cstl 0.0.2

The C++ Standard Template Library (STL) for Python

CSTL : The C++ Standard Template Library (STL) for Python

In this CSTL tool, we wrap several C++ STL containers to use in Python. The containers use native C++ implementation and will not have the copy on write issue like native list, and dict in Python. Though it is designed to solve the CoW issue, it can also be used in scenarios where a standard C++ container is needed.

Install

Install from pip:

pip install CSTL

Build from source:

git clone https://github.com/fuzihaofzh/CSTL.git
cd CSTL
./build.sh
python setup.py install --single-version-externally-managed --record files.txt

Usage

import CSTL
vec = CSTL.VecInt([1,2,3,4,5,6,7])
print(vec[2])      #3
vec[2] = 1
print(vec[2])      #1
vec.append(10)
print(vec[-1])     #10
print(list(vec))   #[1, 2, 1, 4, 5, 6, 7, 10]

Supported Datatype

We support the following data types as the elements in the containers

Python Type	C++ Type	Can be dict key
int	int	Yes
int	std::int64	Yes
str	std::string	Yes
float	float	No
double	double	No
bool	bool	No

Supported Containers

The supported containers are listed as follows

Python Structure	C++ Container
list	std::vector
dict	std::unordered_map
set	std::unordered_set

We also support nested container, namely, structure like std::unordered_map< std::string,std::unordered_map< std::string,std::vector< bool > > > is supported. Currently, at most 3 nested layers are supported.

Copy on Write Issue in Python

This is a feature not a bug for Python. In multi-processing programs, the shared object will be copied to each process if they access the data. However, if the data is large and we use several processes, the memory cannot hold a separate copy for each process. This cannot be solved in Python as all Python's native structures with ref count have such problems. A more detailed discussion can be found at https://github.com/pytorch/pytorch/issues/13246 .