Dockerfile for the EUCP project - based on the Jupyter datascience notebook
View on dockerhub
- master: for regular development and updates. Merging via approved pull requests. Linked to 'latest' tag on dockerhub.
- feature branches: make a separate branch for each PR/feature that you want to add.
- We'll configure the login environment such that you can select which release you want to use for your compute environment.
- Checkout, build and run the docker image locally
- Make desired changes to the environment
- Build and run updated docker image locally for testing
- Use
conda list --export > package_list_for_change_diffs.txt
, download and commit the result - Summarize changes made to the environment in the changelog below
- Make a pull request to the master branch of this repository
- Once the PR is merged, test changed in the 'live' environment by choosing 'latest'.
- Consider making a new release and adding it to the ansible setup
May 20, 2020:
- Inherit from newer jupyter/datascience-notebook
- Updated jupyter hub & jupyter lab versions
- Added explicit dependency of matplotlib
- Removed some packages as they are now part of base- or scipy-nb
- Removed some packages as they were installed as dependencies of others
- Moved all pip-installs to conda
- Add esmvalcore and esmvaltool-python
- Upgrade several packages to newer versions
- Disable not-working jupyter-thredds plugin
May 19, 2020:
- Pin versions to current environment configuration
- Add
package_list_for_change_diffs.txt
for keeping track of environment changes.
May 18, 2020:
- Start changelog
- Enable autobuild on dockerhub
- Create master and stable branches