Make MLBlocks an optional dependency
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- python, tensorflow
- Domain
- build-system, machine-learning
Research direction
Start by tracing PipelineExplorer.score_pipeline, Pipeline.score_template, and MLPipeline loading to see how MLBlocks is currently imported for exploration and scoring. Check the package installation options mentioned in the issue and verify that S3 exploration works without MLBlocks while both scoring entry points remain available with the scoring extra.
Written by the indexing model from the issue text.
Description
MLBlocks is a pretty heavy-weight dependency as it requires tensorflow, xgboost, etc. to be installed. To just "explore" pipelines from S3, users shoudn't need MLBlocks.
Desired functionality:
- for all methods except
PipelineExplorer.score_pipelineandPipeline.score_template, MLBlocks should not need to be installed - if the user wants to score pipelines or templates using
PipelineExplorer.score_pipelineandPipeline.score_template, they can enable this bypip install piex[mlblocks](orpiex[scoring]orpiex[all]or some other variant)
Note that this would require a workaround for using MLPipeline to load pipelines/templates for the express purpose of extracting metadata from them
- Dominant language
- Jupyter Notebook
- Stars
- 17
- Forks
- 6
- PR merge metrics
- No merged PRs in 30d
Getting set up
- No Dockerfile or Docker Compose file
- No pull request template
- Read the contributing 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.
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