mkl-include

import numpy as np import os # --- Check if NumPy is linked against MKL (requires mkl-service or similar detection) --- try: import mkl print(f"MKL-service is imported. MKL version: {mkl.get_version()}") print(f"MKL number of threads: {mkl.get_max_threads()}") print("NumPy is likely using MKL through the loaded mkl-service library.") except ImportError: print("mkl-service not found. Checking numpy config.") # A more direct way to check NumPy's backend (may not explicitly show MKL vs OpenBLAS, etc.) print(f"\nNumPy config:\n{np.show_config()}") # --- Example of using mkl-service to control MKL runtime (if installed) --- if 'mkl' in locals(): # Set MKL to use a specific number of threads for a domain mkl.set_num_threads(2) print(f"\nMKL threads set to: {mkl.get_max_threads()}") # Perform a computation that would benefit from MKL a = np.random.rand(1000, 1000) b = np.random.rand(1000, 1000) c = a @ b # Matrix multiplication, often MKL-accelerated print(f"Matrix multiplication completed. Shape: {c.shape}") # To explicitly use MKL for a library like NumPy, you generally install a MKL-optimized build. # Example (concept, exact command depends on source): # pip install numpy scipy --index-url https://urob.github.io/numpy-mkl # For specific MKL wheels