Uhlolojikelele
I-Kubernetes iwuhlelo lomthombo ovulekile oluhlela ngokuzenzakalelayo, likala, futhi liqale kabusha izinhlelo ezifakwe esitsheni phakathi kweqoqo lemishini. Ukufunda ngomshini, kuvumela amaqembu ukuthi aphake imisebenzi yokuqeqesha elambile i-GPU namaseva emodeli azwela ukubambezeleka kuhadiwe okwabiwe ngaphandle kokugada amaseva ngamanye.
I-Kubernetes ye-ML Workloads iyibhulokhi yokwakha yobuchwepheshe ethinta ikhwalithi yemodeli, izindleko zengqalasizinda, ukubambezeleka, nokuthembeka esikalini.
I-Deep Dive
Yakhelwe okokuqala ku-Google ukuze iqalise amasevisi ewebhu, i-Kubernetes iphatha iqoqo lakho njengechibi elikhulu le-CPU, inkumbulo, nama-GPU, bese inquma ukuthi yimuphi umshini osebenzisa isiqukathi ngasinye. Amaqembu e-ML ancike kuyo ngoba imithwalo yomsebenzi iyaqhuma futhi iyabiza: ukugijima kokuqeqeshwa kungase kudinge ama-GPU ayisishiyagalombili amahora ayisithupha, bese kungekho lutho. I-Kubernetes ihlela lokho ku-pod ku-node enama-GPU amahhala, futhi uma umsebenzi uphela ikhulula i-hardware. Iphinde igcine amaseva wokukhomba ephila, iqale kabusha iziqukathi eziphahlazekile futhi isakaze izifaniso kuyo yonke imishini ukuze iqine. Amathuluzi akhelwe phezulu, njenge-Kubeflow, Ray, kanye ne-KServe, engeza izingcezu eziqondene ne-ML ezifana nama-opharetha okuqeqeshwa asabalele, i-hyperparameter tuning, nama-endpoints emodeli ye-autoscaling, ukuze ososayensi bedatha basebenze ngokudonswa kwezinga eliphezulu esikhundleni se-YAML eluhlaza.
I-Technical Insight
I-Kubernetes yabela ama-GPU ngama-plugin edivayisi akhangisa izinsiza ezifana ne-nvidia.com/gpu, umhleli ofanayo ngokumelene nezicelo ze-pod. Ukungcola nokubekezelela kugcina imisebenzi ye-CPU eshibhile ingekho kumanodi e-GPU abizayo, kuyilapho izikhethi ze-node nemithetho ehambisanayo iphinina ukuqeqeshwa kwezingxenyekazi zekhompuyutha ezithile. Ngokuqeqeshwa kwe-GPU eningi, o-opharetha bakha iqembu lama-pods atholanayo futhi asebenzise izinhlaka ezifana ne-PyTorch DDP noma i-Horovod, abashintshisana ngama-gradient ngenethiwekhi yeqoqo besebenzisa i-NCCL.
Ukwazi kahle i-Kubernetes ye-ML Workloads
I-Kubernetes iwuhlelo lomthombo ovulekile oluhlela ngokuzenzakalelayo, likala, futhi liqale kabusha izinhlelo ezifakwe esitsheni phakathi kweqoqo lemishini. Ukufunda ngomshini, kuvumela amaqembu ukuthi aphake imisebenzi yokuqeqesha elambile i-GPU namaseva emodeli azwela ukubambezeleka kuhadiwe okwabiwe ngaphandle kokugada amaseva ngamanye. I-Kubernetes ye-ML Workloads iyibhulokhi yokwakha yobuchwepheshe ethinta ikhwalithi yemodeli, izindleko zengqalasizinda, ukubambezeleka, nokuthembeka esikalini. Ukuze wakhe ukuqonda okujulile, phatha i-Kubernetes ye-ML Workloads njengemodeli yokusebenza, hhayi isici esisodwa: chaza imiphumela efiselekayo, ucacise ukucabanga, futhi uhlukanise lokho uhlelo olungakwenza ngokwethembeka kulokho okusadinga ukwahlulela kochwepheshe.
Empeleni, amaqembu aqinile asebenzisa i-Kubernetes ye-ML Workloads athuthukisa izakhiwo, idatha, nokukhetha kwengqalasizinda ngokumelene nokuthembeka nezindleko. Babhala imibandela yempumelelo ecacile, ukuhlola okuqhathaniswa nedatha engokoqobo nokugeleza komsebenzi, futhi baphindaphinde ngokusekelwe kumaphethini okuhluleka aqashiwe esikhundleni sokuwina kwebhentshimakhi yesikhathi esisodwa. Yilapho ukuqonda kwethiyori kuguquka kube amandla ahlala njalo kuwo wonke umkhiqizo, inqubomgomo, kanye nokusebenza.
Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka. Ngesikhathi esifanayo, Ukuthuthukisa ibhentshimakhi eyodwa kungafihla ubuthakathaka obubanzi besistimu. Indlela eqine kakhulu iwukuhlanganisa isivinini sokuhlola nesiyalo sokuphatha: qhuba abashayeli bezindiza, bamba ubufakazi, ushicilele amalogi ezinqumo, futhi ubuyekeze izivikelo ngokuqhubekayo njengoba imodeli yokuziphatha, okulindelwe ngabasebenzisi, kanye nezimfuneko zokulawula zishintsha.
I-Strategic Impact
Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka.
Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Imfundo yobuchwepheshe isiza amaqembu ukuthi akhethe isitaki esifanele, hhayi nje esisha.
Imfundo yobuchwepheshe isiza amaqembu ukuthi akhethe isitaki esifanele, hhayi nje esisha. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Izinketho ezingcono zobunjiniyela zinciphisa izehlakalo ezinokwethenjelwa ekukhiqizeni.
Izinketho ezingcono zobunjiniyela zinciphisa izehlakalo ezinokwethenjelwa ekukhiqizeni. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Ukuqaliswa Komhlaba Wangempela
Ilebhu yocwaningo isebenzisa i-Kubeflow Training Operator ukwethula umsebenzi wokuqeqesha osabalalisiwe we-32-GPU PyTorch ezindaweni ezine, bese ikhulula ngokuzenzakalelayo ama-GPU lapho ihlangana.
Inkampani ye-e-commerce isebenzisa imodeli yayo yokuncoma nge-KServe, eyenza i-autoscales iphindaphinde ngesikhathi sokuthengiswa kwe-flash futhi ihlehle ngobusuku obubodwa.
Ibhange lenza imisebenzi yokuqoqa amaphuzu ebusuku njenge-Kubernetes CronJobs, liwabeka kulayini kumanodi e-CPU ayisipele ukuze angaqhudelani nethrafikhi yokunikeza emini.
Isiqalisi sisebenzisa i-Ray ku-Kubernetes ukuze iqalise ukushanela kwe-hyperparameter ehambisanayo, iphotha inqwaba yamaphodi okulinga ehlala isikhashana ngezikhathi ezithile ukuze kwehliswe izindleko.
Amaphethini Okusebenzisa
I-Kubernetes ye-ML Workloads isebenza
Ilebhu yocwaningo isebenzisa i-Kubeflow Training Operator ukwethula umsebenzi wokuqeqesha osabalalisiwe we-32-GPU PyTorch ezindaweni ezine, bese ikhulula ngokuzenzakalelayo ama-GPU lapho ihlangana.
Ilebhu yocwaningo isebenzisa i-Kubeflow Training Operator ukwethula umsebenzi wokuqeqesha osabalalisiwe we-32-GPU PyTorch kuwo wonke ama-node amane, bese ikhulula ngokuzenzakalelayo ama-GPU lapho ihlanganisa Amathimba ngokuvamile athola imiphumela engcono uma echaza imingcele yekhwalithi ngaphambili, agcine indlela yokukhuphuka komuntu ngamacala asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Kubernetes ye-ML Workloads isebenza
Inkampani ye-e-commerce isebenzisa imodeli yayo yokuncoma nge-KServe, eyenza i-autoscales iphindaphinde ngesikhathi sokuthengiswa kwe-flash futhi ihlehle ngobusuku obubodwa.
Inkampani ye-e-commerce isebenzisa imodeli yayo yokuncoma nge-KServe, ephindaphinda i-autoscales phezulu ngesikhathi sokuthengiswa kwe-flash futhi ihlehle ngobusuku obubodwa Amaqembu ngokuvamile athola imiphumela engcono lapho echaza imingcele yekhwalithi ngaphambili, agcine indlela yokukhuphuka kwabantu yamacala asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Kubernetes ye-ML Workloads isebenza
Ibhange lenza imisebenzi yokuqoqa amaphuzu ebusuku njenge-Kubernetes CronJobs, liwabeka kulayini kumanodi e-CPU ayisipele ukuze angaqhudelani nethrafikhi yokunikeza emini.
Ibhange lenza imisebenzi yokuqoqa amaphuzu ebusuku njenge-Kubernetes CronJobs, liwabeka kulayini ezindaweni eziyisipele ze-CPU ukuze angaqhudelani nomsebenzi wethrafikhi wasemini Amaqembu ngokuvamile athola imiphumela engcono uma echaza imingcele yekhwalithi ngaphambili, agcine indlela yokukhuphuka kwabantu ngamacala asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Kubernetes ye-ML Workloads isebenza
Isiqalisi sisebenzisa i-Ray ku-Kubernetes ukuze iqalise ukushanela kwe-hyperparameter ehambisanayo, iphotha inqwaba yamaphodi okulinga ehlala isikhashana ngezikhathi ezithile ukuze kwehliswe izindleko.
Isiqalisi sisebenzisa i-Ray ku-Kubernetes ukuze iqhube ukushanela kwe-hyperparameter ehambisanayo, iphotha inqwaba yama-pods okulinga ehlala isikhathi esifushane ukuze kwehliswe izindleko Amaqembu ngokuvamile athola imiphumela engcono lapho echaza imikhawulo yekhwalithi ngaphambili, agcine indlela yokukhuphuka kwabantu yamacala asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Izingozi & Guardrails
Ukuthuthukisa ibhentshimakhi eyodwa kungafihla ubuthakathaka obubanzi besistimu.
Izindleko zengqalasizinda nezokulungisa zivame ukubukelwa phansi.
Izikhala zokuphepha nokubonakala zingakhula njengoba izinhlelo ziba nzima kakhulu.
Ukuqalisa Umhlahlandlela
Chaza ukubambezeleka, ikhwalithi, nezindleko ezihlosiwe ngaphambi kokuqaliswa.
Chaza ukubambezeleka, ikhwalithi, nezindleko ezihlosiwe ngaphambi kokuqaliswa. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Ibhentshimakhi ngaphansi komthwalo wangempela nezimo zedatha.
Ibhentshimakhi ngaphansi komthwalo wangempela nezimo zedatha. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Ukuqapha amathuluzi amaphutha, ukukhukhuleka, nomthelela wabasebenzisi.
Ukuqapha amathuluzi amaphutha, ukukhukhuleka, nomthelela wabasebenzisi. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Lungiselela izindlela zokuhlehlisa nezigameko ngaphambi kokukala.
Lungiselela izindlela zokuhlehlisa nezigameko ngaphambi kokukala. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.