What does `nb_conda_kernels` do?

nb_conda_kernels got added to my Anaconda installation on an update a few months back. Since then I’ve been confused as to what it does - it seems to make sharing Notebooks harder. What am I missing?

Here’s my workflow:

• Create a new environment for a project using conda
• Make some Notebooks, commit to git, share to collaborator
• Collaborator makes a similar environment, clones my repo
• Using an activated environment the collaborator, having the same named project environment, is asked to select an environment (and presumably this changes the metadata for the Notebook)

I don’t understand why this just doesn’t use the default environment that jupyter notebook was run from. What’s the advantage to me of activating an environment, starting a Notebook, then having to choose the same environment (and/or having a collaborator mess with the metadata when we’re sharing Notebooks)?

What’s the thinking behind fixing a Notebook to a (edit - removed “machine”) specific environment name?

Update - I’ve now upgraded to 2.0 (which still required me to choose a kernel when I started a Notebook), I see on the installation page that I could also run python -m nb_conda_kernels.install --enable --prefix="%CONDA_PREFIX" which I’ve done because it looks magic, but not because I know what it’ll fix.

For reference the Notebook metadata in this case contains:

  "kernelspec": {
    "name": "conda-env-fds-py",
    "display_name": "Python [conda env:fds]",
    "language": "python"
  },```

Hoping someone can set me straight...

[jankatins]

[note: most of this information is based on my usage of the environment_kernel_manager which more or less has the same idea as nb_conda_kernels]

The kernel isn’t specific to the machine, it uses a name defined by the conda environment name: in this case the nb_conda_kernels creates a (virtual) kernel spec for each conda environment (which has a kernel installed?). So if you both have nb_conda_kernels installed and the same name for the projects conda environment, it should work.

Just to comment on the idea for a newish workflow:

• install jupyter notebook in a new/root environment which only contains the notebook (i.e. no “project related” stuff) -> you only need to install the notebook once.
• create a new environment for your project via conda (e.g. via a environment.yml file in your project dir) and install IPykernel in there
• start the notebook server from the root/first environment in the project directory (e.g. via full path to jupyter.exe or whatever it is called on mac/linux)
• create a new notebook by choosing the kernel from the project environment -> you don’t need to activate the env! [… hack and share …]
• get your collaborator to create the same environment(s), especially an environment with the same name as your project environment
• open notebook and it should work

The “default name” is actually depended on having a jupyter kernel in the same environment as the jupyter notebook, which does not need to be the case (at least I tried to remove it, but conda didn’t let me, bu AFAIK there shouldn’t be a technical reason other than not confusing the user).

[damianavila]

Closing this one since the original discussion stopped a long time ago. Please reopen (or open a new issues) if you have further questions.