Speed up training across two processes
one move: average gradients safely
Open the code pane immediately, then use the Spec tab, optional design question, tests, and artifact when you want them.
Finish each move before carrying the artifact forward.
DistributedDataParallel keeps model replicas while all-reducing gradients across workers.
Deliverable: Write the mechanism in one sentence.Split batches across workers, average gradients, and keep updates synchronized.
Deliverable: Type the smallest runnable implementation.Workers must converge to equivalent parameters under a fixed seed.
Deliverable: Record the expected output and one edge case.Vary world size, batch partitioning, and communication timing.
Deliverable: Change one variable and explain the result.A distributed training trace with throughput and synchronization evidence.
Deliverable: Save the code, output, and a short failure note.Workers must converge to equivalent parameters under a fixed seed.
Vary world size, batch partitioning, and communication timing.
A distributed training trace with throughput and synchronization evidence.
Mapped from the supplied Build Everything PDF, source page 54. The five moves are Deriva’s implementation contract for this project.