## ``mkl_random`` -- a NumPy-based Python interface to Intel (R) MKL Random Number Generation functionality
[](https://github.com/IntelPython/mkl_random/actions/workflows/conda-package-cf.yml)
`mkl_random` has started as Intel (R) Distribution for Python optimizations for NumPy.
Per NumPy's community suggestions, voiced in https://github.com/numpy/numpy/pull/8209, it is being released as a
stand-alone package.
Prebuilt `mkl_random` can be installed into conda environment from Intel's channel:
```
conda install -c https://software.repos.intel.com/python/conda mkl_random
```
or from conda forge channel:
```
conda install -c conda-forge mkl_random
```
---
To install mkl_random Pypi package please use following command:
```
python -m pip install -i https://software.repos.intel.com/python/pypi --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 Intel Pypi Cloud:
```
python -m pip install -i https://software.repos.intel.com/python/pypi --extra-index-url https://pypi.org/simple mkl_random numpy==<numpy_version>
```
Where `<numpy_version>` should be the latest version from https://software.repos.intel.com/python/conda/
---
`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'
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"description": "## ``mkl_random`` -- a NumPy-based Python interface to Intel (R) MKL Random Number Generation functionality\r\n[](https://github.com/IntelPython/mkl_random/actions/workflows/conda-package-cf.yml)\r\n\r\n`mkl_random` has started as Intel (R) Distribution for Python optimizations for NumPy.\r\n\r\nPer NumPy's community suggestions, voiced in https://github.com/numpy/numpy/pull/8209, it is being released as a\r\nstand-alone package.\r\n\r\nPrebuilt `mkl_random` can be installed into conda environment from Intel's channel:\r\n\r\n```\r\n conda install -c https://software.repos.intel.com/python/conda mkl_random\r\n```\r\n\r\nor from conda forge channel:\r\n\r\n```\r\n conda install -c conda-forge mkl_random\r\n```\r\n\r\n---\r\n\r\nTo install mkl_random Pypi package please use following command:\r\n\r\n```\r\n python -m pip install -i https://software.repos.intel.com/python/pypi --extra-index-url https://pypi.org/simple mkl_random\r\n```\r\n\r\nIf command above installs NumPy package from the Pypi, please use following command to install Intel optimized NumPy wheel package from Intel Pypi Cloud:\r\n\r\n```\r\n python -m pip install -i https://software.repos.intel.com/python/pypi --extra-index-url https://pypi.org/simple mkl_random numpy==<numpy_version>\r\n```\r\n\r\nWhere `<numpy_version>` should be the latest version from https://software.repos.intel.com/python/conda/\r\n\r\n---\r\n\r\n`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`.\r\n\r\nFor distributions directly supported in Intel (R) Math Kernel Library (MKL), `method` keyword is supported:\r\n\r\n```\r\n mkl_random.standard_normal(size=(10**5, 10**3), method='BoxMuller')\r\n```\r\n\r\nAdditionally, `mkl_random` exposes different basic random number generation algorithms available in MKL. For example to use `SFMT19937` use\r\n\r\n```\r\n mkl_random.RandomState(77777, brng='SFMT19937')\r\n```\r\n\r\nFor generator families, such that `MT2203` and Wichmann-Hill, a particular member of the family can be chosen by specifying ``brng=('WH', 3)``, etc.\r\n\r\nThe list of supported by `mkl_random.RandomState` constructor `brng` keywords is as follows:\r\n\r\n * 'MT19937'\r\n * 'SFMT19937'\r\n * 'WH' or ('WH', id)\r\n * 'MT2203' or ('MT2203', id)\r\n * 'MCG31'\r\n * 'R250'\r\n * 'MRG32K3A'\r\n * 'MCG59'\r\n * 'PHILOX4X32X10'\r\n * 'NONDETERM'\r\n * 'ARS5'\r\n",
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