Overview
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.
Kubernetes yeML Workloads chivakwa chehunyanzvi chinokanganisa mhando yemhando, mutengo wezvivakwa, latency, uye kuvimbika pachiyero.
Deep Dive
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.
Technical Insight
Kubernetes inopa maGPUs kuburikidza nemidziyo plugins inoshambadza zviwanikwa senge nvidia.com/gpu, iyo inoronga inowirirana nezvikumbiro zvepod. Taints uye kushivirira zvinochengeta zvakachipa CPU mabasa kubva pamutengo weGPU node, nepo node selectors uye affinity inotonga pini kudzidziswa kune chaiyo hardware. Kune akawanda-GPU kudzidziswa, vashandisi vanogadzira boka remapods anoonana uye anomhanyisa masisitimu sePyTorch DDP kana Horovod, vachichinjana magradients pamusoro pe network network vachishandisa NCCL.
Mastering Kubernetes yeML 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. Kubernetes yeML Workloads chivakwa chehunyanzvi chinokanganisa mhando yemhando, mutengo wezvivakwa, latency, uye kuvimbika pachiyero. Kuti uvake kunzwisisa kwakadzama, bata Kubernetes yeML Workloads semuenzaniso wekushandisa, kwete chinhu chimwe chete: tsanangura zvinodikanwa, kujekesa fungidziro, uye patsanura izvo zvingaitwe nehurongwa hwakavimbika kubva kune zvichiri kuda kutonga kwenyanzvi.
Mukuita, zvikwata zvakasimba zvinoshandisa Kubernetes yeML Workloads inokwenenzvera zvivakwa, data, uye sarudzo dzezvivakwa zvinopesana nekuvimbika uye mutengo. Ivo vanonyora zvakajeka maitiro ebudiriro, bvunzo vachipokana ne data rechokwadi uye mafambiro ebasa, uye iterate zvichibva pane zvakacherechedzwa maitiro ekutadza kwete kuhwina-nguva imwe chete yebhenji. Apa ndipo apo kunzwisisa kwe theoretical kunoshanduka kuve kugona kwakasimba pane chigadzirwa, mutemo, uye mashandiro.
Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore. Panguva imwecheteyo, Kukwirisa imwe bhenji kunogona kuvanza yakafara system kushaya simba. Nzira yakatsiga ndeyekubatanidza kukurumidza kuyedza nekutonga: mhanyisa vatyairi vendege, tora humbowo, buritsa matanda esarudzo, uye urambe uchivandudza chengetedzo semaitiro emuenzaniso, zvinotarisirwa nemushandisi, uye zvinodikanwa zvekutonga.
Strategic Impact
Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore.
Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete.
Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Sarudzo dzeinjiniya dziri nani dzinoderedza zviitiko zvekuvimbika mukugadzira.
Sarudzo dzeinjiniya dziri nani dzinoderedza zviitiko zvekuvimbika mukugadzira. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Real-World Implementation
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.
Maitiro Ekuita
Kubernetes yeML Workloads mukuita
Lab yekutsvagisa inoshandisa Kubeflow Kudzidzisa Operator kuvhura 32-GPU PyTorch yakagoverwa-basa rekudzidzisa munzvimbo ina, yobva yasunungura maGPU otomatiki kana yasangana.
Labhu yekutsvagisa inoshandisa Kubeflow Kudzidzisa Operator kuvhura 32-GPU PyTorch yakagoverwa-basa rekudzidzisa munzvimbo ina, yobva yasunungura maGPU kana ichichinja Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura hunhu kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
Kubernetes yeML Workloads mukuita
Kambani ye-e-commerce inoshandisa yayo kurudziro modhi neKServe, iyo autoscales replicas kumusoro panguva yekutengesa flash uye kudzoka pasi husiku.
Kambani ye-e-commerce inoshandisa yayo kurudziro modhi neKServe, iyo autoscales replicas kumusoro panguva yekutengesa kweflash uye kudzokera pasi pasi pehusiku Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye kukanganisa mutengo nekufamba kwenguva.
Kubernetes yeML Workloads mukuita
Bhangi rinoita mabasa ehusiku-batch-makina seKubernetes CronJobs, richivamisa pamitsetse yeCPU node kuti vasakwikwidze nekuswera vachishandira traffic.
Bhangi rinoita mabasa ehusiku-batch-makori seKubernetes CronJobs, richivamisa pamitsetse yeCPU node kuti vasakwikwidze nemasikati vanoshandira traffic Matimu anowanzo kuwana mibairo iri nani kana achinge atsanangura zvikumbaridzo zvemhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
Kubernetes yeML Workloads mukuita
Kutanga kunoshandisa Ray paKubernetes kumhanyisa parallel hyperparameter kutsvaira, ichitenderedza akawanda enguva pfupi-yenguva yekuedza pods pane imwe nguva kudzikisa mutengo.
Kutanga kunoshandisa Ray paKubernetes kumhanya kwakafanana hyperparameter kutsvaira, kutenderedza akawanda enguva pfupi-yekuedzwa mapodhi panguva dzakatarwa kuderedza mutengo Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
Njodzi & Guardrails
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.
Implementation Roadmap
Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa.
Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Benchmark pasi pechokwadi mutoro uye data mamiriro.
Benchmark pasi pechokwadi mutoro uye data mamiriro. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro.
Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera.
Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.