Kubernetes maka ibu ọrụ ML
Kubernetes bụ sistemụ mepere emepe nke na-ahazi oge, akpịrịkpa ma malitegharịa mmemme etinyere n'ofe ụyọkọ igwe.
Nchịkọta
For machine learning, it lets teams pack GPU-hungry training jobs and latency-sensitive model servers onto shared hardware without babysitting individual servers.
Ime miri emi
Ewubere na Google iji rụọ ọrụ webụ, Kubernetes na-ewere ụyọkọ gị dị ka otu nnukwu ọdọ mmiri CPU, ebe nchekwa na GPU, wee kpebie igwe na-agba akpa ọ bụla. Ndị otu ML na-adabere na ya n'ihi na ibu ọrụ na-agbawa ma dị oke ọnụ: ọsọ ọzụzụ nwere ike ịchọ GPU asatọ maka awa isii, ọ nweghị ihe ọ bụla. Kubernetes na-ahazi nhazi nke na-agbanye n'ọnụ ọnụ nke nwere GPU n'efu, na mgbe ọrụ ahụ kwụsịrị, ọ na-ahapụ ngwaike ahụ. Ọ na-emekwa ka sava inference dị ndụ, na-amalitegharị arịa ndị mebiri emebi ma na-agbasa ụdị oyiri n'ofe igwe maka nkwụghachi. Ngwá ọrụ ndị e wuru n'elu, dị ka Kubeflow, Ray, na KServe, na-agbakwunye ML kpọmkwem akụkụ dị ka ndị na-ahụ maka ọzụzụ na-ekesa, hyperparameter tuning, na autoscaling model endpoints, ya mere, ndị ọkà mmụta sayensị data na-arụ ọrụ na abstractions dị elu kama ịbụ raw YAML.
Nghọta nka nka
Kubernetes na-ekenye GPUs site na ngwa mgbakwunye ngwaọrụ na-akpọsa akụrụngwa dị ka nvidia.com/gpu, nke onye nhazi ihe kwekọrọ na arịrịọ pọd. Taints na nnabata na-eme ka ọrụ CPU dị ọnụ ala pụọ na ọnụ ọnụ GPU dị ọnụ ahịa, ebe ndị na-ahọpụta ọnụ na iwu mmekọrịta na-etinye ọzụzụ na ngwaike akọwapụtara. Maka ọzụzụ multi-GPU, ndị na-arụ ọrụ na-emepụta otu pọd na-achọpụta ibe ha ma na-agba ọsọ dịka PyTorch DDP ma ọ bụ Horovod, na-agbanwe gradients na netwọk ụyọkọ site na iji NCCL.
Mmetụta atụmatụ
Ọnụ ego na mmefu ego
Mkpebi ihe owuwu ụlọ na-akwalite arụmọrụ yana ọnụ ahịa ọrụ ruo ọtụtụ afọ.
Mkpebi doro anya
Nkà mmụta nka na-enyere ndị otu egwuregwu aka ịhọrọ nchịkọta ziri ezi, ọ bụghị naanị nke kachasị ọhụrụ.
Quality akara
Nhọrọ injinia ka mma na-ebelata ihe omume ntụkwasị obi na mmepụta.
Ọdịnihu nke Kubernetes maka ibu ọrụ ML
Na-atụ anya mwekota ML siri ike: nhazi ndị otu na-ebupụta akwụkwọ nkuzi niile na-ekesa n'otu oge ma ọ bụ ọ bụla ma ọlị, nkesa GPU nke dị obere na nke oge ka ọtụtụ ọrụ ọkụ na-ekekọrịta otu kaadị, yana ntinye topology-mara nke na-asọpụrụ njikọ njikọ NVLink ngwa ngwa. Ntụnye na-enweghị nkesa na Kubernetes, na-eme ka njedebe njedebe na efu n'etiti arịrịọ, na-eto eto. Dị ka balloon ụdị, ndị na-eme nhazi na-ahaziwanye n'ofe ọtụtụ ụyọkọ na igwe ojii, na usoro nkekọrịta ziri ezi dabere na kwụ n'ahịrị dị ka Kueue na Volcano na-aghọ ọkọlọtọ maka ijikwa ike GPU dị ụkọ.
