Comments (5)
auto bump version in the main package.json (currently ocular-publish only bumps versions in modules package.json
lerna bump typically only bumps modules in the workspace. Since example/ and website/ are not in the workspace (and supposedly not), it is not necessary for "bump" to work for them.
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lerna bump typically only bumps modules in the workspace. Since example/ and website/ are not in the workspace (and supposedly not), it is not necessary for "bump" to work for them.
But that will create confusion. Imagine someone new will have to go to modules
subdirectories to find out what current version is.
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Not sure if this is a relevant comment, but I prefer using synchronized versioning of submodules. I.e. all modules get published with the same version even if only some have changed. That version is then in lerna.json
in the root.
from manifold.
Not sure if this is a relevant comment, but I prefer using synchronized versioning of submodules. I.e. all modules get published with the same version even if only some have changed. That version is then in
lerna.json
in the root.
We are already doing that
from manifold.
I changed my mind and I think aligning the versions on website/ and example/ is actually a good idea, because we will have a documentation website later on and the documentation version should align with the module versions, so should be the demo in or referenced by the documentation.
To do so you can make some scripts following the lerna bump to copy over the version number from the modules to the docs/websites.
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Related Issues (20)
- Jupyter notebook - Export Segmentation not working HOT 2
- Module not found error HOT 1
- Segmentation by difference between models' performance
- Support multi-class classification HOT 1
- Allow users to change model names
- Allow users to monitor and change feature types
- Add on-screen tooltips
- Create practical examples using public datasets HOT 1
- Performance metric drop down not working and explanation of feature attribution is not clear HOT 1
- Export report
- Not obvious distributions for categorical features HOT 2
- How to prepare prediction dataset for manifold HOT 1
- Improve "Running demo app locally"
- CSV upload of large data does not work.
- Demo sample data is not working HOT 1
- Python manifold provide alias to model name
- Drill down to data points in clusters HOT 1
- are you planning to create python version ?
- URL does not load HOT 3
- your github.io domain seems to have been hijacked HOT 1
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