Check a prototype of 2nd version of this platform being developed by in collaboration with MLCommons.

Artifact pipeline from

program:noacahan-wavenetautoencoder-github-artifact (v1.0.0)
License: CC BY-SA 4.0
Creation date: 2020-05-01
cID: b0ac08fe1d3c2615:64d9db6af02f4a72

Don't hesitate to get in touch if you encounter any issues or would like to discuss this community project!
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This portable workflow is our attempt to provide a common CLI with Python JSON API and a JSON meta description to automatically detect or install required components (models, data sets, libraries, frameworks, tools), and then build, run, validate, benchmark and auto-tune the associated method (program) across diverse models, datasets, compilers, platforms and environments. Our on-going project is to make the onboarding process as simple as possible via this platform. Please check this CK white paper and don't hesitate to contact us if you have suggestions or feedback!
  • Automation framework: CK
  • Source:
  • Available command lines:
    • ck run program:noacahan-wavenetautoencoder-github-artifact --cmd_key=default (META)
  • Support for host OS: any
  • Support for target OS: any
  • Tags: imported-artifact,raw-artifact,black-box,github,noacahan-wavenetautoencoder,vmaster,branch-master
  • How to get the stable version via the client:
    pip install cbench
    cb download program:noacahan-wavenetautoencoder-github-artifact --version=1.0.0 --all
    ck run program:noacahan-wavenetautoencoder-github-artifact
  • How to get the development version:
    pip install ck

    ck run program:noacahan-wavenetautoencoder-github-artifact

  • CLI and Python API: module:program
  • Dependencies    


    Imported from




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