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Surrogate optimization toolbox for time consuming models

Project description

Surropt

Surrogate optimization toolbox for time consuming models

Installation

To install the module in develop moode, first you need to setup an environment with the following packages:

  • SciPy >= 1.2.0
  • Numpy >= 1.15.0
  • pyDOE2 >= 1.2
  • pydace >= 0.1.1

Having these installed, open a terminal window, navigate to the folder where the setup.py file is located and execute the following command:

$python setup.py develop

After this you are ready to use the package via python command line.

Usage

Optimization server

Server environment installation

Make sure WSL Ubuntu is installed (NOT UBUNTU LTS, IT HAS TO BE PURE UBUNTU) in your system.

Make sure that Anaconda is installed in your WSL system.

Open a WSL terminal and navigate to folder tests_/resources/ipopt_server/.

Install the server by executing the following line in the WSL terminal:

conda env create -f ipopt_server.yaml

Starting the server

Each time you are going to perform a optimization through Caballero's algorithm using the DockerNLPOptions as NLP solver, you have to start the server manually. To do so, execute the following steps:

  1. Open a WSL terminal and navigate to folder tests_/resources/ipopt_server/
  2. Activate the ipopt_server conda environment
  3. Start the server by typing in the WSL terminal: $python server.py
  4. If everything is fine, you should see that a flask server is initialized
  5. To make sure that the server is good to go, open a browser window and type localhost:5000. You should see the following message on your browser: "Hey! I'm running from Flask in a Docker container!". If so, you can close the browser tab (do not close the WSL terminal while performing the optimization!) and proceed normally.

Optimization procedure

  1. Start the optimization server.

  2. See file test_evap.py in folder tests_/surropt/caballero/. You can run it to see how a simple example of usage the Caballero procedure is done.

Project details


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