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Data Parallelism
Data parallelism replicates a model across workers and gives each worker different examples, synchronizing gradients or parameters during training.
Teerera kugwaro iriAI-yakarondedzerwa · ~1 min
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Kudzika Kwakadzika
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.
Kuitwa muNyika yeChokwadi
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.
Ramangwana reData Parallelism
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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- Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa.
- Benchmark pasi pechokwadi mutoro uye data mamiriro.
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Mibvunzo inowanzo bvunzwa
What is Data Parallelism?
Data parallelism replicates a model across workers and gives each worker different examples, synchronizing gradients or parameters during training.
Mune yakajairwa data parallelism, GPU yega yega inobata chii?
Imwe neimwe GPU inochengeta yakazara yakazara modhi uye inogadzirisa chikamu chakasiyana che data batch, izvo ndizvo zvinoita kuti ive 'data' parallelism kwete modhi parallelism.
Ndeipi mashandiro ekutaurirana anochengeta modhi replicas mukuwiriranisa nhanho imwe neimwe?
Mushure mekupfuura kwega kwega kumashure, ma gradients anosanganisirwa pamidziyo kuburikidza ne-ese-kuderedza (kazhinji inopfupikiswa uye yakaenzana) saka replica yega yega inoshandisa zvakafanana kuvandudza.
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.
PyTorch DistributedDataParallel inovanza sei kutaurirana latency?
DDP inotanga kuwiriranisa ma gradients ematanho ekutanga apo gare gare maseru achiri kuverengerwa, achipfuura netiweki kutaurirana nekomputa.
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