Fastai: A Layered API for Deep Learning

Re:Fastai: A Layered API for Deep Learning by jszymborski on Friday February 14, 2020 @08:51AM (#22326456)

For most cases, objects in the fastai framework inherit pytorch objects and are often rather thin. This means that you can usually roll your objects if the fastai ones aren't sufficient. This really help alleviate the feeling that you're "on rails" of the library.

That being said, a current project I'm working on didn't really fit the fastai pipeline and I needed to drop into pytorch. It's a total shame since there are so many little niceties that make things easier.

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Re:Fastai: A Layered API for Deep Learning by dillonmckay on Friday February 14, 2020 @08:55AM (#22326498)

Can you go into more details on why pytorch was a better fit for your particular use-case?

My coworker recently completed a bootcamp using pytorch, and I am working through the fastai course, so it has been interesting to compare experiences.

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Re:Fastai: A Layered API for Deep Learning by jszymborski on Friday February 14, 2020 @02:56PM (#22329756)

I'm training a semi-supervised task which consumes three text samples while training. In retrospect, I might have been able to concatenate them, and treat them as one, but as it was I wasn't able to implement some of the functions fastai's Databunch required.

Might give it another go if I decide to give it another refactor.

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