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JupyterLab Kernels

JupyterLab Kernels

Here is a list of available kernels that can be selected for a jupyter notebook:

  • Note: You can not use the module system from the Beartooth cluster within a Jupyer Notebook - any environment you require needs to run within a specific kernel.

Kernels

Name

Description

GPU Enabled?

Name

Description

GPU Enabled?

Python 3

Python 3.9.5

N

Python 3.11.0

Python 3.11.0

N

PyTorch 1.13.1

Python 3.10.9

Y

Dask 2023.8.1

Python 3.11.4 / pandas 2.0.3 / numpy 1.25.2

N

R4.2.2

A basic R 4.2.2 environment.

N

The following kernels relate to Intel’s Distribution for Python and AI Analytics Toolkit:

Name

Description

GPU Enabled?

Name

Description

GPU Enabled?

Intel Python3 Base 23.0.0

Python 3.9.15

N

Intel Modin 0.17.0.1

Python 3.9.15

N

Intel PyTorch 1.13.0.0

Python 3.9.15

Y

Intel TensorFlow 2.11.0

Python 3.9.15

N

Intel TensorFlow 2.12

Python 3.10.10

N

Finding available packages

Conda: If the kernel has been created from a conda environment, then you can run conda list from a cell to find all the packages/libraries within it:

Python: Call pip list -v from a cell will list all the python packages installed via the conda environment AND any packages you have installed yourself via pip install listed with a location in your home ~/.local/lib/<python-version> folder.

Look at the Location and Installer columns to see what is directly available within the kernel and what has manually been installed. For example:

Package Version Location Installer ----------------------------- ------------------- --------------------------------------------------------- --------- ... entrypoints 0.3 /apps/s/jupyterlab/miniconda3/lib/python3.9/site-packages conda flatbuffers 23.1.4 /pfs/tc1/home/salexan5/.local/lib/python3.9/site-packages pip ...

R: Call installed.packages() from a cell will list all the R packages installed via the conda environment AND any packages you have installed yourself via install.packages('<package-name>') listed with a location in your home ~/R/<architecture/<version> folder.

Look at the LibPath column to see what is directly available within the kernel and what has manually been installed. For example:

Package LibPath Version clipr /pfs/tc1/home/salexan5/R/x86_64-conda-linux-gnu-library/4.2 0.8.0 base /apps/u/opt/jupyter_kernels/r4.2.2/lib/R/library 4.2.2

 

 

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