2016-09-09: General discussion¶
Time: 14:00 UTC
How many repos? ~1100
How many contributors? ~220
New core devs?
Rebuilding binaries for the conda-build 2.0 when the source tarballs that disappear. Bioconda is arching the sources.
Automate process to archive source tarball and test in a container (nice as a service to create a bundle-container to run packages).
Eric Dill (invite)
Peter M. Landwehr (already invited)
conda-build issue (xref?) conda/conda build#1338
continuum compiler toolchain to use gcc (Linux), clang and gfortran (OS X) consistently.
Needs a champion to write a proposal!
* Eric Dill will take this on. Hopefully a CFEP will land within one week, 2016-09-16
Eric suggests having both dev and
-dev labels. The former is for “cutting edge people” but the latter is people who only want to be testing/using the new version of only one thing (plus any dependencies).
dev is a bad name. These packages are more for testing than for development. Testing? RC?
Filipe thinks we should not accept versions earlier than RC (Not really do not accept but encourage people to call their dev version a RC. The thinking is that conda-forge is a place to release binaries and nightly testing builds, for example, are beyond the scope IMO.)
The feather-feedstock maintainers question:
They want to build Python 2.7 with a modern Visual Studio and conda-forge should suggest to them that this will create a different ecosystem that is compatible with conda-forge.
conda-build 2.0 and conda-build-all. Mike asked if we are ready to use conda-build 2.0. conda-forge needs to check:
where are the pins to conda-build <2.0
check the upload script
Use pip in the build script.
On Windows need conda > 4.2
Need to check if the entry_points must be declared or not in the recipe.
Next meeting: can we do 2016-09-16?
Update from the bioconda community. Tarball archiving and automatic Container (Docker, rkt) builds.
Archives: bioconda/bioconda recipes#2194
Container: bioconda/bioconda recipes#2297
Is conda-forge interested in a similar integration?
OSX - getting back to a usable, coherent, stack
libc++ (clang) vs libstdc++ (gcc/g++)
Apple’s Blocks extension to C (these are like lambdas) isn’t in recent (or non-Apple) GCC: https://gcc.gnu.org/ml/gcc/2009-09/msg00264.html
Can we link gfortran and LLVM system/c++ libraries together w/o violating GPL w/runtime exception (compiler_rt + libc++) - not if link is done statically to the best of my knowledge, and also can gfortran be built on top of compiler_rt? These are big unknowns.
Minimum OSX required for clang (10.8, I think?)
Actually clang is usable beginning in 10.7. So, this would be viable given your compatibility constraints.
Also, all the refs I have seen suggest that this will still have C++11 support.
Compatibility with defaults (built on 10.7, uses gcc) - where will people break? I think only if mixing packages - how do we assure that we have all the ones we need?
Metadata unification with Continuum - are we OK with adding some fields to about section to match Anaconda standard?
constrain summary to 80 chars (longer stuff use description)
Can we add this to linter, and add to recipes as we update them?
What support for unicode should we have? Any? Summary/description only?
* Better workflows with staged-recipes * Fast finish AppVeyor on merge ( [conda forge/staged recipes#1142](https://github.com/conda-forge/staged-recipes/pull/1142) ) * Drop Travis CI matrix ( [conda forge/staged recipes#1234](https://github.com/conda-forge/staged-recipes/pull/1234) ) * Use CircleCI for feedstock generation ( [conda forge/staged recipes#916](https://github.com/conda-forge/staged-recipes/issues/916) ) * Keeping recipes out of PRs ( [conda forge/staged recipes#942](https://github.com/conda-forge/staged-recipes/issues/942) ) * Bank work in partial conversion ( [conda forge/staged recipes#915](https://github.com/conda-forge/staged-recipes/issues/915) )
Notifications (how do we stay on top of them)
* Available on defaults - was in conda 4.1.7, but that was pulled. Coming in 4.1.8.
Discussing Ray Donnelly’s work on MSYS2 packages and how we want to use and integrate these into conda-forge.
Some use cases to consider OpenBLAS, FFTW, build tools, others?
* Do we include it in recipes?
