Dubawa
Kubernetes tsarin buɗaɗɗen tushe ne wanda ke tsarawa ta atomatik, ma'auni, da kuma sake kunna shirye-shiryen kwantena a cikin tarin inji. Don koyon inji, yana barin ƙungiyoyi su tattara ayyukan horo na yunwar GPU da sabar ƙirar ƙira a kan kayan aikin da aka raba ba tare da kula da kowane sabar ba.
Kubernetes don ML Workloads wani shingen ginin fasaha ne wanda ke shafar ingancin samfurin, farashin kayan aiki, latency, da aminci a sikelin.
Zurfafa nutsewa
An gina shi a asali a Google don gudanar da ayyukan gidan yanar gizo, Kubernetes yana ɗaukar gungun ku a matsayin babban tafki ɗaya na CPU, ƙwaƙwalwar ajiya, da GPUs, sannan ya yanke shawarar wacce injin ke tafiyar da kowane akwati. Ƙungiyoyin ML sun dogara da shi saboda ayyukan aiki sun fashe da tsada: guduwar horo na iya buƙatar GPUs takwas na sa'o'i shida, sannan ba komai. Kubernetes yana tsara jadawalin da ke kan kumburi tare da GPUs kyauta, kuma lokacin da aikin ya ƙare yana yantar da kayan aikin. Har ila yau, yana kiyaye sabar sabar da rai, tana sake kunna kwantena da suka faɗo da kuma yada kwafi a cikin injina don jurewa. Kayan aikin da aka gina a sama, kamar Kubeflow, Ray, da KServe, suna ƙara takamaiman nau'ikan ML irin su masu aikin horarwa da rarrabawa, daidaitawar hyperparameter, da ƙarshen ƙirar ƙirar autoscaling, don haka masana kimiyyar bayanai suna aiki tare da abstractions mafi girma maimakon raw YAML.
Fahimtar Fasaha
Kubernetes yana sanya GPUs ta hanyar plugins na na'ura waɗanda ke tallata albarkatu kamar nvidia.com/gpu, wanda mai tsarawa yayi daidai da buƙatun kwasfa. Taints da juriya suna kiyaye ayyukan CPU masu arha a kashe nodes na GPU masu tsada, yayin da masu zaɓen kumburi da ƙa'idodin alaƙa suna tura horo zuwa takamaiman kayan aiki. Don horar da GPU da yawa, masu aiki suna ƙirƙirar ƙungiyar kwasfan fayiloli waɗanda ke gano juna kuma suna gudanar da tsarin kamar PyTorch DDP ko Horovod, musayar gradients akan hanyar sadarwar tari ta amfani da NCCL.
Jagorar Kubernetes don Ayyukan Aiki na ML
Kubernetes tsarin buɗaɗɗen tushe ne wanda ke tsarawa ta atomatik, ma'auni, da kuma sake kunna shirye-shiryen kwantena a cikin tarin inji. Don koyon inji, yana barin ƙungiyoyi su tattara ayyukan horo na yunwar GPU da sabar ƙirar ƙira a kan kayan aikin da aka raba ba tare da kula da kowane sabar ba. Kubernetes don ML Workloads wani shingen ginin fasaha ne wanda ke shafar ingancin samfurin, farashin kayan aiki, latency, da aminci a sikelin. Don gina zurfin fahimta, bi Kubernetes don ML Workloads a matsayin samfurin aiki, ba sifa ɗaya ba: ayyana sakamakon da ake so, bayyana zato, kuma raba abin da tsarin zai iya yi da dogaro daga abin da har yanzu yana buƙatar yanke hukunci na ƙwararru.
A aikace, ƙungiyoyi masu ƙarfi masu amfani da Kubernetes don ML Workloads suna haɓaka gine-gine, bayanai, da zaɓin abubuwan more rayuwa akan dogaro da farashi. Suna rubuta ƙayyadaddun ƙa'idodin nasara, gwaji akan bayanan gaskiya da gudanawar aiki, da jujjuyawar bisa ga tsarin gazawar da aka lura maimakon cin nasara na lokaci ɗaya. Wannan shine inda fahimtar ka'idar ta juya zuwa iyawa mai dorewa a cikin samfura, manufofi, da ayyuka.
