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Scaling Model Training with Distributed Computing
Distributed training splits the work across machines, but keeping them in step costs time. In real PyTorch data-parallel runs with one, two and four workers, every replica stayed identical, yet small batches trained slower with more workers and only large batches gained, about one and a half times with four.
Distributed Systems Basics for Data Engineers
The distributed systems ideas behind Spark, Kafka and cloud warehouses, for data engineers: partitioning and hot keys, why modulo hashing moved 74% of real taxi data when a node was added and consistent hashing 23%, replication, what CAP really says, failures and retries, and why clocks cannot be trusted.