CLPython
neanderthal
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CLPython | neanderthal | |
---|---|---|
5 | 5 | |
364 | 1,043 | |
- | 0.2% | |
2.9 | 7.0 | |
6 months ago | about 1 month ago | |
Common Lisp | Clojure | |
GNU General Public License v3.0 or later | Eclipse Public License 1.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
CLPython
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Why Static Languages Suffer From Complexity
C++, ~haskell, python, mathematica... capisce? :)
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You loved running JavaScript in your web browser
we need to go deeper https://common-lisp.net/project/clpython/
- Common Lisp implementation in Python
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Anybody using Common Lisp or clojure for data science
cl-python
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Compiler in Lisp
Python
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.
What are some alternatives?
MicroPython - MicroPython - a lean and efficient Python implementation for microcontrollers and constrained systems
dtype-next - A Clojure library designed to aid in the implementation of high performance algorithms and systems.
IronPython - Implementation of Python 3.x for .NET Framework that is built on top of the Dynamic Language Runtime.
libpython-clj - Python bindings for Clojure
Grumpy - Grumpy is a Python to Go source code transcompiler and runtime.
deep-diamond - A fast Clojure Tensor & Deep Learning library
PySec - OWASP Python Security Project
numcl-benchmarks - benchmarks against numpy, julia
pylisp - A Lisp compiler targeting Python
magicl - Matrix Algebra proGrams In Common Lisp.
py4cl - Call python from Common Lisp
qvm - The high-performance and featureful Quil simulator.