Travis: Install numpy with OpenBLAS instead of MKL#456
Closed
rht wants to merge 3 commits intoQuantEcon:masterfrom
Closed
Travis: Install numpy with OpenBLAS instead of MKL#456rht wants to merge 3 commits intoQuantEcon:masterfrom
rht wants to merge 3 commits intoQuantEcon:masterfrom
Conversation
Contributor
Author
Contributor
Author
|
I added the flake8 commit for the purpose of testing how long it takes for the cache to download (~44s, https://travis-ci.org/QuantEcon/QuantEcon.py/jobs/466303295#L443). It looks like the download size hasn't decreased significantly because the binaries from conda-forge are still fat. I suppose the remaining solution is to use pip. |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
By default, conda installs the non-FLOSS version of numpy, which bloats the installation size by 204.4 MB compressed (~600 MB uncompressed). This slows down the cache creation, even though now the
conda installtime has decreased from ~113s to ~11s.See also: conda-forge/numpy-feedstock#84