{"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-eager-a-multi-stage-python","title":"TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning","arxiv_id":"1903.01855","date":"2019-02-27","proceeding":null,"authors":["Akshay Agrawal","Akshay Naresh Modi","Alexandre Passos","Allen Lavoie","Ashish Agarwal","Asim Shankar","Igor Ganichev","Josh Levenberg","Mingsheng Hong","Rajat Monga","Shanqing Cai"],"abstract":"TensorFlow Eager is a multi-stage, Python-embedded domain-specific language\nfor hardware-accelerated machine learning, suitable for both interactive\nresearch and production. TensorFlow, which TensorFlow Eager extends, requires\nusers to represent computations as dataflow graphs; this permits compiler\noptimizations and simplifies deployment but hinders rapid prototyping and\nrun-time dynamism. TensorFlow Eager eliminates these usability costs without\nsacrificing the benefits furnished by graphs: It provides an imperative\nfront-end to TensorFlow that executes operations immediately and a JIT tracer\nthat translates Python functions composed of TensorFlow operations into\nexecutable dataflow graphs. TensorFlow Eager thus offers a multi-stage\nprogramming model that makes it easy to interpolate between imperative and\nstaged execution in a single package.","url_abs":"http://arxiv.org/abs/1903.01855v1","url_pdf":"http://arxiv.org/pdf/1903.01855v1.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-eager-a-multi-stage-python","repo_url":"https://github.com/tensorflow/tensorflow","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.01855","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}