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+# DKFZ
+#
+#
+# Copyright (c) German Cancer Research Center,
+# Division of Medical Image Computing.
+# All rights reserved.
+#
+# This software is distributed WITHOUT ANY WARRANTY; without
+# even the implied warranty of MERCHANTABILITY or FITNESS FOR
+# A PARTICULAR PURPOSE.
+#
+# See LICENSE
+
+# A hyppopy minimal example optimizing a simple demo function f(x,y) = x**2+y**2
+
+# import the HyppopyProject class keeping track of inputs
+from hyppopy.HyppopyProject import HyppopyProject
+from hyppopy.solvers.GridsearchSolver import GridsearchSolver
+from hyppopy.solvers.MPISolverWrapper import MPISolverWrapper
+from hyppopy.MPIBlackboxFunction import MPIBlackboxFunction
+from mpi4py import MPI
+
+# To configure the Hyppopy solver we use a simple nested dictionary with two obligatory main sections,
+# hyperparameter and settings. The hyperparameter section defines your searchspace. Each hyperparameter
+# is again a dictionary with:
+#
+# - a domain ['categorical', 'uniform', 'normal', 'loguniform']
+# - the domain data [left bound, right bound] and
+# - a type of your domain ['str', 'int', 'float']
+#
+# The settings section has two subcategories, solver and custom. The first contains settings for the solver,
+# here 'max_iterations' - is the maximum number of iteration.
+#
+# The custom section allows defining custom parameter. An entry here is transformed to a member variable of the
+# HyppopyProject class. These can be useful when implementing new solver classes or for control your hyppopy script.
+# Here we use it as a solver switch to control the usage of our solver via the config. This means with the script
+# below your can try out every solver by changing use_solver to 'optunity', 'randomsearch', 'gridsearch',...
+# It can be used like so: project.custom_use_plugin (see below) If using the gridsearch solver, max_iterations is
+# ignored, instead each hyperparameter must specifiy a number of samples additionally to the range like so:
+# 'data': [0, 1, 100] which means sampling the space from 0 to 1 in 100 intervals.
+
+config = {
+    "hyperparameter": {
+        "x": {
+            "domain": "normal",
+            "data": [-10.0, 10.0],
+            "type": float,
+            "frequency": 100
+        },
+        "y": {
+            "domain": "uniform",
+            "data": [-10.0, 10.0],
+            "type": float,
+            "frequency": 100
+        }
+    },
+    "max_iterations": 500
+}
+
+project = HyppopyProject(config=config)
+
+
+# The user defined loss function
+def my_loss_function(data, params):
+    x = params['x']
+    y = params['y']
+    return x**2+y**3
+
+
+solver = MPISolverWrapper(GridsearchSolver(project))
+blackbox = MPIBlackboxFunction(blackbox_func=my_loss_function)
+solver.blackbox = blackbox
+
+# this part should be covered by a MPI solver wrapper. That is just a wrapper around normal solvers, mimicking the HypopySolver interact and just
+# treating run() and get_results() in a special way.
+mpi_rank = MPI.COMM_WORLD.Get_rank()
+if mpi_rank == 0:
+    # This is the master process. From here we run the solver and start all the other processes.
+    solver.run()
+    solver.signal_worker_finished()  # Tell the workes to finish.
+    df, best = solver.get_results()  # gather the results from the different processes.
+
+    print("\n")
+    print("*" * 100)
+    print("Best Parameter Set:\n{}".format(best))
+    print("*" * 100)
+else:
+    # Call the worker and make it wait till it receives input.
+    solver.call_worker()