Home: https://github.com/apple/coremltools
Package license: BSD-3-Clause
Feedstock license: BSD-3-Clause
Summary: Core ML is an Apple framework to integrate machine learning models into your app. Core ML provides a unified representation for all models.
Development: https://github.com/apple/coremltools
Documentation: https://coremltools.readme.io/docs
Core ML is an Apple framework to integrate machine learning models into your app.
Use the coremltools Python package to convert models from third-party training libraries such as TensorFlow and PyTorch to the Core ML format. You can then use Core ML to integrate the models into your app.
Core ML provides a unified representation for all models. Your app uses Core ML APIs and user data to make predictions, and to fine-tune models, all on the user’s device. Running a model strictly on the user’s device removes any need for a network connection, which helps keep the user’s data private and your app responsive.
Core ML optimizes on-device performance by leveraging the CPU, GPU, and Neural Engine while minimizing its memory footprint and power consumption.
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Installing coremltools from the conda-forge channel can be achieved by adding conda-forge to your channels with:
conda config --add channels conda-forge
Once the conda-forge channel has been enabled, coremltools can be installed with:
conda install coremltools
It is possible to list all of the versions of coremltools available on your platform with:
conda search coremltools --channel 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 CircleCI, AppVeyor and TravisCI it is possible to build and upload installable packages to the conda-forge Anaconda-Cloud 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.
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)
If you would like to improve the coremltools 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/coremltools-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/numberback to 0.