During the 2025 Datathon, environments on JupyterHub are managed with UV, a package and environment manager like pip or conda.
We've pre-configured several of these based on the contents of the summer school sessions. These include:
pandas-env: General data science and data engineering packagespytorch-env: General data science/data engineering packages + packages to use Pytorch/Torchvisiontensorflow-env: General data science/data engineering packages + packages to use Tensorflowlinear_probing: Environment used during the summer school session on linear probing with embeddingsWe've also prepared an R kernel so users that wish to can use R for data analysis.
User environments are stored in /mnt/efs/envs (or alternatively are symlinked from each users home directory at ~/envs). This location is not writable, so users who want to add new environments should create them in their home directory with UV or Python.
If you'd like to modify an existing environment, we recommend copying the source environment folder from /mnt/efs/envs to your home directory, then using uv add PACKAGE to add new packages. You can register this environment in your JupyterHub by running (please ensure you're inside your package directory first):
# replace NEW_ENV_NAME with your desired environment name
uv run ipython kernel install --user --env VIRTUAL_ENV $(pwd)/.venv --name=NEW_ENV_NAME