---
$schema: https://gitlab.com/crim.ca/clients/cubewerx/quote-estimator/-/raw/main/src/quote-estimator/schema.yaml
inputs:
  # metadata that describes the expected process inputs
  process-input:
    size: 209715200  # 200 MiB
config:
  # rates applied for this quote estimation of the process
  # for this example, assume a base rate and a process that highly depends on variable memory usage
  # other estimator/rates could be added as needed for advanced use cases: [duration, storage, GPU usage, CPU, usage]
  flat_rate: 10
  memory_rate: 0.005
  memory_estimator:
    # mapping of any ONNX-compatible model to process inputs
    # this is a pretrained model that should be obtained from some previous usage analysis of the process
    inputs:
      input: process-input
    output: variable
    model:
      irVersion: '8'
      producerName: skl2onnx
      producerVersion: '1.13'
      domain: ai.onnx
      modelVersion: '0'
      docString: ''
      opsetImport:
        - domain: ai.onnx.ml
          version: '1'
        - domain: ''
          version: '17'
      graph:
        node:
          - input:
              - input
            output:
              - variable
            name: LinearRegressor
            opType: LinearRegressor
            attribute:
              - name: coefficients
                floats:
                  - 7.394776e-08
                type: FLOATS
              - name: intercepts
                floats:
                  - 10.522503
                type: FLOATS
            domain: ai.onnx.ml
        name: c0ebe96ccead470493f9742d6980bcf0
        input:
          - name: input
            type:
              tensorType:
                elemType: 1
                shape:
                  dim:
                    - { }
                    - dimValue: '1'
        output:
          - name: variable
            type:
              tensorType:
                elemType: 1
                shape:
                  dim:
                    - { }
                    - dimValue: '1'