InoteveraGaidhi rinotevera
Kugona kuziva zvirwere zvedoro (AI Dental X-Ray Cavity Detection)
AI Yekutarisa
Nhungamiro yehunyanzvi
Ray yeDistributed AI
Ray ndeye yakavhurika-sosi sisitimu inoita kuti zvive nyore kuyera Python neAI mitoro yebasa kubva palaptop kuenda kuboka rezviuru zvemichina.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
It matters because it gives a simple, unified way to distribute training, tuning, data processing, and serving without rewriting your code for each.
Kudzika Kwakadzika
Pfungwa yaRay yepakati kushandura akajairwa Python mabasa uye makirasi kuita zvikamu zvakagoverwa zvine shanduko shoma. Basa rakanyorwa se "basa" riri kure rinoita asynchronously pane chero mushandi ari musumbu; kirasi yamaka se 'actor' ari kure inova sevhisi ine hunyanzvi inogara pamushandi. Ray anodzorera remangwana rakareruka (mareferensi echinhu) uye anobata kuronga, kufamba kwedata kuburikidza nechitoro chechinhu chakagovaniswa, uye kushivirira kukanganisa. Pamusoro peiyi musimboti gara-akavakirwa maraibhurari: Ray Chitima cheyakagoverwa modhi yekudzidziswa, Ray Tune yekutsvaga hyperparameter, Ray Dhata yekutepfenyura data mapaipi, RLlib yekusimbisa kudzidza, uye Ray Serve ye scalable modhi inoshumira. Izvi zvinoita kuti sumbu rimwe ribate yese ML workflow kupera kusvika kumagumo.
Kuitwa muNyika yeChokwadi
Kumhanya Ray Tune kutsvaga mazana emusanganiswa we hyperparameter mukufambirana nepakati peGPU cluster kuti uwane yakanakisa modhi yekumisikidza.
Kushandisa Ray Chitima kugovera kudzidziswa kweiyo yakadzama yekudzidza modhi kune akawanda maGPU uye node ine kushoma kodhi shanduko
Kuvaka batch-inference pombi neRay Data kuhwina mamirioni emarekodhi nekuaburitsa kuburikidza nemuenzaniso muboka.
Kuendesa akawanda modhi kuseri kweimwe autoscaling endpoint neRay Serve kubata akasiyana ekugadzira traffic
Ramangwana raRay reKuparadzirwa kweAI
Ray yave musana weAI yakakura-yakakura, inonyanya kushandiswa mukudzidzisa uye kushandira mhando dzemitauro mikuru. Tarisira kukura muLLM-chaiyo kushandira (Ray Serve nevLLM), heterogeneous GPU kuronga, kusimba kusanganisa nedhata madziva uye Kubernetes kuburikidza neKubeRay, uye zvirinani autoscaling kune spiky generative mabasa. Sezvo mamodheru achikura, basa raRay mukuronga akawanda-node kudzidziswa, RLHF mapaipi, uye batch inference kuzviuru zveanomhanyisa anogona kuwedzera.
Kuzvishandisa
Nei zvichikosha
- Mutengo uye bhajeti. Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore.
- Sarudzo dzakajeka. Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete.
- Kudzora kwemhando yepamusoro. Sarudzo dzeinjiniya dziri nani dzinoderedza zviitiko zvekuvimbika mukugadzira.
Chenjera
- 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.
Matanho anotevera
- Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa.
- Benchmark pasi pechokwadi mutoro uye data mamiriro.
- Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro.
- Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera.
Ramba Uchiongorora
Tsamba yemahara
Tora iyo yemazuva ese AI pfupiso
Nhatu dzakasimbiswa dzeAI nyaya mangwanani ega ega pakati pevhiki, dzakanyorwa neChirungu chakajeka. Yemahara zvachose, hapana ads.
Imwe email zuva rega rega revhiki. Bvisa kunyoresa kamwe chete. Hatimbotengese kana kugovera kero yako.
Zviedze iwe pachako
Take the Ray for Distributed AI quiz
Mhinduro yepakarepo pamhinduro yega yega, uye chitupa chinogovaniswa chine ID inobatika kana wapedza kosi.
Tsigira dzidzo yemahara yeAI. AI Understanding ndeye 501(c)(3) isingabatsiri - hapana ads, hapana paywall, zvachose. Ita chipo
Mibvunzo inowanzo bvunzwa
What is Ray for Distributed AI?
Ray ndeye yakavhurika-sosi sisitimu inoita kuti zvive nyore kuyera Python neAI mitoro yebasa kubva palaptop kuenda kuboka rezviuru zvemichina. Izvo zvine basa nekuti inopa yakapusa, yakabatana nzira yekugovera kudzidziswa, tuning, kugadzirisa data, uye kushumira pasina kunyora kodhi yako yega yega.
Idambudziko ripi rinonyanya kugadziriswa naRay?
Ray inopa yakabatana, yakapusa nzira yekugovera komputa mumakina mazhinji pasina kunyorazve kodhi yako kune yega yega basa remhando.
Muna Ray, ndeupi musiyano uripo pakati pe 'basa' ne 'mutambi'?
Mabasa ndeasingaverengeki mabasa ari kure anomhanya achienderana, nepo vatambi vari zvinhu zvenguva refu zvinochengeta mamiriro, senge yakaremerwa modhi.
Ray anodzokerei nekukasika paunotanga basa riri kure?
Ray anodzorera remangwana rakareruka ipapo saka basa rinomhanya asynchronously; unofonera ray.get() kuti utore mhedzisiro chaiyo kana ichidikanwa.
Ndeipi raibhurari yaRay yakagadzirirwa kugoverwa hyperparameter yekutsvaga?
Ray Tune anonyanya kumhanyisa miedzo yakawanda ye hyperparameter inopindirana musumbu.
Which object type do Ray’s docs explicitly say can be read from same-node object-store memory without a copy?
Ray documents zero-copy reads for NumPy arrays stored in the object store when workers on the same node access them. Other values may require deserialization, and data requested across nodes must be transferred.
Ramba uchidzidza
Mazano anoenderana
Mamwe madhairekitori akasarudzirwa nyaya iyi