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Dataflow, parameter servers and automatic differentiation have earlier origins.", "domain": "Machine-learning systems and compilers" }, "sources": [ { "citation": "TensorFlow: A system for large-scale machine learning", "url": "https://arxiv.org/abs/1605.08695v2" }, { "citation": "Abadi and colleagues: TensorFlow distributed stateful dataflow system — institutional research record", "url": "https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi" } ], "statement": "# Abadi and colleagues: TensorFlow distributed stateful dataflow system\n\nUnified computation and mutable shared state in dataflow graphs mapped over heterogeneous devices and distributed machines for training and inference.\n\n## Scientific scope\n\nYear denotes the OSDI system paper, not the 2015 release. Dataflow, parameter servers and automatic differentiation have earlier origins.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nMartín Abadi; Paul Barham; Jianmin Chen; Zhifeng Chen; Andy Davis; Jeffrey Dean; Matthieu Devin; Sanjay Ghemawat; Geoffrey Irving; Michael Isard; Manjunath Kudlur; Josh Levenberg; Rajat Monga; Sherry Moore; Derek G. Murray; Benoit Steiner; Paul Tucker; Vijay Vasudevan; Pete Warden; Martin Wicke; Yuan Yu; Xiaoqiang Zheng\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [TensorFlow: A system for large-scale machine learning](https://arxiv.org/abs/1605.08695v2)\n- [Abadi and colleagues: TensorFlow distributed stateful dataflow system — institutional research record](https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi)\n", "summary": "Unified computation and mutable shared state in dataflow graphs mapped over heterogeneous devices and distributed machines for training and inference.", "title": "Abadi and colleagues: TensorFlow distributed stateful dataflow system" }, "record_id": "urn:uuid:73fadc3b-8108-4458-ab4a-d602815c15c6", "version": 1, "source_ref": { "record_id": "urn:uuid:73fadc3b-8108-4458-ab4a-d602815c15c6", "version": 1, "content_hash": "sha256:fd502f409c2de9138129b3949ea3f515a9e5d898f1f86a3651c554fdc6199d3a" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:c6459f6d4beb801c2f5a13c19e3902e51497ffdc9ec83a8ad95c31df86500ecb" }, "target": { "record_id": "urn:uuid:73fadc3b-8108-4458-ab4a-d602815c15c6", "version": 1, "content_hash": "sha256:fd502f409c2de9138129b3949ea3f515a9e5d898f1f86a3651c554fdc6199d3a" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "abadi-et-al-tensorflow-system-2016", "entry_id": "abadi-et-al-tensorflow-system-2016", "revision": 1, "title": "Abadi and colleagues: TensorFlow distributed stateful dataflow system", "context": "Machine-learning systems and compilers", "track": "Human-designed research method or resource", "band": "A", "modifier": "+", "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Unified computation and mutable shared state in dataflow graphs mapped over heterogeneous devices and distributed machines for training and inference.", "scope": "Unified computation and mutable shared state in dataflow graphs mapped over heterogeneous devices and distributed machines for training and inference. Year denotes the OSDI system paper, not the 2015 release. Dataflow, parameter servers and automatic differentiation have earlier origins.", "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Year denotes the OSDI system paper, not the 2015 release. 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