What kinds do we allow if any (e.g. icons)?
How do we verify the licensing?
How do we verify that they are safe?
Dev releases: Where do they happen?
* Do we do them at conda-forge? * Maybe add a label. * Do we let others do them with a feedstock on their own repo?
How do we enforce whatever we decide?
* Can this point be a little bit explained? I thought about this as well and would like to contribute to this point. * Eric Dill has put together a script for copying a package from one channel to another here: [conda forge/conda forge.github.io#134](https://github.com/conda-forge/conda-forge.github.io/pull/134) * I have a really, really crude script that copies all of the packages in one channel to another that I just put at: (https://gist.github.com/mwcraig/8473cf840f6d29236d6d8af699404555)[https://gist.github.com/mwcraig/8473cf840f6d29236d6d8af699404555](https://gist.github.com/mwcraig/8473cf840f6d29236d6d8af699404555) * conda-build-all can copy from one channel to another: `conda build-all --inspect-channels conda-forge --upload-channels astropy some_packge_recipe` will copy the `some_package` from the channel conda-forge to astropy if it can, or build it if it doesn't exist on conda-forge. Discussion about what the desired behavior should be has started at: [SciTools/conda build all#46](https://github.com/SciTools/conda-build-all/issues/46)
* Is it sacred?
Do we rebase/force push?
* If so, under what conditions?
How do we avoid multiple people doing this simultaneously?
* I don't think you can. * IMHO, if it's just one author in staged recipes, sure. If feedstock, no force push - only to PRs to feedstock. If people don't mind merge PRs, it sure is a lot simpler to not rebase. I have messed up rebasing a few times recently... =(
Drop numpy 1.10 and reduce our build matrix. (Numba now works with numpy 1.11.)
Feedstocks philosophy: Explicit vs implicit / reproducible vs redundant
* Should be easy to do. ( (http://conda.pydata.org/docs/signed-packages.html)[http://conda.pydata.org/docs/signed-packages.html](http://conda.pydata.org/docs/signed-packages.html) )
There has been some interest previously.
HTTPError: 503 Server Error: Service Unavailable: Back-end server is at capacity for url…
* Seems we are regularly running into this issue under normal usage conditions.
Had discussed previously caching packages on AppVeyor and trying to reuse those to start.
Maybe we need to consider caching on all CIs.
Building our own Miniconda-like self-extracting scripts with packages via
There have been improvements on Continuum’s side that should help this. In short, repodata (the package index for a given channel) was being generated for each anaconda.org query. This was unnecessarily high cost, and some caching schemes have been implemented.
Handling removal of unpinned/improperly pinned packages.
* Has been done manually thus far.
This doesn’t scale well though.
Should we (semi) automate removal?
Should we hot-fix broken packages? ( conda forge/conda forge.github.io#170 )
Should we label them as broken
Not currently buildable packages
* In particular open source code that is out of scope for CIs.
Examples include Qt4, Qt5, possibly PyQt4, possibly PyQt5, gcc, VTK, etc.
How do we indicate they are built manually?
Are we ok with uploading non-built binaries?
When do we determine something is ok to be built manually?
What procedures should people follow for building manually?
* Use a standard build docker image, VM, or vagrant file
Implement reproducible builds where feasible (linux)
What changes do we need to make in conda-smithy elsewhere?
What other build infrastructure could we utilize?
* Would be nice to provide some volunteer builder abstraction, so that we could have an elastic worker farm that would be somewhat resilient.
Standardizing build images is probably (relatively) easy - how to orchestrate, though?
Windows BLAS Solutions
* Still don't have a BLAS for Windows yet need something.
Don’t build a BLAS
* NumPy has a small subset of BLAS functionality.
Not sure what to do with SciPy (unable to find Windows wheels for them either).
Build OpenBLAS with C support only.
* Will be pretty slow.
Should work on all Pythons.
Build OpenBLAS with MinGW compilers.
* Works with Python 2.7 and 3.4.
Won’t work with Python 3.5?
Reuse something like R’s BLAS.
* Is there a package for something like this?
Will it have the same issues with Python 3.5?