Working with Pyomo Models

This section gives an overview of commonly used scripting commands when working with Pyomo models. These commands must be applied to a concrete model instance or in other words an instantiated model.

Repeated Solves

>>> import pyomo.environ as pyo
>>> from pyomo.opt import SolverFactory
>>> model = pyo.ConcreteModel()
>>> model.nVars = pyo.Param(initialize=4)
>>> model.N = pyo.RangeSet(model.nVars)
>>> model.x = pyo.Var(model.N, within=pyo.Binary)
>>> model.obj = pyo.Objective(expr=pyo.summation(model.x))
>>> model.cuts = pyo.ConstraintList()
>>> opt = SolverFactory('glpk')
>>> opt.solve(model) 

>>> # Iterate, adding a cut to exclude the previously found solution
>>> for i in range(5):
...    expr = 0
...    for j in model.x:
...        if pyo.value(model.x[j]) < 0.5:
...            expr += model.x[j]
...        else:
...            expr += (1 - model.x[j])
...    model.cuts.add( expr >= 1 )
...    results = opt.solve(model)
...    print ("\n===== iteration",i)
...    model.display() 

To illustrate Python scripts for Pyomo we consider an example that is in the file iterative1.py and is executed using the command

python iterative1.py

Note

This is a Python script that contains elements of Pyomo, so it is executed using the python command. The pyomo command can be used, but then there will be some strange messages at the end when Pyomo finishes the script and attempts to send the results to a solver, which is what the pyomo command does.

This script creates a model, solves it, and then adds a constraint to preclude the solution just found. This process is repeated, so the script finds and prints multiple solutions. The particular model it creates is just the sum of four binary variables. One does not need a computer to solve the problem or even to iterate over solutions. This example is provided just to illustrate some elementary aspects of scripting.

# iterative1.py
import pyomo.environ as pyo
from pyomo.opt import SolverFactory

# Create a solver
opt = pyo.SolverFactory('glpk')

#
# A simple model with binary variables and
# an empty constraint list.
#
model = pyo.AbstractModel()
model.n = pyo.Param(default=4)
model.x = pyo.Var(pyo.RangeSet(model.n), within=pyo.Binary)
def o_rule(model):
    return pyo.summation(model.x)
model.o = pyo.Objective(rule=o_rule)
model.c = pyo.ConstraintList()

# Create a model instance and optimize
instance = model.create_instance()
results = opt.solve(instance)
instance.display()

# Iterate to eliminate the previously found solution
for i in range(5):
    expr = 0
    for j in instance.x:
        if pyo.value(instance.x[j]) == 0:
            expr += instance.x[j]
        else:
            expr += (1-instance.x[j])
    instance.c.add( expr >= 1 )
    results = opt.solve(instance)
    print ("\n===== iteration",i)
    instance.display()

Let us now analyze this script. The first line is a comment that happens to give the name of the file. This is followed by two lines that import symbols for Pyomo. The pyomo namespace is imported as pyo. Therefore, pyo. must precede each use of a Pyomo name.

# iterative1.py
import pyomo.environ as pyo
from pyomo.opt import SolverFactory

An object to perform optimization is created by calling SolverFactory with an argument giving the name of the solver. The argument would be 'gurobi' if, e.g., Gurobi was desired instead of glpk:

# Create a solver
opt = pyo.SolverFactory('glpk')

The next lines after a comment create a model. For our discussion here, we will refer to this as the base model because it will be extended by adding constraints later. (The words “base model” are not reserved words, they are just being introduced for the discussion of this example). There are no constraints in the base model, but that is just to keep it simple. Constraints could be present in the base model. Even though it is an abstract model, the base model is fully specified by these commands because it requires no external data:

model = pyo.AbstractModel()
model.n = pyo.Param(default=4)
model.x = pyo.Var(pyo.RangeSet(model.n), within=pyo.Binary)
def o_rule(model):
    return pyo.summation(model.x)
model.o = pyo.Objective(rule=o_rule)

The next line is not part of the base model specification. It creates an empty constraint list that the script will use to add constraints.

model.c = pyo.ConstraintList()

The next non-comment line creates the instantiated model and refers to the instance object with a Python variable instance. Models run using the pyomo script do not typically contain this line because model instantiation is done by the pyomo script. In this example, the create function is called without arguments because none are needed; however, the name of a file with data commands is given as an argument in many scripts.

