Package: cornflow
Cornflow is an open source multi-solver optimization server with a REST API built usingflask,airflowandpulp.While most deployment servers are based on the solving technique (MIP, CP, NLP, etc.), Cornflow focuses on the optimization problems themselves. However, it does not impose any constraint on the type of problem and solution method to use.With Cornflow you can deploy a Traveling Salesman Problem solver next to a Knapsack solver or a Nurse Rostering Problem solver. As long as you describe the input and output data, you can upload any solution method for any problem and then use it with any data you want.Cornflow helps you formalize your problem by proposing development guidelines. It also provides a range of functionalities around your deployed solution method, namely:storage of users, instances, solutions and solution logs.deployment and maintenance of models, solvers and algorithms.scheduling of executions in remote machines.management of said executions: start, monitor, interrupt.centralizing of commercial licenses.scenario storage and comparison.user management, roles and groups.Table of ContentsInstallation instructionsUsing cornflow to solve a PuLP modelUsing cornflow to deploy a solution methodUsing cornflow to solve a problemRunning tests and coverageInstallation instructionsCornflow is tested with Ubuntu 20.04, python >= 3.8 and git.Download the Cornflow project and install requirements:python3 -m venv venv
venv/bin/pip3 install cornflowinitialize the sqlite database:source venv/bin/activate
export FLASK_APP=cornflow.app
export DATABASE_URL=sqlite:///cornflow.db
flask db upgrade
flask access_init
flask create_service_user  -u airflow -e airflow_test@admin.com -p airflow_test_password
flask create_admin_user  -u cornflow -e cornflow_admin@admin.com -p cornflow_admin_passwordactivate the virtual environment and run Cornflow:source venv/bin/activate
export FLASK_APP=cornflow.app
export SECRET_KEY=THISNEEDSTOBECHANGED
export DATABASE_URL=sqlite:///cornflow.db
export AIRFLOW_URL=http://127.0.0.1:8080/
export AIRFLOW_USER=airflow_user
export AIRFLOW_PWD=airflow_pwd
flask runCornflow needs a running installation of Airflow to operate and more configuration. Checkthe installation docsfor more details on installing airflow, configuring the application and initializing the database.Using cornflow to solve a PuLP modelWe’re going to test the cornflow server by using thecornflow-clientand thepulppython package:pip install cornflow-client pulpInitialize the api client:from cornflow_client import CornFlow
email = 'some_email@gmail.com'
pwd = 'Some_password1'
username = 'some_name'
client = CornFlow(url="http://127.0.0.1:5000")Create a user:config = dict(username=username, email=email, pwd=pwd)
client.sign_up(**config)Log in:client.login(username=username, pwd=pwd)Prepare an instance:import pulp
prob = pulp.LpProblem("test_export_dict_MIP", pulp.LpMinimize)
x = pulp.LpVariable("x", 0, 4)
y = pulp.LpVariable("y", -1, 1)
z = pulp.LpVariable("z", 0, None, pulp.LpInteger)
prob += x + 4 * y + 9 * z, "obj"
prob += x + y <= 5, "c1"
prob += x + z >= 10, "c2"
prob += -y + z == 7.5, "c3"
data = prob.to_dict()
insName = 'test_export_dict_MIP'
description = 'very small example'Send instance:instance = client.create_instance(data, name=insName, description=description, schema="solve_model_dag",)Solve an instance:config = dict(
    solver = "PULP_CBC_CMD",
    timeLimit = 10
)
execution = client.create_execution(
    instance['id'], config, name='execution1', description='execution of a very small instance',
    schema="solve_model_dag",
)Check the status of an execution:status = client.get_status(execution["id"])
print(status['state'])
# 1 means "finished correctly"Retrieve a solution:results = client.get_solution(execution['id'])
print(results['data'])
# returns a json with the solved pulp object
_vars, prob = pulp.LpProblem.from_dict(results['data'])Retrieve the log of the solver:log = client.get_log(execution['id'])
print(log['log'])
# json format of the solver logUsing cornflow to deploy a solution methodTo deploy a cornflow solution method, the following tasks need to be accomplished:Create an Application for the new problemDo a PR to a compatible repo linked to a server instance (e.g., likethis one).For more details on each part, check thedeployment guide.Using cornflow to solve a problemFor this example we only need the cornflow_client package. We will test the graph-coloring demo definedhere. We will use the test server to solve it.Initialize the api client:from cornflow_client import CornFlow
email = 'readme@gmail.com'
pwd = 'some_password'
username = 'some_name'
client = CornFlow(url="https://devsm.cornflow.baobabsoluciones.app/")
client.login(username=username, pwd=pwd)solve a graph coloring problem and get the solution:data = dict(pairs=[dict(n1=0, n2=1), dict(n1=1, n2=2), dict(n1=1, n2=3)])
instance = client.create_instance(data, name='gc_4_1', description='very small gc problem', schema="graph_coloring")
config = dict()
execution = client.create_execution(
    instance['id'], config, name='gc_4_1_exec', description='execution of very small gc problem',
    schema="graph_coloring",
)
status = client.get_status(execution["id"])
print(status['state'])
solution = client.get_solution(execution["id"])
print(solution['data']['assignment'])Running tests and coverageThen you have to run the following commands:export FLASK_ENV=testingFinally you can run all the tests with the following command:python -m unittest discover -s cornflow.testsIf you want to only run the unit tests (without a local airflow webserver):python -m unittest discover -s cornflow.tests.unitIf you want to only run the integration test with a local airflow webserver:python -m unittest discover -s cornflow.tests.integrationAfter if you want to check the coverage report you need to run:coverage run  --source=./cornflow/ -m unittest discover -s=./cornflow/tests/
coverage report -mor to get the html reports:coverage html