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Posted to dev@ignite.apache.org by Yury Babak <y....@gmail.com> on 2018/08/01 14:13:03 UTC
Re: [ML] Machine Learning Pipeline Improvement
Sure, https://issues.apache.org/jira/browse/IGNITE-9158.
Regards,
Yury
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Re: [ML] Machine Learning Pipeline Improvement
Posted by Alexey Zinoviev <za...@gmail.com>.
Dear Manu
it could be a great idea!
Could you please provide any examples of Apache Arrow integration for speed
up ML computation in another ML frameworks, it would be very helpful!
Sincerely yours
Alexey Zinovyev
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Re: [ML] Machine Learning Pipeline Improvement
Posted by Manu <ma...@hotmail.com>.
Hi, all!
Could be viable to integrate Apache Arrow to improve ML computation using
GPU?
Out of this thread, could be viable to integrate Apache Arrow to improve
Indexing computation using GPU?
Regards
https://rapids.ai <https://rapids.ai>
https://arrow.apache.org <https://arrow.apache.org>
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Re: [ML] Machine Learning Pipeline Improvement
Posted by Alexey Zinoviev <za...@gmail.com>.
The prototype of the API will look like that
PipelineMdl mdl = new Pipeline<Integer, Object[]> ()
.addFeatureExtractor(featureExtractor)
.addLabelExtractor(lbExtractor)
.addStage(new EncoderTrainer<Integer, Object[]>()
.withEncoderType(EncoderType.STRING_ENCODER)
.withEncodedFeature(1)
.withEncodedFeature(6))
.addStage(new ImputerTrainer<Integer, Object[]>())
.addStage(new MinMaxScalerTrainer<Integer,
Object[]>())
.addStage(new NormalizationTrainer<Integer,
Object[]>()
.withP(1))
.addFinalStage(new
DecisionTreeClassificationTrainer(5, 0))
.fit(ignite, dataCache);
Also, I've added separate ticket for the update of ParamGrid/CrossValidation
API to support tune hyperparameters not only in final trainers but in
intermideate preprocessing stages too.
https://issues.apache.org/jira/browse/IGNITE-9497
I suggest to add this feature in 2.8 because it doesn't change the current
API of algorithms and has no serialized issues
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