Thanks, Denis, no, the initial H2O integration is not the same as TensorFlow integration, it perform the prediction phase (than you have trained model) nor training as I mentioned "As a result we could predict something on Ignite data via the model trained in H2O system."
It's the same as XGBost and Spark ML integration, but I hope that we could extend our integration here and provide something like Ignite data-source for training in H2O in the future. чт, 31 окт. 2019 г. в 16:48, Denis Magda <dma...@apache.org>: > Hi Alexey, > > That’s great to see that our ML and DL capabilities keep growing via > integrations! > > Is it correct to say that H2O uses Ignite as a data source in a way similar > to what we did for TensorFlow? > > Btw, do you have any plans to spread the word about the additions through > blogging? H2O, Spark ML, XGBoost deserve their own articles. > > Denis > > On Thursday, October 31, 2019, Alexey Zinoviev <zaleslaw....@gmail.com> > wrote: > > > Hi, Igniters, > > I happy to announce the new part of Ignite ML functionality. > > > > The integration with the popular distributed library H2O > > https://github.com/h2oai was added. > > As a result we could predict something on Ignite data via the model > trained > > in H2O system. > > > > Many thanks to Michal Kurka (https://github.com/michalkurka), waiting > him > > on our dev-list, hope that he will help with the backward integration > from > > Ignite to H2O system. > > > > Remind, that now we have integrations with XGBoost, TensorFlow, Spark ML > > via SparkMlParser and MLeap integration and waiting for the new > > integrations with Dl4j or PyTorch for example > > > > Sincerely yours, > > Alexey Zinoviev > > > > > -- > - > Denis >
