2013/3/5 Kenneth C. Arnold <kcarn...@seas.harvard.edu>:
> It was a pretty easy build on Mac -- I just used MacPorts to install and
> select an llvm. Of course Anaconda is even easier.
>
> I'd say Numba is a medium-term consideration. It's enough trouble getting
> everybody using C compilers, so adding LLVM to the mix is probably way too
> much of a change for the short term. Not to mention that Numba is still
> young, e.g., its error messages were totally awful when I tried it a couple
> of weeks ago. But in there are a number of very interesting advantages in
> the medium term:
>
> 1. It will likely become easier to get an LLVM toolchain installed,
> potentially even easier than other compiler toolchains, if tools like
> Anaconda take off. Numba could probably be built as a self-contained binary
> package too.
>
> 2. Numba will allow greater just-in-time specialization (through on-demand
> compilation) and optimization (through JIT) than Cython.
>
> 3. Code will be more readable and maintainable, since it's just Python. This
> will reduce the barrier for optimized implementations.
>
> 4. It looks like they're going to start targeting GPUs too, so we'd get that
> basically free.


I agree. If people are interested, please feel free to start
experimenting with implementations of standard machine learning using
numba and following the scikit-learn coding guidelines and API.

That will make it easier for future discussions to have accumulated
some experience on a real sklearn-like code base.

-- 
Olivier
http://twitter.com/ogrisel - http://github.com/ogrisel

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