InoteveraGaidhi rinotevera
Autoscaling Model Inference on Kubernetes
Tekinoroji
Nhungamiro yehunyanzvi
KServe uye Model Kushanda paKubernetes
KServe inzvimbo yakamisikidzwa, Kubernetes-yekuzvarwa chikuva chekushandira muchina wekufunda modhi pachiyero.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
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.
Kudzika Kwakadzika
Aimbozivikanwa seKFServing uye akazvarwa kubva kuKubeflow chirongwa, KServe inotsanangura InferenceService tsika sosi. Iwe unonyora ipfupi YAML faira inonongedza pamuenzaniso wakachengetwa mukuchengetedza chinhu (S3, GCS, Azure Blob), uye KServe inobata zvimwe. Inotsigira zvese zvekufungidzira uye, zvichiwedzera, generative LLM inoshumira. KServe ngarava dzakafanovakwa 'sevhisi yekumhanya' kune akajairwa masisitimu (TensorFlow Serving, TorchServe, Triton, scikit-dzidza, XGBoost, Hugging Face) uye inotsigira midziyo yetsika. Yakavakwa pamusoro peKnative Serving uye networking layer (Istio kana yakafanana), inopa chikumbiro-inotyairwa otomatiki inosanganisira yechokwadi chiyero-kusvika-zero, saka mamodheru asina basa haadyi compute. Iyo zvakare inomisikidza API yekufungidzira yakatenderedza Open Inference Protocol, saka vatengi vanotaura kune yega modhi nenzira imwechete zvisinei nehurongwa.
Kuitwa muNyika yeChokwadi
Bhengi rinoshandisa kiredhiti-chibodzwa modhi nekunyora gumi-mitsara InferenceService YAML inonongedza modhi muS3, ine KServe inobata autoscaling uye ingress.
Chikwata che e-commerce chinoshandisa KServe canary rollouts kutumira gumi muzana yetraffic kune nyowani yekurudziro modhi, zvino ramps kusvika zana muzana kana metrics ichiita seine hutano.
Lab yekutsvagisa inoshandira akawanda emhando dzisingawanzo shandiswa ane chikero-kusvika-zero, saka imwe neimwe modhi inotenderera chete kana chikumbiro chasvika uye isingadye GPU isina basa.
Chikwata cheMLOps chinoshandisa KServe transformer component kumhanyisa mufananidzo resize uye normalization isati yafanotaura isati yashandisa Triton-served vision model.
Ramangwana reKServe uye Model Kushanda paKubernetes
KServe iri kukurumidza kushanduka yakananga kukugadzira AI, ichiwedzera track yakatarisana neLLM ine maficha akaita seKV-cache-aware routing, modhi caching, uye disaggregated prefill/decode inoshandira mhando dzemitauro mikuru. Tarisira kusanganisa kwakadzama neinjini dzekufungidzira senge vLLM, zvirinani-node yakawanda inoshandira mamodheru akawandisa kune imwe GPU, uye gedhi-nhanho nzira yekuyera-yakavakirwa mutoro kuenzanisa. SeCNCF-incubating purojekiti, yave kuita iyo de facto yakavhurika mwero wekuisa modhi kuseri kweKubernetes, ichidzikisa mukaha uripo pakati pekutsvagisa zvigadzirwa uye magumo ekugadzira akasimba.
Kuzvishandisa
Nei zvichikosha
- Mutengo uye bhajeti. Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore.
- Sarudzo dzakajeka. Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete.
- Kudzora kwemhando yepamusoro. Sarudzo dzeinjiniya dziri nani dzinoderedza zviitiko zvekuvimbika mukugadzira.
Chenjera
- Kugadzirisa imwe bhenji kunogona kuvanza yakafara system kushaya simba.
- Infrastructure uye mari yekugadzirisa inowanzotarisirwa pasi.
- Chengetedzo uye kucherechedzwa mapundu anogona kukura sezvo masisitimu anowedzera kuoma.
Matanho anotevera
- Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa.
- Benchmark pasi pechokwadi mutoro uye data mamiriro.
- Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro.
- Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera.
Ramba Uchiongorora
Tsamba yemahara
Tora iyo yemazuva ese AI pfupiso
Nhatu dzakasimbiswa dzeAI nyaya mangwanani ega ega pakati pevhiki, dzakanyorwa neChirungu chakajeka. Yemahara zvachose, hapana ads.
Imwe email zuva rega rega revhiki. Bvisa kunyoresa kamwe chete. Hatimbotengese kana kugovera kero yako.
Zviedze iwe pachako
Take the KServe and Model Serving on Kubernetes quiz
Mhinduro yepakarepo pamhinduro yega yega, uye chitupa chinogovaniswa chine ID inobatika kana wapedza kosi.
Tsigira dzidzo yemahara yeAI. AI Understanding ndeye 501(c)(3) isingabatsiri - hapana ads, hapana paywall, zvachose. Ita chipo
Mibvunzo inowanzo bvunzwa
What is KServe and Model Serving on Kubernetes?
KServe inzvimbo yakamisikidzwa, Kubernetes-yekuzvarwa chikuva chekushandira muchina wekufunda modhi pachiyero. Inopa zvikwata nzira imwe chete, yekuzivisa yekuendesa modhi ine autoscaling, canary rollouts, uye chiyero-kusvika-zero, ichibvisa mazhinji eKubernetes pombi dzemvura.
Nderipi raiva zita rekare reKServe risati rava chirongwa chakazvimirira?
KServe yakatanga seKFSKushandira mukati meKubeflow chirongwa isati yave yakazvimirira chikuva.
Ndeipi yekutanga Kubernetes tsika sosi yaunotsanangura kuendesa modhi neKServe?
Iwe unozivisa iyo InferenceService tsika sosi, kazhinji muYAML, kuendesa uye kugadzirisa yakashumirwa modhi.
Ndeupi kugona kunoita kuti maKServe mamodheru ashandise zero compute kusvika chikumbiro chasvika?
Yakavakwa paKnative, KServe inotsigira chikero-kusvika-zero, ichidonhedza replicas kusvika zero kana pasina traffic uye kutonhora-kutanga pane zvinodiwa.
Ndeipi yepasi purojekiti inopa KServe's chikumbiro-inotungamirwa autoscaling?
KServe inovaka paKnative Serving, iyo inoyera replicas zvichienderana nemari kana chikumbiro chiyero uye inogonesa chiyero-kusvika-zero.
KServe inowanzowana sei zvigadzirwa zvemhando mupodhi inoshumira pakutanga?
Matangi ekuchengetera anodhawunirodha maodhisheni emhando kubva kuchengetedzo yechinhu (seS3 kana GCS) mupodhi, kubatanidza mhando kubva pamufananidzo.
Ramba uchidzidza
Mazano anoenderana
Mamwe madhairekitori akasarudzirwa nyaya iyi