KServe da Model Hidima akan Kubernetes
KServe daidaitaccen dandamali ne na Kubernetes na asali don ba da samfuran koyan inji a sikeli.
Dubawa
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.
Zurfafa nutsewa
Wanda aka fi sani da KFServing kuma an haife shi daga aikin Kubeflow, KServe yana fayyace albarkatu na al'ada Sabis na Inference. Kuna rubuta ɗan gajeren fayil ɗin YAML yana nunawa a ƙirar da aka adana a cikin ma'ajin abu (S3, GCS, Azure Blob), kuma KServe yana ɗaukar sauran. Yana goyan bayan duka tsinkayar tsinkaya da, ƙara, samar da sabis na LLM. KServe yana jigilar 'lokacin yin hidima' da aka riga aka gina don tsarin gama gari (TensorFlow Serving, TorchServe, Triton, scikit-learn, XGBoost, Hugging Face) kuma yana goyan bayan kwantena na al'ada. An gina shi a saman Sabis ɗin Knative da layin sadarwar (Istio ko makamancin haka), yana ba da ƙididdiga mai sarrafa buƙatu gami da ma'auni na gaskiya-zuwa-sifili, don haka samfuran marasa aiki suna cinye ƙididdiga. Hakanan yana daidaita API ɗin tsinkaya a kusa da Ƙaddamarwa ta Buɗe, don haka abokan ciniki suna magana da kowane ƙira iri ɗaya ba tare da la'akari da tsarin ba.
Fahimtar Fasaha
KServe's autoscaling yana dogara ne akan Knative, wanda ke daidaita ƙididdige ƙididdigewa bisa la'akari ko buƙatun-dakika guda kuma zai iya faɗuwa zuwa kwafin sifili lokacin da zirga-zirgar ababen hawa ta tsaya, sannan sanyi-fara kan buƙata. Sabis ɗin InferenceService yana ƙaddamar da cikakken bututun ƙididdigewa zuwa mai tsinkaya, mai canzawa (pre/post-processing), da abubuwan bayani. Samfuran suna ɗaukar kaya daga ma'ajin abu ta hanyar 'ma'ajiyar kayan ajiya' waɗanda ke jawo kayan tarihi zuwa cikin kwafsa yayin farawa, keɓance ma'ajin ƙira daga hoton kwantena.
Dabarun Tasiri
Kudin da kasafin kuɗi
Hukunce-hukuncen gine-gine suna haifar da aiki da tsadar aiki na shekaru.
Shawarwari masu haske
Ilimin fasaha yana taimaka wa ƙungiyoyi su zaɓi tari mai kyau, ba kawai sabon abu ba.
Kula da inganci
Zaɓuɓɓukan injiniya mafi kyau suna rage abin dogaro a cikin samarwa.
Makomar KServe da Model Hidima akan Kubernetes
KServe yana ci gaba da sauri zuwa ga haɓaka AI, yana ƙara waƙa mai mai da hankali kan LLM tare da fasali kamar KV-cache-aware routing, caching model, da rarrabuwa prefill/decode hidima ga manyan harsuna. Yi tsammanin haɗin kai mai zurfi tare da injunan ƙididdigewa kamar vLLM, mafi kyawun kumburi masu yawa don samfura masu girma da yawa ga GPU ɗaya, da matakin matakin ƙofa don daidaita nauyi na tushen alamar. A matsayin aikin CNCF-incubating, yana zama madaidaicin buɗaɗɗen gaskiya don sanya samfura a bayan Kubernetes, yana rage rata tsakanin kayan aikin bincike da ƙarshen samarwa.
Aiwatar da Gaskiyar Duniya
Banki yana ƙaddamar da ƙirar ƙima ta hanyar rubuta layin InferenceService YAML mai lamba 10 yana nuni a ƙirar a cikin S3, tare da KServe sarrafa autoscaling da shiga.
Ƙungiyar kasuwancin e-commerce tana amfani da sauye-sauye na KServe don aika kashi 10 na zirga-zirga zuwa sabon samfurin shawarwarin, sannan ta kai kashi 100 da zarar ma'auni ya yi kyau.
Gidan binciken bincike yana ba da nau'ikan nau'ikan da ba a cika amfani da su ba tare da sikelin-zuwa-sifili, don haka kowane ƙirar yana jujjuyawa kawai lokacin da buƙatu ta zo kuma ba ta cinye GPU yayin aiki.
Ƙungiya ta MLOps tana amfani da ɓangaren mai canzawa na KServe don gudanar da girman hoto da daidaitawa kafin mai tsinkaya ya gudanar da samfurin hangen nesa na Triton.
Hatsari & Tsare-tsare
Haɓaka ma'auni ɗaya na iya ɓoye manyan raunin tsarin.
Sau da yawa ana raina kayan more rayuwa da kuma kuɗin kulawa.
Tsaro da gibin lura na iya girma yayin da tsarin ke ƙara haɓaka.
Taswirar Hanya
Ƙayyade latency, inganci, da maƙasudin farashi kafin aiwatarwa.
Alamar ma'auni a ƙarƙashin ainihin kaya da yanayin bayanai.
Kula da kayan aiki don kurakurai, ɗigo, da tasirin mai amfani.
Shirya bijirowa da hanyoyin mayar da martani kafin sikeli.
Ci gaba da Bincike
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Jagora na gaba
Gyaran Hasashen don Samfuran Code
Tambayoyin da ake yawan yi
What is KServe and Model Serving on Kubernetes?
KServe daidaitaccen dandamali ne na Kubernetes na asali don ba da samfuran koyan inji a sikeli. Yana ba ƙungiyoyi guda ɗaya, hanyar bayyanawa don ƙaddamar da ƙira tare da autoscaling, canary rollouts, da sikelin-zuwa-sifi, yana kawar da mafi yawan bututun Kubernetes.
Menene sunan KServe a baya kafin ya zama aiki mai zaman kansa?
KServe ya samo asali ne azaman KFServing a cikin aikin Kubeflow kafin ya zama dandamali mai zaman kansa.
Menene ainihin tushen tushen Kubernetes na al'ada da kuka ayyana don tura samfuri tare da KServe?
Kuna ayyana albarkatu na al'ada na Sabis na Inference, yawanci a cikin YAML, don turawa da daidaita samfurin da aka yi aiki.
Wanne damar ke ba da damar ƙirar KServe marasa aiki su cinye lissafin sifili har sai buƙatar ta zo?
Gina kan Knative, KServe yana goyan bayan sikeli-zuwa-sifili, yana sauke kwafi zuwa sifili lokacin da babu zirga-zirga da sanyin farawa akan buƙata.
Wanne aikin da ke ƙasa ya samar da autoscaling na buƙatar buƙatar KServe?
KServe yana ginawa akan Hidimar Knative, wanda ke daidaita ma'auni bisa la'akari da ƙima ko ƙimar buƙata kuma yana ba da damar sikeli-zuwa-sifili.
Ta yaya KServe yawanci ke samun kayan aikin ƙira a cikin kwandon hidima a farawa?
Masu farawa na ajiya suna zazzage kayan tarihi na samfuri daga ma'ajin abu (kamar S3 ko GCS) cikin kwafsa, zazzage ƙira daga hoton.