I have one final question. I've mixed feelings about this discussion. You are saying that there is no point in doing mapreduce implementation of neural netoworks (with pretraining). Then you are thinking that non map reduce would of substatial interest. On the other hand you say that it would be easy and it beats the purpose of doing it of doing it on mahout (because it is not a mr version). Finally you are saying that building something simple and working is a good thing.
I do not really know what to think about it. Could you give me some advice whether I should write a proposal or not? (And if I should: Should I propose MapReduce or not MapReduce verison? There is already NN algorithm but without pretraining.) Thanks, Maciej Mazur On Fri, Feb 28, 2014 at 5:44 AM, peng <[email protected]> wrote: > Oh, thanks a lot, I missed that one :) > +1 on easiest one implemented first. I haven't think about difficulty > issue, need to read more about YARN extension. > > Yours Peng > > > On Thu 27 Feb 2014 08:06:27 PM EST, Yexi Jiang wrote: > >> Hi, Peng, >> >> Do you mean the MultilayerPerceptron? There are three 'train' method, and >> only one (the one without the parameters trackingKey and groupKey) is >> implemented. In current implementation, they are not used. >> >> Regards, >> Yexi >> >> >> 2014-02-27 19:31 GMT-05:00 Ted Dunning <[email protected]>: >> >> Generally for training models like this, there is an assumption that >>> fault >>> tolerance is not particularly necessary because the low risk of failure >>> trades against algorithmic speed. For reasonably small chance of >>> failure, >>> simply re-running the training is just fine. If there is high risk of >>> failure, simply checkpointing the parameter server is sufficient to allow >>> restarts without redundancy. >>> >>> Sharding the parameter is quite possible and is reasonable when the >>> parameter vector exceed 10's or 100's of millions of parameters, but >>> isn't >>> likely much necessary below that. >>> >>> The asymmetry is similarly not a big deal. The traffic to and from the >>> parameter server isn't enormous. >>> >>> >>> Building something simple and working first is a good thing. >>> >>> >>> On Thu, Feb 27, 2014 at 3:56 PM, peng <[email protected]> wrote: >>> >>> With pleasure! the original downpour paper propose a parameter server >>>> >>> from >>> >>>> which subnodes download shards of old model and upload gradients. So if >>>> >>> the >>> >>>> parameter server is down, the process has to be delayed, it also >>>> requires >>>> that all model parameters to be stored and atomically updated on (and >>>> fetched from) a single machine, imposing asymmetric HDD and bandwidth >>>> requirement. This design is necessary only because each -=delta >>>> operation >>>> has to be atomic. Which cannot be ensured across network (e.g. on HDFS). >>>> >>>> But it doesn't mean that the operation cannot be decentralized: >>>> >>> parameters >>> >>>> can be sharded across multiple nodes and multiple accumulator instances >>>> >>> can >>> >>>> handle parts of the vector subtraction. This should be easy if you >>>> >>> create a >>> >>>> buffer for the stream of gradient, and allocate proper numbers of >>>> >>> producers >>> >>>> and consumers on each machine to make sure it doesn't overflow. >>>> Obviously >>>> this is far from MR framework, but at least it can be made homogeneous >>>> >>> and >>> >>>> slightly faster (because sparse data can be distributed in a way to >>>> minimize their overlapping, so gradients doesn't have to go across the >>>> network that frequent). >>>> >>>> If we instead using a centralized architect. Then there must be >=1 >>>> >>> backup >>> >>>> parameter server for mission critical training. >>>> >>>> Yours Peng >>>> >>>> e.g. we can simply use a producer/consumer pattern >>>> >>>> If we use a producer/consumer pattern for all gradients, >>>> >>>> On Thu 27 Feb 2014 05:09:52 PM EST, Yexi Jiang wrote: >>>> >>>> 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) >>>>>>> >>>>>>> >>>>>>> >>>>>> >>>>> >>>>> >>> >> >> >>
