I am on an Asus notebbok with an i7 8550 processor, OS is Ubuntu 18.04.
I am trying to make my python3/numpy scripts go faster, by using MKL which supposedly will use many or all processor cores/threads.
I want to install intel-numpy or numpy-mkl (clarification needed!) in a pyenv/virtualenv environment with the `pip install` command. (Python version of that environment is 3.7.6)
I NEED CLEAR INSTRUCTIONS HOW TO PROCEED (which packages to install)
SOME DETAILS OF WHAT I DID:
- I have registered the intel repositories in `apt`, see the file /etc/apt/intelproducts.list in code section below
- I ran apt update and apt upgrade (succesfully)
- activated the mkl virtual environment (pyenv activate mkl), and checked the available packages with pip search mkl and pip search intel. There are many (see list below) and I am in doubt of what to install.
- then I tried the following pip install commands
- pip install mkl (succeeded)
- pip install intel-numpy (error message follows)
- same with intel-scipy and numpy-mkl (same error message)
- NB. I didn't need to pip uninstall numpy because mkl environment is brand new and no numpy is in there. Should I maybe install something else before the above commands?
Content of /etc/apt/sources.list.d/:
$ cat /etc/apt/sources.list.d/intelproducts.list # intelpython is disabled because not signed (apt upgrade gives error message) # deb https://apt.repos.intel.com/intelpython binary/ deb https://apt.repos.intel.com/mkl all main deb https://apt.repos.intel.com/ipp all main deb https://apt.repos.intel.com/tbb all main deb https://apt.repos.intel.com/daal all main deb https://apt.repos.intel.com/mpi all main
List of available pip packages:
$ pip search mkl mkl-fft (1.0.6) - MKL-based FFT transforms for NumPy arrays sparse-dot-mkl (0.4.1) - Intel MKL wrapper for sparse matrix multiplication mkl (2019.0) - Math library for Intel and compatible processors INSTALLED: 2019.0 (latest) mkl-random (126.96.36.199) - NumPy-based implementation of random number generation sampling using Intel (R) Math Kernel Library, mirroring numpy.random, but exposing all choices of sampling algorithms available in MKL. mxnet-mkl (1.6.0) - MXNet is an ultra-scalable deep learning framework. This version uses MKLDNN. mkl-devel (2018.0.3) - Math library for Intel and compatible processors cyanure-mkl (0.21.post3) - optimization toolbox for machine learning mkl-include (2019.0) - Math library for Intel and compatible processors numpy-mkl (1.10.2) - NumPy: array processing for numbers, strings, records, and objects. spams-mkl (2.6.1) - Python interface for SPAMS mkl-static (2018.0.0) - Math library for Intel and compatible processors cyanure-mkl-no-openmp (0.21.post3) - optimization toolbox for machine learning pyMKL (0.0.3) - Python wrapper of Intel MKL routines intel-numpy (1.15.1) - NumPy optimized with Intel(R) MKL library intel-scipy (1.1.0) - SciPy optimized with Intel(R) MKL library numkl (0.0.4) - A thin cython/python wrapper on some routines from Intel MKL tensorflow-kernels (0.1.2) - A package with Tensorflow (both CPU and GPU) implementation of most popular Kernels for kernels methods (SVM, MKL...).
pip install typical error message:
# python3 -m pip install intel-numpy ERROR: Could not find a version that satisfies the requirement intel-numpy (from versions: none) ERROR: No matching distribution found for intel-numpy
I am available and willing to contribute further detail into the matter.
PS. Last but not least, I struggled to find an appropriate category to post this question (it probably would be MKL or Intel Python but there's no such category), please make one! 🙂
Thank you for posting in the Intel Community.
I have found the following links that might help:
but if you need farther assistance please