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Frank McQuillan reassigned MADLIB-1268: --------------------------------------- Assignee: Frank McQuillan > 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: v2.0 > > > Story > `As a MADlib developer` > I want investigate convergence behaviour 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). > * Does not need to use an aggregate for this spike, if that is too > inconvenient, since performance is not the focus of this story. It's about > convergence. > * In defining the merge function, review [2] for single-server, multi-GPU > merge function. Perhaps we can do the exact same thing for multi-server? > * For dataset, consider MNIST and/or CIFAR-10. > * See page 11 of [8] re synchronous data parallel in TF > 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 [2] or > something else? Does it operate on weights only or does it need gradients? > 3) What does the architecture look like? Draw a diagram showing sync/merge > step for distributed model training. > 4) What tests do we need to do to convince ourselves that the architecture is > valid? > 5) Do we need to write different merge functions, or have a different > approach, for each different neural net type algorithm? Or is there a > general approach that we can use that will apply to this class of algorithms? > References > [2] 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 > [5] 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/ > [6] Why are GPUs necessary for training Deep Learning models? > https://www.analyticsvidhya.com/blog/2017/05/gpus-necessary-for-deep-learning/ > [7] Deep Learning vs Classical Machine Learning > https://towardsdatascience.com/deep-learning-vs-classical-machine-learning-9a42c6d48aa > [8] TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed > Systems > https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf -- This message was sent by Atlassian JIRA (v7.6.3#76005)