fnndsc/pl-unirep_analysis repository overview

        Paper: https://www.nature.com/articles/s41592-019-0598-1

    unirep_analysis                                                     \
                                [--dimension <modelDimension>]          \
                                [--batch_size <batchSize>]              \
                                [--learning_rate <learningRate>]        \
                                [--inputFile <inputFileToProcess>]      \
                                [--inputGlob <inputGlobPattern>]        \
                                [--modelWeightPath <pathToWeights>]     \
                                [--outputFile <resultOutputFile>]       \
                                [--topModelTraining]                    \
                                [--jointModelTraining]                  \
                                [--json]                                \
                                <inputDir>
                                <outputDir>

docker pull fnndsc/pl-unirep_analysis

    [--dimension <modelDimension>]
    By default, the <modelDimension> is 64. However, the value can be changed
    to 1900 (full) or 256 and the corresponding weights files (present inside
    the container) will be used.

    [--batch_size <batchSize>]
    This represents the batch size of the babbler. Default value is 12.

    [--learning_rate <learningRate>]
    Needed to build the model. Default is 0.001.

    [--inputFile <inputFileToProcess>]
    The name of the input text file that contains your amino acid sequences.
    The default file name is an empty string. The full path to the
    <inputFileToProcess> is constructed by concatenating <inputDir>

            <inputDir>/<inputFileToProcess>

    [--inputGlob <inputGlob>]
    A glob pattern string, default '**/*txt', that specifies the file containing
    an amino acid sequence. This parameter allows for dynamic searching in the
    input space a sequence file, and the first "hit" is grabbed.

    [--modelWeightPath <path>]
    A path to a directory containing model weight files to use for inference.

    [--outputFile <resultOutputFile>]
    The name of the output or formatted 'txt' file. Default name is 'format.txt'
    
    [--topModelTraining]
    If specified, run a training model just optimizing top model
    
    [--jointModelTraing]
    If specified, jointly train top model and mLSTM

    [-h]
    Display inline help

    [--json]
    If specified, print a JSON representation of the app.

To run using ``docker``, be sure to assign an "input" directory to ``/incoming`` and an output directory to ``/outgoing``. *Make sure that the* ``$(pwd)/out`` *directory is world writable!*

Now, prefix all calls with

.. code:: bash

    docker run --rm -v $(pwd)/out:/outgoing                        \
            fnndsc/pl-unirep_analysis                              \
            unirep_analysis                                        \

Thus, getting inline help is:

.. code:: bash

    mkdir in out && chmod 777 out
    docker run --rm -v $(pwd)/in:/incoming -v $(pwd)/out:/outgoing      \
            fnndsc/pl-unirep_analysis                                   \
            unirep_analysis                                             \
            -h                                                          \
            /incoming /outgoing

Examples
--------

Assuming that the ``<inputDir>`` layout conforms to

.. code:: bash

    <inputDir>
        │
        └──█ sequence.txt


to process this (by default on a GPU) do

.. code:: bash

   docker run   --rm --gpus all                                             \
                -v $(pwd)/in:/incoming -v $(pwd)/out:/outgoing              \
                fnndsc/pl-unirep_analysis unirep_analysis                   \
                --inputFile sequence.txt --outputFile formatted.txt         \
                /incoming /outgoing

(note the ``--gpus all`` is not necessarily required) which will create in the ``<outputDir>``:

.. code:: bash

    <outputDir>
        │
        └──█ formatted.txt


Development
-----------

To perform in-line debugging of the container, do

.. code:: bash

    docker run --rm -it --userns=host  -u $(id -u):$(id -g)                                     \
        -v $PWD/unirep_analysis.py:/usr/local/lib/python3.5/dist-packages/unirep_analysis.py:ro \
        -v $PWD/src:/usr/local/lib/python3.5/dist-packages/src                                  \
           -v $PWD/in:/incoming:ro -v $PWD/out:/outgoing:rw -w /outgoing                        \
           local/pl-unirep_analysis2 unirep_analysis /incoming /outgoing

Note, if you want to use `pudb` for debugging, then omit the ``-u $(id -u):$(id -g)``:

.. code:: bash

    docker run --rm -it --userns=host                                                           \
        -v $PWD/unirep_analysis.py:/usr/local/lib/python3.5/dist-packages/unirep_analysis.py:ro \
        -v $PWD/src:/usr/local/lib/python3.5/dist-packages/src                                  \
           -v $PWD/in:/incoming:ro -v $PWD/out:/outgoing:rw -w /outgoing                        \
           local/pl-unirep_analysis2 unirep_analysis /incoming /outgoing

Of course, in both cases above, use approrpiate CLI args if required.

.. image:: https://raw.githubusercontent.com/FNNDSC/cookiecutter-chrisapp/master/doc/assets/badge/light.png
    :target: https://chrisstore.co

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