<filename>mlflow/pyfunc/scoring_server.py<gh_stars>1-10
"""
Scoring server for python model format.
The passed int model is expected to have function:
   predict(pandas.Dataframe) -> pandas.DataFrame

Input, expected intext/csv or application/json format,
is parsed into pandas.DataFrame and passed to the model.

Defines two endpoints:
    /ping used for health check
    /invocations used for scoring
"""
from __future__ import print_function

import json

import pandas as pd
import flask

try:
    from StringIO import StringIO
except ImportError:
    from io import StringIO

from mlflow.utils import get_jsonable_obj


def init(model):
    """
    Initialize the server. Loads pyfunc model from the path.
    """
    app = flask.Flask(__name__)

    @app.route('/ping', methods=['GET'])
    def ping():  # pylint: disable=unused-variable
        """
        Determine if the container is working and healthy.
        We declare it healthy if we can load the model successfully.
        """
        health = model is not None
        status = 200 if health else 404
        return flask.Response(response='\n', status=status, mimetype='application/json')

    @app.route('/invocations', methods=['POST'])
    def transformation():  # pylint: disable=unused-variable
        """
        Do an inference on a single batch of data. In this sample server,
        we take data as CSV or json, convert it to a pandas data frame,
        generate predictions and convert them back to CSV.
        """
        # Convert from CSV to pandas
        if flask.request.content_type == 'text/csv':
            data = flask.request.data.decode('utf-8')
            s = StringIO(data)
            data = pd.read_csv(s)
        elif flask.request.content_type == 'application/json':
            data = flask.request.data.decode('utf-8')
            s = StringIO(data)
            data = pd.read_json(s, orient="records")
        else:
            return flask.Response(
                response='This predictor only supports CSV or JSON  data, got %s' % str(
                    flask.request.content_type), status=415, mimetype='text/plain')

        # Do the prediction
        predictions = get_jsonable_obj(model.predict(data))
        result = json.dumps(predictions)
        return flask.Response(response=result, status=200, mimetype='application/json')

    return app

<filename>mlflow/pyfunc/scoring_server.py<gh_stars>1-10
"""
Scoring server for python model format.
The passed int model is expected to have function:
   predict(pandas.Dataframe) -> pandas.DataFrame

Input, expected intext/csv or application/json format,
is parsed into pandas.DataFrame and passed to the model.

Defines two endpoints:
    /ping used for health check
    /invocations used for scoring
"""
from __future__ import print_function

import json

import pandas as pd
import flask

try:
    from StringIO import StringIO
except ImportError:
    from io import StringIO

from mlflow.utils import get_jsonable_obj


def init(model):
    """
    Initialize the server. Loads pyfunc model from the path.
    """
    app = flask.Flask(__name__)

    @app.route('/ping', methods=['GET'])
    def ping():  # pylint: disable=unused-variable
        """
        Determine if the container is working and healthy.
        We declare it healthy if we can load the model successfully.
        """
        health = model is not None
        status = 200 if health else 404
        return flask.Response(response='\n', status=status, mimetype='application/json')

    @app.route('/invocations', methods=['POST'])
    def transformation():  # pylint: disable=unused-variable
        """
        Do an inference on a single batch of data. In this sample server,
        we take data as CSV or json, convert it to a pandas data frame,
        generate predictions and convert them back to CSV.
        """
        # Convert from CSV to pandas
        if flask.request.content_type == 'text/csv':
            data = flask.request.data.decode('utf-8')
            s = StringIO(data)
            data = pd.read_csv(s)
        elif flask.request.content_type == 'application/json':
            data = flask.request.data.decode('utf-8')
            s = StringIO(data)
            data = pd.read_json(s, orient="records")
        else:
            return flask.Response(
                response='This predictor only supports CSV or JSON  data, got %s' % str(
                    flask.request.content_type), status=415, mimetype='text/plain')

        # Do the prediction
        predictions = get_jsonable_obj(model.predict(data))
        result = json.dumps(predictions)
        return flask.Response(response=result, status=200, mimetype='application/json')

    return app

Scoring server for python model format. The passed int model is expected to have function:    predict(pandas.Dataframe) -> pandas.DataFrame  Input, expected intext/csv or application/json format, is parsed into pandas.DataFrame and passed to the model.  Defines two endpoints:     /ping used for health check     /invocations used for scoring Initialize the server. Loads pyfunc model from the path. # pylint: disable=unused-variable Determine if the container is working and healthy.         We declare it healthy if we can load the model successfully. # pylint: disable=unused-variable Do an inference on a single batch of data. In this sample server,         we take data as CSV or json, convert it to a pandas data frame,         generate predictions and convert them back to CSV. # Convert from CSV to pandas # Do the prediction