![]() 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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