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.
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.
We (Python maintenance team) already did a similar thing with
fedora-python-tox:
https://github.com/fedora-python/fedora-python-tox
Do you know about some good examples we can start with?
LumÃr
>
> peace o/
>
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