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GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

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Dehao Chen · Quoc Le · Yanping Huang · Zhifeng Chen

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Published 2018-11-16 authorship evidence authorship evidence authorship evidence authorship evidence

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GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism arXiv · published 2018-11-16 · arXiv abstract page, retrieved for the candidate pack on 2026-10-28 (SHA-256 95a12ca1bacf12a3d166afbb7febc0522911d2ca99bafbc9598dca58986dc135). Submission history as arXiv records it (UTC): v1 16 November 2018; v2 19 November 2018; v3 20 November 2018; v4 12 December 2018; v5 25 July 2019. The page serves the latest version; each version is a separate expression, and only the one a record names was read.
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism arXiv · published 2018-11-16 · arXiv:1811.06965v1, submitted 16 November 2018 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 58bb403c7ccb7982d149bc21a0ba7345da1e7966719cf1ba15b8a70333698281). The author list is the byline as printed on the first page.

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In its own wordsThe paper presents GPipe, a pipeline parallelism library for training very large deep neural networks across accelerators.“GPipe is a scalable pipeline parallelism library that enables learning of giant deep neural networks.” arXiv v1, PDF p. 1, abstract2018-11-16 · publication · Stated in source · Supported Inspect claim & evidence ↗