← 💻 Coding for Infra
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Producer-Consumer Pipelines
A data loader, a log shipper, a batch inference job and a checkpoint writer are the same program: stages connected by bounded buffers, each running at its own pace, the slowest setting the throughput and the buffers absorbing the jitter between them. The coding screen asks you to build one (read, decode, batch, feed a consumer) and then pushes on the production questions: buffer sizes, clean stops, failure propagation, and why it runs at a third of the expected speed. This page derives throughput from stage times, implements the pipeline in threads and asyncio, and works the stop and failure semantics.
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PRACTICE THIS IN REAL QUESTIONS
AI Infrastructure System DesignDesign the pipeline that produces 15 trillion training tokens: ingest, dedup, tokenize, shard, serve. Throughput per stage.→Coding for InfraImplement a batcher that flushes when the batch is full or when a timeout expires. What breaks in the timer path?→Coding for InfraDesign the API for a GPU job scheduler in 45 minutes. What are the resources, states and semantics?→Coding for InfraWrite a producer-consumer pipeline with a bounded queue. What are the three bugs that show up in every first attempt?→Coding for InfraIn Go, download many model shards concurrently with a concurrency limit, a timeout and clean cancellation. Write it.→Networking, Interconnects & StorageWhy does object storage behave badly for random reads, and what does a training pipeline do about it?→
