Distributed Training

Data parallelism gives workers different batches and combines their gradients while maintaining a shared model. Sharded data parallelism distributes parameter, gradient or optimizer state to reduce per-device memory. Tensor parallelism partitions operations within layers, and pipeline parallelism assigns different layers to stages that process microbatches. These approaches can be combined, but impose different communication and scheduling requirements. A distributed process group coordinates collectives such as reductions and gathers. Effective batch size depends on local batches, accumulation and participating workers, while averaging conventions determine the scale of the resulting update. Distributed execution is a systems choice rather than a new learning objective.

Measure model-state memory, compute time and network capacity to choose a partitioning strategy. Establish a correct single-device reference, then test a small distributed run with controlled data and randomness. Ensure samplers, accumulation, gradient scaling and scheduler steps agree across workers. Profile time spent computing, communicating and waiting for input. Validate full checkpoint recovery and test how partial failures terminate or resume the job. The deliverable should show correct update semantics and useful scaling for the workload, with resource and recovery assumptions explicit enough to reproduce the experiment.

Communication, imbalance and input bottlenecks can erase expected scaling gains. Numerical reduction order and mixed precision can alter results, and bitwise reproducibility is not always attainable. More workers can change optimization if effective batch or scheduling is left uncontrolled. Sharding complicates checkpoint loading and inspection. Distinguish data distribution from model partitioning, and assess total run cost rather than reporting device count as evidence of efficiency. A successful launch alone does not establish correct synchronized learning.