Python package for visualizing cluster measures.
Python package for computing cluster measures.
- Free software: MIT license
- Documentation: https://viewclust.readthedocs.io.
- Calculates job usage based on inputs from Slurm or other formatted data frames
- Plots cumulative and instantaneous usage
- Accepts dynamic targets for account and system analysis
- Provides a basic interface for generating job data frames from Slurm
For more plotting options and dashboard suites see the ViewClust-Vis package.
- Optimize job use calculations
- Add support for horizon measures when studying job usage
- Code linting fixes
- Revert modern plotting features and added to ViewClust-Vis
- Job use now calculates more theoretical lines
- Added return handles for figures that were missing them
- Added parameter for groupby frequency inside of job use
- Bug fix on sacct poller
- Added ability to sacct poll all accounts in slurm submodule
- Refactor calculations for eqv use_unit measures
- Completed support for gpu eqv usage calculations
- Minor formatting to mem info parsing
- Can now serialize user run
- Crediting Sergio!
- Add new support for plotting to the terminal
- Bug fix stderr output
- Docs update for new changes
- Bug fix for the init py file
- Major versioning bump
- User area colouring in usage figures
- Mem info plotting support
- Serialization of user
- Removed violin plot. Moved to ViewClust-Vis
- Removed use_suite. Moved to ViewClust-Vis
- Removed job_stack. Moved to ViewClust-Vis
- Deprecated cumu/insta plotting function. Supported versions moved to ViewClust-Vis
- Added support for user breakdown of activity on plots
- Fixed other half of instaplot bug
- Fix use suite string passing bug
- Fix insta plot pre reference bug
- Fix syntax based errors
- Fix failing doc build
- GPU usage added back
- README fixes to include credits and features
- Figures now return their handles for extra processing if needed
- Figures can now have their polling rates downsampled for ease of sharing
- Prepared responses for figures section added in to documentation
- Inclusion of API documentation
- Overhauled the documentation usage section
- Reduced versioning complexity
- Testing distributions changed
- File inclusion
- First release on PyPI.
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