Anaconda vs python numpy full#
It seems that on such a small instance of VM the Intel distribution does not show its full capabilities.This is mainly caused by the Intel MKL library, and I recommend you to read more about it in this article. You are definitely better off with a custom distribution than with the vanilla Python.Intel distribution takes these from its own channels. To perform the tests, I activated the distribution (for both Anaconda and Intel distro) and created a virtual environment, and installed all main dependencies using conda – namely sklearn, scipy, numpy and pandas. First one is a clean Ubuntu with clean Python 3.6.5, second one was running the Anaconda distribution, and the third had a full Intel Python distribution installed.
More about the installation can be found here. Source /opt/intel/intelpython3/bin/activateĬonda create -n intelpython -override-channels -channel intel python=3.6 intelpython scipy pydaal scikit-learn numpy pandas
Anaconda vs python numpy install#
Install all Intel performance libraries.To activate the Intel distribution, these are the main steps:.Install numpy, sklearn, scipy using conda install (not pip!).If you cannot pair the numpy with MKL (and other libraries), even though you have sourced the intelpython or anaconda.Always check, whether numpy is paired with MKL library using.These information might be useful for bughunting: It comes with 2 cores from Intel Xeon 2673v4. DS11v2 is an instance optimized for data science workloads, and has 14GB of RAM. Regarding methodology, I decided to create three Azure VM instances DS11v2, all with Ubuntu 18.04 installed. So how does a clean Python 3.6 with Anaconda distribution compare, and does Intel bring any other additional benefit to this setup?
However, Intel also provides other libraries, such as Data Analytics Acceleration Library (DAAL) and Threading Building Blocks (TBB) among others. # All requested packages already installed.Īnd if I tried to remove NumPy to install it again, it will remove everything, not just NumPy.The Intel distribution is built on top of Anaconda, and both distributions already come with the Intel MKL library. In Anaconda Prompt I get: Solving environment: done
Anaconda vs python numpy how to#
There is only how to "install", and when I use conda install numpy I searched on the web, but I didn't find a way to "reinstall". It looks like that I need to "reinstall" NumPy. Original error was: DLL load failed: The specified module could not be found. If you're working with a numpy git repo, try `git clean -xdf` (removes allįiles not under version control). Likely you are trying to import a failed build of numpy. Importing the multiarray numpy extension module failed. When I try to import sklearn I get an error: Traceback (most recent call last):įile "c:\Users\azzam\machinelearning.py", line 1, in įile "C:\Anaconda3\lib\site-packages\sklearn\_init_.py", line 134, in įile "C:\Anaconda3\lib\site-packages\sklearn\base.py", line 10, in įile "C:\Anaconda3\lib\site-packages\numpy\_init_.py", line 142, in įile "C:\Anaconda3\lib\site-packages\numpy\add_newdocs.py", line 13, in įile "C:\Anaconda3\lib\site-packages\numpy\lib\_init_.py", line 8, in įile "C:\Anaconda3\lib\site-packages\numpy\lib\type_check.py", line 11, in įile "C:\Anaconda3\lib\site-packages\numpy\core\_init_.py", line 26, in I am using Anaconda 5.3.0's Python interpreter in Visual Studio Code.
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