4 ms·

Can this compete with the momentum/performance of Julia? I'd be stoked if this became widely adopted but community size seems to be a huge determinant of succe

Can this compete with the momentum/performance of Julia? I'd be stoked if this became widely adopted but community size seems to be a huge determinant of success with these types of languages.

medo-bear 4y ago
the rally call of lisps is to get a lot done with less, so it is definitely possible that it can compete in some sense. also common lisp packages such as these can cater to the programmer-first type of data scientist by allowing greater (interactive) tinkering with the underlying code
- sieste 4y ago
  >> cater to the programmer-first type of data scientist Exactly. Especially those who want to dabble with lisp by playing with familiar problems and applications. I find it much more instructive to play with linear regression code in a new language than with, say, a game development engine, because I have a pretty good idea about the strengths and quirks of `lm` in R.
lukego 4y ago
I migrated from Lisp to Julia for the ecosystem. It hasn't been worth it from my point of view. I'll migrate back to Lisp eventually. So I can easily imagine packages like this becoming widely adopted /within/ the Lisp community.
- tpejrl23432 4y ago
  Could you elaborate on this a bit ? Lisp has next to no numerical libraries IIRC ?
  - vindarel 4y ago
    won't answer but a good starting point is https://github.com/CodyReichert/awesome-cl#numerical-and-scientific https://github.com/CodyReichert/awesome-cl#numerical-and-sci..., did you see it? It has some libraries, for example magicl is developed by and for Quantum companies.
  - medo-bear 4y ago
    > Lisp has next to no numerical libraries IIRC ? https://github.com/CodyReichert/awesome-cl#machine-learning https://github.com/CodyReichert/awesome-cl#machine-learning https://github.com/CodyReichert/awesome-cl#numerical-and-scientific https://github.com/CodyReichert/awesome-cl#numerical-and-sci...
    - tpejrl23432 4y ago
      You can hardly compare any of these one-off projects to something like numpy (let alone something like pytorch etc. which Julia is going after).
      - medo-bear 4y ago
        what do you mean a one-off project? some of these have corporate/institutional backing but thats not the point. if you know how to implement machine learning models then there should be nothing (except maybe availability of pretrained models) preventing you from using common lisp. if you dont want to dig deep and are happy using just the front end (perfectly acceptable) then python is a much better bet all the needed ingredients are there to build a custom production class ml solution in common lisp: vectorization, cuda, blas, lapack
        p1esk 4y ago
        Do any of those libraries linked above provide an example of training a resnet or bert on a GPU? I’m not looking for anything fancy, or production quality, just a basic example.
        medo-bear 4y ago
        although not necessarily bert or resnet the following probably has all the ingredients for what you are looking for. the author of this library is a research scientist at deepmind since 2015 https://github.com/melisgl/mgl#x-28MGL-BP-3A-40MGL-BP-20MGL-PAX-3ASECTION-29 https://github.com/melisgl/mgl#x-28MGL-BP-3A-40MGL-BP-20MGL-...
  - Lisp-Stat 4y ago
    I think this depends on what part of the statistics universe you're working in. For example, within Lisp-Stat the statistics routines [1] were written by an econometrician working for the Austrian government (Julia folks might know him - Tamas Papp). It would not be exaggerating to say his job depending on it. These are state of the art, high performance algorithms, equal to anything available in R or Python. So, if you're doing econometrics, or something related, everything you need is already there in the tin. For machine learning, there's CLML [2], developed by NTT. This is the largest telco in Japan, equivalent to ATT in the USA. As well, there is MGL [3], used to win the Higgs Boson challenge a few years back. Both actively maintained. For linear algebra, MagicCL was mention elsewhere in the thread. My favourite is MGL-MAT [4], also by the author of MGL. This supports both BLAS and CUBLAS (CUDA for GPUs) for solutions. Finally, there's the XLISP-STAT archive [5]. Prior to Luke Tierney, the author of XLISP-Stat joining the core R team, XLISP-STAT was the dominate statistical computing platform. There's heaps of stuff in the archive, most at least as good as what's in base R, that could be ported to Lisp-Stat. Common Lisp is a viable platform for statistics and machine learning. It isn't (yet) quite as well organised as R or Python, but it's all there. [1] https://github.com/Lisp-Stat/numerical-utilities/blob/master/src/statistics.lisp https://github.com/Lisp-Stat/numerical-utilities/blob/master... [2] https://github.com/mmaul/clml https://github.com/mmaul/clml [3] https://github.com/melisgl/mgl https://github.com/melisgl/mgl [4] https://github.com/melisgl/mgl-mat https://github.com/melisgl/mgl-mat [5] https://lisp-stat.dev/docs/resources/xlisp/ https://lisp-stat.dev/docs/resources/xlisp/

