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program:mnist-armnn-tf (v3.0.0)
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Copyright: See copyright in the source repository
License: See license in the source repository
Creation date: 2019-02-12
Source: GitHub
cID: b0ac08fe1d3c2615:dee85e62d40959a9

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Description  

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
  • Development repository: ck-ml
  • Source: GitHub
  • Available command lines:
    • ck run program:mnist-armnn-tf --cmd_key=minimal (META)
  • Support for host OS: any
  • Support for target OS: linux, windows
  • Tags: test,mnist,tf,armnn,lang-cpp
  • How to get the stable version via the client:
    pip install cbench
    cb download program:mnist-armnn-tf --version=3.0.0 --all
    ck run program:mnist-armnn-tf
  • How to get the development version:
    pip install ck
    ck pull repo:ck-ml
    ck run program:mnist-armnn-tf
  • CLI and Python API: module:program

    • Dependencies    

    ReadMe  

    MNIST classification example for ArmNN/TF

    Compile (4 versions) :

    $ ck compile program:mnist-armnn-tf                                     # compile reference implementation (works on Intel)

$ ck compile program:mnist-armnn-tf --env.USE_NEON                      # compile for using CPU acceleration (Arm only)

$ ck compile program:mnist-armnn-tf --env.USE_OPENCL                    # compile for using GPU acceleration (Arm only)

$ ck compile program:mnist-armnn-tf --env.USE_NEON --env.USE_OPENCL     # compile for using both CPU and GPU acceleration

    Run (4 versions, should match USE_* parameters during compilation) :

    $ ck run program:mnist-armnn-tf --env.CK_FILE_NUMBER=0                  # run reference implementation (works on Intel)

$ ck run program:mnist-armnn-tf --env.USE_NEON                          # run for using CPU acceleration (Arm only)

$ ck run program:mnist-armnn-tf --env.USE_OPENCL --env.CK_FILE_NUMBER=0 # run for using GPU acceleration (Arm only)

$ ck run program:mnist-armnn-tf --env.USE_NEON --env.USE_OPENCL         # run for using both CPU and GPU acceleration

    Note:

    CK_FILE_NUMBER is the file number in MNIST dataset (0..9999)

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