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GUIDE teknik
Paralelismu done
Data parallelism replicates a model across workers and gives each worker different examples, synchronizing gradients or parameters during training.
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In standard data parallelism, each worker holds a model replica, processes a local batch, and participates in gradient synchronization. This can improve throughput, but each replica still needs memory for the model and optimizer state. PyTorch DistributedDataParallel synchronizes gradients and can overlap communication with backward computation. Scaling depends on compute, communication, batch size, network topology, and input pipeline; adding GPUs does not guarantee proportional throughput.
Doxal ci àdduna dëgg
Replicate a model across GPUs and split each batch across workers.
Use gradient accumulation to increase effective batch size across steps.
Profile all-reduce communication as worker count increases.
Compare scaling efficiency at matched model, batch, and hardware.
Ëlëgu paralelismu done
Distributed training continues to combine data, tensor, and pipeline parallelism. Benchmark throughput and convergence on the target system instead of assuming ideal linear scaling.
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Moytul
- Optimize benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi.
- Njëg li ñuy fay ci infrastructure yi ak ci toppatoo dañuy faral di suufeel.
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- Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo.
- Benchmark ci biir sargal ak done yu dëggu.
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Take the Data Parallelism quiz
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Laaj yi ñuy faral di laaj
Luy paralelismu done?
Data parallelism replicates a model across workers and gives each worker different examples, synchronizing gradients or parameters during training.
Ci parallelism done buñ miin, lan la GPU bu nekk di tëye?
GPU bu nekk dafay denc koppi bu mat sëkk ci model bi ba noppi di liggéey ci wàll wu wuute ci done yi, moo tax mu nekk parallelism 'done' moo gën parallelism model.
Ban jëfu jokkoo mooy tëye replica yu model yi ci jéego bu nekk?
Ginaaw bépp dellu ginaaw, gradient yi dañuy boole ci aparey yi jaaraleko ci all-reduce (dañu koy boole ba noppi moyenne) suko defee replica bu nekk di jëfandikoo benn yeesal bi.
What memory limit remains in plain data parallelism?
The proposed guide says plain DDP replicates model parameters and optimizer state on each device; data parallelism therefore does not shard the model to fit a larger checkpoint.
Which factors does NCCL use when selecting collective algorithms?
NCCL documentation describes algorithm selection in terms of the collective, message size, and system topology.
naka la ko PyTorch di nëbbee ay done yuñ séddale parallel?
DDP mingi tàmbali synchroniser gradient yi ngir layers yu njëkk ya, fekk layers yu ci topp yi ñu ngi doon calculer, di jaxasoo jokkoo reso bi ak calcul bi.
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