jbdatascience/nb_conda_kernels

Package for managing conda environment-based kernels inside of Jupyter

nb_conda_kernels

Manage your conda environment-based kernels inside the Jupyter Notebook.

This package defines a custom KernelSpecManager that automatically creates KernelSpecs for each conda environment. When you create a new notebook, you can choose a kernel corresponding to the environment you wish to run within. This will allow you to have different versions of python, libraries, etc. for different notebooks.

Important Note : To use a conda environment as a kernel, don't forget to install ipykernel in this environment or it won't show up in the kernel list.

Installation

conda install -c conda-forge nb_conda_kernels

Getting Started

You'll need conda installed, either from Anaconda or miniconda.

conda create -n nb_conda_kernels python=YOUR_FAVORITE_PYTHON
conda install -n nb_conda_kernels --file requirements.txt -c r
source activate nb_conda_kernels
python setup.py develop
python -m nb_conda_kernels.install --enable --prefix="${CONDA_PREFIX}"
# or on windows
python -m nb_conda_kernels.install --enable --prefix="%CONDA_PREFIX"

We still use npm for testing things, so then run:

npm install

Finally, you are ready to run the tests!

npm run test

Changelog

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