view inference/chapmlaas.xml @ 127:d8baf7dafbd8 draft

planemo upload for repository https://github.com/CHESSComputing/ChessAnalysisPipeline commit dfe15e79689bca8ddcd843f9cf47eff29f6ee2e4-dirty

<tool id="CHAP_inference_demo" name="CHAP MLaaS" version="0.1.0+galaxy0" python_template_version="3.5" profile="21.05">
    <macros>
        <import>../macros.xml</import>
    </macros>
    <requirements>
        <requirement type="package" version="2.28.2">requests</requirement>
	<expand macro="chap-requirement" />
    </requirements>
    <command detect_errors="exit_code"><![CDATA[
        cp '$input' data.csv &&
        cp '$image' img.png &&
        CHAP --config '$config' &&
        cp preds.json '$output'
    ]]></command>
    <inputs>
        <param type="data" name="config" format="yaml" />
        <param type="data" name="input" format="csv" />
        <param type="data" name="image" format="png" />
    </inputs>
    <outputs>
        <data name="output" format="json" />
    </outputs>
    <tests>
        <test>
            <param name="config" value="config.yaml"/>
            <param name="input" value="data.csv"/>
            <param name="image" value="img.png"/>
        </test>
    </tests>
    <help><![CDATA[
CHESS Analysis Pipeline (CHAP) with Machine Learning as a Service (MLaaS)

To run it you need the following:
1. Working TFaaS server
2. A pipeline config with TFaaS, e.g.

pipeline:
  - reader.Reader:
      filename: data.csv
  - processor.Processor: {}
  - reader.BinaryFileReader:
      filename: img.png
  - processor.TFaaSImageProcessor:
      url: "http://localhost:8083"
      model: mnist
      verbose: true
  - writer.Writer:
      filename: preds.json

    ]]></help>
    <citations>
        <expand macro="chap-citation" />
    </citations>
</tool>