How severe does this issue affect your experience of using Ray?
- Medium: It contributes to significant difficulty to complete my task, but I can work around it.
with the recent major API changes coming in
ray 2.0.0, I wonder if the community can provide some “best practice” mixture of version compatibility regarding the major libraries.
I am thinking about the following
ray1 version 1.13.0 or 1.12.0 works well with
- numpy version XX
- tensorflow version XX
- torch version XX
I am asking that because even within the soft-dependent libraries I observe ground-breaking changes, e.g. in
tensorflow when suddenly I get the message of “missing module keras” (caused by a call within
rllib code), or the removal of
np.bool in numpy>=1.20.
In the last days I already came across
pipdeptree, but this is rather post-mortem analysis and a good twin brother for
Thanks in advance!