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:
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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 RBM patch was withdrawn.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 RBM patch was withdrawn.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?
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