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Comment by amelius | Hacker News Reader

What is so appealing about a delayed execution model? Why can't we just perform tensor math as in numpy, and let the library figure out the fastest way to do it behind the scenes? I think the whole "graph" approach is making things needlessly complicated.

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Check out our roadmap: https://deeplearnjs.org/docs/roadmap.html (we are working on eager mode / define-by-run computation)

That would be nice for experimentation but for production use cases knowing the whole graph unlocks a number of optimizations.

This is a meme that keeps getting repeated, and I don't know why. Tensorflow, for example, despite several years of development, does basically little to no graph optimizations and for tons of tasks ends up much slower than PyTorch / Chainer / DyNet (Tensorflow is developing a "JIT compiler" but it is still in alpha).

It goes without saying that a framework that does define-by-run also knows the whole graph.

Author of deeplearnjs here. We hear you, and we 100% agree. Stay tuned.

That's great to hear.

By the way, if you'd make your interface more general than deep learning, your library could be the start of an alternative for numpy/scipy on JS, and it would be even faster than the original Python version because it uses the GPU. Just a thought ...

(One small downside is that JS doesn't have the nice operator overloading that Python has, afaik)

We call ourselves deeplearn.js, but you can use it for general linear algebra! Our NDArrayMath layer is analogous to NumPy, and we support a large subset of it (we support many of the linear algebra kernels, broadcasting, axis reduction, etc).

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