On Thu, Aug 22, 2019 at 3:37 AM Lumir Balhar <lbalhar(a)redhat.com> wrote:
Hello.
On 8/21/19 3:37 PM, Michael McCune wrote:
> hi all,
>
> i am writing today to get a sense for how the group feels about
> creating content that focuses on containerized machine learning
> workflows on fedora.
>
> i have been creating cloud native ml applications for a few years now
> and most of my work begins on fedora inside of containers. from the
> wiki page[0], it seems apparent that one of the major focuses of this
> group is around packaging various frameworks and tools for fedora, but
> i am curious how the sig feels about embracing a more container
> focused approach to machine learning development on fedora.
>
> i think the initial outputs from a push like this could easily see us
> creating some tutorial content and fedora based images for doing work
> that uses modern machine learning frameworks (eg spark, tensorflow,
> etc). i also think that focusing on containers gives us an easier way
> to tell stories about machine learning on silverblue and fedora
> coreos.
>
> with that said, what does the group think about this?
I think that this is a great idea.
One of our goals is to gather success stories about how the Fedora is
used in AI/ML industry and write some blog posts about it with a "hello
world" examples using the tools already available in Fedora.
so, i guess what i am proposing are stories about how a fedora user
can unlock new power through the use of podman and buildah ;)
If we identify that some of the working pipelines make sense also as
a
containerized Fedora instance we can definitely create and maintain a
ready-to-use container and use that also in blog posts mentioned before
to simplify things and/or offer a different approach how to get it working.
a "ready-to-use" container sounds like an interesting idea, i'd love
to hear more thoughts about that.
We (Python maintenance team) already did a similar thing with
fedora-python-tox:
https://github.com/fedora-python/fedora-python-tox
i will check this out
Do you know about some good examples we can start with?
indeed, i do. i contribute to a community (radanalytics.io) that
focuses on machine learning on openshift. we containerize tools like
apache spark, and then demonstrate how to create application
pipelines. much of the spark work i do begins on fedora using these
containers from the community. although spark is not packaged for
fedora (for good reason), we can still utilize it by running the
community images (centos based).
if this style of workflow is useful to the fedora community, i can
definitely write some fedora-specific blogs about how users can run
spark-based machine learning workflows. i guess my larger question is,
is it appropriate for us to demonstrate workflows that use tools which
aren't packaged for fedora?
peace o/
> LumÃr
> >
> > peace o/
> >
> > [0]
https://fedoraproject.org/wiki/SIGs/ML
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