conda install jupycon/label/dev::nb_conda_kernels
conda install defaults::nb_conda_kernels
conda install conda-forge::nb_conda_kernels

nb_conda_kernels

This extension enables a Jupyter Notebook or JupyterLab application in one conda environment to access kernels for Python, R, and other languages found in other environments. When a kernel from an external environment is selected, the kernel conda environment is automatically activated before the kernel is launched. This allows you to utilize different versions of Python, R, and other languages from a single Jupyter installation.

The package works by defining a custom KernelSpecManager that scans the current set of conda environments for kernel specifications. It dynamically modifies each KernelSpec so that it can be properly run from the notebook environment. When you create a new notebook, these modified kernels will be made available in the selection list.

Installation

This package is designed to be managed solely using conda. It should be installed in the environment from which you run Jupyter Notebook or JupyterLab. This might be your base conda environment, but it need not be. For instance, if the environment notebook_env contains the notebook package, then you would run

conda install -n notebook_env nb_conda_kernels

Any other environments you wish to access in your notebooks must have an appropriate kernel package installed. For instance, to access a Python environment, it must have the ipykernel package; e.g.

conda install -n python_env ipykernel

To utilize an R environment, it must have the r-irkernel package; e.g.

conda install -n r_env r-irkernel

For other languages, their corresponding kernels must be installed.

Use with nbconvert, voila, papermill,...

This extension works out of the box only with Jupyter notebooks and JupyterLab.

A new kernel discovery system is being developed that should enable the wider Jupyter ecosystem to take advantage of these external kernels. This package will require modification to function properly in this new system.

But you can activate a workaround for it to work with Jupyter Console, nbconvert, and other tools. As these tools were not designed to allow for the use of custom KernelSpecs, you can set the configuration parameter kernelspec_path to tell this extension to add dynamically the conda environment to the kernel list. To set it up:

1. Create a configuration file for jupyter named jupyter_config.json in the folder returned by jupyter --config-dir.
2. Add the following configuration to install all kernel spec for the current user:

   {
   "CondaKernelSpecManager": {
   "kernelspec_path": "--user"
   }
   }

3. Execute the command (or open the classical Notebook or JupyterLab UI):

   python -m nb_conda_kernels list

4. Check that the conda environment kernels are discovered by jupyter:

   jupyter kernelspec list

   The previous command should list the same kernel than nb_conda_kernels.

You are now all set. nbconvert, voila, papermill,... should find the conda environment kernels.

Configuration

This package introduces two additional configuration options:

• conda_only: Whether to include only the kernels not visible from Jupyter normally or not (default: False except if kernelspec_path is set)

• env_filter: Regex to filter environment path matching it. Default: None (i.e. no filter)

• kernelspec_path: Path to install conda kernel specs to if not None. Default: None (i.e. don't install the conda environment as kernel specs for other Jupyter tools)
  Possible values are:

  • "" (empty string): Install for all users
  • --user: Install for the current user instead of system-wide
  • --sys-prefix: Install to Python's sys.prefix
  • PREFIX: Specify an install prefix for the kernelspec. The kernel specs will be written in PREFIX/share/jupyter/kernels. Be careful that the PREFIX may not be discoverable by Jupyter; set JUPYTER_DATA_DIR to force it or run jupyter --paths to get the list of data directories.

• name_format: String name format
  Default: '{language} [conda env:{environment}]'
  Available field names within the string:

  • {0} = Language
  • {1} = Environment name
  • {conda_kernel} = Dynamically built kernel name for conda environment
  • {display_name} = Kernel displayed name (as defined in the kernel spec)
  • {environment} = Environment name (identical to {1})
  • {kernel} = Original kernel name (name of the folder containing the kernel spec)
  • {language} = Language (identical to {0})

• enable_debugger: Override kernelspec debugger metadata Default: None Possible values are:

  • True: Override environment kernelspec metadata and set the debugger flag to true
  • False: Override environment kernelspec metadata and set the debugger flag to false

In order to pass a configuration option in the command line use python -m nb_conda_kernels list --CondaKernelSpecManager.env_filter="regex" where regex is the regular expression for filtering envs "this|that|and|that" works. To set it in jupyter config file, edit the jupyter configuration file (py or json) located in your jupyter --config-dir

• for jupyter_config.py - add a line "c.CondaKernelSpecManager.env_filter = 'regex'"
• for jupyter_config.json - add a json key

{
  "CondaKernelSpecManager": {
    "env_filter": "regex"
}

Development

1. Install Anaconda or Miniconda. If you are on Windows, make sure you have a Bash shell on your path.

2. Create and activate the testbed environment by running

   source testbed/build.sh

   This performs the following steps:

   • Builds a new root conda environment in ../nbckdev, or in CONDA_ROOT if that environment variable is defined. (Note that the default directory ../nbckdev is at the same level as your copy of the repository. This is because we do not want conda build to try to capture the entire testbed into the build workspace.)
   • Installs conda-build and the necessary dependencies to locally test the package
   • Installs the package in development mode
   • Creates a set of environments that the test scripts require to fully exercise the package.
   • Activates the environment, including a deliberate scrubbing of variables and paths from your primary conda environment.

   If the environment already exists, testbed/build.sh will quickly exit, so it is safe to run it if you are not sure.

3. Run pytest to test the package.

   pytest tests

4. The root environment of our testbed uses Python 3.7. If you would like to test nb_conda_kernels with a different Python version, create a new child environment:

   conda create -n ptest python=... notebook pytest pytest-cov requests mock
   conda install backports.functools_lru_cache # python 2 only
   conda activate ptest
   pip install -e .
   python -m nb_conda_kernels.install --enable
   pytest tests

Changelog

2.3.2

• Added code to clear zombie child processes left behind by the conda info subprocess call in manager.py

2.3.1

• Provide more options to set the display name of an environment (see name_format setting)
• Improve the runner script by activating the environment only if required
• Installation script improvements
• Fix GitHub CI actions

2.3.0

• Provide a mechanism for using nb_conda_kernels with tools such as voila, papermill, nbconvert
• Preserve kernel metadata properly
• Testbed improvements

2.2.4

• Tested support for noarch packages
• Windows bug fixes
• Better documentation for env_filter
• Fixes to kernel metadata

2.2.3

• Restore compatibiltiy with Jupyter V6
• Testing and support for Python 3.8
• Enhanced kernelSpec metadata

2.2.2

• Adds project name to kernel name for environments that live outside of the default environment location
• Improved runner scripts: linear execution, better handling of environment variables
• Migrate from nosetests to pytest

2.2.1

• Put the default environment back into the conda-env list; the redundancy is worth the elimination of confusion.
• Fix post-link scripts on windows

2.2.0

• Perform full activation of kernel conda environments
• Discover kernels from their kernel specs, enabling the use of kernels besides Python and R
• Support for spaces and accented characters in environment paths, with properly validating kernel names
• Configurable format for kernel display names
• Remove NodeJS-based testing

2.1.1

• move to a full conda-based approach to build and test
• add support for conda 4.4 and later, which can remove conda from the PATH

2.1.0

• add support for regex-based filtering of conda environments that should not appear in the list

2.0.0

• change kernel naming scheme to leave default kernels in place

1.0.3

• ignore build cleanup on windows due to poorly-behaved PhantomJS processes

1.0.2

• use Travis-CI for continuous integration
• use Coveralls for code coverage
• use a conda-forge for cross-platform conda package building

1.0.1

• minor build changes

1.0.0

• update to notebook 4.2