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Tomas Kulhanek edited this page Feb 12, 2019 · 17 revisions

This wiki is intended as a draft analysis, design and recommendation for developing CCPi Virtual Machine (VM) and CCPi framework and related components as a whole in order to deliver efficient environment in VM and probably in other identified environments.

CCPi VM brings exemplar configuration with all software installed on selected OS, library versions, etc. without hassle to install and configure it on your host machine.

VM analysis and design documents:

  • CCPi 1 VM analysis
    • recommendation: consider refactoring (systematic approach in directory structure in git repositories), repository naming convention (some have ccpi prefix, some doesn't), etc.
    • conclusion: CCPi-VirtualMachine will include all important CCPi components identified above installed from binaries and optional scripts or configuration will be made to install component from sources (if available).
  • CCPi 2 VM plan and implementation

Broader picture of CCPi and possible enhancements:

  • CCPi 3 State of The Art
    • conclusion: missing data management, data acquisition, grid-computing cloud-computing support? Some terminology different from other image processing disciplines.
    • recommendation: have bigger ambition to cover data storage, data communication and other gaps within CCPi roadmap. A lot of missing gaps can be addressed by recommendation, best practices to use or integrate existing tools and infrastructure for data management (Zenodo, EUDAT, ...). Document API and algorithm usage in interactive documents (e.g. Jupyter notebooks, samples in https://github.com/vais-ral/CCPi-VirtualMachine/tree/master/notebooks )
  • CCPi 4 Building and Releasing
    • describe conda build and configuration in Jenkins
    • reuse build scripts in CCPi VM (from sources option)
    • analyse existing unit tests, add new one. Integrate into automatic build
    • automatic build when new commit appears in master branch of git repository.
  • CCPi-5-Unit-Testing
    • keep/move multiple unit test file into /test directory,
    • call unit tests from run_tests.py,
    • automate/include tests in conda-build process - run_tests.py in recipe directory
    • preprocessing -create new run_tests, from existing demo code
    • quantification - create run_tests, call existing tests from it
    • reconstruction - create run_tests, call existing tests from it

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