[
https://issues.apache.org/jira/browse/MAHOUT-1329?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Sergey Svinarchuk updated MAHOUT-1329:
--
Attachment: 1329-3-additional.diff
Mahout for hadoop 2
---
[
https://issues.apache.org/jira/browse/MAHOUT-1329?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Sergey Svinarchuk updated MAHOUT-1329:
--
Attachment: (was: 1329-3-additional.diff)
Mahout for hadoop 2
[
https://issues.apache.org/jira/browse/MAHOUT-1329?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Sergey Svinarchuk updated MAHOUT-1329:
--
Attachment: 1329-3-additional.patch
Mahout for hadoop 2
---
[
https://issues.apache.org/jira/browse/MAHOUT-1329?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13914484#comment-13914484
]
Sergey Svinarchuk commented on MAHOUT-1329:
---
I think that will be better add
[
https://issues.apache.org/jira/browse/MAHOUT-1329?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13914484#comment-13914484
]
Sergey Svinarchuk edited comment on MAHOUT-1329 at 2/27/14 1:21 PM:
[
https://issues.apache.org/jira/browse/MAHOUT-1329?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13914494#comment-13914494
]
Gokhan Capan commented on MAHOUT-1329:
--
Sure I can.
Although my vote would be
[
https://issues.apache.org/jira/browse/MAHOUT-1329?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13914508#comment-13914508
]
Sergey Svinarchuk commented on MAHOUT-1329:
---
You can use
{noformat}
mvn clean
[
https://issues.apache.org/jira/browse/MAHOUT-1426?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=13913488#comment-13913488
]
Maciej Mazur edited comment on MAHOUT-1426 at 2/27/14 2:41 PM:
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?
Peng,
Can you provide more details about your thought?
Regards,
2014-02-27 16:00 GMT-05:00 peng pc...@uowmail.edu.au:
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
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
Hi Yexi,
I was reading your code and found the MLP class is abstract-ish (both
train functions throws exception). Is there a thread or ticket for
shippable implementation?
Yours Peng
On Thu 27 Feb 2014 06:56:51 PM EST, peng wrote:
With pleasure! the original downpour paper propose a
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
I would like to start a conversation about where we want Mahout to be for
1.0. Let's suspend for the moment the question of how to achieve the
goals. Instead, let's converge on what we really would like to have happen
and after that, let's talk about means that will get us there.
Here are some
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 ted.dunn...@gmail.com:
This sounds good, but sounds like a whole different project or projects.
For example where does R appear from, what non-MR implementations, etc,
what is the no Hadoop implementation?
On Feb 28, 2014 12:38 AM, Ted Dunning ted.dunn...@gmail.com wrote:
I would like to start a conversation about
Well, Mahout has had (kinda sorta awful) classifiers and clustering from
day one. It isn't like the only goal is recommendations.
The non-MR, non-Hadoop comments are really more user centric requirements
than implementations. It is important that users be able to start without
a cluster and
Yes. Wasn't questioning the part about algorithms. I think each of several
other of these points are probably on their own several times the amount of
work that has been put into this project over the past year so I'm
wondering if this close to realistic as a to do list for 1.0 of this
project.
On Thu, Feb 27, 2014 at 5:25 PM, Sean Owen sro...@gmail.com wrote:
And whether the goal here should look more like polish
up and maintain.
That sounds like defeatism to me. I think that new things are quite
possible here.
On Thu, Feb 27, 2014 at 5:25 PM, Sean Owen sro...@gmail.com wrote:
I think each of several
other of these points are probably on their own several times the amount of
work that has been put into this project over the past year so I'm
wondering if this close to realistic as a to do list for
If we approach this form purely marketing standpoint, i would look at it
from two points: why is Mahout used, and why it is not used.
Mahout is not used because it is a collection of methods that are fairly
non-uniform in their api, especially embedded api, and generaly has zero
encouragement to
(5) Another thing i would suggest is to look at feature prep
standartization -- outlier detection, scaling, hash-tricking etc. etc.
Again, with abilities to customize, or it would be useless.
On Thu, Feb 27, 2014 at 6:08 PM, Dmitriy Lyubimov dlie...@gmail.com wrote:
If we approach this form
With the announcement of http://deeplearning4j.org yesterday which is various
Neural Networks implementations on Hadoop 2/JBlas that had been talked about in
one of the other discussion threads on this mailing list. Do we wanna duplicate
a similar effort in Mahout?
In addition to what
Thanks for starting the conversation, Ted. I'm relatively new to the
project though I've been using Mahout for a couple years in production, and
am happy to see things move forward in whatever way makes sense.
I think Mahout needs to ship a production-ready version if it's going to be
called
I agree with b) and c); haven't used seq2sparse enough to grok a).
On Thu, Feb 27, 2014 at 6:30 PM, Suneel Marthi suneel_mar...@yahoo.comwrote:
With the announcement of http://deeplearning4j.org yesterday which is
various Neural Networks implementations on Hadoop 2/JBlas that had been
Yes. THis is a big and important addition.
On Thu, Feb 27, 2014 at 6:19 PM, Dmitriy Lyubimov dlie...@gmail.com wrote:
(5) Another thing i would suggest is to look at feature prep
standartization -- outlier detection, scaling, hash-tricking etc. etc.
Again, with abilities to customize, or it
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
On Thu, Feb 27, 2014 at 7:01 PM, Andrew Musselman
andrew.mussel...@gmail.com wrote:
And I'm not sure if this is what Dmitriy meant in his comments (3), but I'd
love to be able to do Mathematica-style work in an interactive shell and/or
symbolic system where I could do A*B' and it just
28 matches
Mail list logo