instance = model.create_instance()

The next line invokes the solver and refers to the object contain results with the Python variable results.

results = opt.solve(instance)

The solve function loads the results into the instance, so the next line writes out the updated values.

instance.display()

The next non-comment line is a Python iteration command that will successively assign the integers from 0 to 4 to the Python variable i, although that variable is not used in script. This loop is what causes the script to generate five more solutions:

for i in range(5):

An expression is built up in the Python variable named expr. The Python variable j will be iteratively assigned all of the indexes of the variable x. For each index, the value of the variable (which was loaded by the load method just described) is tested to see if it is zero and the expression in expr is augmented accordingly. Although expr is initialized to 0 (an integer), its type will change to be a Pyomo expression when it is assigned expressions involving Pyomo variable objects:

    expr = 0
    for j in instance.x:
        if pyo.value(instance.x[j]) == 0:
            expr += instance.x[j]
        else:
            expr += (1-instance.x[j])

During the first iteration (when i is 0), we know that all values of x will be 0, so we can anticipate what the expression will look like. We know that x is indexed by the integers from 1 to 4 so we know that j will take on the values from 1 to 4 and we also know that all value of x will be zero for all indexes so we know that the value of expr will be something like

0 + instance.x[1] + instance.x[2] + instance.x[3] + instance.x[4]

The value of j will be evaluated because it is a Python variable; however, because it is a Pyomo variable, the value of instance.x[j] not be used, instead the variable object will appear in the expression. That is exactly what we want in this case. When we wanted to use the current value in the if statement, we used the value function to get it.

The next line adds to the constaint list called c the requirement that the expression be greater than or equal to one:

    instance.c.add( expr >= 1 )

The proof that this precludes the last solution is left as an exerise for the reader.

The final lines in the outer for loop find a solution and display it:

    results = opt.solve(instance)
    print ("\n===== iteration",i)
    instance.display()

Note

The assignment of the solve output to a results object is somewhat anachronistic. Many scripts just use

>>> opt.solve(instance) # doctest: +SKIP

since the results are moved to the instance by default, leaving the results object with little of interest. If, for some reason, you want the results to stay in the results object and not be moved to the instance, you would use

>>> results = opt.solve(instance, load_solutions=False) # doctest: +SKIP

This approach can be usefull if there is a concern that the solver did not terminate with an optimal solution. For example,

>>> results = opt.solve(instance, load_solutions=False) # doctest: +SKIP
>>> if results.solver.termination_condition == TerminationCondition.optimal: # doctest: +SKIP
...     instance.solutions.load_from(results) # doctest: +SKIP

Changing the Model or Data and Re-solving

The iterative1.py example above illustrates how a model can be changed and then re-solved. In that example, the model is changed by adding a constraint, but the model could also be changed by altering the values of parameters. Note, however, that in these examples, we make the changes to the concrete model instances. This is particularly important for AbstractModel users, as this implies working with the instance object rather than the model object, which allows us to avoid creating a new model object for each solve. Here is the basic idea for users of an AbstractModel:

  1. Create an AbstractModel (suppose it is called model)
  2. Call model.create_instance() to create an instance (suppose it is called instance)
  3. Solve instance
  4. Change someting in instance
  5. Solve instance again

Note

Users of ConcreteModel typically name their models model, which can cause confusion to novice readers of documentation. Examples based on an AbstractModel will refer to instance where users of a ConcreteModel would typically use the name model.

If instance has a parameter whose name is Theta that was declared to be mutable (i.e., mutable=True) with an index that contains idx, then the value in NewVal can be assigned to it using

>>> instance.Theta[idx] = NewVal

For a singleton parameter named sigma (i.e., if it is not indexed), the assignment can be made using

>>> instance.sigma = NewVal

Note

If the Param is not declared to be mutable, an error will occur if an assignment to it is attempted.

For more information about access to Pyomo parameters, see the section in this document on Param access Accessing Parameter Values. Note that for concrete models, the model is the instance.

Fixing Variables and Re-solving

Instead of changing model data, scripts are often used to fix variable values. The following example illustrates this.

