Guide-yakabatana mibvunzo · Zvakaoma Nhanho

Yakazara Yakagoverwa Data Parallel Quiz

Inovhara FSDP sharding, zvese-kuunganidza uye kuderedza-kuparadzira, kutaurirana kunopindirana, uye ndangariro-inopesana-network tradeoffs mune yakakura yakagoverwa modhi yekudzidziswa.

Nzira dzekutungamira dzinoenderanaFully Shared Data Parallel
Mubvunzo 1 ye 8

Chii chinoita FSDP shard mhiri kweGPUs iyo yakajairwa data parallelism HAZVI?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Chii chinoita FSDP shard mhiri kweGPUs iyo yakajairwa data parallelism HAZVI?
    • Parameters, gradients, uye optimizer nyika
    • Mabhechi ekupinza chete
    • Chete chirongwa chechiyero chekudzidza
    • Iyo yekudzidzira dataset mafaera pane dhisiki
  2. Ndeipi mashandiro emubatanidzwa anoshandiswa neFSDP kugadzirazve huremu hwakazara huremu pamberi pekombuta?
    • Nhepfenyuro-chete
    • Zvose-unganidza
    • Kuparadzira
    • Chipingamupinyi
  3. Nei FSDP ichisunungura iyo yakaunganidzwa maremu akazara nekukurumidza mushure meiyo layer's computation?
    • Kushatisa magadhi asina kushandiswa
    • Kumanikidza kunonoka kudzidzira loop
    • Kuchengeta chisingaperi chiyeuchidzo chetsoka kune shard imwe chete pamudziyo
    • Nekuti PyTorch inovabvisa zvisina tsarukano
  4. FSDP yakanyanya kufemerwa neipi yekutanga ndangariro-optimization maitiro?
    • Google's MepuReduce
    • Apache Spark RDDs
    • NVIDIA's CUDA cores
    • Microsoft's ZeRO (Zero Redundancy Optimizer)
  5. Ko FSDP inovanza sei yakawanda yetiweki latency kubva pakuunganidza uremu?
    • Nekupindirana kutaurirana nekombuta, kufanofeta mafaera emutsetse unotevera
    • Nekudzima nzira yekumashure
    • Nekumanikidza uremu kune imwe bhiti
    • Nekudzidziswa paCPU chete
  6. Ndeipi mashandiro emubatanidzwa anogovera akasanganiswa gradient zvimedu kudzokera kune yavo maGPU mumashure ekupfuura?
    • Zvese-kune-zvese kudzokorora
    • Deredza-kuparadzira
    • Imwe-GPU kopi
    • Unganidza-chete
  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