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If you download the package and compile it (yourself or via the AUR) you can optimize those flags and compiler options.

As such, the flags used in the compilation will be intended to work in a wide variety of cpus.

#ANACONDA 3 INSTALL INSTALL#

Except that with PKGBUILD you will need to do more editing of the makefile.īut with pacman is a different story: pacman will install precompiled libraries and binaries. )Īnyway, time to read up on AUR helpers for the Nth timeĬorrect, there is no much difference between PKGBUILD/AUR and downloading and compiling yourself. Do you suggest virtualenv and friends for this? ( I suppose this is a whole other debate. It was probably redundant most of the time, but on the odd occasion it could be useful. I also get access to convenient channels/repos for 'science' oriented (read: poorly packaged) software, which could apparently be hard to build on my own.īefore, I used to make new conda envs for each project (this is often recommended in python courses or by professors etc). Specifically, conda provides the Intel Math Kernel Library (or binaries compiled with it?) and cuDNN (a deep neural network, haven't actually used this). Some context, to be completely transparent about my concerns: this reddit thread and this post/article. It's just a bit intimidating since I've ony been on arch for about a week which conda can handle, but PKGBUILDS probably work too. I was concerned about handling other dependencies like C/FORTRAN etc. Just took a peek at the AUR again and it seems that at least some of what I need has been packaged already, e.g.

#ANACONDA 3 INSTALL UPDATE#

When conda is used, my current advice is to 1) use a python3-based conda install (py2 installs don't seem to be tested upstream - the latest repo and package format changes certainly caused a lot of trouble), 2) never touch base - create envs to install the packages you need, 3) keep envs as minimal as possible - the more packages you install into one, the longer dependency resolution will take (it's slow enough at the best of times.), and the more likely it is that it'll break during an update (and debugging a broken conda env is Not Fun™).Īlright thanks for the heads up.

There are other cases where using conda (ideally Miniconda rather than Anaconda) makes sense, notably when you're working with a group and you just want to be able to set up and export your environment for others to import, or you need to have multiple versions of packages installed for processing different datasets. I'd recommend writing a PKGBUILD (basing it on the conda recipe if necessary & available) and using native package management over a secondary package management system.

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Because to me, anaconda is just a way of giving users the ability to install the software they need on systems that they otherwise wouldn't be able to install software on (i.e.

Anaconda 3 install