Like this one:
I tried to used KNIME with conda environment. I tested the workflow from this tutorial with the workflow .
I install a new conda env containing:
tensorboard 2.10.0 py38haa95532_0
tensorboard-data-server 0.6.1 py38haa95532_0
tensorboard-plugin-wit 1.8.1 py38haa95532_0
tensorflow 2.10.0 mkl_py38ha5c4042_0
tensorflow-base 2.10.0 mkl_py38h6a7f48e_0
tensorflow-estimator 2.10.0 py38haa955…
I have all the environment setup for best compatibility in my other AI/ML tasks.
Unfortunately, Knime wants to use its own as given by the use of a deprecated pandas library.
Github issue: GitHub - knime/knime-deeplearning: KNIME Deep Learning Integration
Have also been disabled which I think slows down its resolution.
Any fixes to this?
@james0001 welcome to the KNIME forum. I can offer this information. Especially on Apple silicon chips the keras and tensor flow integrations need some improvements.
Currently you will have to work with specific combination of older packages and for the M chips you need a workaround.
@jordanm55 welcome to the KNIME forum. It is something of an art to have the right combination of packages for the deep learning nodes to work with python.
If you found a working combination it is best to store, document and keep it. Until knime changes the nodes there should not be any updates but currently you would have to take care of them yourself.
I hope there will be a better integration of DL and a knime python integration like for basic packages.
Sometimes there are workarounds lik…