Different versions of Jupyter can be usedįor different conda environments, but this option might be a bit of overkill. Jupyter will be completely installed in the conda environment. Jupyter notebook # start server + kernel inside my-conda-env In short, there are three options how to use a conda environment and Jupyter: Option 1: Run Jupyter server and kernel inside the conda environmentĭo something like: conda create -n my-conda-env # creates new virtual envĬonda activate my-conda-env # activate environment in terminalĬonda install jupyter # install jupyter + notebook If nb_conda_kernels is used, additional to statically configured kernels, a separate kernel for each conda environment with ipykernel installed will be available in Jupyter notebooks. Kernels are configured by specifying the interpreter and a name and some other parameters (see Jupyter documentation) and configuration can be stored system-wide, for the active environment (or virtualenv) or per user. The kernel can be a different Python installation (in a different conda environment or virtualenv or Python 2 instead of Python 3) or even an interpreter for a different language (e.g. Jupyter runs the user's code in a separate process called kernel. Disclaimer: ATM tested only in Ubuntu and Windows (see comments to this answer).
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