{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tensorflow-a-system-for-large-scale-machine","title":"TensorFlow: A system for large-scale machine learning","arxiv_id":"1605.08695","date":"2016-05-27","proceeding":null,"authors":["Martín 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"],"abstract":"TensorFlow is a machine learning system that operates at large scale and in\nheterogeneous environments. TensorFlow uses dataflow graphs to represent\ncomputation, shared state, and the operations that mutate that state. It maps\nthe nodes of a dataflow graph across many machines in a cluster, and within a\nmachine across multiple computational devices, including multicore CPUs,\ngeneral-purpose GPUs, and custom designed ASICs known as Tensor Processing\nUnits (TPUs). This architecture gives flexibility to the application developer:\nwhereas in previous \"parameter server\" designs the management of shared state\nis built into the system, TensorFlow enables developers to experiment with\nnovel optimizations and training algorithms. TensorFlow supports a variety of\napplications, with particularly strong support for training and inference on\ndeep neural networks. Several Google services use TensorFlow in production, we\nhave released it as an open-source project, and it has become widely used for\nmachine learning research. In this paper, we describe the TensorFlow dataflow\nmodel in contrast to existing systems, and demonstrate the compelling\nperformance that TensorFlow achieves for several real-world applications.","url_abs":"http://arxiv.org/abs/1605.08695v2","url_pdf":"http://arxiv.org/pdf/1605.08695v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tensorflow-a-system-for-large-scale-machine","repo_url":"https://github.com/tensorflow/tensorflow","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"tensorflow-a-system-for-large-scale-machine","repo_url":"https://github.com/Allen-Czyysx/Paper-Reading","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"management","task_name":"Management"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1605.08695","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.08695"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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