Try our new documentation site.


mip1_remote.py


import gurobipy as gp
from gurobipy import GRB

# Variation of mip1.py, with a focus on remote services
#
# When remote resources are tied to the optimization process, such as a token
# server, compute server, or Instant Cloud, extra care should be taken to
# ensure that such resources are released once they are no longer needed.
# Technically, such resources are managed by a gurobipy.Env object
# ("environment").  This example shows best practices for acquiring and
# releasing such shared resources via Env objects.
#
# See also https://www.gurobi.com/documentation/9.1/refman/environments.html

def populate_and_solve(m):
    # This function formulates and solves the following MIP model (see mip1.py):
    #  maximize
    #        x +   y + 2 z
    #  subject to
    #        x + 2 y + 3 z <= 4
    #        x +   y       >= 1
    #        x, y, z binary

    # Create variables
    x = m.addVar(vtype=GRB.BINARY, name="x")
    y = m.addVar(vtype=GRB.BINARY, name="y")
    z = m.addVar(vtype=GRB.BINARY, name="z")

    # Set objective
    m.setObjective(x + y + 2 * z, GRB.MAXIMIZE)

    # Add constraint: x + 2 y + 3 z <= 4
    m.addConstr(x + 2 * y + 3 * z <= 4, "c0")

    # Add constraint: x + y >= 1
    m.addConstr(x + y >= 1, "c1")

    # Optimize model
    m.optimize()

    for v in m.getVars():
        print('%s %g' % (v.VarName, v.X))

    print('Obj: %g' % m.ObjVal)

# Put any connection parameters for Gurobi Compute Server, Gurobi Cluster
# Manager or Gurobi Token server here, unless they are set already
# through the license file.

connection_params = {
# For Compute Server you need at least this
#       "ComputeServer": "<server name>",
#       "UserName": "<user name>",
#       "ServerPassword": "<password>",

# For Cluster Manager you need at least this
#       "CSManager": "<manager name>",
#       "CSAPIAccessID": "<access ID>",
#       "CSAPISecret": "<secret>",

# For Instant cloud you need at least this
#       "CloudAccessID": "<access id>",
#       "CloudSecretKey": "<secret>",
        }

with gp.Env(params=connection_params) as env:
    # 'env' is now set up according to the connection parameters.
    # The environment is disposed of automatically through the context manager
    # upon leaving this block.
    with gp.Model(env=env) as model:
        # 'model' is now an instance tied to the enclosing Env object 'env'.
        # The model is disposed of automatically through the context manager
        # upon leaving this block.
        try:
            populate_and_solve(model)
        except:
            # Add appropriate error handling here.
            raise

Try Gurobi for Free

Choose the evaluation license that fits you best, and start working with our Expert Team for technical guidance and support.

Evaluation License
Get a free, full-featured license of the Gurobi Optimizer to experience the performance, support, benchmarking and tuning services we provide as part of our product offering.
Academic License
Gurobi supports the teaching and use of optimization within academic institutions. We offer free, full-featured copies of Gurobi for use in class, and for research.
Cloud Trial

Request free trial hours, so you can see how quickly and easily a model can be solved on the cloud.

Search