A rigorous, well-organized reference on the parallelism strategies (data, tensor, pipeline, MoE, expert-choice routing) and memory-saving techniques that make large-scale model training feasible — useful for anyone reasoning about distributed training tradeoffs.
[Updated on 2022-03-13: add expert choice routing .] [Updated on 2022-06-10]: Greg and I wrote a shorted and upgraded version of this post, published on OpenAI Blog: “Techniques for Training Large Neural Networks”
Transcript
[Updated on 2022-03-13: add expert choice routing .] [Updated on 2022-06-10]: Greg and I wrote a shorted and upgraded version of this post, published on OpenAI Blog: “Techniques for Training Large Neural Networks”
