How to Verify BLAS and LAPACK Libraries Used by NumPy and SciPy in Python

BLAS (Basic Linear Algebra Subprograms) and LAPACK (Linear Algebra PACKage) are fundamental libraries that provide optimized implementations of linear algebra routines for numerical computations. NumPy and SciPy rely on these libraries for efficient performance.

Why Check Linkage?

• Performance Optimization: Knowing the specific BLAS/LAPACK libraries linked to NumPy helps you assess performance characteristics. Different libraries offer varying levels of optimization for your hardware.
• Troubleshooting: If you encounter issues with linear algebra operations in your Python code, checking the linkage can help identify potential conflicts or missing libraries.

Unfortunately, there's no built-in function within NumPy or SciPy to directly query the linked BLAS/LAPACK libraries. However, here are two common approaches:

1. Using numpy.__config__.show():

   • Import the numpy module.
   • Access the __config__ attribute, which contains information about the NumPy configuration.
   • Call the show() method on __config__ to print a detailed report that includes the BLAS and LAPACK library versions (if available).

   import numpy as np
   
   np.__config__.show()
   

   In the output, look for sections like blas_opt_info and lapack_info. These might contain details about the linked libraries, but it depends on how NumPy was built.

2. Consulting Installation Documentation:

   • Refer to the documentation or installation instructions for NumPy or SciPy in your environment.

Additional Considerations:

• The information provided by numpy.__config__.show() might not be exhaustive.
• If you're still unsure, consider searching for specific details about your Python environment or package manager installation.
• The specific BLAS/LAPACK versions might not be directly exposed within your Python code, but understanding their linkage can help with performance optimization and troubleshooting.

By following these steps, you can gain valuable insights into the BLAS/LAPACK libraries used by your NumPy and SciPy installation in Python.

import numpy as np

# Approach 1: Using numpy.__config__.show()
print("Checking BLAS/LAPACK linkage using numpy.__config__.show():")
np.__config__.show()

# Approach 2: Consulting installation documentation (not directly in code)
print("\nApproach 2: Refer to the installation documentation or information")
print("provided by your package manager (e.g., Anaconda) for details on the")
print("specific BLAS/LAPACK versions used during installation.")

Explanation:

1. Approach 1: We print a header and then call np.__config__.show() to display detailed configuration information. Look for sections like blas_opt_info and lapack_info for potential library details.
2. Approach 2: This approach highlights the need to consult external documentation. Since the version information might not be directly accessible in code, we recommend checking the installation instructions or package manager output for details on the BLAS/LAPACK versions used during setup.

Remember: The information provided by numpy.__config__.show() may vary depending on your specific NumPy installation. Use both approaches in conjunction for a more comprehensive understanding.

System-Specific Tools (Advanced):

• If you're comfortable with terminal commands and system-level tools, you can attempt to inspect the loaded libraries at runtime. However, this approach has limitations:
  • The specific commands vary depending on your operating system (Linux, macOS, Windows).
  • The output might not be readily interpretable, requiring familiarity with library naming conventions and system details.

Example (Linux/macOS):

ldd <path_to_numpy_library>

Replace <path_to_numpy_library> with the actual location of a NumPy library file. This might vary based on your installation. Be cautious using this method, as incorrect usage of system tools can potentially impact your system.

Third-Party Libraries (Experimental):

• Some third-party libraries claim to provide information about linked libraries, but use these with caution. Thoroughly research their reputation and compatibility before employing them in your project.

Indirect Performance Profiling:

• While not a direct method for identifying the linked libraries, you can use profiling tools to measure the performance of certain linear algebra operations. By comparing performance with known benchmarks or results from different environments, you might gain insights into the underlying BLAS/LAPACK implementation. However, this approach is more indirect and less definitive.

Reinstallation with Specific Libraries:

• If you have control over your environment and know the desired BLAS/LAPACK libraries, consider reinstalling NumPy and SciPy while specifying the preferred library during the installation process. This ensures you get the desired linkage, but may require more advanced configuration options for your package manager.

Remember that the first approach (numpy.__config__.show()) and consulting installation documentation are generally the safest and most reliable methods. The other approaches require more technical expertise and should be used cautiously.