Claim · 2018-11-16 · publication · Stated in source · Supported

The paper presents GPipe, a pipeline parallelism library for training very large deep neural networks across accelerators.

One recorded claim. The claim panel gives its qualification, the source passages with their place in each source, and how it was checked.

What it connects

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism → states its contribution → The paper presents GPipe, a pipeline parallelism library for training very large deep neural networks across accelerators.

Where it appears

No collection page holds this claim. It is listed with its records in the explorer.

Claim and evidence

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelismstates its contribution ↓The paper presents GPipe, a pipeline parallelism library for training very large deep neural networks across accelerators.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2018-11-16 · publication Records dated 2018 →

Scope and limitsThe paper's own description of its contribution; not an assessment of it.

The evidence 01

primary sourceSupports the claim

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

arXiv · 2018-11-16

Cite the original source

Cite the original document, not Selekoa. Check these details against it.

Source excerpt

“GPipe is a scalable pipeline parallelism library that enables learning of giant deep neural networks.”

Location in source arXiv v1, PDF p. 1, abstract

Machine-checked · 2026-10-31

Source details and check notes

Authors: Yanping Huang; Youlong Cheng; Dehao Chen; HyoukJoong Lee; Jiquan Ngiam; Quoc V. Le; Zhifeng Chen
Version: 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.
Version date: 2018-11-16
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Yanping Huang, Youlong Cheng, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Zhifeng Chen, “GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism”, 2018-11-16.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, key papers (research/key-papers). The quotation (15 words) matched the text retrieved for the candidate pack on 2026-10-28 (research/key-papers/verification.json). Matching ignored differences of whitespace, case, quotation marks, dashes and line-break hyphens, so the source’s typography can differ. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. A paper or other publication describes its own contribution, quoted from the expression the record names, usually its abstract or introduction. The record is the document’s own description, not an assessment: a claim of priority or novelty it contains is the document’s claim, flagged as such, and says nothing about earlier or later work.

Date. Date of the arXiv version quoted, submitted 16 November 2018 (UTC); later versions may word it differently.

Record ID kp-states-gpipe-efficient-training-giant-neural-2018-1