Uhlolojikelele
I-Triton Inference Server iyinkundla yomthombo ovulekile ye-NVIDIA yokuthumela nokunikeza amamodeli we-AI ekukhiqizeni ngezinga eliphezulu. Kubalulekile ngoba kulinganisa ukuthi mangaki amamodeli - kuzo zonke izinhlaka ezihlukene - asingathwa, ahlanganiswe, futhi afinyelelwa ngemuva kwe-API eyodwa esebenza kahle.
I-Triton Inference Server iyibhulokhi yokwakha yobuchwepheshe ethinta ikhwalithi yemodeli, izindleko zengqalasizinda, ukubambezeleka, nokuthembeka esikalini.
I-Deep Dive
I-Triton ihlala phakathi kwamamodeli akho aqeqeshiwe kanye nezinhlelo zokusebenza eziwabizayo. Ilayisha amamodeli ukusuka 'kwindawo yesikhombi' futhi iwanikeze nge-HTTP/REST kanye ne-gRPC. Isici sayo esivelele siwuhlaka-agnostic: isibonelo esisodwa se-Triton singasebenza kanyekanye i-PyTorch, i-TensorFlow, i-ONNX, i-TensorRT, kanye ne-Python noma ama-backends angokwezifiso. Amakhono abalulekile ahlanganisa ukuhlanganisa okuguquguqukayo, okuhlanganisa ngokuzenzakalelayo izicelo ezingenayo ezifika eduze kusenesikhathi ukuze kusetshenziswe i-GPU ngokuphumelelayo; ukusetshenziswa kwemodeli ngesikhathi esisodwa, ukusebenzisa amamodeli amaningi noma amakhophi amaningi ku-GPU eyodwa; kanye namamodeli ahlanganisayo/i-business-logic scripting, ehlanganisa ukucutshungulwa kusengaphambili, ukuqagela, nokucubungula ngemva kwepayipi elilodwa lohlangothi lweseva. Idalula amamethrikhi e-Prometheus, isekela inguqulo yemodeli, nezikali kahle ku-Kubernetes.
I-Technical Insight
I-Dynamic batching iwumgogodla we- throughput lever. Ama-GPU asebenza ngempumelelo kakhulu amaqoqo amakhulu, kodwa izicelo zokukhiqiza zifika eyodwa ngesikhathi. I-Triton iphethe izicelo zewindi elincane elilungisekayo (isb., ama-millisecond ambalwa), izihlanganise zibe inqwaba, iqhube okukodwa, bese ihlukanisa imiphumela emuva kofonayo ngamunye. Lokhu kuphakamisa ngokuphawulekayo ukusetshenziswa kwe-GPU ngezindleko ezincane zokubambezeleka. Ukwenza ngesikhathi esisodwa namaqembu esibonelo semodeli ngayinye avumela i-GPU eyodwa ukuthi ihlale imatasa kuwo wonke amamodeli ambalwa ngesikhathi esisodwa.
I-Mastering Triton Inference Server
I-Triton Inference Server iyinkundla yomthombo ovulekile ye-NVIDIA yokuthumela nokunikeza amamodeli we-AI ekukhiqizeni ngezinga eliphezulu. Kubalulekile ngoba kulinganisa ukuthi mangaki amamodeli - kuzo zonke izinhlaka ezihlukene - asingathwa, ahlanganiswe, futhi afinyelelwa ngemuva kwe-API eyodwa esebenza kahle. I-Triton Inference Server iyibhulokhi yokwakha yobuchwepheshe ethinta ikhwalithi yemodeli, izindleko zengqalasizinda, ukubambezeleka, nokuthembeka esikalini. Ukuze wakhe ukuqonda okujulile, phatha i-Triton Inference Server 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-Triton Inference Server athuthukisa ukwakheka, 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
Ukusingatha imodeli yokuhlonza ukukhwabanisa, imodeli yesincomo, kanye nesihlukanisi sesithombe kuseva eyodwa ye-GPU eyabelwe kusetshenziswa ukusetshenziswa kwemodeli efanayo
Ukusebenzisa i-dynamic batching ukuze kusetshenziswe i-API yokuqashelwa kwesithombe esinethrafikhi ephezulu ukuze izicelo ezihlakazekile ziqoqwe ukuze kutholwe i-GPU ephumelelayo.
Ukwakha inhlanganisela yohlangothi lweseva esebenzisa ukucutshungulwa kwangaphambili kwesithombe, umtshina we-TensorRT, kanye nokulebula ukucubungula ngemva kwepayipi elilodwa le-Triton
Kusetshenziswa i-LLM ene-backend ye-TensorRT-LLM e-Triton ukuze kusakazwe izimpendulo ze-chatbot ezinkulungwaneni zabasebenzisi ngasikhathi sinye
Amaphethini Okusebenzisa
I-Triton Inference Server isebenza
Ukusingatha imodeli yokuhlonza ukukhwabanisa, imodeli yesincomo, kanye nesihlukanisi sesithombe kuseva eyodwa ye-GPU eyabelwe kusetshenziswa ukusetshenziswa kwemodeli efanayo.
Ukusingatha imodeli yokuhlonza ukukhwabanisa, imodeli yesincomo, kanye nesihlukanisi sesithombe kuseva ye-GPU eyodwa eyabelwe kusetshenziswa imodeli efanayo Amathimba ngokuvamile athola imiphumela engcono uma echaza imikhawulo yekhwalithi ngaphambili, egcina indlela yokukhuphuka komuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Triton Inference Server isebenza
Kusetshenziswa i-dynamic batching ukuze kunikezwe i-API yokuqashelwa kwesithombe sethrafikhi ephezulu ukuze izicelo ezihlakazekile ziqoqwe ukuze kutholwe i-GPU esebenza kahle.
Kusetshenziswa i-batching eguquguqukayo ukuze kunikezwe i-API yokuqashelwa kwezithombe zethrafikhi ephezulu ukuze izicelo ezihlakazekile ziqoqwe ukuze kutholwe i-GPU ephumelelayo Amaqembu ngokuvamile athola imiphumela engcono uma echaza imingcele yekhwalithi ngaphambili, agcine indlela yokukhuphuka komuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Triton Inference Server isebenza
Ukwakha inhlanganisela yohlangothi lweseva esebenzisa ukucutshungulwa kwangaphambili kwesithombe, umtshina we-TensorRT, kanye nokulebula ukucubungula ngemva kwepayipi elilodwa le-Triton.
Ukwakha inhlanganisela yohlangothi lweseva esebenzisa ukucutshungulwa kwangaphambili kwesithombe, umtshina we-TensorRT, kanye nokulungiswa kwangemuva kwelebula epayipini elilodwa le-Triton Amaqembu ngokuvamile athola imiphumela engcono uma echaza imingcele yekhwalithi ngaphambili, agcine indlela yokukhuphuka komuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Triton Inference Server isebenza
Kusetshenziswa i-LLM ene-backend ye-TensorRT-LLM e-Triton ukuze kusakazwe izimpendulo ze-chatbot ezinkulungwaneni zabasebenzisi ngasikhathi sinye.
Ukukhipha i-LLM ene-backend ye-TensorRT-LLM e-Triton ukuze kusakazwe izimpendulo ze-chatbot ezinkulungwaneni zabasebenzisi ngesikhathi esisodwa Amaqembu ngokuvamile athola imiphumela engcono uma echaza izilinganiso zekhwalithi ngaphambili, agcina indlela yokukhuphuka kwabantu yamakesi 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.