pipeline
AI infra interview questions tagged pipeline, across every topic.
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Concepts behind "pipeline"
The curriculum that explains the ideas these questions test.
Foundational
Dataset Lifecycle: Ingest, Shard and RetainA training dataset is not a file, it is a pipeline with four stages and a retention policy, and each stage has a different bottleneck. Ingest is metadata-bound rather than bandwidth-bound. Tokenization is CPU work that should happen once offline rather than every epoch. Sharding decides whether the training read is a stream or a storm of small files. And retention decides how much of the bill is paid for bytes nobody reads.🔌 Networking & Storage
Advanced
Communication Volume EstimatesEvery parallelism strategy is a promise to move a certain number of bytes between GPUs every step, and the fabric either affords it or it does not. This page derives the per-rank volume for data parallelism, ZeRO/FSDP, tensor parallelism, pipeline parallelism and expert parallelism, works each for a 70B model at 8 and 64 ranks, and turns the bytes into seconds on NVLink and on a 400 Gb/s NIC. The result is the rule that decides every 3D layout: per-layer traffic stays on NVLink, per-step traffic can cross the fabric.🧮 Napkin Math & Capacity🔒 Premium