HardTechnical screen30 min to answer

How does PyTorch Fully Sharded Data Parallel work?

Asked at

NVIDIA

1Give yourself 30 minutes

2Answer out loud, not in your head

3Then compare with the answer below

Stuck? Show a way to structure it+

01Clarify objective, users, constraints, and failure tolerance.
02Cover parameter sharding, collectives, memory, trade-offs in a causal structure.
03Compare at least two credible approaches.
04Specify evaluation, rollout, monitoring, and rollback.

Reference answer

strong answer

I would begin by defining the target outcome and acceptable failure boundary. For Distributed training, I would analyze parameter sharding, collectives, memory, trade-offs, compare alternatives on quality, latency, cost, and risk, and validate on representative slices before a monitored rollout.

parameter sharding
collectives
memory
trade-offs

Requires scenario-specific numbers
Should include relevant personal experience

alternative answer

I would start with the riskiest assumption in Distributed training, create the cheapest discriminating experiment, then use the evidence to choose an architecture and release threshold.

experimentation
risk-first sequencing
decision clarity

Needs deeper implementation detail

Grade yourself

parameter sharding

weight 30%

StrongExplains parameter sharding with mechanisms, trade-offs, and validation.

WeakNames parameter sharding without operational implications.

collectives

weight 25%

StrongExplains collectives with mechanisms, trade-offs, and validation.

WeakNames collectives without operational implications.

memory

weight 25%

StrongExplains memory with mechanisms, trade-offs, and validation.

WeakNames memory without operational implications.

trade-offs

weight 20%

StrongExplains trade-offs with mechanisms, trade-offs, and validation.

WeakNames trade-offs without operational implications.

Then expect these follow-ups

Which assumption is most likely to invalidate your approach?
Tests: depth
What would you measure offline and in production?
Tests: evaluation
How would your answer change with one-tenth the data or compute?
Tests: adaptability

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