Launch Jupyter kernels from Conda-Store
Project description
nb_conda_store_kernels
This extension enables a Jupyter Notebook or JupyterLab application to access environments stored in Conda-Store and run kernels for Python, R, and other languages. When a kernel from an external environment is selected, the environment is downloaded, extracted, conda environment is automatically activated, and finally the kernel is launched. This package was heavily inspired by nb_conda_kernels.
Any notebook launched via a nb_conda_store_kernel
kernel will have
notebook metadata about the environment used. This provides a powerful
mechanism for running the notebook later in a reproducible way.
{
...,
"kernelspec": {
...,
"name": "conda-store://<namespace>/<environment_name>:<build-id>",
...,
},
...
}
The package works by defining a custom KernelSpecManager that calls the Conda-Store REST API for available conda environments with required packages. 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. Additionally without Conda-Pack
Usage
This package does not need pip
or conda
to run properly but
currently only run on Linux. It should be installed in the environment
from which you run Jupyter Notebook or JupyterLab. It is recommended
for install nb_conda_store_kernsl
via Conda due to activation hooks
which simplify the installation but there is no strong reason to use
conda.
conda install -c conda-forge nb_conda_store_kernels
Alternatively pip
works as well but requires one additional step.
pip install nb_conda_store_kernels
python -m nb_conda_store_kernels.install --enable
# python -m nb_conda_store_kernels.install --disable # to disable
python -m nb_conda_store_kernels.install --enable
simply modifies a
single jupyter setting. If that did not work (it should and is a bug
if not) you need to add the following setting to jupyter_config.py
.
mkdir ~/.jupyter
echo 'c.JupyterApp.kernel_spec_manager_class = "nb_conda_store_kernels.manager.CondaStoreKernelSpecManager"' > ~/.jupyter/jupyter_conifg.py
In order to nb_conda_store_kernels
to connect properly to
Conda-Store it needs several environment variables set. Under the
covers conda-store
the client is being using and has detailed
documentation
on configuration.
export CONDA_STORE_URL=http(s)://...
export CONDA_STORE_AUTH=none|basic|token
# export CONDA_STORE_USERNAME=... # using basic auth
# export CONDA_STORE_PASSWORD=... # using basic auth
# export CONDA_STORE_TOKEN=... # using token auth
Finally launch JupyterLab!
jupyter lab
Connecting to existing conda-store server
Visit conda-store server you are using and get a token for your given
user via vising the /user
endpoint after you login.
docker run -p 8888:8888 -e CONDA_STORE_TOKEN=... quansight/nb-conda-store-kernels:v0.1.5
Use with nbconvert, voila, papermill,...
Since nb_conda_store_kernels
uses the conda-store
client under the
covers these use cases are supported by the client with proper
environment variables being set. There are plans to extend other tools
to use Conda-Store.
conda-store run namespace/environment -- python -c "print('Hello, Conda-Store!')"
Development
Start Conda-Store server
docker-compose up --build
jupyter lab
Login to Conda-Store at localhost:5000 and create a new environment.
Copyright 2022 Quansight LLC
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Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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