About dlib-split-feedstock

Feedstock license: BSD-3-Clause

Home: http://dlib.net

Package license: BSL-1.0

Summary: toolkit for making real world machine learning and data analysis applications

Development: https://github.com/davisking/dlib

Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. See http://dlib.net for the main project documentation and API reference.

Current build status

Azure

Variant Status linux_64_c_compiler_version11cuda_compilernvcccuda_compiler_version11.8cxx_compiler_version11numpy2.0python3.10.____cpython linux_64_c_compiler_version11cuda_compilernvcccuda_compiler_version11.8cxx_compiler_version11numpy2.0python3.11.____cpython linux_64_c_compiler_version11cuda_compilernvcccuda_compiler_version11.8cxx_compiler_version11numpy2.0python3.12.____cpython linux_64_c_compiler_version11cuda_compilernvcccuda_compiler_version11.8cxx_compiler_version11numpy2.0python3.9.____cpython linux_64_c_compiler_version11cuda_compilernvcccuda_compiler_version11.8cxx_compiler_version11numpy2python3.13.____cp313 linux_64_c_compiler_version12cuda_compilercuda-nvcccuda_compiler_version12.0cxx_compiler_version12numpy2.0python3.10.____cpython linux_64_c_compiler_version12cuda_compilercuda-nvcccuda_compiler_version12.0cxx_compiler_version12numpy2.0python3.11.____cpython linux_64_c_compiler_version12cuda_compilercuda-nvcccuda_compiler_version12.0cxx_compiler_version12numpy2.0python3.12.____cpython linux_64_c_compiler_version12cuda_compilercuda-nvcccuda_compiler_version12.0cxx_compiler_version12numpy2.0python3.9.____cpython linux_64_c_compiler_version12cuda_compilercuda-nvcccuda_compiler_version12.0cxx_compiler_version12numpy2python3.13.____cp313 linux_64_c_compiler_version13cuda_compilerNonecuda_compiler_versionNonecxx_compiler_version13numpy2.0python3.10.____cpython linux_64_c_compiler_version13cuda_compilerNonecuda_compiler_versionNonecxx_compiler_version13numpy2.0python3.11.____cpython linux_64_c_compiler_version13cuda_compilerNonecuda_compiler_versionNonecxx_compiler_version13numpy2.0python3.12.____cpython linux_64_c_compiler_version13cuda_compilerNonecuda_compiler_versionNonecxx_compiler_version13numpy2.0python3.9.____cpython linux_64_c_compiler_version13cuda_compilerNonecuda_compiler_versionNonecxx_compiler_version13numpy2python3.13.____cp313 linux_aarch64_c_compiler_version11cuda_compiler_version11.8cxx_compiler_version11numpy2.0python3.10.____cpython linux_aarch64_c_compiler_version11cuda_compiler_version11.8cxx_compiler_version11numpy2.0python3.11.____cpython linux_aarch64_c_compiler_version11cuda_compiler_version11.8cxx_compiler_version11numpy2.0python3.12.____cpython linux_aarch64_c_compiler_version11cuda_compiler_version11.8cxx_compiler_version11numpy2.0python3.9.____cpython linux_aarch64_c_compiler_version11cuda_compiler_version11.8cxx_compiler_version11numpy2python3.13.____cp313 linux_aarch64_c_compiler_version13cuda_compiler_versionNonecxx_compiler_version13numpy2.0python3.10.____cpython linux_aarch64_c_compiler_version13cuda_compiler_versionNonecxx_compiler_version13numpy2.0python3.11.____cpython linux_aarch64_c_compiler_version13cuda_compiler_versionNonecxx_compiler_version13numpy2.0python3.12.____cpython linux_aarch64_c_compiler_version13cuda_compiler_versionNonecxx_compiler_version13numpy2.0python3.9.____cpython linux_aarch64_c_compiler_version13cuda_compiler_versionNonecxx_compiler_version13numpy2python3.13.____cp313 osx_64_numpy2.0python3.10.____cpython osx_64_numpy2.0python3.11.____cpython osx_64_numpy2.0python3.12.____cpython osx_64_numpy2.0python3.9.____cpython osx_64_numpy2python3.13.____cp313 osx_arm64_numpy2.0python3.10.____cpython osx_arm64_numpy2.0python3.11.____cpython osx_arm64_numpy2.0python3.12.____cpython osx_arm64_numpy2.0python3.9.____cpython osx_arm64_numpy2python3.13.____cp313 win_64_cuda_compilerNonecuda_compiler_versionNonenumpy2.0python3.10.____cpython win_64_cuda_compilerNonecuda_compiler_versionNonenumpy2.0python3.11.____cpython win_64_cuda_compilerNonecuda_compiler_versionNonenumpy2.0python3.12.____cpython win_64_cuda_compilerNonecuda_compiler_versionNonenumpy2.0python3.9.____cpython win_64_cuda_compilerNonecuda_compiler_versionNonenumpy2python3.13.____cp313 win_64_cuda_compilercuda-nvcccuda_compiler_version12.0numpy2.0python3.10.____cpython win_64_cuda_compilercuda-nvcccuda_compiler_version12.0numpy2.0python3.11.____cpython win_64_cuda_compilercuda-nvcccuda_compiler_version12.0numpy2.0python3.12.____cpython win_64_cuda_compilercuda-nvcccuda_compiler_version12.0numpy2.0python3.9.____cpython win_64_cuda_compilercuda-nvcccuda_compiler_version12.0numpy2python3.13.____cp313 win_64_cuda_compilernvcccuda_compiler_version11.8numpy2.0python3.10.____cpython win_64_cuda_compilernvcccuda_compiler_version11.8numpy2.0python3.11.____cpython win_64_cuda_compilernvcccuda_compiler_version11.8numpy2.0python3.12.____cpython win_64_cuda_compilernvcccuda_compiler_version11.8numpy2.0python3.9.____cpython win_64_cuda_compilernvcccuda_compiler_version11.8numpy2python3.13.____cp313

Current release info

Name	Downloads	Version	Platforms

Installing dlib-split

Installing dlib-split 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, dlib, dlib-cpu, dlib-gpu can be installed with conda:

conda install dlib dlib-cpu dlib-gpu

or with mamba:

mamba install dlib dlib-cpu dlib-gpu

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

conda search dlib --channel conda-forge

or with mamba:

mamba search dlib --channel conda-forge

Alternatively, mamba repoquery may provide more information:

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

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

# List dependencies of `dlib`:
mamba repoquery depends dlib --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 dlib-split-feedstock

If you would like to improve the dlib-split 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/dlib-split-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

• @Tobias-Fischer
• @aldanor
• @patricksnape