_TF_REQ is not compatible with tensorflow-cpu requirements using pip

[kimdwkimdw]

When tensorflow-cpu is already installed, tensorflow-serving-api package cannot detect tensorflow installation.

https://pypi.org/project/tensorflow-serving-api/ should follow tensorflow-cpu

https://pypi.org/project/tensorflow-serving-api-gpu/ is now folloing tensorflow-gpu requirement.

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[kimdwkimdw]

@googlebot I signed it!

[netfs]

this will break for people who have tensorflow package installed:

https://pypi.org/project/tensorflow/

is there a way to specify a list of packages, where any one of them can be installed to satisfy the requirement?

[kimdwkimdw]

@netfs

How about this?

f183188

import pkg_resources

pkg_released = set(["tensorflow", "tensorflow-cpu"])
pkg_installed = set(map(lambda pkg: pkg.project_name, pkg_resources.working_set))
tensorflow_installed = list(pkg_released & pkg_installed) + ["tensorflow"]

_TF_REQ = [tensorflow_installed[0]+_TF_VERSION_SANITIZED]

[netfs]

for my understanding, is pkg_resources.working_set from the machine where this package is going to be installed OR is this from the machine where the package is being built? we obviously want the former (list packages installed on the target).

[kimdwkimdw]

It was my misunderstanding. That is for the latter, not the former one.

I think there is no solution for conditional requirements.

Creating new package is another solution with tensorflow-serving-api-cpu
And then add tensorflow-cpu to _TF_REQ when package name is tensorflow-serving-api-cpu
ex) https://pypi.org/project/tensorflow-serving-api-cpu/

Same as tensorflow package, https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py