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You aren't making a fair comparison here. Flux is more generic, flexible and thus harder to build. Julia could have been there already if the small ML team w
by amkkma 6y ago
You aren't making a fair comparison here.
Flux is more generic, flexible and thus harder to build. Julia could have been there already if the small ML team w was working with TF type design goals. But they aren't, and therefore are ahead of TF/pytorch in many respects.
Also not as much effort has gone into making this polished for standard deep learning stuff, but you can see the fruit in things like reusing the same code for ML on holomophically encrypted data, faster than pytorch/tf deep learning on graphs and 3d inputs, faster, easier composability with ODEs and prob programming etc.
- eigenspace 6y agoA more fair comparison is KNet.jl which is a very worthy competitor to things like TensorFlow or PyTorch with comparable speed and roughly equivalent flexibility. It's just not talked about as much by Julia people because what most Julia people really care about is the flexibility and composability of Flux, so they're willing to forgive it lagging a bit in raw performance (as long as they believe it'll catch up eventually).