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XGBoost Performance Issues

See original GitHub issue


I ran some JMH benchmarks that show MLeap to be significantly slower than other libraries for evaluating XGBoost models.


Here you can see throughput (ops / sec) as a function of library and batch size, where:

xgboost4j = xgboost-predictor-java = yelp-xgboost = mleap =

Given that Mleap makes use of xgboost4j-spark does anyone know why it would have half the throughput of xgboost4j? Also, is there a reason why mleap does not observe constant throughput scaling like xgboost4j does?

Thanks! -Ryan

Issue Analytics

  • State:closed
  • Created 4 years ago
  • Reactions:5
  • Comments:12 (9 by maintainers)

github_iconTop GitHub Comments

ancasarbcommented, Feb 4, 2020

That sounds like a good plan @lucagiovagnoli.

lucagiovagnolicommented, Feb 1, 2020

Hi Anca! So, we’ve run some tests above and noticed that xgboost4j is much slower than xgboost-predictor-java 😦 Historically we’ve used a fork of xgboost-predictor at Yelp (yelp-xgboost) so we’re hitting a performance issue when running MLeap cause xgboost4j seems thousands of times slower 😕

  • I was thinking to make a PR to allow deserializing the model binary as either an xgboost-predictor OR xgboost4j (based users’ preference). Fortunately it seems that xgboost-predictor supports loading from xgboost4j binaries, see the ModelReader. I just wanted to check what you think before we get to it.

PS: I also noticed that @hollinwilkins looked into xgboost-predictor in the past, he commented on the xgboost-predictor project about deploying it to Maven Central (comment here). I wonder if they considered that rather than xgboost4j and why it didn’t work out ?


Read more comments on GitHub >

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