Hi all, we are trying to provide built wheels of TensorFlow. TensorFlow can be built with various configuration options that include/exclude certain functionality for the resulting wheel artifact - we would like to provide multiple wheels based on desired configuration. An example can be support for kafka, s3, xla, hdfs. The main issue we are struggling with is a lack of specifying such features in python packages for users. Considering the current wheel binary package format (PEP 491) there is no way to specify such options in package names in the wheel naming convention.
Even if there would be such ability, there are issues on how to specify different configurations from the available ones. Considering different wheels (e.g. tensorflow-1.8.0-py3-cp36m-linux_x86_64+hdfs+s3+kafka+xla.whl) there is no straightforward way to specify desired features that an installed wheel should provide. As a user, I don’t want to consume packages that are named “tensorflow+hdfs+s3+kafka+xla” so I think this deserves broader discussion in community and design decisions for providing configuration and platform specific features for wheels. Are there any plans from community to incorporate such configuration options for wheels? Also, how these compile time options should be included in the resulting wheel as metadata? Thanks, Fridolin
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