Ukufana kwedatha
Ukufana kwedatha kuqeqesha imodeli eyodwa ngokushesha ngokuyiphindaphinda kuwo wonke ama-GPU amaningi, nge-GPU ngayinye icubungula ucezu oluhlukile lwenqwaba yedatha.
Uhlolojikelele
It is the workhorse technique that lets teams scale to dozens or thousands of accelerators.
I-Deep Dive
Ekufaneni kwedatha, yonke i-GPU ibamba ikhophi efanayo yezisindo zemodeli kodwa icubungula iqoqo elincane elihlukile lezibonelo zokuqeqeshwa. Idivayisi ngayinye ibala iphasi eya phambili nangemuva ngokuzimela, ikhiqize isethi yayo yama-gradient. Ngaphambi kokubuyekezwa kwezisindo, ama-gradient alinganiswa kuwo wonke ama-GPU kusetshenziswa umsebenzi wokuxhumana wokunciphisa konke, ngakho-ke yonke ikhophi ihlala ivumelanisiwe futhi iziphatha njengokungathi iqeqeshelwe kubheshi eyodwa enkulu ehlanganisiwe. Lokhu kuphindaphinda ngempumelelo: Ama-GPU angu-8 angahlafuna cishe izikhathi ezingu-8x idatha ngesinyathelo ngasinye. Okubanjiwe ukuthi i-GPU ngayinye kufanele ilingane nayo yonke imodeli, ama-gradients ayo, nesimo se-optimizer enkumbulweni, ukuze ukufana kwedatha okusobala akusizi uma imodeli inkulu kakhulu kudivayisi eyodwa.
I-Technical Insight
Umsebenzi oyinhloko owokunciphisa konke, ohlanganisa ama-gradient kuwo wonke amadivayisi bese usabalalisa kabusha umphumela. Khalisa konke ukunciphisa, okusetshenziswa imitapo yolwazi efana ne-NCCL ne-Horovod, kudlula izingcezu ze-gradient eduze nendandatho enengqondo ukuze ukuxhumana okuphelele kuzimele ekubalweni kwe-GPU. I-PyTorch's DistributedDataParallel idlula lokhu kuxhumana nokudlula emuva, ikhiphe ukuvumelanisa kwe-gradient kuzendlalelo zangaphambili kuyilapho izendlalelo zakamuva zisasebenza ngekhompyutha, zifihla ukubambezeleka okuningi kwenethiwekhi.
I-Strategic Impact
Izindleko kanye nesabelomali
Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka.
Izinqumo ezicacile
Imfundo yobuchwepheshe isiza amaqembu ukuthi akhethe isitaki esifanele, hhayi nje esisha.
Ukulawulwa kwekhwalithi
Izinketho ezingcono zobunjiniyela zinciphisa izehlakalo ezinokwethenjelwa ekukhiqizeni.
Ikusasa Lokufana Kwedatha
Ukufana kwedatha okumsulwa kuya ngokuya kuhlanganiswa nokushadi nokufana kwemodeli kube amasu ayingxube 'ye-nD parallelism' kumamodeli wamapharamitha ayizigidigidi. Lindela ukuminyanisa kwe-gradient okuhlakaniphile, ukuxhumana okuvumelanayo nokugqagqene, kanye ne-topology-aware all-reduction esebenzisa i-NVLink esheshayo ngaphakathi kwenodi kanye ne-InfiniBand ehamba kancane kuwo wonke amanodi. Njengoba amaqoqo akhula, ukwehlisa isilinganiso sokuxhumana-kuya-kukhompyutha kuhlala kuyinselelo emaphakathi yobunjiniyela yokugcina izinkulungwane zama-GPU ematasa.
Ukuqaliswa Komhlaba Wangempela
Ukuqeqesha isigaba sesithombe se-ResNet kuwo wonke ama-GPU angu-8 kuseva eyodwa kusetshenziswa i-PyTorch DistributedDataParallel, i-GPU ngayinye iphatha okungu-32 kwenqwaba yezithombe ezingu-256.
Ukukala ukuqeqeshwa kwangaphambili kwe-BERT kumakhulukhulu ama-GPU nge-Horovod, kusetshenziswa indandatho yokunciphisa konke ukuze uvumelanise ama-gradients isinyathelo ngasinye.
Ukushuna kahle imodeli yokuncoma kuqoqo le-multi-node lapho inodi ngayinye icubungula izingcezwana ezihlukene zokusebenzisana nabasebenzisi.
Kusetshenziswa i-TensorFlow's MirroredStrategy ukusabalalisa ukuqeqeshwa kwemodeli yombono kuwo wonke ama-GPU amaningi endaweni yokusebenza eyodwa enoshintsho oluncane lwekhodi.
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.
Ibhentshimakhi ngaphansi komthwalo wangempela nezimo zedatha.
Ukuqapha amathuluzi amaphutha, ukukhukhuleka, nomthelela wabasebenzisi.
Lungiselela izindlela zokuhlehlisa nezigameko ngaphambi kokukala.
Qhubeka Uhlole
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Umhlahlandlela olandelayo
I-AI Data Governance
Imibuzo evame ukubuzwa
What is Data Parallelism?
Ukufana kwedatha kuqeqesha imodeli eyodwa ngokushesha ngokuyiphindaphinda kuwo wonke ama-GPU amaningi, nge-GPU ngayinye icubungula ucezu oluhlukile lwenqwaba yedatha. Kuyindlela ye-workhorse evumela amaqembu ukuthi afinyelele kumashumi noma izinkulungwane zama-accelerator.
Ekufaneni kwedatha okujwayelekile, i-GPU ngayinye iphetheni?
I-GPU ngayinye igcina ikhophi egcwele yemodeli futhi icubungule ingxenye ehlukile yenqwaba yedatha, okuyikhona okuyenza ifane 'yedatha' kunokuba imodeli ihambisane.
Imuphi umsebenzi wokuxhumana ogcina izifaniso zemodeli zivumelanisa isinyathelo ngasinye?
Ngemva kokudlula ngakunye okubuyela emuva, ama-gradient ahlanganiswa kuwo wonke amadivayisi ngokunciphisa konke (okuvame ukufinyezwa bese kuba nesilinganiso) ukuze yonke ikhophi isebenzisa isibuyekezo esifanayo.
Uyini umkhawulo oyinhloko wokufana kwedatha engenalutho?
Ngenxa yokuthi yonke i-GPU iphethe ikhophi egcwele yayo yonke into, ukufana kwedatha akwenzi lutho ukusiza lapho imodeli imane iyinkulu kakhulu ukuthi ingene kudivayisi eyodwa.
Kungani indandatho inciphisa konke ikhanga ukubala okukhulu kwe-GPU?
Amaphase okunciphisa konke okuyiziqephu zegrediyenti ezungeze iringi enengqondo, ukuze isamba somkhawulokudonsa i-GPU ngayinye esithumelayo sihlale sinjalo kungakhathaliseki ukuthi mangaki ama-GPU abamba iqhaza.
I-PyTorch DistributedDataParallel ikufihla kanjani ukubambezeleka kokuxhumana?
I-DDP iqala ukuvumelanisa ama-gradient ngezendlalelo zangaphambili kuyilapho izendlalelo zakamuva zisabalwa ngekhompyutha, ukuxhumana kwenethiwekhi okugqagqene nokubala.