conda-forge / cudadecon-feedstock

A conda-smithy repository for cudadecon.

About cudadecon-feedstock

Feedstock license: BSD-3-Clause

Home: https://github.com/scopetools/cudaDecon

Package license: LicenseRef-Janelia-Open-Source

Summary: GPU accelerated 3D image deconvolution using CUDA

Development: https://github.com/scopetools/cudaDecon

Documentation: https://github.com/scopetools/cudaDecon#readme

GPU accelerated 3D image deconvolution using CUDA. Developed in the Betzig lab at Janelia by Lin Shao and Dan Milkie.

Current build status

Azure

Variant Status linux_64_c_stdlib_version2.17cuda_compilernvcccuda_compiler_version11.8 win_64_cuda_compilernvcccuda_compiler_version11.8

Current release info

Name	Downloads	Version	Platforms

Installing cudadecon

Installing cudadecon from the conda-forge channel can be achieved by adding conda-forge to your channels with:

conda config --add channels conda-forge
conda config --set channel_priority strict

Once the conda-forge channel has been enabled, cudadecon can be installed with conda:

conda install cudadecon

or with mamba:

mamba install cudadecon

It is possible to list all of the versions of cudadecon available on your platform with conda:

conda search cudadecon --channel conda-forge

or with mamba:

mamba search cudadecon --channel conda-forge

Alternatively, mamba repoquery may provide more information:

# Search all versions available on your platform:
mamba repoquery search cudadecon --channel conda-forge

# List packages depending on `cudadecon`:
mamba repoquery whoneeds cudadecon --channel conda-forge

# List dependencies of `cudadecon`:
mamba repoquery depends cudadecon --channel conda-forge

About conda-forge

conda-forge is a community-led conda channel of installable packages. In order to provide high-quality builds, the process has been automated into the conda-forge GitHub organization. The conda-forge organization contains one repository for each of the installable packages. Such a repository is known as a feedstock.

A feedstock is made up of a conda recipe (the instructions on what and how to build the package) and the necessary configurations for automatic building using freely available continuous integration services. Thanks to the awesome service provided by Azure, GitHub, CircleCI, AppVeyor, Drone, and TravisCI it is possible to build and upload installable packages to the conda-forge anaconda.org channel for Linux, Windows and OSX respectively.

To manage the continuous integration and simplify feedstock maintenance conda-smithy has been developed. Using the conda-forge.yml within this repository, it is possible to re-render all of this feedstock's supporting files (e.g. the CI configuration files) with conda smithy rerender.

For more information please check the conda-forge documentation.

Terminology

feedstock - the conda recipe (raw material), supporting scripts and CI configuration.

conda-smithy - the tool which helps orchestrate the feedstock. Its primary use is in the construction of the CI .yml files and simplify the management of many feedstocks.

conda-forge - the place where the feedstock and smithy live and work to produce the finished article (built conda distributions)

Updating cudadecon-feedstock

If you would like to improve the cudadecon recipe or build a new package version, please fork this repository and submit a PR. Upon submission, your changes will be run on the appropriate platforms to give the reviewer an opportunity to confirm that the changes result in a successful build. Once merged, the recipe will be re-built and uploaded automatically to the conda-forge channel, whereupon the built conda packages will be available for everybody to install and use from the conda-forge channel. Note that all branches in the conda-forge/cudadecon-feedstock are immediately built and any created packages are uploaded, so PRs should be based on branches in forks and branches in the main repository should only be used to build distinct package versions.

In order to produce a uniquely identifiable distribution:

• If the version of a package is not being increased, please add or increase the build/number.
• If the version of a package is being increased, please remember to return the build/number back to 0.

Feedstock Maintainers

• @dmilkie
• @tlambert03
• @zbarry

cudadecon-feedstock's Issues

Fix Linux CUDA 11.8 GCC 11 builds

Recently when adding CUDA 11.8 builds in PR ( #26 ), we ran into some issues with the Linux builds. On further investigation, it appears that the issue was related to the use of GCC 11 in the Linux CUDA 11.8 builds

Looking at the result from CI (also attached log for posterity), we saw errors like this one:

/home/conda/feedstock_root/build_artifacts/cudadecon_1698498913015/_build_env/x86_64-conda-linux-gnu/include/c++/11.4.0/type_traits:79:52: error: redefinition of 'constexpr const _Tp std::integral_constant<_Tp, __v>::value'
   79 |   template<typename _Tp, _Tp __v>
      |                                                    ^                           
/home/conda/feedstock_root/build_artifacts/cudadecon_1698498913015/_build_env/x86_64-conda-linux-gnu/include/c++/11.4.0/type_traits:67:29: note: 'constexpr const _Tp value' previously declared here
   67 |       static constexpr _Tp                  value = __v;
      |      

This error did not occur with GCC 10. So something changed between GCC 10 & 11. Maybe the compiler got stricter and so is catching an error? Would need further investigation to know for sure

For now we have pinned to GCC 10 in the CUDA 11.8 builds as a workaround, which allows those to pass. Have highlighted the relevant lines below

cudadecon-feedstock/recipe/meta.yaml

Lines 19 to 24 in 3c13e13

	# For Linux CUDA 11.8 builds, pin to GCC 10 to workaround issues with GCC 11.
	# xref: https://github.com/conda-forge/cudadecon-feedstock/issues/29
	- {{ compiler('c') }} # [not (linux and cuda_compiler_version == "11.8")]
	- {{ compiler('cxx') }} # [not (linux and cuda_compiler_version == "11.8")]
	- {{ c_compiler }}_{{ target_platform }} 10 # [linux and cuda_compiler_version == "11.8"]
	- {{ cxx_compiler }}_{{ target_platform }} 10 # [linux and cuda_compiler_version == "11.8"]

Once the underlying issue is resolved we should remove the workaround by reverting commit: 4cb1263

Relaxing GCC pins (currently using GCC 7)

It looks like GCC is currently pinned in this recipe (and has been for a while). Wondering if it is possible to relax this pinning and use the conda-forge default GCC (currently GCC 12)

cudadecon-feedstock/recipe/conda_build_config.yaml

Lines 1 to 6 in df80cf2

	c_compiler_version: # [linux]
	- 7 # [linux]
	cxx_compiler_version: # [linux]
	- 7 # [linux]
	fortran_compiler_version: # [linux]
	- 7 # [linux]