# iterative2.py

import pyomo.environ as pyo
from pyomo.opt import SolverFactory

# Create a solver
opt = pyo.SolverFactory('cplex')

#
# A simple model with binary variables and
# an empty constraint list.
#
model = pyo.AbstractModel()
model.n = pyo.Param(default=4)
model.x = pyo.Var(pyo.RangeSet(model.n), within=pyo.Binary)
def o_rule(model):
    return summation(model.x)
model.o = pyo.Objective(rule=o_rule)
model.c = pyo.ConstraintList()

# Create a model instance and optimize
instance = model.create_instance()
results = opt.solve(instance)
instance.display()

# "flip" the value of x[2] (it is binary)
# then solve again

if pyo.value(instance.x[2]) == 0:
    instance.x[2].fix(1)
else:
    instance.x[2].fix(0)

results = opt.solve(instance)
instance.display()

In this example, the variables are binary. The model is solved and then the value of model.x[2] is flipped to the opposite value before solving the model again. The main lines of interest are:


if pyo.value(instance.x[2]) == 0:
    instance.x[2].fix(1)
else:
    instance.x[2].fix(0)

results = opt.solve(instance)

This could also have been accomplished by setting the upper and lower bounds:

>>> if instance.x[2] == 0:
...     instance.x[2].setlb(1)
...     instance.x[2].setub(1)
... else:
...     instance.x[2].setlb(0)
...     instance.x[2].setub(0)

Notice that when using the bounds, we do not set fixed to True because that would fix the variable at whatever value it presently has and then the bounds would be ignored by the solver.

For more information about access to Pyomo variables, see the section in this document on Var access Accessing Variable Values.

Note that

>>> instance.x.fix(2)

is equivalent to

>>> instance.y.value = 2
>>> instance.y.fixed = True
and
>>> instance.x.fix()

is equivalent to

>>> instance.x.fixed = True

Extending the Objective Function

One can add terms to an objective function of a ConcreteModel (or and instantiated AbstractModel) using the expr attribute of the objective function object. Here is a simple example:

>>> import pyomo.environ as pyo
>>> from pyomo.opt import SolverFactory

>>> model = pyo.ConcreteModel()

>>> model.x = pyo.Var(within=pyo.PositiveReals)
>>> model.y = pyo.Var(within=pyo.PositiveReals)

>>> model.sillybound = pyo.Constraint(expr = model.x + model.y <= 2)

>>> model.obj = pyo.Objective(expr = 20 * model.x)

>>> opt = SolverFactory('glpk') 
>>> opt.solve(model) 

>>> model.pprint() 

>>> print ("------------- extend obj --------------") 
>>> model.obj.expr += 10 * model.y

>>> opt = SolverFactory('cplex') 
>>> opt.solve(model) 
>>> model.pprint() 

Activating and Deactivating Objectives

Multiple objectives can be declared, but only one can be active at a time (at present, Pyomo does not support any solvers that can be given more than one objective). If both model.obj1 and model.obj2 have been declared using Objective, then one can ensure that model.obj2 is passed to the solver as shown in this simple example:

>>> model = pyo.ConcreteModel()
>>> model.obj1 = pyo.Objective(expr = 0)
>>> model.obj2 = pyo.Objective(expr = 0)

>>> model.obj1.deactivate()
>>> model.obj2.activate()

For abstract models this would be done prior to instantiation or else the activate and deactivate calls would be on the instance rather than the model.

Activating and Deactivating Constraints

Constraints can be temporarily disabled using the deactivate() method. When the model is sent to a solver inactive constraints are not included. Disabled constraints can be re-enabled using the activate() method.

>>> model = pyo.ConcreteModel()
>>> model.v = pyo.Var()
>>> model.con = pyo.Constraint(expr=model.v**2 + model.v >= 3)
>>> model.con.deactivate()
>>> model.con.activate()

Indexed constraints can be deactivated/activated as a whole or by individual index:

>>> model = pyo.ConcreteModel()
>>> model.s = pyo.Set(initialize=[1,2,3])
>>> model.v = pyo.Var(model.s)
>>> def _con(m, s):
...    return m.v[s]**2 + m.v[s] >= 3
>>> model.con = pyo.Constraint(model.s, rule=_con)
>>> model.con.deactivate()   # Deactivate all indices
>>> model.con[1].activate()  # Activate single index

Accessing Variable Values

Primal Variable Values

Often, the point of optimization is to get optimal values of variables. Some users may want to process the values in a script. We will describe how to access a particular variable from a Python script as well as how to access all variables from a Python script and from a callback. This should enable the reader to understand how to get the access that they desire. The Iterative example given above also illustrates access to variable values.

