{"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/an-mpi-based-python-framework-for-distributed","title":"An MPI-Based Python Framework for Distributed Training with Keras","arxiv_id":"1712.05878","date":"2017-12-16","proceeding":null,"authors":["Dustin Anderson","Jean-Roch Vlimant","Maria Spiropulu"],"abstract":"We present a lightweight Python framework for distributed training of neural\nnetworks on multiple GPUs or CPUs. The framework is built on the popular Keras\nmachine learning library. The Message Passing Interface (MPI) protocol is used\nto coordinate the training process, and the system is well suited for job\nsubmission at supercomputing sites. We detail the software's features, describe\nits use, and demonstrate its performance on systems of varying sizes on a\nbenchmark problem drawn from high-energy physics research.","url_abs":"http://arxiv.org/abs/1712.05878v1","url_pdf":"http://arxiv.org/pdf/1712.05878v1.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":"an-mpi-based-python-framework-for-distributed","repo_url":"https://github.com/SelimaC/large-scale-sparse-neural-networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}