Kedis ku xidhan hagaha · Adag Heerka

Kediska Isbarbar yaaca Xogta oo Dhamaystiran

Daboolaashaha FSDP kala shaandheynta, dhammaan-ururinta iyo yaraynta-kala-firdhinta, is-gaadhsiinta is-gaarsiinta, iyo xusuusta-ka-soo-horjeedka-isku-xidhka ganacsiga ee tababarka moodeelka ee ballaaran.

Wadooyinka haga ee la xidhiidhaIsbarbar-dhigga Xogta Si Buuxda Loola Qaybsaday
Su'aal 1 ee 8

Maxay FSDP ku kala firdhisaa GPU-yada oo dhan ee xogta caadiga ahi aanay isbarbar dhigin?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Maxay FSDP ku kala firdhisaa GPU-yada oo dhan ee xogta caadiga ahi aanay isbarbar dhigin?
    • Parameters, gradients, iyo gobolada wanaajiya
    • Kaliya qaybaha wax gelinta
    • Kaliya jadwalka heerka waxbarashada
    • Faylasha xogta tababarka ee diskka
  2. Hawlgalkee wadareed ayay FSDP isticmaashaa si ay dib ugu dhisto miisaan buuxa lakabka ka hor inta aanay xisaabin?
    • Baahinta-kaliya
    • Dhammaan-urur
    • Kala firdhiso
    • Caqabad
  3. Maxay FSDP u xoraynaysaa miisaankii la ururiyey isla markiiba ka dib xisaabinta lakabka?
    • Si loo musuqmaasuqo gradients aan la isticmaalin
    • Si loo qasbo wareeg tababar oo gaabis ah
    • Si loo ilaaliyo raadadka xusuusta joogtada ah oo kaliya hal jeex qalabkiiba
    • Sababtoo ah PyTorch waxay tirtirtaa si aan kala sooc lahayn
  4. FSDP waxaa si weyn u dhiirigeliyay habkee hore ee hagaajinta xusuusta?
    • Khariidadda Google
    • Apache Spark RDDs
    • Xirmooyinka CUDA ee NVIDIA
    • Microsoft's ZeRO (Zero Redundancy Optimizer)
  5. Sidee FSDP u qarisaa inta badan daahitaanka shabkada ururinta miisaanka?
    • Adigoo isku dhejinaya isgaadhsiinta xisaabinta, horudhac u samaynta xuduudaha lakabka xiga
    • Adigoo curyaaminaya baaska danbe
    • Adigoo ku cadaadinaya miisaanka hal xoogaa
    • By tababar ku saabsan CPU kaliya
  6. Hawlgalkee wadareed u qaybiya jeexyada gradient-ka la isku daray ee u qaybiya GPU-da ay iyagu leeyihiin ee kaarka danbe?
    • Isku-duubnida-dhan-dhan
    • Iska yaree-kala firdhiso
    • Hal koob-GPU
    • Ururi-kaliya
  7. Why can fully sharding state lower persistent per-GPU memory compared with replicated data parallelism?
    • It removes all model parameters during training
    • It converts optimizer states into network requests
    • Each rank stores a shard of model state instead of a full copy
    • It places every activation permanently on the CPU
  8. What is a central systems tradeoff in FSDP?
    • Lower memory and no communication at any point
    • More replicated parameters in exchange for fewer data samples
    • Replacing training with inference
    • Lower per-device memory use in exchange for collective communication