Coding & Design — Weighted Data Batcher with Checkpointing

A research-track 1-of-N coding-and-design problem. Build a `DataBatcher` that samples weighted batches from a `DataRegistry`, supports deterministic save/resume via an `offset` argument, and handles the case where `batch_size` is not divisible by the sum of weights. Requirements The DataRegistry (provided in Colab) exposes something like: registry.get_iterator(name: str, offset: int = 0) -> Iterator[Example] Implement DataBatcher(registry, weights: dict[str, int], batch_size: int) with: Part 1 — Weighted sampling next_batch() returns a list of batch_size examples drawn from the registry's datasets in proportion to weights. Assume batch_size is divisible by sum(weights) for now. The order within a batch matters for deterministic reproducibility — define and commit to a convention. Part 2 — Deterministic save/resume Implement state_dict() / load_state_dict() so a training run can be paused and resumed with byte-identical batches. The supplied iterator interface accepts offset — use it; don't try to fast-forward by re-iterating. Resume must be O(1) in offset lookups, not O(n) replay. Part 3 — Non-divisible batch size Drop the divisibility assumption. Two reasonable approaches: Round-robin: maintain remainder counters; allocate the leftover slots in a defined order each batch. Stochastic: sample dataset names per slot with probabilities proportional to weights; expected ratio is correct, exact ratio is not. The interviewer expects you to discuss tradeoffs (variance, determinism, exposure bias) rather than pick blindly. Follow-ups Corner cases the interviewer fishes for: dataset shorter than its weighted quota, weights changing mid-training, multi-GPU sharding of the iterator. Test plan you'd write before deploying this in real training. Notes The canonical weighted-pick primitive — prefix-sum array of weights + binary-search a uniform draw in [0, total) — is the right building block for the stochastic non-divisible variant. Build it once, call it batch_size times per next_batch(), and the expected per-dataset count matches weights in O(log K) per slot. This round runs in Google Colab with the registry and helper imports pre-supplied. Read the cell carefully — the registry interface is the spec. An older variant of the prompt ("data batcher with sampling API offset") has been corrected: it is the offset on get_iterator, not a top-level sampling API offset. Several candidates report needing to write and re-write Part 2 — practice it ahead of time. Preparation Implement a divisible-batch weighted sampler in pure Python in under 15 minutes. Layer in state_dict / load_state_dict so a paused-then-resumed batcher emits the same next batch as an uninterrupted batcher would. Test it. Practice the non-divisible variant both ways (deterministic round-robin and stochastic) and be able to argue when you'd pick which.