I've used this to install rdkit in base:
conda install -c rdkit rdkit
while it shows this:
UnsatisfiableError: The following specifications were found
to be incompatible with the existing python installation in your environment:
Specifications:
- rdkit -> python[version='2.6.*|2.7.*|3.5.*|3.6.*|>=2.7,<2.8.0a0|>=3.6,<3.7.0a0|>=3.7,<3.8.0a0|>=3.5,<3.6.0a0|3.4.*']
Your python: python=3.8
If python is on the left-most side of the chain, that's the version you've asked for.
When python appears to the right, that indicates that the thing on the left is somehow
not available for the python version you are constrained to. Note that conda will not
change your python version to a different minor version unless you explicitly specify
that.
I thought it means i should try lower version python, but i checked another docker and rdkit runs well with python 3.8, that's confusing.
Before I decided to reinstall a python 3.7, I also tried to set kernel in rdkit environment (which i named as rdkit), and I succeed. Though in that kernel rdkit, none of tensorflow or tons of other packages can be used in my previous kernel works, which is quite inconvenient.
I checked both kernels' kernel.json but they are just the same. The only difference I found is the installation path of the kernel, the previous (named python3) is in /opt/anaconda3/share/jupyter/kernels/python3, while the kernel rdkit is in /usr/local/share/jupyter/kernels/rdkit. How to transfer all the pakages to new kernel, or install a new kernel based on rdkit environment while keeping other pakages?