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app-image-classification-onnx-py

Automatically generated README for this automation recipe: app-image-classification-onnx-py

Category: Modular AI/ML application pipeline

License: Apache 2.0

  • Notes from the authors, contributors and users: README-extra

  • CM meta description for this script: _cm.yaml

  • Output cached? False

Reuse this script in your project

Install MLCommons CM automation meta-framework

Pull CM repository with this automation recipe (CM script)

cm pull repo mlcommons@cm4mlops

cmr "modular python app image-classification onnx" --help

Run this script

Run this script via CLI
cm run script --tags=modular,python,app,image-classification,onnx[,variations] [--input_flags]
Run this script via CLI (alternative)
cmr "modular python app image-classification onnx [variations]" [--input_flags]
Run this script from Python
import cmind

r = cmind.access({'action':'run'
              'automation':'script',
              'tags':'modular,python,app,image-classification,onnx'
              'out':'con',
              ...
              (other input keys for this script)
              ...
             })

if r['return']>0:
    print (r['error'])
Run this script via Docker (beta)
cm docker script "modular python app image-classification onnx[variations]" [--input_flags]

Variations

  • Group "target"

    Click here to expand this section.

    • _cpu (default)
      • ENV variables:
        • USE_CPU: True
    • _cuda
      • ENV variables:
        • USE_CUDA: True
Default variations

_cpu

Input Flags

  • --input: Path to JPEG image to classify
  • --output: Output directory (optional)
  • --j: Print JSON output

Script flags mapped to environment

  • --input=value → CM_IMAGE=value
  • --output=value → CM_APP_IMAGE_CLASSIFICATION_ONNX_PY_OUTPUT=value

Default environment

These keys can be updated via --env.KEY=VALUE or env dictionary in @input.json or using script flags.

  • CM_BATCH_COUNT: 1
  • CM_BATCH_SIZE: 1

Native script being run

Script output

cmr "modular python app image-classification onnx [variations]" [--input_flags] -j