{"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/accurate-large-minibatch-sgd-training","title":"Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour","arxiv_id":"1706.02677","date":"2017-06-08","proceeding":null,"authors":["Priya Goyal","Piotr Dollár","Ross Girshick","Pieter Noordhuis","Lukasz Wesolowski","Aapo Kyrola","Andrew Tulloch","Yangqing Jia","Kaiming He"],"abstract":"Deep learning thrives with large neural networks and large datasets. However,\nlarger networks and larger datasets result in longer training times that impede\nresearch and development progress. Distributed synchronous SGD offers a\npotential solution to this problem by dividing SGD minibatches over a pool of\nparallel workers. Yet to make this scheme efficient, the per-worker workload\nmust be large, which implies nontrivial growth in the SGD minibatch size. In\nthis paper, we empirically show that on the ImageNet dataset large minibatches\ncause optimization difficulties, but when these are addressed the trained\nnetworks exhibit good generalization. Specifically, we show no loss of accuracy\nwhen training with large minibatch sizes up to 8192 images. To achieve this\nresult, we adopt a hyper-parameter-free linear scaling rule for adjusting\nlearning rates as a function of minibatch size and develop a new warmup scheme\nthat overcomes optimization challenges early in training. With these simple\ntechniques, our Caffe2-based system trains ResNet-50 with a minibatch size of\n8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using\ncommodity hardware, our implementation achieves ~90% scaling efficiency when\nmoving from 8 to 256 GPUs. Our findings enable training visual recognition\nmodels on internet-scale data with high efficiency.","url_abs":"http://arxiv.org/abs/1706.02677v2","url_pdf":"http://arxiv.org/pdf/1706.02677v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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