NumPyPy vs Theano?

Question

I am wondering: do these two projects basically have the same goal -- to speed up numerical work in Python?

What are the similarities and differences?

I know that Theano does not aim to re-implement all of NumPy like NumPyPy does, but from what I've read, Theano can already lead to some really impressive speedup results. So why do we need NumPyPy if we can just write code for Theano that runs fast?

Answer 1

Well for one thing : millions of lines of code use numpy, so porting Numpy to pypy would be a great step forward for the porting of many other (scientific and other) librairies to Pypy.

Re-implementing all Numpy in pypy may sound like a chore, and it is, but the alternative in just insane : re-implementing hundreds or librairies to use XXX instead.

And by the way I don't know theano really well, but I know it isn't a substitute for Numpy. they are different projects, with different features.

Answer 2

Theano, seeks to improve NumPy, NumPy is a prerequisite for Theano.

A large feature of Theano, is it's transparent use of CUDA GPU's, when possible.