Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/17894
gentle ping @VinceShieh for @WeichenXu123's comment.
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Github user AmplabJenkins commented on the issue:
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Github user AmplabJenkins commented on the issue:
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Github user WeichenXu123 commented on the issue:
https://github.com/apache/spark/pull/17894
I am also interested in implementation by level-3 BLAS. Can you post a
design doc first?
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Github user VinceShieh commented on the issue:
https://github.com/apache/spark/pull/17894
@sethah yes, we only take 100 samples and trained with 3 iterations,
numClasss is 20 of our test dataset for single node testing.
Yeah, I also believe it'd have a better result if it's
Github user sethah commented on the issue:
https://github.com/apache/spark/pull/17894
@VinceShieh Thanks for posting your results. You tested these on datasets
with only 100 samples correct? That's probably not a representative use case of
a normal workload... Also, how many classes
Github user VinceShieh commented on the issue:
https://github.com/apache/spark/pull/17894
Forgot to mention, we observed a nearly 2x performance gain with the help
of nativeBLAS- MKL, without a fine tuning, so if we can also make F2J version
run faster in distributed cluster than the
Github user VinceShieh commented on the issue:
https://github.com/apache/spark/pull/17894
sorry for late update!
we tested on this PR against the current implementation with both dense and
sparse(0.95 sparsity):
Github user VinceShieh commented on the issue:
https://github.com/apache/spark/pull/17894
@sethah Sorry for the late response. Setting as WIP. We have performance
data for dense features, data for the sparse feature will be ready soon. thanks.
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