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Sample Tensorflow application plugin for Chris Project. The application is a digit-identification application based on MNIST data.

usage: tensorflowapp.py [-h] [--json] [--savejson DIR] [--inputmeta INPUTMETA]

[--saveinputmeta] [--saveoutputmeta] [--prefix PREFIX] [--inference_path imagedir] inputdir outputdir

Runs the tensorflow application.

positional arguments:

inputdir directory containing the input files outputdir directory containing the output files/folders inference_path directory containing the test digit images

optional arguments:

-h, --help	show this help message and exit
--json	show json representation of app and exit
--savejson DIR	save json representation file to DIR and exit
--inputmeta INPUTMETA
	meta data file containing the arguments passed to this app
--saveinputmeta
	save arguments to a JSON file
--saveoutputmeta
	save output meta data to a JSON file
--prefix PREFIX
	prefix for file names

Assign an "input" directory to /incoming and an output directory to /outgoing. The input is prepopulated with mnist data.

mkdir -p input && mkdir -p output

To train the mnist model. Below command will train a mnist model and output the accuracy to a file in $(pwd)/output folder.

docker run --rm -v $(pwd)/input:/incoming -v $(pwd)/output:/outgoing   \
        submod/pl-tensorflowapp tensorflowapp.py            \
        --prefix mnist-                                     \
        /incoming /outgoing

To train the mnist model & also run inference. Below command will train a mnist model also run inference on the test image against the mnist model.

docker run --rm -v $(pwd)/input:/incoming -v $(pwd)/output:/outgoing   \
        submod/pl-tensorflowapp tensorflowapp.py            \
        --prefix mnist-                                     \
        --inference_path /incoming/test/test.png            \
        /incoming /outgoing

Make sure that the host $(pwd)/output directory is world writable!