nebari-dev / nebari-dev/mlflow-pack
Integrate MLflow with Jupyterhub
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- Dominant language
- Makefile
- Stars
- 1
- Forks
- 2
- Avg merge
- 23h 29m
- Merged PRs (30d)
- 4
Description
MLflow has an environment variable MLFLOW_TRACKING_URI that will need to be set in jupyterhub for users of the data science pack to utilize the service.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by locating the JupyterHub or data science pack configuration that supplies user environment variables. Check how service settings are exposed to users and verify the MLflow integration can read MLFLOW_TRACKING_URI. Done means users of the data science pack have the variable available and can utilize the MLflow service.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter
- Domain
- devops
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100