Ni kikun Sharded Data Parallel adanwo

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Kini FSDP shard kọja awọn GPUs pe afiwera data boṣewa kii ṣe?
   • Awọn paramita, gradients, ati awọn ipinlẹ iṣapeye
   • Awọn ipele titẹ sii nikan
   • Nikan iṣeto oṣuwọn ikẹkọ
   • Awọn faili dataset ikẹkọ lori disk
2. Iṣiṣẹ apapọ wo ni FSDP nlo lati tun ṣe awọn iwuwo kikun Layer kan ni deede ṣaaju ṣiṣe iširo rẹ?
   • Igbohunsafefe-nikan
   • Gbogbo-kó
   • Tuka
   • Idena
3. Kini idi ti FSDP ṣe ominira awọn iwuwo kikun ti o pejọ lẹsẹkẹsẹ lẹhin iṣiro Layer kan?
   • Lati ba awọn gradients ti ko lo
   • Lati fi ipa mu loop ikẹkọ losokepupo
   • Lati tọju ifẹsẹtẹ iranti ayeraye si shard kan fun ẹrọ kan
   • Nitori PyTorch pa wọn laileto
4. FSDP ni atilẹyin pupọ nipasẹ ọna iṣapeye iranti iṣaaju?
   • Map Idinku _AIU_PROTECTED_11__
   • Apache Spark RDDs
   • Awọn ohun kohun CUDA NVIDIA
   • Microsoft's ZeRO (Odo Imudara Apopada odo)
5. Bawo ni FSDP ṣe tọju pupọ ti aipe nẹtiwọọki lati ikojọpọ awọn iwuwo?
   • Nipa agbekọja ibaraẹnisọrọ pẹlu iširo, ṣaju awọn aye ti Layer atẹle
   • Nipa pipasẹ ẹhin sẹhin
   • Nipa fisinuirindigbindigbin òṣuwọn si ọkan bit
   • Nipa ikẹkọ lori Sipiyu nikan
6. Iṣiṣẹ apapọ wo ni o pin kaakiri awọn ege gradient ni idapo pada si awọn GPU ti wọn ni iwọle sẹhin?
   • Gbogbo-si-gbogbo išẹpo
   • Din-tuka
   • Ẹda-GPU ẹyọkan
   • Kojọpọ-nikan
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