py4cl2
Petalisp
py4cl2 | Petalisp | |
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11 | 17 | |
40 | 445 | |
- | - | |
5.6 | 8.5 | |
16 days ago | 7 days ago | |
Common Lisp | Common Lisp | |
GNU General Public License v3.0 or later | GNU Affero General Public License v3.0 |
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py4cl2
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An Idea for Piggybacking Python (language) ecosystem
I... recently got that working: https://github.com/digikar99/py4cl2/tree/master/cffi - Yes, CFFI! Yes, passing CL array data by reference!
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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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interested in learning lisp, (specifically for games, but also for everything else including tui and gui applications for linux. currently have next to no programming knowledge, can i get forwarded some resources and some tips on what exactly i should do? any videos i should watch?
Python: Blender and Panda3D (game engine used for Disney's Toontown way back when) are both scriptable with Python. I've been able to successfully call Panda from Py4CL2 (thanks digikar for the help with that), but I have not tried with Blender yet. I think it's doable.
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Rewrite Your Scripts In LISP - with Roswell
While you are at it I may as well mention https://github.com/digikar99/py4cl2
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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.
- Using Lisp as a Dynamic Library
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What are the advantages of Hy/Hissp over python bindings for CL/Clojure?
py4cl2 (not py4cl!) author here. From the v2.9.0 docs:
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Design patterns for Lisp interop with other languages?
py4cl and py4cl2 represent a fairly pragmatic example of method 1, using an OS child process to communicate back and forth with your python code. Python is fairly popular and well-enabled with libraries, so you can delegate things to python that leverage those libraries.
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Image classification in CL? Help with starting point
If you can structure your code so that data de/serialization is not a bottleneck, then you could access the python libraries using py4cl/2.
Petalisp
- Petalisp: Elegant High Performance Computing
- Is there a tutorial for automatic differentiation with petalisp?
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Is there a language with lisp syntax but C semantics?
While not "as fast as C" (C is not the absolute pinnacle of performance), Common Lisp is incredibly fast compared to the majority of programming languages around today. There is even a huge amount of ongoing work being done to make it faster still. We are seeing many interesting projects that make better use of the hardware in your computer (e.g. https://github.com/marcoheisig/Petalisp).
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Common Lisp Implementations in 2023
i think lisp-stat library is actually being developed. however one numerical cl library that doesnt get enough mention and is being constantly developed is petalisp for HPC
https://github.com/marcoheisig/Petalisp
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numericals - Performance of NumPy with the goodness of Common Lisp
However, if you have a lisp library that puts those semantics to use, then you could get it to employ magicl/ext-blas and cl-bmas to speed it up. (petalisp looks relevant, but I lack the background to compare it with APL.)
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New Lisp-Stat Release
> his means cl pagckages can be "done".
this is true if there is nothing functional that can be added to a package. however its very much not true for ml frameworks right now. new things are being added all the time in the field. however even in the package i linked you have the necessary ingredients for any deep learning model: cuda and back propagation. the other person mentioned convolution which i think is pretty trivial to implement but still, if you expect everything for you to be ready made then you should probably stick to tf and pytorch. if you want to explore the cutting edge and push the boundaries then i think common lisp is a good tool. as an aside it might also be interesting to note that a common lisp package (Petalisp) is being used for high performance computing by a german university
https://github.com/marcoheisig/Petalisp
- The Julia language has a number of correctness flaws
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When a young programmer who has been using C for several years is convinced that C is the best possible programming language and that people who don't prefer it just haven't use it enough, what is the best argument for Lisp vs C, given that they're already convinced in favor of C?
One trick is that Common Lisp can generate and compile code at runtime, whereas static languages typically do not have a compiler available at runtime. This lets you make your own lazy person's JIT/staged compiler, which is useful if some part of the problem is not known at compile-time. Such an approach has been used at least for array munging, type munging and regular expression munging.
What are some alternatives?
py4cl - Call python from Common Lisp
awesome-cl - A curated list of awesome Common Lisp frameworks, libraries and other shiny stuff.
cl-cuda - Cl-cuda is a library to use NVIDIA CUDA in Common Lisp programs.
JWM - Cross-platform window management and OS integration library for Java
numcl - Numpy clone in Common Lisp
farolero - Thread-safe Common Lisp style conditions and restarts for Clojure(Script) and Babashka.
magicl - Matrix Algebra proGrams In Common Lisp.
vega-lite - A concise grammar of interactive graphics, built on Vega.
lish - Lisp Shell
numericals - CFFI enabled SIMD powered simple-math numerical operations on arrays for Common Lisp [still experimental]
StatsBase.jl - Basic statistics for Julia