tensorflow / tensorflow/probability
ImportError dependent on import order
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Description
Hi,
Working on an M3 macbook pro in macOS 15.6.
Using miniforge3.
Creating a conda env from scratch.
Running
conda install python pip ipython
pip install tensorflow tensorflow_probablity tensorflow-keras
to install python 3.13 and
tensorflow==2.20.0
tensorflow-probability==0.24.0
tensorflow_keras==0.1
then in a python terminal
In [1]: import tensorflow_probability
In [2]: import tensorflow
succeeds, and I can use tf and tf keras and import and use my own modules which use them . But
In [1]: import tensorflow
In [2]: import tensorflow_probability
returns the (familiar) error
File ~/miniforge3/envs/tensorflow/lib/python3.13/site-packages/tensorflow_probability/python/__init__.py:79, in _validate_tf_environment(package)
77 if required_tensorflow_version[0] == '2':
78 try:
---> 79 import tf_keras # pylint: disable=unused-import
80 except (ImportError, ModuleNotFoundError):
81 # Print more informative error message, then reraise.
82 print('\n\nFailed to import TF-Keras. Please note that TF-Keras is not '
83 'installed by default when you install TensorFlow Probability. '
84 'This is so that JAX-only users do not have to install TensorFlow '
(...) 87 'This can be be done through installing the '
88 'tensorflow-probability[tf] extra.\n\n')
ModuleNotFoundError: No module named 'tf_keras'
When importing tf and tfp at the top of my own modules, this error also appears when I import mymodule no matter what order the import of tf and tfp happens in the module file.
I am note even sure how to debug this! I guess something to do with namespaces getting messed up, but I am very much a user not a dev...
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Research direction
Reproduce the conda and pip installation, then run the two import orders shown in the report. Start in tensorflow_probability/python/init.py at _validate_tf_environment and inspect why importing tensorflow first leaves tf_keras unavailable; done means the documented installation imports successfully in either order.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100