neanderthal
py4cl
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neanderthal | py4cl | |
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5 | 21 | |
1,043 | 221 | |
0.2% | - | |
7.0 | 2.3 | |
about 1 month ago | 6 months ago | |
Clojure | Common Lisp | |
Eclipse Public License 1.0 | GNU General Public License v3.0 or later |
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neanderthal
- AI’s compute fragmentation: what matrix multiplication teaches us
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Having trouble setting up Neanderthal.
There is the official Hello World https://github.com/uncomplicate/neanderthal/tree/master/examples/hello-world
- Da li u Srbiji , generalno prostoru balkana , ima "Ozbiljnih" Open source kreatora?
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Anybody using Common Lisp or clojure for data science
Did you have any occasion to evaluate neanderthal during your research? People seem to prefer it over core.matrix because it focus on primitive speed and sticking to BLAS idioms (as well as offering a decent api for working with GPU backends via cuda and opencl). I am curious to see if you did and found anything lacking there. I have a project on the backburner to try and target neanderthal for local search stuff, expressing problems in a high-level API that can then be baked into some numerically-friendly representation for efficient execution. It's often easier (trivial) to express solution representations, neighborhood functions, and objectives/constraints in a general purpose language, of which none of the things we like (sparse data structures, dynamically allocated stuff) are amenable to the contiguous memory, primitive numeric model that the hardware wants.
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I want to quit my data analyst job and learn and become a Clojure developer
Do clojure as a side gig or in free time. Let day job pay the bills. If you can, maybe incorporate clojure into work job to solve small problems (https://github.com/clj-python/libpython-clj and https://github.com/scicloj/clojisr provide bridges to/from python and r). There is a lot of effort going into the data science side as well; the scicloj effort has resulted in a lot of growth over the last 2 years. tech.ml.dataset, tech.ml (now scicloj.ml). Dragan has a bunch of excellent stuff in neanderthal and deep diamond. There are also bindings to other jvm libraries from multiple languages.
py4cl
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Need recommendation for IPC with Go
py4cl and cl4py rely on uiop:launch-program and python's subprocess respectively. These are portable to the extent uiop and subprocess are portable and do not require any additional installation.
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Lisp-Stick on a Python
If you want to use Python libs from CL, see py4cl: https://github.com/bendudson/py4cl the other way around, calling your efficient CL library from Python: https://github.com/marcoheisig/cl4py/ There might be more CL libraries than you think! https://github.com/CodyReichert/awesome-cl (or at least a project sufficiently advanced on your field to join forces ;) )
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The German School of Lisp (2011)
FYI you can call Python from CL: https://github.com/bendudson/py4cl and CL from Python: https://github.com/marcoheisig/cl4py/
If you don't know Emacs, see other editors: https://lispcookbook.github.io/cl-cookbook/editor-support.ht... If you want the more Smalltalk-like experience I'd go with the free LispWorks version: it has many GUI panes that allow to watch and discover the state of the program.
I personally couldn't stay long with Hylang. You won't get CL niceties: more language features, performance, standalone binaries, interactive debugger (all the niceties of an image-based development)…
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Plotting
I ended up using a fair bit of matplotlib through college and with colleagues. I too don't want to use python, but I also don't like throwing away its libraries, and I'm too lazy to invest in other* plotting ecosystems. In effect, I use up using matplotlib through py4cl/2.
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numericals - Performance of NumPy with the goodness of Common Lisp
Note that it is not my aim to replace the python ecosystem; I think that is far too lofy a goal to be of any good. My original intention was to interoperate with python through py4cl/2 or the likes, but felt that one needs a Common Lisp library for "small" operations, while "large" operations can be offloaded to python libraries through py4cl/2.
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Good Lisp libraries for math
If performance is absolutely not a concern, then third option is using python libraries through py4cl/2. To put it differently, if calling python from lisp is not the bottleneck, then this is a feasible option.
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Why Hy?
I encourage people to try out Common Lisp because, unlike with Hy, you will get: speed, ability to build binaries, truly interactive image-based development (yes, more interactive than ipython), more static type checks, more language features (no closures in Hy last time I checked), language stability… To reach to Python libs, you have https://github.com/bendudson/py4cl My comparison of Python and CL: https://lisp-journey.gitlab.io/pythonvslisp/
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Tutorial Series to learn Common Lisp quickly
> Not sure if such a thing already exists for CL
couple of solutions exist for this
https://github.com/bendudson/py4cl
https://github.com/pinterface/burgled-batteries
- Calling Python from Common Lisp
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(define (uwu) (display "nya~\n"))
Ahh, makes sense. Well, if you ever wanna steal some of python's thunder, libpython-clj worked great for me lol. Supposedly py4cl fills a similar role in Common Lisp.
What are some alternatives?
dtype-next - A Clojure library designed to aid in the implementation of high performance algorithms and systems.
py4cl2 - Call python from Common Lisp
libpython-clj - Python bindings for Clojure
magicl - Matrix Algebra proGrams In Common Lisp.
deep-diamond - A fast Clojure Tensor & Deep Learning library
cl-cuda - Cl-cuda is a library to use NVIDIA CUDA in Common Lisp programs.
numcl-benchmarks - benchmarks against numpy, julia
hy - A dialect of Lisp that's embedded in Python
qvm - The high-performance and featureful Quil simulator.
coalton - Coalton is an efficient, statically typed functional programming language that supercharges Common Lisp.