GitHub - Suzhou-Tongyuan/jnumpy: Writing Python C extensions in Julia within 5 m...
source link: https://github.com/Suzhou-Tongyuan/jnumpy
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JNumPy: writing high-performance C extensions for Python in minutes
Install JNumPy
Requirements:
- Python >= 3.7
You can install the Python package jnumpy
with the following command:
pip install julia-numpy
.
Note that JNumPy will install julia in JNUMPY_HOME
for you, if there is no Julia installation available.
-
write and export julia functions in file
example.jl
module example using TyPython using TyPython.CPython @export_py function mat_mul(a::StridedArray, b::StridedArray)::StridedArray return a * b end function init() @export_pymodule example begin jl_mat_mul = Pyfunc(jl_mat_mul) end end end
-
initialize and import the julia functions in Python
from jnumpy import init_jl, exec_julia, include_src import jnumpy as np init_jl() include_src("example.jl", __file__) exec_julia("example.init()") from example import jl_mat_mul x = np.array([[1,2],[3,4]]) y = np.array([[4,3],[2,1]]) jl_mat_mul(x, y) # array([[ 8, 5], # [20, 13]])
Environment Variables
-
JNUMPY_HOME
:The home directory for JNumPy-specific settings. The default value is
~/.jnumpy
. JNumPy runs julia in a default environment ($JNUMPY_HOME/envs/default
). In case that you don't have a julia executable, JNumPy installs julia into$JNUMPY_HOME
using jill.py. -
TYPY_JL_EXE
:The path of the julia executable in use.
-
TYPY_JL_OPTS
:Command-line options when launching julia. If you want to use a custom environment, you could set
--project=<dir>
.TYPY_JL_OPTS
is the same as those arguments passed tojulia
.
Examples
There are several examples presented in the demo
directory. Those examples are standalone Python packages created using JNumPy, and can be imported if you have JNumPy installed.
-
demo/basic
: a tiny Python package to give an example of how to use JNumPy. -
demo/kmeans
: a tiny Python package wrapping ParallelKMeans.jl. It produces a 10x performance gain against Scikit-Learn. -
demo/fft
: a tiny Python package wrapping FFTW.jl. It allows users to access FFT plans for accelerating FFTs.
Contributions
Open-source contributions are kindly accepted and appreciated including bug reports, documentations, pull requests, and general suggestions.
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