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Reproducing Tutorials Fails: Error processing property 'data' of _TupleWrapper( .. #840

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vschram opened this issue May 10, 2024 · 1 comment
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bug Something isn't working

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@vschram
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vschram commented May 10, 2024

Dear Secondmind Team,

Describe the bug
When I am trying to reproduce the Trieste tutorials, e.g. : https://secondmind-labs.github.io/trieste/3.1.0/notebooks/expected_improvement.html or https://secondmind-labs.github.io/trieste/3.1.0/notebooks/inequality_constraints.html
I am getting the following error:

\Anaconda3\envs\clone_BayesOpt_env\Lib\site-packages\tensorflow\python\module\module.py", line 428, in _flatten_module
raise ValueError("Error processing property {!r} of {!r}".format(
ValueError: Error processing property 'data' of _TupleWrapper((<tf.Variable 'Variable:0' shape=(None, 2) dtype=float64, numpy=
array([[4.05741721, 0.49318051],
[5.55239509, 1.09468531],
[1.60541616, 5.99931802],
[0.37097807, 4.92991607],
[2.9573046 , 4.75401206]])>, <tf.Variable 'Variable:0' shape=(None, 1) dtype=float64, numpy=
array([[-1.02023562],
[-0.61750123],
[ 0.0417215 ],
[ 0.52161525],
[ 0.22206321]])>))

To reproduce
Steps to reproduce the behaviour:

  1. Run https://secondmind-labs.github.io/trieste/3.1.0/notebooks/expected_improvement.html

Expected behaviour
Expected to see: Optimization completed without errors

System information

  • OS: Windows
  • Python version: 3.12.3
  • Trieste version: 0.13.2 (the pip version, release tag or commit hash)
  • TensorFlow version: 2.16.1
  • GPflow version: 2.9.1

Thanks in advance for any hint.

@vschram vschram added the bug Something isn't working label May 10, 2024
@vschram vschram changed the title Reproducing Tutorials Fails: pyto Reproducing Tutorials Fails: Error processing property 'data' of _TupleWrapper( .. May 10, 2024
@uri-granta
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Thanks for the report!

I expect the issue is that you are using an old (pre 1.0) version of trieste. Are you able to update to a more recent one?

There may also be an issue with the fact that the current version of GPflow doesn't fully work with TensorFlow 2.16 due to breaking changes in the latter (though the upcoming GPflow release will). You can work around this either by downgrading to TensorFlow 2.15, or by following the suggestion in this comment.

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