2_Strategies/model/predictor.py [19:79]:
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prefix = '/opt/ml/'
model_path = os.path.join(prefix, 'model')

# A singleton for holding the model. This simply loads the model and holds it.
# It has a predict function that does a prediction based on the model and the
# input data.
class ScoringService(object):
    model = None                # Where we keep the model when it's loaded

    @classmethod
    def get_model(cls):
        """
        Get the model object for this instance,
        loading it if it's not already loaded.
        """
        if cls.model is None:
            cls.model = load_model(
                os.path.join(model_path, 'model.h5'))
        return cls.model

    @classmethod
    def predict(cls, input):
        """For the input, do the predictions and return them.

        Args:
            input (a pandas dataframe): The data on which to do the
            predictions.

            There will be one prediction per row in the dataframe
        """
        sess = K.get_session()
        with sess.graph.as_default():
            clf = cls.get_model()
            return clf.predict(input)

# The flask app for serving predictions
app = flask.Flask(__name__)


@app.route('/ping', methods=['GET'])
def ping():
    """
    Determine if the container is working and healthy.
    In this sample container, we declare it healthy if we can load the model
    successfully.
    """
    # Health check -- You can insert a health check here
    health = True
    status = 200 if health else 404
    return flask.Response(
        response='{"status":"ok"}',
        status=status,
        mimetype='application/json')


@app.route('/invocations', methods=['POST'])
def transformation():
    return flask.Response(
        response='{"status":"ok"}',
        status=status,
        mimetype='application/json')
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4_Kinesis/model/predictor.py [19:79]:
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prefix = '/opt/ml/'
model_path = os.path.join(prefix, 'model')

# A singleton for holding the model. This simply loads the model and holds it.
# It has a predict function that does a prediction based on the model and the
# input data.
class ScoringService(object):
    model = None                # Where we keep the model when it's loaded

    @classmethod
    def get_model(cls):
        """
        Get the model object for this instance,
        loading it if it's not already loaded.
        """
        if cls.model is None:
            cls.model = load_model(
                os.path.join(model_path, 'model.h5'))
        return cls.model

    @classmethod
    def predict(cls, input):
        """For the input, do the predictions and return them.

        Args:
            input (a pandas dataframe): The data on which to do the
            predictions.

            There will be one prediction per row in the dataframe
        """
        sess = K.get_session()
        with sess.graph.as_default():
            clf = cls.get_model()
            return clf.predict(input)

# The flask app for serving predictions
app = flask.Flask(__name__)


@app.route('/ping', methods=['GET'])
def ping():
    """
    Determine if the container is working and healthy.
    In this sample container, we declare it healthy if we can load the model
    successfully.
    """
    # Health check -- You can insert a health check here
    health = True
    status = 200 if health else 404
    return flask.Response(
        response='{"status":"ok"}',
        status=status,
        mimetype='application/json')


@app.route('/invocations', methods=['POST'])
def transformation():
    return flask.Response(
        response='{"status":"ok"}',
        status=status,
        mimetype='application/json')
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