<tool id="wine_quality_train_eval" name="wine_quality_train_eval"
 version="1.0.0">

  <description>wine_quality_train_eval</description>

  <requirements>

    <!-- singularity A REMETTRE (avant essais)
    <container type="singularity">oras://registry.forgemia.inra.fr/nathalie.rousse/use/dnn_fidlemore:1906137478520a9f71a4d8996c0a8827f27aed16</container -->




    <!-- singularity ESSAIS -->
    <container type="singularity">oras://registry.forgemia.inra.fr/nathalie.rousse/use/dnn_fidlemore:ea8ac4fa4f127a69480c70e8966353df331d26ed</container>




    <!-- image locale
    <container type="singularity">/home/nrousse/workspace_git/SIWAA_regroup/USE_branch_dnn/use/DNN/containers/fidlemore.simg</container -->

  </requirements>

  <environment_variables>
    <environment_variable name="FIDLE_DATASETS_DIR">/fidle-tp/datasets-fidle</environment_variable>
  </environment_variables>
  <command detect_errors="aggressive">
    <![CDATA[
    bash -e -c "source /fidle-tp/fidle-env/bin/activate &&
    export FIDLE_DATASETS_DIR=/fidle-tp/datasets-fidle &&
    export IPYTHONDIR=\$(realpath -s ipython_dir) &&
    mkdir cache_dir &&
    export TRANSFORMERS_CACHE=\$(realpath -s cache_dir) &&
    mkdir mpl_dir &&
    export MPLCONFIGDIR=\$(realpath -s mpl_dir) &&
    python3 $__tool_directory__/toto_wine_quality_train_eval.py -dataset_filepath ${dataset_csv} &&
    cp OUTPUTS/model.ckpt ${model_ckpt} &&
    cp OUTPUTS/norm_config.json ${norm_config_json} &&
    cp OUTPUTS/report.json ${report_json}"
    ]]>
  </command>

  <inputs>
    <param name="dataset_csv" optional="true" type="data" format="csv"
           label="File of dataset used to train and test the model (.csv)"/>
  </inputs>

  <outputs>
    <data format="ckpt" name="model_ckpt"
          label="model_ckpt model file (.ckpt)"/>
    <data format="json" name="norm_config_json"
          label="norm_config normalization configuration (.json)"/>
    <data format="json" name="report_json" label="report (.json)"/>
  </outputs>

  <tests>
  </tests>

  <help><![CDATA[

wine_quality_train_eval.xml

===========================

Code:

----

- wine_quality_train_eval.py

Inputs:

-------

- dataset file (.csv) :

  File containing dataset used to train and test the model.

  The dataset will be splitted in 2 parts (training part, validation part).
  
  If not given, an embedded default file used.

Outputs:

--------

- Model file (.ckpt)

- Normalization configuration file (.json)

- Report file (.json)

Credits:

--------

-  Author: Nathalie Rousse nathalie.rousse@inrae.fr
-  Copyright: INRAE

  ]]>
  </help>

</tool>