Modifying kernelspec used at launch

I’m working on setting up a Jupyter notebook that uses the R kernel (IRkernel) inside a conda environment named contrib-viz. When I launch the binder, the kernel is not found, as seen in the following screenshot

The problem seems to be that the local notebook is saved with a kernlespec that includes the name of the conda environment:

"kernelspec": { 
   "display_name": "R [conda env:contrib-viz]", 
   "language": "R", 
   "name": "conda-env-contrib-viz-r" 
  }

Jupyter launched through binder is trying to use a different kernelspec:

"kernelspec": {
   "display_name": "R [conda env:conda]",
   "language": "R",
   "name": "conda-env-conda-r"
  }

Selecting the kernel R [conda env:conda] that is offered in the drop down menu in the screenshot is a workaround, but I expect that will deter novice users who would most benefit from the binder. I can also edit the .ipynb file to make the kernelspec match what binder expects, but then I get the kernel not found error when I run the notebook in my local environment. Renaming my conda environment would probably work, but I have multiple environments for this project and prefer to keep them distinct if possible. Is there a better general solution?

The binder link is https://mybinder.org/v2/gh/agitter/meta-review/binder?filepath=analyses/deep-review-contrib/02.contrib-viz.ipynb This is a temporary branch for a pull request that will be deleted soon. Currently, this is the version with an edited .ipynb file that works in binder but not locally.

Thanks for the binder service. I see a lot of opportunities to use this.

[agitter]

This is working now so I’ll leave some final notes for anyone who has a similar problem. I decided that modifying the .ipynb notebook file in the postBuild script was the best way to accomplish what I wanted. I can still use nb_conda_kernels in my local environment. The modified notebook has an edited kernelspec to match a kernel that is available in Binder.

I was unable to get conda create --name new_name --clone old_name working. I received the error

CommandNotFoundError: Your shell has not been properly configured to use 'conda activate'.

even after briefly attempting to enable conda in postBuild. Because conda clean -tipsy has already run before the conda create in the postBuild script, creating the environment is slow anyway. conda has to re-download all of the packages again.

Part of my initial confusion was already related to this issue https://github.com/jupyter/repo2docker/issues/411 I had assumed that using Binder with an environment.yml file would work similarly to

conda env create --file environment.yml
conda activate <new_environment>

After looking through the repo2docker conda buildpack code I realized that the environment in environment.yml is not created or activated. It is used to update the base conda environment. That could be documented in https://mybinder.readthedocs.io/en/latest/config_files.html#environment-yml and I’m happy to suggest something in another issue.

[betatim]

The environment.yml is used to update the environment so even for packages that are installed by default the version specified in the repositories environment.yml should “win”. Otherwise please report a bug in https://github.com/jupyter/repo2docker.

The best we have to offer right now for running things locally is http://repo2docker.readthedocs.io/ which is the tool that Binder uses to build your environment. You can install it with pip install jupyter-repo2docker and run repo2docker https://github.com/myorg/myrepo to get the same experience as on mybinder.org (with some patience to build the image). For actual local development repo2docker --editable some/local/dir is more useful as it will mount your local directory read-write into the container so you can keep using your favourite editor and tools. Polishing the run-locally-for-day-to-day-dev experience is something we are working on and welcome contributions. IN particular in making things faster.