Docker image with ATLAS AnalysisBase 24.2.X, Dask, and Scikit-HEP tools.
A version of this image is available for use at the US ATLAS Analysis Facility at UChicago through the JupyterHub service. When configuring the Jupyter Lab instance for your session select the image from the "Image" drop-down menu.
docker pull sslhep/analysis-dask-base:latestdocker run --rm -ti --publish 8888:8888 --volume $PWD:/analysis sslhep/analysis-dask-base:latest(as using Jupytext right click .py files to open as a Jupyter notebook)
docker run --rm -ti --publish 8888:8888 --volume $PWD:/analysis sslhep/analysis-dask-base:latest /bin/bashLists of all AnalysisBase releases that could be used as base images are provided on the ATLAS Twikis:
More easily though, you can just use crane to get a listing of all images from the command line
crane ls gitlab-registry.cern.ch/atlas/athena/analysisbaseAll the Python dependencies installed into the default Python virtual environment in the container image are installed from a lock file. To update the dependencies and the lock file:
-
Make a new branch.
-
Figure out what the version of the dependency you want to install is with
python -m pip index versions <dependency name>
-
Add this dependency and version to the
docker/requirements.txtwith the version pinned.Example:
dask-labextension==7.0.0 -
Rebuild the lock file with
make lock(this also verifies that the environment can be installed). -
Add and commit the updated
docker/requirements.txtanddocker/requirements.lock. -
Open a PR with the changes and wait for the CI to verify the build passes.
To trigger an image rebuild use the workflow dispatch feature of the base_builder workflow.
- Visit the
base_builderworkflow GitHub Actions page. - Select the "Run workflow" button on the top right side of the workflow runs table.
- Select which branch you would like to build from in the "Use workflow from" drop down menu.
- Click the "Run workflow" button.