{"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/theano-mpi-a-theano-based-distributed","title":"Theano-MPI: a Theano-based Distributed Training Framework","arxiv_id":"1605.08325","date":"2016-05-26","proceeding":null,"authors":["He Ma","Fei Mao","Graham W. Taylor"],"abstract":"We develop a scalable and extendable training framework that can utilize GPUs\nacross nodes in a cluster and accelerate the training of deep learning models\nbased on data parallelism. Both synchronous and asynchronous training are\nimplemented in our framework, where parameter exchange among GPUs is based on\nCUDA-aware MPI. In this report, we analyze the convergence and capability of\nthe framework to reduce training time when scaling the synchronous training of\nAlexNet and GoogLeNet from 2 GPUs to 8 GPUs. In addition, we explore novel ways\nto reduce the communication overhead caused by exchanging parameters. Finally,\nwe release the framework as open-source for further research on distributed\ndeep learning","url_abs":"http://arxiv.org/abs/1605.08325v1","url_pdf":"http://arxiv.org/pdf/1605.08325v1.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":"theano-mpi-a-theano-based-distributed","repo_url":"https://github.com/uoguelph-mlrg/Theano-MPI","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"auxiliary-classifier","method_name":"Auxiliary Classifier"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"googlenet","method_name":"GoogLeNet"},{"method_slug":"inception-module","method_name":"Inception Module"},{"method_slug":"local-response-normalization","method_name":"Local Response Normalization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}