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ml-platform-engineering

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HAYDARKILIC / distributed_systems_for_ml

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A 6-week masterclass in Distributed Deep Learning Infrastructure from scratch. Covers the $\alpha-\beta$ communication model, custom Ring All-Reduce primitives, Parameter Servers (Hogwild!), 1F1B Pipeline Parallelism, ZeRO/FSDP memory sharding, gradient compression with error feedback, and fault-tolerant elastic training simulators.

python distributed-systems high-performance-computing parameter-server distributed-training pipeline-parallelism fsdp all-reduce zero-redundancy-optimizer ml-platform-engineering
  • Updated May 28, 2026
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