This episode breaks down the research paper "GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism," which proposes a new method for training very large neural networks by partitioning the model across multiple accelerators and using a novel batch-splitting pipelining algorithm. This approach allows for the efficient training of larger models than previously possible, achieving almost linear speedup with the number of accelerators.Audio : (Spotify) https://open.spotify.com/episode/4zXyQKSdiSUFK7HkAi6pxO?si=eWWrNsURSqGtw6Phf4tpJgPaper: https://arxiv.org/abs/1811.06965

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