Can this compete with the momentum/performance of Julia? I'd be stoked if this became widely adopted but community size seems to be a huge determinant of success with these types of languages.

medo-bear 4y ago

the rally call of lisps is to get a lot done with less, so it is definitely possible that it can compete in some sense. also common lisp packages such as these can cater to the programmer-first type of data scientist by allowing greater (interactive) tinkering with the underlying code

sieste 4y ago
>> cater to the programmer-first type of data scientist Exactly. Especially those who want to dabble with lisp by playing with familiar problems and applications. I find it much more instructive to play with linear regression code in a new language than with, say, a game development engine, because I have a pretty good idea about the strengths and quirks of `lm` in R.

lukego 4y ago

I migrated from Lisp to Julia for the ecosystem. It hasn't been worth it from my point of view. I'll migrate back to Lisp eventually. So I can easily imagine packages like this becoming widely adopted /within/ the Lisp community.

tpejrl23432 4y ago
Could you elaborate on this a bit ? Lisp has next to no numerical libraries IIRC ?
- vindarel 4y ago
  won't answer but a good starting point is https://github.com/CodyReichert/awesome-cl#numerical-and-scientific https://github.com/CodyReichert/awesome-cl#numerical-and-sci..., did you see it? It has some libraries, for example magicl is developed by and for Quantum companies.
- medo-bear 4y ago
  > Lisp has next to no numerical libraries IIRC ? https://github.com/CodyReichert/awesome-cl#machine-learning https://github.com/CodyReichert/awesome-cl#machine-learning https://github.com/CodyReichert/awesome-cl#numerical-and-scientific https://github.com/CodyReichert/awesome-cl#numerical-and-sci...
  - tpejrl23432 4y ago
    You can hardly compare any of these one-off projects to something like numpy (let alone something like pytorch etc. which Julia is going after).
    - medo-bear 4y ago
      what do you mean a one-off project? some of these have corporate/institutional backing but thats not the point. if you know how to implement machine learning models then there should be nothing (except maybe availability of pretrained models) preventing you from using common lisp. if you dont want to dig deep and are happy using just the front end (perfectly acceptable) then python is a much better bet all the needed ingredients are there to build a custom production class ml solution in common lisp: vectorization, cuda, blas, lapack
      - p1esk 4y ago
        Do any of those libraries linked above provide an example of training a resnet or bert on a GPU? I’m not looking for anything fancy, or production quality, just a basic example.
        medo-bear 4y ago
        although not necessarily bert or resnet the following probably has all the ingredients for what you are looking for. the author of this library is a research scientist at deepmind since 2015 https://github.com/melisgl/mgl#x-28MGL-BP-3A-40MGL-BP-20MGL-PAX-3ASECTION-29 https://github.com/melisgl/mgl#x-28MGL-BP-3A-40MGL-BP-20MGL-...
- Lisp-Stat 4y ago
  I think this depends on what part of the statistics universe you're working in. For example, within Lisp-Stat the statistics routines [1] were written by an econometrician working for the Austrian government (Julia folks might know him - Tamas Papp). It would not be exaggerating to say his job depending on it. These are state of the art, high performance algorithms, equal to anything available in R or Python. So, if you're doing econometrics, or something related, everything you need is already there in the tin. For machine learning, there's CLML [2], developed by NTT. This is the largest telco in Japan, equivalent to ATT in the USA. As well, there is MGL [3], used to win the Higgs Boson challenge a few years back. Both actively maintained. For linear algebra, MagicCL was mention elsewhere in the thread. My favourite is MGL-MAT [4], also by the author of MGL. This supports both BLAS and CUBLAS (CUDA for GPUs) for solutions. Finally, there's the XLISP-STAT archive [5]. Prior to Luke Tierney, the author of XLISP-Stat joining the core R team, XLISP-STAT was the dominate statistical computing platform. There's heaps of stuff in the archive, most at least as good as what's in base R, that could be ported to Lisp-Stat. Common Lisp is a viable platform for statistics and machine learning. It isn't (yet) quite as well organised as R or Python, but it's all there. [1] https://github.com/Lisp-Stat/numerical-utilities/blob/master/src/statistics.lisp https://github.com/Lisp-Stat/numerical-utilities/blob/master... [2] https://github.com/mmaul/clml https://github.com/mmaul/clml [3] https://github.com/melisgl/mgl https://github.com/melisgl/mgl [4] https://github.com/melisgl/mgl-mat https://github.com/melisgl/mgl-mat [5] https://lisp-stat.dev/docs/resources/xlisp/ https://lisp-stat.dev/docs/resources/xlisp/