You are viewing a plain text version of this content. The canonical link for it is here.
Posted to dev@ignite.apache.org by Alexey Zinoviev <za...@gmail.com> on 2019/10/31 12:45:49 UTC
H2O integration
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
Re: H2O integration
Posted by Alexey Zinoviev <za...@gmail.com>.
Yes, you are correct
чт, 31 окт. 2019 г., 20:29 Denis Magda <dm...@apache.org>:
> Basically it means a model trained with H2O can be transformed to Ignite’s
> one and executed in our cluster? Sorry if I’m still missing something.
>
> Denis
>
> On Thursday, October 31, 2019, Alexey Zinoviev <za...@gmail.com>
> wrote:
>
> > 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 <dm...@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.sin@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
> > >
> >
>
>
> --
> -
> Denis
>
Re: H2O integration
Posted by Denis Magda <dm...@apache.org>.
Basically it means a model trained with H2O can be transformed to Ignite’s
one and executed in our cluster? Sorry if I’m still missing something.
Denis
On Thursday, October 31, 2019, Alexey Zinoviev <za...@gmail.com>
wrote:
> 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 <dm...@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 <za...@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
> >
>
--
-
Denis
Re: H2O integration
Posted by Alexey Zinoviev <za...@gmail.com>.
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 <dm...@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 <za...@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
>
Re: H2O integration
Posted by Denis Magda <dm...@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 <za...@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