add_solution program.optimization
Component: cr-action:add_solution program.optimization (v1.0.0)
Added by: gfursin (2019-10-16 07:56:43)
Authors: Grigori Fursin
License: BSD 3-clause (code) and CC BY-SA 4.0 (data)
Source: GitHub
Creation date: 2015-12-31 17:14:32
CID: 081173242a88bc94:3e5416e9eeed7503cr-action:add_solution program.optimization  )

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How to get and run stable version (under development):
  pip install codereef
  cr download module:program.optimization --version=1.0.0 --all
  ck add_solution program.optimization --help

How to get and run development version:
  pip install ck
  ck pull repo:ck-crowdtuning
  ck add_solution program.optimization --help

How to run from Python:
   import ck.kernel as ck

   r=ck.access({'action':'add_solution',
                'module_uoa':'program.optimization',
                ... See JSON API below ...
               })
   if r['return']>0: return r
   ...
Info about the CK module with this action: program.optimization
Workflow framework: CK
Development repository: ck-crowdtuning
Module description: program optimization
Workflow: program optimization front-end (workflow)
API Python code: Link
JSON API:
        "    Input:  {
              packed_solution         - new packed points
              scenario_module_uoa     - scenario UID
              meta                    - meta to search
              (meta_extra)            - extra meta to add

              exchange_repo           - where to record (local or remote)
              exchange_subrepo        - where to recrod (if remote, local repo in remote machine)

              (packed_solution)       - new packed solution (experimental points ready to be sent via Internet 
                                        if communicating with crowd-server)

              (solution_uid)          - new solution UID (if found)

              solutions               - list of solutions (pre-existing and new with re-classification)

              (workload)              - workload dict to classify distinct optimizations
                                        (useful for collaborative machine learning and run-time adaptation)

              (user)                  - user email/ID to attribute found solutions (optional for privacy)          

              (first_key)             - first key (to record max speedup)
            }

    Output: {
              return              - return code =  0, if successful
                                                >  0, if error
              (error)             - error text if return > 0

              (recorded)          - if 'yes', submitted solution was recorded

              if recorded=='yes':
              (recorded_info)     - dict with recorded entry {'repo_uoa', 'module_uoa', 'data_uoa'}
            }

"
       


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