One Variable from a Python Script

Assuming the model has been instantiated and solved and the results have been loded back into the instance object, then we can make use of the fact that the variable is a member of the instance object and its value can be accessed using its value member. For example, suppose the model contains a variable named quant that is a singleton (has no indexes) and suppose further that the name of the instance object is instance. Then the value of this variable can be accessed using pyo.value(instance.quant). Variables with indexes can be referenced by supplying the index.

Consider the following very simple example, which is similar to the iterative example. This is a concrete model. In this example, the value of x[2] is accessed.

# noiteration1.py

import pyomo.environ as pyo
from pyomo.opt import SolverFactory

# Create a solver
opt = SolverFactory('glpk')

#
# A simple model with binary variables and
# an empty constraint list.
#
model = pyo.ConcreteModel()
model.n = pyo.Param(default=4)
model.x = pyo.Var(pyo.RangeSet(model.n), within=pyo.Binary)
def o_rule(model):
    return summation(model.x)
model.o = pyo.Objective(rule=o_rule)
model.c = pyo.ConstraintList()

results = opt.solve(model)

if pyo.value(model.x[2]) == 0:
    print("The second index has a zero")
else:
    print("x[2]=",pyo.value(model.x[2]))

Note

If this script is run without modification, Pyomo is likely to issue a warning because there are no constraints. The warning is because some solvers may fail if given a problem instance that does not have any constraints.

All Variables from a Python Script

As with one variable, we assume that the model has been instantiated and solved. Assuming the instance object has the name instance, the following code snippet displays all variables and their values:

>>> for v in instance.component_objects(pyo.Var, active=True):
...     print("Variable",v)  # doctest: +SKIP
...     for index in v:
...         print ("   ",index, pyo.value(v[index]))  # doctest: +SKIP

Alternatively,

>>> for v in instance.component_data_objects(pyo.Var, active=True):
...     print(v, pyo.value(v))  # doctest: +SKIP

This code could be improved by checking to see if the variable is not indexed (i.e., the only index value is None), then the code could print the value without the word None next to it.

Assuming again that the model has been instantiated and solved and the results have been loded back into the instance object. Here is a code snippet for fixing all integers at their current value:

>>> for var in instance.component_data_objects(pyo.Var, active=True):
...     if not var.is_continuous():
...         print ("fixing "+str(v))  # doctest: +SKIP
...         var.fixed = True # fix the current value

Another way to access all of the variables (particularly if there are blocks) is as follows (this particular snippet assumes that instead of import pyomo.environ as pyo from pyo.environ import * was used):

for v in model.component_objects(Var, descend_into=True):
    print("FOUND VAR:" + v.name)
    v.pprint()

for v_data in model.component_data_objects(Var, descend_into=True):
    print("Found: "+v_data.name+", value = "+str(value(v_data)))

Accessing Parameter Values

Accessing parameter values is completely analogous to accessing variable values. For example, here is a code snippet to print the name and value of every Parameter in a model:

>>> for parmobject in instance.component_objects(pyo.Param, active=True):
...     nametoprint = str(str(parmobject.name))
...     print ("Parameter ", nametoprint)  # doctest: +SKIP
...     for index in parmobject:
...         vtoprint = pyo.value(parmobject[index])
...         print ("   ",index, vtoprint)  # doctest: +SKIP

Accessing Duals

Access to dual values in scripts is similar to accessing primal variable values, except that dual values are not captured by default so additional directives are needed before optimization to signal that duals are desired.

To get duals without a script, use the pyomo option --solver-suffixes='dual' which will cause dual values to be included in output. Note: In addition to duals (dual) , reduced costs (rc) and slack values (slack) can be requested. All suffixes can be requested using the pyomo option --solver-suffixes='.*'

Warning

Some of the duals may have the value None, rather than 0.

Access Duals in a Python Script

To signal that duals are desired, declare a Suffix component with the name “dual” on the model or instance with an IMPORT or IMPORT_EXPORT direction.

# Create a 'dual' suffix component on the instance
# so the solver plugin will know which suffixes to collect
instance.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)

See the section on Suffixes Suffixes for more information on Pyomo’s Suffix component. After the results are obtained and loaded into an instance, duals can be accessed in the following fashion.

