NumPy is one of the foundational libraries for scientific computing in Python. While it typically relies on dynamic linking, there are situations where a static linked version of NumPy is desired, such as in embedded systems or standalone applications. This guide will take you through the steps to build a static linked version of NumPy, making it easier to integrate with other components without worrying about dynamic library dependencies.
Understanding the Problem
The need arises when developers want a version of NumPy that does not require shared libraries at runtime. A static build integrates the library directly into the executable, eliminating dependencies on external .so files. However, building a static version can be more complex due to the dependencies NumPy has on libraries like BLAS and LAPACK.
Original Code for Building NumPy
Before diving into the solution, let’s start with the common command to build NumPy in a usual dynamic linked setup:
pip install numpy
Now, let’s explore how to create a static linked version of NumPy.
Steps to Build a Static Linked Version of NumPy
Prerequisites
- Python: Ensure you have Python installed (preferably Python 3.6+).
- C Compiler: You need a working C compiler (GCC for Linux, MSVC for Windows).
- BLAS/LAPACK Libraries: Get the static versions of these libraries as NumPy relies on them for many of its operations.
Step 1: Install Required Dependencies
Begin by installing the required dependencies if you haven't already. You can use apt-get on Ubuntu, for example:
sudo apt-get install build-essential libblas-dev liblapack-dev
Make sure you install the static versions of these libraries as well. You can usually find them in your package manager or by downloading from the library's official sites.
Step 2: Download NumPy Source Code
You can download the NumPy source code from its GitHub repository. Clone the repository using:
git clone https://github.com/numpy/numpy.git
cd numpy
Step 3: Modify the Build Configuration
To build NumPy statically, you need to modify the setup.py file in the NumPy source code. Open the file and look for where the build configurations are set. You'll want to ensure that the blas and lapack flags point to the static versions of the libraries. For instance, modify the library flags as follows:
# Example changes (ensure paths are correct)
libraries = ['blas', 'lapack']
library_dirs = ['/path/to/static/libs']
extra_compile_args = ['-static']
Step 4: Build the Static Version
After modifying the configuration, run the following command in your terminal:
python setup.py build
This command will compile the NumPy library into a static version. If everything is set up correctly, you should see a build process starting, eventually producing static libraries (.a files).
Step 5: Install the Static Version
To install the static version after building, use:
python setup.py install
This command installs the library into your Python environment. Ensure that your environment's path is correctly set up so that you can import the static NumPy in your Python scripts.
Practical Example
After successfully installing a static version of NumPy, you can verify it by running a simple Python script:
import numpy as np
# Create a simple array
arr = np.array([1, 2, 3, 4, 5])
print("NumPy Array:", arr)
If the script runs without any issues, you have successfully built and installed a static linked version of NumPy.
Conclusion
Building a static linked version of NumPy can seem daunting at first, especially with the multiple dependencies involved. However, with careful configuration and the right libraries, you can achieve a fully static build suited for various applications. This method not only helps in reducing runtime dependencies but also facilitates easier deployment across different environments.
Useful Resources
By following these steps and guidelines, you should be well-equipped to build a static version of NumPy tailored to your needs.