Hukunce-hukuncen gine-gine suna haifar da aiki da tsadar aiki na shekaru. A lokaci guda, Haɓaka ma'auni ɗaya na iya ɓoye manyan raunin tsarin. Hanyar da ta fi dacewa ita ce haɗa saurin gwaji tare da horon gudanarwa: gudanar da matukin jirgi, kama shaida, buga rajistan ayyukan yanke shawara, da ci gaba da sabunta abubuwan tsaro kamar yadda halayen ƙira, tsammanin mai amfani, da buƙatun tsari ke tasowa.
Dabarun Tasiri
Hukunce-hukuncen gine-gine suna haifar da aiki da tsadar aiki na shekaru.
Hukunce-hukuncen gine-gine suna haifar da aiki da tsadar aiki na shekaru. A cikin ƙawance masu inganci, ana fassara wannan zuwa ƙa'idodin aiki waɗanda za a iya aunawa, iyakokin ikon mallaka, da kuma bita-da-kullin bita don ƙungiyoyi su iya haɓaka kwarin gwiwa a maimakon ɓata shakku.
Ilimin fasaha yana taimaka wa ƙungiyoyi su zaɓi tari mai kyau, ba kawai sabon abu ba.
Ilimin fasaha yana taimaka wa ƙungiyoyi su zaɓi tari mai kyau, ba kawai sabon abu ba. A cikin ƙawance masu inganci, ana fassara wannan zuwa ƙa'idodin aiki waɗanda za a iya aunawa, iyakokin ikon mallaka, da kuma bita-da-kullin bita don ƙungiyoyi su iya haɓaka kwarin gwiwa a maimakon ɓata shakku.
Zaɓuɓɓukan injiniya mafi kyau suna rage abin dogaro a cikin samarwa.
Zaɓuɓɓukan injiniya mafi kyau suna rage abin dogaro a cikin samarwa. A cikin ƙawance masu inganci, ana fassara wannan zuwa ƙa'idodin aiki waɗanda za a iya aunawa, iyakokin ikon mallaka, da kuma bita-da-kullin bita don ƙungiyoyi su iya haɓaka kwarin gwiwa a maimakon ɓata shakku.
Aiwatar da Gaskiyar Duniya
Gidan binciken bincike yana amfani da Ma'aikacin Horar da Kubeflow don ƙaddamar da 32-GPU PyTorch aikin horarwa da aka rarraba a cikin kuɗaɗe huɗu, sannan yana 'yantar da GPUs ta atomatik lokacin da ta haɗu.
Kamfanin kasuwancin e-commerce yana ba da samfurin shawarwarin sa tare da KServe, wanda ke ɗaukar kwafin kwafi yayin siyar da walƙiya da koma baya cikin dare.
Banki yana gudanar da ayyukan batch na dare kamar Kubernetes CronJobs, yana yi musu layi akan nodes na CPU don kada su yi gasa tare da zirga-zirgar rana.
Farawa yana amfani da Ray akan Kubernetes don gudanar da share fage na hyperparamita daidai gwargwado, yana jujjuya ɗimbin fakitin gwaji na ɗan gajeren lokaci akan abubuwan tabo don rage farashi.
Hanyoyin Aiwatarwa
Kubernetes don ML Workloads a aikace
Gidan binciken bincike yana amfani da Ma'aikacin Horar da Kubeflow don ƙaddamar da 32-GPU PyTorch aikin horarwa da aka rarraba a cikin kuɗaɗe huɗu, sannan yana 'yantar da GPUs ta atomatik lokacin da ta haɗu.