# display all duals
print ("Duals")
for c in instance.component_objects(pyo.Constraint, active=True):
    print ("   Constraint",c)
    for index in c:
        print ("      ", index, instance.dual[c[index]])

The following snippet will only work, of course, if there is a constraint with the name AxbConstraint that has and index, which is the string Film.

# access one dual
print ("Dual for Film=", instance.dual[instance.AxbConstraint['Film']])

Here is a complete example that relies on the file abstract2.py to provide the model and the file abstract2.dat to provide the data. Note that the model in abstract2.py does contain a constraint named AxbConstraint and abstract2.dat does specify an index for it named Film.

# driveabs2.py
from __future__ import division
import pyomo.environ as pyo
from pyomo.opt import SolverFactory

# Create a solver
opt = SolverFactory('cplex')

# get the model from another file
from abstract2 import model

# Create a model instance and optimize
instance = model.create_instance('abstract2.dat')

# Create a 'dual' suffix component on the instance
# so the solver plugin will know which suffixes to collect
instance.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)

results = opt.solve(instance)
# also puts the results back into the instance for easy access

# display all duals
print ("Duals")
for c in instance.component_objects(pyo.Constraint, active=True):
    print ("   Constraint",c)
    for index in c:
        print ("      ", index, instance.dual[c[index]])

# access one dual
print ("Dual for Film=", instance.dual[instance.AxbConstraint['Film']])

Concrete models are slightly different because the model is the instance. Here is a complete example that relies on the file concrete1.py to provide the model and instantiate it.

# driveconc1.py
from __future__ import division
import pyomo.environ as pyo
from pyomo.opt import SolverFactory

# Create a solver
opt = SolverFactory('cplex')

# get the model from another file
from concrete1 import model

# Create a 'dual' suffix component on the instance
# so the solver plugin will know which suffixes to collect
model.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)

results = opt.solve(model) # also load results to model

# display all duals
print ("Duals")
for c in model.component_objects(pyo.Constraint, active=True):
    print ("   Constraint",c)
    for index in c:
        print ("      ", index, model.dual[c[index]])



Accessing Slacks

The functions lslack() and uslack() return the upper and lower slacks, respectively, for a constraint.

Accessing Solver Status

After a solve, the results object has a member Solution.Status that contains the solver status. The following snippet shows an example of access via a print statement:

results = opt.solve(instance)
#print ("The solver returned a status of:"+str(results.solver.status))

The use of the Python str function to cast the value to a be string makes it easy to test it. In particular, the value ‘optimal’ indicates that the solver succeeded. It is also possible to access Pyomo data that can be compared with the solver status as in the following code snippet:

from pyomo.opt import SolverStatus, TerminationCondition

#...

if (results.solver.status == SolverStatus.ok) and (results.solver.termination_condition == TerminationCondition.optimal):
     print ("this is feasible and optimal")
elif results.solver.termination_condition == TerminationCondition.infeasible:
     print ("do something about it? or exit?")
else:
     # something else is wrong
     print (str(results.solver))

Alternatively,

from pyomo.opt import TerminationCondition

...

results = opt.solve(model, load_solutions=False)
if results.solver.termination_condition == TerminationCondition.optimal:
    model.solutions.load_from(results)
else:
    print ("Solution is not optimal")
    # now do something about it? or exit? ...

Display of Solver Output

To see the output of the solver, use the option tee=True as in

results = opt.solve(instance, tee=True)

This can be useful for troubleshooting solver difficulties.

Sending Options to the Solver

Most solvers accept options and Pyomo can pass options through to a solver. In scripts or callbacks, the options can be attached to the solver object by adding to its options dictionary as illustrated by this snippet:

optimizer = pyo.SolverFactory['cbc']
optimizer.options["threads"] = 4

If multiple options are needed, then multiple dictionary entries should be added.

Sometime it is desirable to pass options as part of the call to the solve function as in this snippet:

results = optimizer.solve(instance, options="threads=4", tee=True)

The quoted string is passed directly to the solver. If multiple options need to be passed to the solver in this way, they should be separated by a space within the quoted string. Notice that tee is a Pyomo option and is solver-independent, while the string argument to options is passed to the solver without very little processing by Pyomo. If the solver does not have a “threads” option, it will probably complain, but Pyomo will not.

