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?
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