mkl_random -- a NumPy-based Python interface to Intel (R) MKL Random Number Generation functionality
mkl_random has started as Intel (R) Distribution for Python optimizations for NumPy.
Per NumPy's community suggestions, voiced in numpy/numpy#8209, it is being released as a stand-alone package.
Prebuilt mkl_random can be installed into conda environment from Intel's channel on Anaconda cloud:
conda install -c intel mkl_random
To install mkl_random Pypi package please use following command:
python -m pip install --i https://pypi.anaconda.org/intel/simple -extra-index-url https://pypi.org/simple mkl_random
If command above installs NumPy package from the Pypi, please use following command to install Intel optimized NumPy wheel package from Anaconda Cloud:
python -m pip install --i https://pypi.anaconda.org/intel/simple -extra-index-url https://pypi.org/simple mkl_random numpy==<numpy_version>
Where <numpy_version> should be the latest version from https://anaconda.org/intel/numpy
mkl_random is not fixed-seed backward compatible drop-in replacement for numpy.random, meaning that it implements sampling from the same distributions as numpy.random.
For distributions directly supported in Intel (R) Math Kernel Library (MKL), method keyword is supported:
mkl_random.standard_normal(size=(10**5, 10**3), method='BoxMuller')
Additionally, mkl_random exposes different basic random number generation algorithms available in MKL. For example to use SFMT19937 use
mkl_random.RandomState(77777, brng='SFMT19937')
For generator families, such that MT2203 and Wichmann-Hill, a particular member of the family can be chosen by specifying brng=('WH', 3), etc.
The list of supported by mkl_random.RandomState constructor brng keywords is as follows:
- 'MT19937'
- 'SFMT19937'
- 'WH' or ('WH', id)
- 'MT2203' or ('MT2203', id)
- 'MCG31'
- 'R250'
- 'MRG32K3A'
- 'MCG59'
- 'PHILOX4X32X10'
- 'NONDETERM'
- 'ARS5'