṣiṣe awọn awoṣe ikẹkọ ẹrọ ni iwọn. O fun awọn ẹgbẹ ni ẹyọkan, ọna asọye lati fi awọn awoṣe ransẹ pẹlu adaṣe adaṣe, awọn iyipo canary, ati iwọn-si-odo, yiyọ kuro pupọ julọ ti Plumbing Kubernetes.
Kini orukọ KServe tẹlẹ ṣaaju ki o to di iṣẹ akanṣe kan?
KServe ti bẹrẹ bi KFServing laarin iṣẹ akanṣe Kubeflow ṣaaju ki o to di pẹpẹ ti ominira.
Kini orisun orisun aṣa Kubernetes akọkọ ti o ṣalaye lati ran awoṣe kan pẹlu KServe?
O kede ohun elo aṣa Iṣẹ InferenceService kan, ni igbagbogbo ni YAML, lati ran ati tunto awoṣe iṣẹ kan.
Agbara wo ni o jẹ ki awọn awoṣe KServe ti ko ṣiṣẹ jẹ iṣiro odo titi ti ibeere kan yoo fi de?
Ti a ṣe lori Knative, KServe ṣe atilẹyin iwọn-si-odo, sisọ awọn ẹda silẹ si odo nigbati ko ba si ijabọ ati tutu-bẹrẹ lori ibeere.
Ise agbese ti o wa ni abẹlẹ wo ni o pese autoscaling ti ibeere KServe?
KServe ṣe agbero lori Sisin Knative, eyiti o ṣe iwọn awọn ẹda ti o da lori concurrency tabi oṣuwọn ibeere ati mu iwọn-si-odo ṣiṣẹ.
Bawo ni KServe ṣe deede gba awọn ohun-ọṣọ awoṣe sinu adarọ ese ni ibẹrẹ?
Awọn olupilẹṣẹ ibi ipamọ ṣe igbasilẹ awọn ohun-ọṣọ awoṣe lati ibi ipamọ ohun (bii S3 tabi GCS) sinu adarọ-ese, sisọ awọn awoṣe lati aworan naa.