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.
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 | N |
Intel Python3 Base 23.0.0 | This is kernel of Intel’s Distribution for Python: Python 3.9.15 | N |
Intel Modin 0.17.0.1 | N | |
Intel PyTorch 1.13.0.0 | Y | |
Intel TensorFlow 2.11.0 | N | |
Intel TensorFlow 2.12 | N | |
R4.2.2 | A basic R 4.2.2 environment. | N |
Finding available packages:
If the kernel has been updated 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.
R:
in your home/.lc.
To find the full list of available packages, start required kernel and within a cell, run:
For Python:
pip listR:
installed.packages()
Testing for GPUs: