How to use

Read the ML Engine manual: 
https://cloud.google.com/ml-engine/docs/tensorflow/getting-started-training-prediction


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TRAINING THE MODEL LOCALLY
======================================================================================
Define environmental variables:

MODEL_DIR=<folder_name> (e.g ./output)

Run the task locally:

gcloud ml-engine local train \
    --module-name trainer_tf.task \
    --package-path trainer_tf \
    --job-dir $MODEL_DIR

======================================================================================
TRAINING THE MODEL IN THE CLOUD
======================================================================================

Define environmental variables:

JOB_NAME=<job_name>
OUTPUT_PATH=gs://$BUCKET_NAME/$JOB_NAME

Run the task in the Google Cloud (e.g. trainer_name = trainer_tf | trainer_keras | 
trainer_tf_with_LRD | trainer_tf_softmaxreg):

gcloud ml-engine jobs submit training $JOB_NAME \    	
	--job-dir $OUTPUT_PATH \    	
	--runtime-version 1.8 \    	
	--module-name <trainer_name>.task \    	
	--package-path <trainer_name>/ \    	
	--region $REGION \    	
	--verbosity debug

======================================================================================
CREATING VERSION IN THE CLOUD
======================================================================================

gcloud ml-engine models create "<model_name>"

DEPLOYMENT_SOURCE=gs://<BUCKET_NAME>/<JOB_NAME>/export/
gcloud ml-engine versions create "<version_name>"\    	
	--model "<model_name>" 
	--origin $DEPLOYMENT_SOURCE

======================================================================================
GETTING ONLINE PREDICTIONS
======================================================================================

MODEL_NAME="[MODEL-NAME]"
INPUT_DATA_FILE=“input.json"VERSION_NAME="[VERSION-NAME]”

gcloud ml-engine predict --model $MODEL_NAME  \                         --version $VERSION_NAME \                         --json-instances $INPUT_DATA_FILE

As an input.json you can use "image.json", "images_3.json" or "images_10.json"
in this example