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Re: Dataflow programming on CPU and GPU using AI::MXNet

by Ea (Chaplain)
on Dec 19, 2019 at 13:45 UTC ( [id://11110390]=note: print w/replies, xml ) Need Help??


in reply to Dataflow programming on CPU and GPU using AI::MXNet

Do you think you will be able to make a series of posts about how to use AI::MXNet? It's something that I would appreciate reading.

I've been meaning to dip my toes into PDL as a way of working my way into Data Science-y things without needing to do everything according to Python recipes. I think Ovid's post has stirred up some interest in exploring and promoting tools in Perl. There's a lot out there in CPAN already. It just needs some love and blogging, by which I mean taking the time to explain how to do fun things with numbers, etc.

Which is just what we do.

such as 100 PDL exercises and 101 PDL exercises

Ea

Sometimes I can think of 6 impossible attributes before breakfast.

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Re^2: Dataflow programming on CPU and GPU using AI::MXNet
by bliako (Monsignor) on Dec 20, 2019 at 08:02 UTC
    Do you think you will be able to make a series of posts about how to use AI::MXNet?

    Yes, that's my intention. I will post here when I make more progress. The above was my first visit to MXNet-land. And I wanted to stress the fact that 1) you get GPU access on the side and 2) The underlying Dataflow "engine" by MXNet does parallelisation and lazy evaluation as it sees fit without the user being concerned, so this is quite higher up than more traditional ways to do such computations. And applications using these features do not have to be within neural networks...

    If anyone has any real applications outside neural networks, involving tensor/matrix/vector operations then we can work out a demo of the above 2 features. This (i mean non-neural-network apps) will expand the user-base of AI::MXNet

    Next stop neural networks. Possibly sooner than a month.

    bw, bliako

      PDL natively has forward dataflow (if you opt in by using $pdl->doflow - see the next PDL CPAN version for an updated PDL::Dataflow doc that lays all this out, or check out the GitHub version in Basic/Pod), and has for decades.

      Lazy evaluation currently only happens with slice-like operations, but there are plans to switch to lazy evaluation in order to have not only loop-fusion by creating custom operations that would e.g. do the a*b + c*d with only one round-trip from RAM through the CPU to RAM, but also GPU processing. See https://github.com/PDLPorters/pdl/issues/349 for discussion and/or to participate!

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