Created attachment 172947 [details] py-numba shar archive Numba is a LLVM optimizing compiler for python and in numpy aware, it requires py-llvmlite, which was submitted earlier.
Due to an outstanding issue, it appears to be a bug with FreeBSD. In order to properly use Numba currently the user is expected to import numpy first such as, import numpy import numba An issue has been opened up on numba at: https://github.com/numba/numba/issues/2001
Created attachment 172982 [details] [UPDATED] py-numba shell archive Updated port to handle python requirements properly. Tested and passed with ports-mgmt/poudriere. Requires py-llvmlite referenced in Bug 211346.
Created attachment 172983 [details] poudriere py-numba log Poudriere log which indicates build success for py-numba.
Created attachment 172996 [details] [UPDATED] py-numba shell archive Added RUN_DEPENDS where necessary
Created attachment 172997 [details] [UPDATED] poudriere py-numba log Updated poudriere log for py27-numba
Thanks David, Minor nits (as long as you've already run portlint -AC): * Remove "Numba is an" from the COMMENT See: https://www.freebsd.org/doc/en_US.ISO8859-1/books/porters-handbook/book.html#makefile-comment * BUILD_DEPENDS+= ${PYTHON_PKGNAMEPREFIX}numpy>1.7:${PORTSDIR}/math/py-numpy Change to = (its not appending anything) and put it above USES section (after LICENSE as it doesn't need anything that pre.mk provides so goes before the include. This is unlike the conditional RUN_DEPENDS, which uses PYTHON_REL, which *does* need to be after pre.mk. Also ${PORTSDIR}/ is no longer required for dependency lines * Unconditional RUN_DEPENDS (llvmlite/argparse) go up top along with other *_DEPENDS
Created attachment 173000 [details] [UPDATED+] py-numba shell archive
Created attachment 173002 [details] [UPDATE+] py-numba shell archive Thanks for the feedback and help! I have adjusted the files as you requested, to be more consistent with the Handbook. Also last attachment was an attempt in elinks and didn't go through as expetced.
Created attachment 173003 [details] [UPDATE+] poudriere py-numba log poudriere py-numba log, success
Comment on attachment 172997 [details] [UPDATED] poudriere py-numba log This should be obsolete, forgot to hit the checkbox
After consulting with the numba team, it appears that LD_LIBRARY_PATH requires to be set: LD_LIBRARY_PATH=/usr/local/lib/gcc48 fixes the issue so that a user can just call `import numba` without invoking import numpy import numba but setting LD_LIBRARY_PATH isn't a preferred solution from what I gather. I will look for possible solutions, postpone the acceptance of this port until this is resolved, or you have a solution for me to add quickly.
@David to put an issue on hold, you can obsolete the existing attachments (with comment if needed), so its clear to committers there's nothing to do :)
Created attachment 174228 [details] py-numba shar archive, with included pkg-message I believe I have found the issue with py-numba, libgcc_s.so.1 is loaded from base, not from gcc48. However I think this might not be handled correctly within numba itself, but I need time to go over it. A ugly fix is modify /etc/libmap.conf which I have included as pkg-message in the port. Would this be acceptable for now, while I dig around? Unfortunately, I wasn't able to obsolete any files before, the only way to get the option I found was to go to 'add attachment', but that required another attachment to replace it.
I'm testbuilding right now.
Created attachment 174355 [details] [UPDATE] py-numba v0.28.1 shar archive Updated port to latest version. Removed the pkg-message, due to finding out the issue of libgcc_s.so.1 was presented in py-llvmlite (which needs to be updated)
Thanks, restarted testbuilds (will take a long time to get gcc etc...)
Testbuilds are fine.
Committed, thanks!
A commit references this bug: Author: pi Date: Mon Sep 5 17:19:22 UTC 2016 New revision: 421379 URL: https://svnweb.freebsd.org/changeset/ports/421379 Log: New port: devel/py-numba Numba gives you the power to speed up your applications with high performance functions written directly in Python. With a few annotations, array-oriented and math-heavy Python code can be just-in-time compiled to native machine instructions, similar in performance to C, C++ and Fortran, without having to switch languages or Python interpreters. WWW: http://numba.pydata.org/ PR: 211347 Submitted by: David Kalliecharan <dave@dal.ca> Changes: head/devel/Makefile head/devel/py-numba/ head/devel/py-numba/Makefile head/devel/py-numba/distinfo head/devel/py-numba/pkg-descr