InoteveraGaidhi rinotevera
Cloud Cost Optimization for ML Workloads
Tekinoroji
Nhungamiro yehunyanzvi
Kubernetes yeML Workloads
Kubernetes ndeye yakavhurika-sosi sisitimu iyo inoronga otomatiki, zvikero, uye kudzoreredza zvirongwa zvakaiswa mumidziyo muboka remichina.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
For machine learning, it lets teams pack GPU-hungry training jobs and latency-sensitive model servers onto shared hardware without babysitting individual servers.
Kudzika Kwakadzika
Yakavakwa pa Google kuti ishandise masevhisi ewebhu, Kubernetes inobata cluster yako sedziva guru reCPU, memory, uye GPUs, yobva yafunga kuti ndeupi muchina unofambisa mudziyo wega wega. Zvikwata zveML zvinotsamira pazviri nekuti basa rakawandisa uye rinodhura: kumhanya kwekudzidzira kungangoda masere maGPU kwemaawa matanhatu, ipapo hapana. Kubernetes inoronga iyo pod pane node ine emahara maGPU, uye kana basa rapera inosunungura iyo hardware. Iyo zvakare inochengeta maseva ekufungidzira ari mapenyu, kutangazve midziyo yakapwanyika uye kuparadzira replicas pamakina ese ekusimba. Zvishandiso zvakavakwa pamusoro, senge Kubeflow, Ray, uye KServe, wedzera ML-chaiwo zvidimbu zvakaita sevakagovera-vanodzidzisa vanoshanda, hyperparameter tuning, uye autoscaling modhi endpoints, saka masayendisiti edata anoshanda nepamusoro-level abstractions pachinzvimbo cheYAML mbishi.
Kuitwa muNyika yeChokwadi
Lab yekutsvagisa inoshandisa Kubeflow Kudzidzisa Operator kuvhura 32-GPU PyTorch yakagoverwa-basa rekudzidzisa munzvimbo ina, yobva yasunungura maGPU otomatiki kana yasangana.
Kambani ye-e-commerce inoshandisa yayo kurudziro modhi neKServe, iyo autoscales replicas kumusoro panguva yekutengesa flash uye kudzoka pasi husiku.
Bhangi rinoita mabasa ehusiku-batch-makina seKubernetes CronJobs, richivamisa pamitsetse yeCPU node kuti vasakwikwidze nekuswera vachishandira traffic.
Kutanga kunoshandisa Ray paKubernetes kumhanyisa parallel hyperparameter kutsvaira, ichitenderedza akawanda enguva pfupi-yenguva yekuedza pods pane imwe nguva kudzikisa mutengo.
Ramangwana reKubernetes reML Workloads
Teams can combine queue admission, suitable GPU resource exposure, and topology-aware placement with their ML workload controllers. Group admission helps avoid partial worker allocation, while model servers may support scale-to-zero with an appropriate serving configuration. These behaviors depend on installed components and settings. Operators should test recovery, resource limits, interconnects, and cold-start latency rather than assume the container orchestrator alone supplies every training or serving feature.
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 Kubernetes for ML Workloads 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 Kubernetes for ML Workloads?
Kubernetes ndeye yakavhurika-sosi sisitimu iyo inoronga otomatiki, zvikero, uye kudzoreredza zvirongwa zvakaiswa mumidziyo muboka remichina. Pakudzidza kwemichina, inoita kuti zvikwata zvirongedze GPU-nzara yekudzidzira mabasa uye latency-sensitive modhi maseva pane yakagovaniswa Hardware pasina kubatirira maseva ega.
Nderipi basa rekutanga reKubernetes scheduler yeML mabasa?
Iyo inoronga inofananidza zvikumbiro zvepod zvikumbiro (CPU, ndangariro, maGPU) vachipesana nenzvimbo dziripo uye inoisa iyo pod painokwana. Iyo haibate modhi kodhi kana data.
Kubernetes anowanzoita sei kuti maGPU awanikwe kune pod?
Mapulagi emudziyo anofumura maGPU sechinhu chinorongeka (semuenzaniso, nvidia.com/gpu), achirega mapods achikumbira ivo uye kuwanikwa kweanoronga track.
With a NoSchedule taint on a GPU node, what does a matching pod toleration do?
A toleration removes the matching taint barrier. Other resource and scheduling requirements still apply.
Ndechipi chishandiso chinowedzera ML-chaiyo hunyanzvi sevakagovera-vanodzidzisa vanoshanda pamusoro peKubernetes?
Kubeflow layers ML workflows kupinda Kubernetes, kusanganisira kudzidzisa maoperator, mapaipi, uye tuning, saka zvikwata zvinodzivirira nemaoko-kunyora-yakaderera-level cluster config.
Nei Kubernetes yakanyatsokodzera kune kuputika kweML mabasa?
Kudzidziswa kune spiky: akawanda maGPU muchidimbu, saka hapana. Kubernetes anoisa basa kana zviwanikwa zvakasununguka uye ozviburitsa pakupedza, nekuvandudza mashandisirwo.
Ramba uchidzidza
Mazano anoenderana
Mamwe madhairekitori akasarudzirwa nyaya iyi