Jython is definitely not an option. I have found Py4J
<http://py4j.sourceforge.net/> relatively usable when needing to call Java
from Python; I gather it can do the opposite too. There's still some
network overhead and memory copying.
On 4 July 2014 03:00, Eustache DIEMERT <[email protected]> wrote:
> For a new project I'm researching different options for integrating
> machine learned models into a stream processing system based on [1].
>
> I've considered other frameworks (Spark MLLib for instance) but so far it
> seems to me none of them is as mature and agile as sklearn. Neither do I
> have enormous scalability needs.
>
> So one of the options would be to have a "prediction server" that is
> called over, say, HTTP from the processing pipeline. I've done that in the
> past using Cyclone [2]. The main drawback is transporting the features data
> over HTTP which can be quite network / marshaling heavy.
>
> Another option is to call the prediction from the processing code
> directly, which is in Java. I consider making a native call to the python
> interpreter with a suspicious eye as I've encountered many disappointments
> in the past with such settings (horrible performance, difficult error
> handling...)
>
> Jython for sklearn seems irrealistic at first sight.
>
> So I'm wondering if there are other viable options...
>
> Any comment or feedback welcome :)
>
> Eustache
>
> [1] https://storm.incubator.apache.org/
> [2] http://cyclone.io/ https://twistedmatrix.com
>
>
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