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I think this is cool... but... personally, I feel that PyTorch’s API is more or less where I want a deep learning API to be. TensorFlow is too low level. Fasta
by king_magic 7y ago
I think this is cool... but... personally, I feel that PyTorch’s API is more or less where I want a deep learning API to be.
TensorFlow is too low level. Fastai abstracts too much away and feels way too magical - once things start going wrong, or if you need to do something totally outside the bounds of the library, good luck.
Guess I’m mostly just a bit tired of frameworks for what feels like frameworks sake.
- jszymborski 7y agoFor 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.
- dillonmckay 7y agoCan 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.
- jszymborski 7y agoI'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.
- zneveu 7y agoI feel the same way about Fastai v1. Hard to use bits and pieces of it without rewriting everything. Apparently they heard this and addressed it with fastai V2 though so you can just pull in the critical bits you need.