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Frank McQuillan commented on MADLIB-1268: ----------------------------------------- We will go with a basic model weight averaging for 1.16 release. Later releases will look at other model merge techniques to improve convergence as a function of iterations and time. > Spike - CNN convergence, data parallel with merge > ------------------------------------------------- > > Key: MADLIB-1268 > URL: https://issues.apache.org/jira/browse/MADLIB-1268 > Project: Apache MADlib > Issue Type: New Feature > Components: Deep Learning > Reporter: Frank McQuillan > Assignee: Frank McQuillan > Priority: Major > Fix For: v1.16 > > Attachments: CIFAR-10 validation accuracy by iteration.png, > screenshot-1.png, screenshot-2.png, training time vs accuracy for different > cluster sizes.png > > > Story > `As a MADlib developer` > I want investigate convergence behavior when running a single distributed CNN > model across the Greenplum cluster using Keras with a Tensorflow backend > `so that` > I can see if it converges in a predictable and expected way. > Details > * By "single distributed CNN model" I mean data parallel with merge (not > model parallel) [6,7,8]. > * In defining the merge function, review [1] for single-server, multi-GPU > merge function, or use standard MADlib weighted average approach. > * For dataset, consider MNIST and/or CIFAR-10. A bigger data set like Places > http://places2.csail.mit.edu/ may also be useful. > Acceptance > 1) Plot characteristic curves of loss vs. iteration number. Compare with > MADlib merge (this story) vs. without merge. > 2) Define what the merge function is for CNN. Is it the same as [1] or > something else? Does it operate on weights only or does it need gradients? > References > [1] Check for “# Merge outputs under expected scope” section in the python > program > > https://github.com/keras-team/keras/blob/bf1378f39d02b7d0b53ece5458f9275ac8208046/keras/utils/multi_gpu_utils.py > [2] Single Machine Data Parallel multi GPU Training > https://www.pyimagesearch.com/2017/10/30/how-to-multi-gpu-training-with-keras-python-and-deep-learning/ > [3] Why are GPUs necessary for training Deep Learning models? > https://www.analyticsvidhya.com/blog/2017/05/gpus-necessary-for-deep-learning/ > [4] Deep Learning vs Classical Machine Learning > https://towardsdatascience.com/deep-learning-vs-classical-machine-learning-9a42c6d48aa > [5] TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed > Systems > https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf > [6] Demystifying Parallel and Distributed Deep Learning: An In-Depth > Concurrency Analysis > https://arxiv.org/pdf/1802.09941.pdf > * see section 7.4.2 for discussion on model averaging > [7] Deep learning with Elastic Averaging SGD > https://papers.nips.cc/paper/5761-deep-learning-with-elastic-averaging-sgd.pdf > * use momentum and Nesterov methods in model averaging computation > [8] SCALABLE TRAINING OF DEEP LEARNING MACHINES BY INCREMENTAL BLOCK > TRAINING WITH INTRA-BLOCK PARALLEL OPTIMIZATION AND BLOCKWISE > MODEL-UPDATE FILTERING > https://www.microsoft.com/en-us/research/wp-content/uploads/2016/08/0005880.pdf > * similar to [7], uses momentum methods -- This message was sent by Atlassian JIRA (v7.6.3#76005)