Peng, Can you provide more details about your thought?
Regards, 2014-02-27 16:00 GMT-05:00 peng <[email protected]>: > That should be easy. But that defeats the purpose of using mahout as there > are already enough implementations of single node backpropagation (in which > case GPU is much faster). > > Yexi: > > Regarding downpour SGD and sandblaster, may I suggest that the > implementation better has no parameter server? It's obviously a single > point of failure and in terms of bandwidth, a bottleneck. I heard that > MLlib on top of Spark has a functional implementation (never read or test > it), and its possible to build the workflow on top of YARN. Non of those > framework has an heterogeneous topology. > > Yours Peng > > > On Thu 27 Feb 2014 09:43:19 AM EST, Maciej Mazur (JIRA) wrote: > >> >> [ https://issues.apache.org/jira/browse/MAHOUT-1426?page= >> com.atlassian.jira.plugin.system.issuetabpanels:comment- >> tabpanel&focusedCommentId=13913488#comment-13913488 ] >> >> Maciej Mazur edited comment on MAHOUT-1426 at 2/27/14 2:41 PM: >> --------------------------------------------------------------- >> >> I've read the papers. I didn't think about distributed network. I had in >> mind network that will fit into memory, but will require significant amount >> of computations. >> >> I understand that there are better options for neural networks than map >> reduce. >> How about non-map-reduce version? >> I see that you think it is something that would make a sense. (Doing a >> non-map-reduce neural network in Mahout would be of substantial >> interest.) >> Do you think it will be a valueable contribution? >> Is there a need for this type of algorithm? >> I think about multi-threded batch gradient descent with pretraining (RBM >> or/and Autoencoders). >> >> I have looked into these old JIRAs. RBM patch was withdrawn. >> "I would rather like to withdraw that patch, because by the time i >> implemented it i didn't know that the learning algorithm is not suited for >> MR, so I think there is no point including the patch." >> >> >> was (Author: maciejmazur): >> I've read the papers. I didn't think about distributed network. I had in >> mind network that will fit into memory, but will require significant amount >> of computations. >> >> I understand that there are better options for neural networks than map >> reduce. >> How about non-map-reduce version? >> I see that you think it is something that would make a sense. >> Do you think it will be a valueable contribution? >> Is there a need for this type of algorithm? >> I think about multi-threded batch gradient descent with pretraining (RBM >> or/and Autoencoders). >> >> I have looked into these old JIRAs. RBM patch was withdrawn. >> "I would rather like to withdraw that patch, because by the time i >> implemented it i didn't know that the learning algorithm is not suited for >> MR, so I think there is no point including the patch." >> >> GSOC 2013 Neural network algorithms >>> ----------------------------------- >>> >>> Key: MAHOUT-1426 >>> URL: https://issues.apache.org/jira/browse/MAHOUT-1426 >>> Project: Mahout >>> Issue Type: Improvement >>> Components: Classification >>> Reporter: Maciej Mazur >>> >>> I would like to ask about possibilites of implementing neural network >>> algorithms in mahout during GSOC. >>> There is a classifier.mlp package with neural network. >>> I can't see neighter RBM nor Autoencoder in these classes. >>> There is only one word about Autoencoders in NeuralNetwork class. >>> As far as I know Mahout doesn't support convolutional networks. >>> Is it a good idea to implement one of these algorithms? >>> Is it a reasonable amount of work? >>> How hard is it to get GSOC in Mahout? >>> Did anyone succeed last year? >>> >> >> >> >> -- >> This message was sent by Atlassian JIRA >> (v6.1.5#6160) >> > -- ------ Yexi Jiang, ECS 251, [email protected] School of Computer and Information Science, Florida International University Homepage: http://users.cis.fiu.edu/~yjian004/
