view inference/chapmlaas.xml @ 137:0bd8fb3aae96 draft

planemo upload for repository https://github.com/CHESSComputing/ChessAnalysisPipeline commit 85285b9b70eaf7c5db051ed50b000339023ccdf2-dirty

<tool id="CHAP_inference_demo" name="CHAP MLaaS" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" python_template_version="@PYTHON_TEMPLATE_VERSION@" profile="@PROFILE@">
  <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>