comment out tensorflow dependency
@@ -32,20 +32,31 @@ requirements:
#
# After splitting out of tensorflow, the tensorboard pypi package is problematic:
- # - the tensorflow package depends on tensorboard (but does not need tensorboard for anything, it is just for backwards compatibility)
- # - the tensorboard package does logically depend on tensorflow (but does not do implicitly to avoid cyclical dependencies)
- # In fact, the first pypi release of tensorboard had a circular dependency with tensorflow, which was nasty to us tensorboard-gpu users.
- # They fixed it in 0.1.5 (see https://github.com/tensorflow/tensorboard/releases/tag/0.1.5) by not depending on tensorflow anymore.
- # But now the package in pypi is not anymore standalone (you need to install tensorflow for it to work, even if TF is not a explicit dep).
#
- # In conda-land we must break that vicious cycle and use the proper dependency graph:
- # tensorboard depends on tensorflow, tensorflow does not depend on tensorboard.
+ # - tensorflow does not need tensorboard to work
+ # (but depends on it, just to avoid surprising the users that were used to have tensorboard in the package)
+ #
+ # - tensorboard does need tensorflow to work
+ # (but does not depend on it explicitly to avoid cyclical dependencies)
+ #
+ # This means that the tensorboard package is not standalone and should always be installed
+ # by installing tensorflow. At the moment we are mirroring in conda-forge the wrongs on
+ # pypi.
#
# Also note that this makes the noarch nature of the package a bit of a wishful thinking
# (we can use tensorboard only on platforms with a tensorflow package available).
#
-
- - tensorflow >=1.3.0
+ # See:
+ # Issue in TB tracker: https://github.com/tensorflow/tensorboard/issues/410
+ # Initial recipe PR: https://github.com/conda-forge/staged-recipes/pull/3866
+ # Issue in the TF recipe: https://github.com/conda-forge/tensorflow-feedstock/issues/42
+ #
+ # An alternative solution would be to uncomment the tensorflow dependency down here
+ # and remove the tensorflow dependency on the tensorboard package. Pros: this would create
+ # a saner dependency cycle. Cons: it would be inconsitent with the official pypi releases.
+ #
+ # - tensorflow >=1.3.0
+ #
test:
imports:
