Check a prototype of 2nd version of this platform
being developed by cKnowledge.org in collaboration with
MLCommons.
filter math.frontier
ck-action:filter math.frontier (v3.0.0)
License: BSD 3-clause (code) and CC BY-SA 4.0 (data)
Creation date: 2015-05-19
Source: GitHub
cID: 081173242a88bc94:963b46fe8fdf6564
Don't hesitate to get in touch if you encounter any issues or would like to discuss this community project!
Creation date: 2015-05-19
Source: GitHub
cID: 081173242a88bc94:963b46fe8fdf6564
Description
- Automation framework: CK
- Development repository: ck-ml
- Source: GitHub
- How to get the stable version via the CK client:
pip install cbench
cb download module:math.frontier --version=3.0.0 --all
ck filter math.frontier --help - How to get the development version:
pip install ck
ck pull repo:ck-ml
ck filter math.frontier --help
- How to run from Python:
import ck.kernel as ck
r=ck.access({'action':'',
'module_uoa':'',
... See JSON API below ...
})
if r['return']>0: return r
...
- Module description: detecting (Pareto) frontier for multi-objective optimizations
- API Python code: Link
- JSON API:
" Input: { points - dict with points, each has dict with optimization dimensions (should have the same names) (frontier_keys) - list of keys to leave only best points during multi-objective autotuning (multi-objective optimization) If omited, use all keys (reverse_keys) - list of values associated with above keys. If True, reverse sorting for a give key (by default descending) - can't be used without "frontier_keys" due to lack of order in python dicts2 (margins) - list of margins when comparing values, i.e. Vold/Vnew < this number (such as 1.10 instead of 1). will be used if !=None } Output: { return - return code = 0, if successful > 0, if error (error) - error text if return > 0 points - filtered points! deleted_points - deleted points } "