Skip to content

[MNT] comment on MKL in index.rst #421

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
dswah wants to merge 7 commits into
base: main
Choose a base branch
from
Open

[MNT] comment on MKL in index.rst #421

Changes from all commits
Commits
Show all changes
7 commits
Select commit Hold shift + click to select a range
261e053
comment on MKL in index.rst
dswah Dec 22, 2025
a31fab9
Update index.rst
dswah Dec 22, 2025
e16ccb1
Update index.rst
dswah Dec 22, 2025
c4ac42c
Update index.rst
dswah Dec 22, 2025
326e2fa
Update index.rst
dswah Dec 22, 2025
ceba304
Update index.rst
dswah Dec 22, 2025
11a851e
Update index.rst
dswah Dec 22, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
21 changes: 9 additions & 12 deletions docs/index.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -48,24 +48,21 @@ Bleeding Edge
You can install the bleeding edge from github using ``pip``.
First clone the repo, ``cd`` into the main directory and do: ::

pip install . # for an unstable "latest" dev version install
pip install . # for an unstable "latest" dev version
# or
pip install -e . # for an editable developer/contributor install
pip install -e . # for an editable developer/contributor version

Acceleration
------------
Most of pyGAM's computations are linear algebra operations.

Optional
--------
To speed up optimization on large models with constraints, it helps to
have `scikit-sparse <https://github.com/scikit-sparse/scikit-sparse>`_ installed because it contains a slightly faster,
sparse version of Cholesky factorization. The import from
``scikit-sparse`` references ``nose``, so you'll need that too.
To speed up optimization on large models with constraints, it helps to have `intel MKL <https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl.html>`_ installed.

The easiest way is to use Conda: ::
It is currently a bit tricky to install both NumPy and SciPy linked to the MKL routines with Conda because you have to be careful with which channel you are using. Pip's NumPy-MKL is outdated.

conda install -c conda-forge scikit-sparse nose
An alternative is to use a third-party build like https://urob.github.io/numpy-mkl: ::

More information is available in the `scikit-sparse docs
<https://scikit-sparse.readthedocs.io/en/latest/overview.html>`_.
pip install numpy scipy --extra-index-url https://urob.github.io/numpy-mkl

|

Expand Down