semvec/pypi-packages huggingface dataset:
ndsharray 1.1.4
Sharing numpy ndarray with a simple API between different python processes with shared memory.
License: BSD 3-Clause License
Requires Python: >=3.6
Keywords: sharing numpy arrays, inter process communication, subprocess, multiple process reading a numpy ndarray

Overview
========
ndarray + sharing = ndsharray

Introduction
------------
This python module let you share a numpy ndarray within different processes (either via python's multiprocessing or 
sharing between different python instances). The library behind this package is the lib 
[mmap](https://docs.python.org/3/library/mmap.html) from official python - no extra library, except numpy, is needed. 
The mmap approach is using the shared memory, which can be accessed by different CPUs/python instances. Using shared 
memory is much 
faster than the pickle approach - you can even do a video streaming on a Raspberry Pi / Jetson Nano over multiple 
python processes.\
This library is easy-to-use, just initialize the shared array with a unique tag and write/read! You can even change the 
numpy array dimension/shape/dtype during runtime - the mmap will be silently rebuild if there is a change in the 
numpy array  size/shape/dtype.

Small Example Code:
```python
import numpy as np
from ndsharray import NdShArray
    
shared_array = NdShArray("my_unique_tag", r_w='w')  # r_w must be specified

my_array = np.random.random((400, 800))
shared_array.write(my_array)

print(my_array)
```

That's all for writing into shared memory. How to read? Open a second python instance:
```python
import numpy as np
from ndsharray import NdShArray

shared_array = NdShArray("my_unique_tag", r_w='r')  # r_w must be specified

status, my_array = shared_array.read()

print(my_array)
```

Documentation
-------------
Can be found here: https://ndsharray.readthedocs.io/en/latest/


Requirements
------------ 
- Python â‰¥ 3.6
- numpy

Tested with example codes on 
- Windows 10, amd64, Python 3.6 / 3.8
- Ubuntu 20, amd64, Python 3.6 /3.8
- NVIDIA Jetson Nano with Ubuntu 18.04, ARM64-bit (aarch64), Python 3.6

Some technical notes
--------------------
This library shall be an easy-to-use library and also shall be faster than pickling numpy arrays to another process. 
Please note that the python's provided 
[shared_memory](https://docs.python.org/3/library/multiprocessing.shared_memory.html) does the same as ndsharray, but 
is using byte array instead of numpy array! However, shared_memory is available since python 3.8 and not 
supported for python 3.6.


Installation from Github
------------------------
Make sure to have git, python and pip in your environment path or activate your python environment.\
To install enter in cmd/shell:
```console
git clone https://github.com/monzelr/ndsharray.git

cd ndsharray

pip install .
```

Alternative with python:
```console
python setup.py install
```