Mmejuputa n'ezie n'ụwa
Ụlọ nyocha na-eji Kubeflow Training Operator malite ọrụ ọzụzụ nkesa 32-GPU PyTorch gafee ọnụ anọ, wee hapụ GPU ndị ahụ na-akpaghị aka mgbe ọ na-agbakọta.
Otu ụlọ ọrụ e-azụmahịa na-eji KServe na-eme ihe nrịbama ya, nke na-emezigharị ihe n'oge ire ọkụ wee laghachi n'otu abalị.
Otu ụlọ akụ na-arụ ọrụ ị nweta akara abalị dị ka Kubernetes CronJobs, na-adọba ha n'ahịrị na oghere CPU mapụtara ka ha ghara ịsọ mpi na okporo ụzọ na-eje ozi ụbọchị ehihie.
Mmalite na-eji Ray na Kubernetes na-eme mkpochapụ hyperparameter yiri ya, na-atụgharị ọtụtụ pọd nnwale dị mkpụmkpụ n'oge ntụpọ iji belata ọnụ ahịa.
Ihe ize ndụ & okporo ụzọ nche
Ịkwalite otu akara ngosi nwere ike zoo adịghị ike sistemụ sara mbara.
A na-eledakarị ihe akụrụngwa na ụgwọ ọrụ anya.
Ọdịiche nchekwa na nleba anya nwere ike itolite ka sistemu na-adịwanye mgbagwoju anya.
Map mmejuputa
Kọwaa latency, ịdịmma na ebumnuche ọnụ ahịa tupu mmejuputa ya.
Benchmark n'okpuru ibu dị adị na ọnọdụ data.
Nleba anya akụrụngwa maka mperi, ịkpafu na mmetụta onye ọrụ.
Kwadebe ụzọ nzaghachi azụghachi azụ na ihe omume tupu ịchachaa.
Nọgide na-eme nchọpụta
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Ntuziaka na-esote
Nlele A/B maka Ụdị ML
Ajụjụ a na-ajụkarị
What is Kubernetes for ML Workloads?
Kubernetes bụ sistemụ mepere emepe nke na-ahazi oge, akpịrịkpa ma malitegharịa mmemme etinyere n'ofe ụyọkọ igwe. Maka mmụta igwe, ọ na-ahapụ ndị otu na-ebukọta ọrụ ọzụzụ agụụ GPU na sava ihe nlere anya na-enwe mmetụta n'ime ngwaike nkekọrịta na-enweghị ilekọta sava ọ bụla.
Kedu ọrụ bụ isi nke Kubernetes nhazi maka ibu ọrụ ML?
Onye nhazi ihe dakọtara arịrịọ akụrụngwa pọd (CPU, ebe nchekwa, GPUs) megide ọnụ dịnụ wee debe pọd ebe ọ dabara. Ọ naghị emetụ koodu nlereanya ma ọ bụ data aka.
Kedu ka Kubernetes si eme ka GPU dị na pọd?
Ngwa mgbakwunye ngwaọrụ na-ekpughe GPU dị ka akụrụngwa nwere ike ịhazi (dịka, nvidia.com/gpu), na-enye ohere ka pọd rịọ ha na ndị na-ahazi usoro ihe dị.
Kedu ihe bụ taints na nnabata a na-ejikarị maka ụyọkọ ML?
Unyi na-achupu pọmpụ site na ọnụ; naanị pọd ndị nwere nkwado dabara adaba nwere ike ịdaba ebe ahụ. Nke a na-edobe oghere GPU dị ụkọ maka ọrụ ndị chọrọ GPU n'ezie.
Kedu ngwá ọrụ na-agbakwụnye ikike ML kpọmkwem dị ka ndị na-arụ ọrụ ọzụzụ na-ekesa n'elu Kubernetes?
Kubeflow layers ML workflows na Kubernetes, gụnyere ndị na-arụ ọrụ ọzụzụ, pipeline, na ntuzigharị, yabụ ndị otu na-ezere nhazi ụyọkọ ọkwa dị ala.
Kedu ihe kpatara Kubernetes ji dabara nke ọma maka ibu ọrụ ML gbawara agbawa?
Ọzụzụ dị oke egwu: ọtụtụ GPU dị nkenke, yabụ ọ nweghị. Kubernetes na-etinye ọrụ ahụ mgbe akụrụngwa nweere onwe ya wee wepụta ha ka emechara, na-emeziwanye ojiji.