Lab ɗin bincike yana amfani da Ma'aikacin Horar da Kubeflow don ƙaddamar da aikin 32-GPU PyTorch wanda aka rarraba-ayyukan horarwa a cikin kuɗaɗe huɗu, sannan ya 'yantar da GPUs ta atomatik lokacin da yake haɗa ƙungiyoyi yawanci suna samun sakamako mafi kyau lokacin da suka ayyana ma'auni masu inganci a gaba, kiyaye hanyar haɓaka ɗan adam don ƙararraki, da bin diddigin abubuwan samarwa da ƙimar kuskure akan lokaci.
Kubernetes don ML Workloads a aikace
Kamfanin kasuwancin e-commerce yana ba da samfurin shawarwarin sa tare da KServe, wanda ke ɗaukar kwafin kwafi yayin siyar da walƙiya da koma baya cikin dare.
Kamfanin kasuwancin e-commerce yana ba da samfurin shawarwarin sa tare da KServe, wanda ke yin kwafin kwafi yayin siyarwar walƙiya da koma baya na dare Ƙungiyoyi yawanci suna samun ingantacciyar sakamako lokacin da suka ayyana ƙofofin inganci a gaba, kiyaye hanyar haɓakar ɗan adam don shari'o'i, da bin diddigin nasarorin samarwa da ƙimar kuskure akan lokaci.
Kubernetes don ML Workloads a aikace
Banki yana gudanar da ayyukan batch na dare kamar Kubernetes CronJobs, yana yi musu layi akan nodes na CPU don kada su yi gasa tare da zirga-zirgar rana.
Banki yana gudanar da ayyukan batch na dare kamar Kubernetes CronJobs, yana yin layi a kan nodes na CPU don kada su yi gasa tare da zirga-zirgar zirga-zirgar rana yawanci suna samun sakamako mafi kyau lokacin da suka ayyana ma'auni masu inganci a gaba, kiyaye hanyar haɓaka ɗan adam don ƙararraki, da bin diddigin abubuwan samarwa da ƙimar kuskure akan lokaci.
Kubernetes don ML Workloads a aikace
Farawa yana amfani da Ray akan Kubernetes don gudanar da share fage na hyperparamita daidai gwargwado, yana jujjuya ɗimbin fakitin gwaji na ɗan gajeren lokaci akan abubuwan tabo don rage farashi.
Farawa yana amfani da Ray akan Kubernetes don gudanar da share fage na hyperparamita daidai, yana jujjuya dozin na ɗan gajeren lokaci na gwaji akan lokuta don rage farashi Ƙungiyoyi yawanci suna samun sakamako mafi kyau lokacin da suka ayyana ma'auni masu inganci a gaba, kiyaye hanyar haɓaka ɗan adam don ƙararraki, da bin diddigin nasarorin samarwa da ƙimar kuskure akan lokaci.
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.
Ƙayyade latency, inganci, da maƙasudin farashi kafin aiwatarwa. Ɗauki kowane mataki azaman ƙofar shaida: idan ba a cika sharuɗɗa ba, dakatar da fitar, rufe tazarar, sannan kawai faɗaɗa amfani.
Alamar ma'auni a ƙarƙashin ainihin kaya da yanayin bayanai.
Alamar ma'auni a ƙarƙashin ainihin kaya da yanayin bayanai. Ɗauki kowane mataki azaman ƙofar shaida: idan ba a cika sharuɗɗa ba, dakatar da fitar, rufe tazarar, sannan kawai faɗaɗa amfani.
Kula da kayan aiki don kurakurai, ɗigo, da tasirin mai amfani.
Kula da kayan aiki don kurakurai, ɗigo, da tasirin mai amfani. Ɗauki kowane mataki azaman ƙofar shaida: idan ba a cika sharuɗɗa ba, dakatar da fitar, rufe tazarar, sannan kawai faɗaɗa amfani.
Shirya bijirowa da hanyoyin mayar da martani kafin sikeli.
Shirya bijirowa da hanyoyin mayar da martani kafin sikeli. Ɗauki kowane mataki azaman ƙofar shaida: idan ba a cika sharuɗɗa ba, dakatar da fitar, rufe tazarar, sannan kawai faɗaɗa amfani.