[Fix] ImportError: cannot import name ‘dtensor’ from ‘tensorflow.compat.v2.experimental’

Recently, I was trying to import the dtensor, it’s showing the error: ImportError: cannot import name ‘dtensor’ from ‘tensorflow.compat.v2.experimental’ in Python.

Below I would be sharing the all fixes in details which I tried and also can work for you.

Why ImportError: cannot import name ‘dtensor’ from ‘tensorflow.compat.v2.experimental’ Error occurs?

This Error is seen because of incompatibility between your tensorflow and your keras versions. So if you are seeing the below error:

ImportError: cannot import name ‘dtensor’ from ‘tensorflow.compat.v2.experimental’

Most probably reason is the compatibility issue between tensorflow and keras versions you are using right now. This is the version compatibility issue between tensorflow and keras version, so either updating and downgrading one of version should help you to fix the error.

How to Fix the ImportError: cannot import name ‘dtensor’ from ‘tensorflow.compat.v2.experimental’ Error?

To fix the ImportError: cannot import name ‘dtensor’ from ‘tensorflow.compat.v2.experimental’ Error, either update your tensorflow version or downgrade your keras version. Once both versions are compatible, this error will be resolved.

Use the below command to update the tensorflow version:

pip install tensorflow==2.8

OR Use the below command to downgrade the keras version:

pip install keras==2.6

Once both tensorflow and keras versions are compatible to each other, run the below command again to import the dtensor:

from tensorflow.compat.v2.experimental import dtensor

Now error should be resolved and you should be able to import the dtensor without any issue.

Conclusion

To fix ImportError: cannot import name ‘dtensor’ from ‘tensorflow.compat.v2.experimental’, update your tensorflow version or downgrade your keras version. Once both versions are compatible, this error will be resolved.

ImportError: cannot import name ‘dtensor’ from ‘tensorflow.compat.v2.experimental’ Error is seen because of uncompatibility between yours tensorflow and keras versions.