8 ms·

Exciting to hear -- I have used Julia for prototyping, and have found it to be excellent for that: In my experience there are still some rough edges as compare

by preygel 10y ago

Exciting to hear -- I have used Julia for prototyping, and have found it to be excellent for that: In my experience there are still some rough edges as compared to the Python ecosystem (of course!), which together with the 0.x status make it impractical for many production situations. However it is fantastic for prototyping numerical code, the type system is a pleasure, and the JuMP mathematical optimization library is a gem. Being able to have fast code be "first class," as opposed to the impedance mismatch of dealing with numba/Cython, feels great and is a real boon for trying new things. Then, it is fairly straightforward to port the final solution to whatever production language you use (e.g., Python with a sprinkle of numba).

baldfat 10y ago
Really interested in why you would use a "faster" language in julia then move to a "slower" language like Python or R?
- williamstein 10y ago
  Numba and Cython can be very competitive with Julia.
- preygel 10y ago
  Typically a mix of reasons: - Needing to interact with an existing codebase, and an existing developer base. If everyone knows and uses Python and only a few use Julia, it is too early to put Julia in production. If there are proprietary libraries, now may not be the best time to commit to porting them to Julia. - Language and ecosystem stability. I started something on 0.4, and with 0.5 there were a raft of deprecations. If the code will live several years, that's a support commitment with unclear value. - Library maturity. If I need to build a web app, read an Excel, read a CSV with dates quickly, consume a SOAP endpoint, etc etc in Python -- no problem. With Julia I will mostly be fine, but am likely to run into some cases that are not yet 100% there. - Most code does not need the extra performance, so once you have a fast prototype as a performance target it is often not that hard to hit similar performance with Python + numba/Cython. Note for that last point: there is a lot of value to not worrying about this in the exploratory stage, and getting a performance target (for later optimization) as a nice byproduct.

by preygel 10y ago

baldfat 10y ago

Really interested in why you would use a "faster" language in julia then move to a "slower" language like Python or R?

williamstein 10y ago
Numba and Cython can be very competitive with Julia.
preygel 10y ago
Typically a mix of reasons: - Needing to interact with an existing codebase, and an existing developer base. If everyone knows and uses Python and only a few use Julia, it is too early to put Julia in production. If there are proprietary libraries, now may not be the best time to commit to porting them to Julia. - Language and ecosystem stability. I started something on 0.4, and with 0.5 there were a raft of deprecations. If the code will live several years, that's a support commitment with unclear value. - Library maturity. If I need to build a web app, read an Excel, read a CSV with dates quickly, consume a SOAP endpoint, etc etc in Python -- no problem. With Julia I will mostly be fine, but am likely to run into some cases that are not yet 100% there. - Most code does not need the extra performance, so once you have a fast prototype as a performance target it is often not that hard to hit similar performance with Python + numba/Cython. Note for that last point: there is a lot of value to not worrying about this in the exploratory stage, and getting a performance target (for later optimization) as a nice byproduct.