Distributed Training Methods and Parallelization Techniques - Lecture 19

Learn about distributed training fundamentals in machine learning through a recorded MIT lecture that explores parallelization methods, data parallelism, and memory optimization techniques. Dive into essential concepts including communication primitives, ZeRO and FSDP memory reduction strategies, pipeline parallelism, tensor parallelism, and sequence parallelism. Taught by Professor Song Han, the 70-minute lecture provides comprehensive coverage of background, motivation, and various parallelization approaches used in modern distributed ML training systems.