Distributed trainingRapid Fire
Distributed Training Fundamentals 1
Answer seven questions about dividing training computation, memory and communication across GPUs.
Unlock with Full AccessThe round
Work through the mechanisms behind data, tensor and pipeline parallelism. The round also asks about state sharding, activation checkpointing and communication costs. Explain the trade-offs and use the stated setup for each calculation.
What you'll practise
Parallel training
Explain what is distributed across GPUs and where those workers need to communicate.
Memory trade-offs
Distinguish state sharding from recomputing activations during training.
Training efficiency
Reason about pipeline idle time and the time required to transfer data.
Distributed training fundamentals
Answer seven questions
Get feedback on each answer when you finish the round.
All questions and feedback are included with Full Access.