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- My question
I haven't found the technical documentation of Numpy on intelpython3_full. I don't know which classes, methods, and functions of intelpython3_full can be called in python or used in numpy.
I need a function and method documentation of Numpy, pandas, and scipy on intelpython3_full, similar to daal4py's technical documentation.
(Https://intelpython.github.io/daal4py/contents.html)
- Question details
On this page:
There is a case:
Cannot find "random_intel" at www.numpy.org.
Refer to: from numpy import random, random_intel usage, I use: from numpy import sin, sin_intel, the result is an error:
Where can I find more function documentation ending in _intel?
- Further details
I delete the virtual environment of intelpython3_full, and I can run "random_intel" successfully
Explain that "random_intel" may be a function or class in Numpy.
Are there more functions ending in _intel? Please recommend the documentation link, thank you very much! !
- System information:
win10 Professional 20H2,
Anaconda3-2019.10-Windows-x86_64
- Work done
According to the intel documentation, I installed intelpython with conda create -n idp intelpython3_full python=3.7.
And I found the technical documentation of daal4py (https://intelpython.github.io/daal4py/contents.html)
By studying this document, I can use daal4py smoothly.
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Hi Heming,
https://www.intel.com/content/www/us/en/developer/articles/technical/faster-random-number-generation-in-intel-distribution-for-python.html, this article is outdated, random_intel is deprecated, and we use mkl_random instead. The numpy in Intel Distribution for Python is built to use mkl_random by default.
So, to use numpy in Intel Distribution for Python, you just use the same API that standard numpy supports and you can get performance boost, there are no additional numpy packages or numpy APIs.
If you are interested in optimizations in oneMKL, you can refer here: https://www.intel.com/content/www/us/en/develop/documentation/onemkl-linux-developer-guide/top.html
I will close this ticket, feel free to re-open if you have further questions. Thanks!
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Hi,
Thanks for posting in Intel Forums.
Also thanks for your feedback. We are looking into it. We will get back to you soon.
Regards
Raviranjan
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Hi,
we had taken your feedback and reported to the internal team. Please let us know if there is any other issue we could help with?
Thanks
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Hi,
Thank you for your feedback. We have provided your feedback to the relevant team. At this moment there is no visibility when it will be implemented and available for use. Please let me know if we can go ahead and close this case?
Thanks
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Hi Heming,
https://www.intel.com/content/www/us/en/developer/articles/technical/faster-random-number-generation-in-intel-distribution-for-python.html, this article is outdated, random_intel is deprecated, and we use mkl_random instead. The numpy in Intel Distribution for Python is built to use mkl_random by default.
So, to use numpy in Intel Distribution for Python, you just use the same API that standard numpy supports and you can get performance boost, there are no additional numpy packages or numpy APIs.
If you are interested in optimizations in oneMKL, you can refer here: https://www.intel.com/content/www/us/en/develop/documentation/onemkl-linux-developer-guide/top.html
I will close this ticket, feel free to re-open if you have further questions. Thanks!
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