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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