Hey Ryan, Great progress! This is one of the projects that I believe is quite useful for IoT and cloud latency mitigation in RT apps, so exited to see development on this front.
A couple of notes that may help: 1) If you haven't tried yet, you can play a bit with the different dependencies to get an even smaller footprint. I.e. explore other BLAS/LAPACK implementations. 2) Disk footprint is composed by library size (~4MB in your case) + model size. I've recently discovered that some models (when saved) are 10x bigger than let's say sklearn models. Maybe it's a stretch goal, but you could probably explore how to downsize serialized models as well. After all, a real world application would need both the library and the model to fit into i.e. 10MB. Please keep us updated, amazing work! Regards, German ________________________________ From: mlpack <[email protected]> on behalf of Ryan Birmingham <[email protected]> Sent: Wednesday, May 27, 2020 10:19 AM To: Omar Shrit <[email protected]> Cc: [email protected] <[email protected]> Subject: Re: [mlpack] GSOC'20 mlpack on constrained devices - weekly updates Cool project and update! Thanks for sharing! -Ryan Birmingham On Wed, May 27, 2020 at 12:38 PM Omar Shrit <[email protected]<mailto:[email protected]>> wrote: Hello everyone, You can find here my weekly updates on my GSOC project for the last week. https://shrit.me/blog/ Best regards, Omar _______________________________________________ mlpack mailing list [email protected]<mailto:[email protected]> http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
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