There are no default values for options on a SolverFactory object. If you directly modify its options dictionary, as was done above, those options will persist across every call to optimizer.solve(…) unless you delete them from the options dictionary. You can also pass a dictionary of options into the opt.solve(…) method using the options keyword. Those options will only persist within that solve and temporarily override any matching options in the options dictionary on the solver object.

Specifying the Path to a Solver

Often, the executables for solvers are in the path; however, for situations where they are not, the SolverFactory function accepts the keyword executable, which you can use to set an absolute or relative path to a solver executable. E.g.,

opt = pyo.SolverFactory("ipopt", executable="../ipopt")

Warm Starts

Some solvers support a warm start based on current values of variables. To use this feature, set the values of variables in the instance and pass warmstart=True to the solve() method. E.g.,

instance = model.create()
instance.y[0] = 1
instance.y[1] = 0

opt = pyo.SolverFactory("cplex")

results = opt.solve(instance, warmstart=True)

Note

The Cplex and Gurobi LP file (and Python) interfaces will generate an MST file with the variable data and hand this off to the solver in addition to the LP file.

Warning

Solvers using the NL file interface (e.g., “gurobi_ampl”, “cplexamp”) do not accept warmstart as a keyword to the solve() method as the NL file format, by default, includes variable initialization data (drawn from the current value of all variables).

Solving Multiple Instances in Parallel

Use of parallel solvers for PySP is discussed in the section on parallel PySP Solving Sub-problems in Parallel and/or Remotely.

Solvers are controlled by solver servers. The pyro mip solver server is launched with the command pyro_mip_server. This command may be repeated to launch as many solvers as are desired. A name server and a dispatch server must be running and accessible to the process that runs the script that will use the mip servers as well as to the mip servers. The name server is launched using the command pyomo_ns and then the dispatch server is launched with dispatch_srvr. Note that both commands contain an underscore. Both programs keep running until terminated by an external signal, so it is common to pipe their output to a file. The commands are:

  • Once: pyomo_ns
  • Once: dispatch_srvr
  • Multiple times: pyro_mip_server

This example demonstrates how to use these services to solve two instances in parallel.

# parallel.py
from __future__ import division
from pyomo.environ import *
from pyomo.opt import SolverFactory
from pyomo.opt.parallel import SolverManagerFactory
import sys

action_handle_map = {} # maps action handles to instances

# Create a solver
optsolver = SolverFactory('cplex')

# create a solver manager
# 'pyro' could be replaced with 'serial'
solver_manager = SolverManagerFactory('pyro')
if solver_manager is None:
    print "Failed to create solver manager."
    sys.exit(1)

#
# A simple model with binary variables and
# an empty constraint list.
#
model = AbstractModel()
model.n = Param(default=4)
model.x = Var(RangeSet(model.n), within=Binary)
def o_rule(model):
    return summation(model.x)
model.o = Objective(rule=o_rule)
model.c = ConstraintList()

# Create two model instances
instance1 = model.create()

instance2 = model.create()
instance2.x[1] = 1
instance2.x[1].fixed = True

# send them to the solver(s)
action_handle = solver_manager.queue(instance1, opt=optsolver, warmstart=False, tee=True, verbose=False)
action_handle_map[action_handle] = "Original"
action_handle = solver_manager.queue(instance2, opt=optsolver, warmstart=False, tee=True, verbose=False)
action_handle_map[action_handle] = "One Var Fixed"

# retrieve the solutions
for i in range(2): # we know there are two instances
    this_action_handle = solver_manager.wait_any()
    solved_name = action_handle_map[this_action_handle]
    results = solver_manager.get_results(this_action_handle)
    print "Results for",solved_name
    print results

This example creates two instances that are very similar and then sends them to be dispatched to solvers. If there are two solvers, then these problems could be solved in parallel (we say “could” because for such trivial problems to be actually solved in parallel, the solvers would have to be very, very slow). This example is non-sensical; the goal is simply to show solver_manager.queue to submit jobs to a name server for dispatch to solver servers and solver_manager.wait_any to recover the results. The wait_all function is similar, but it takes a list of action handles (returned by queue) as an argument and returns all of the results at once.

Changing the temporary directory

A “temporary” directory is used for many intermediate files. Normally, the name of the directory for temporary files is provided by the operating system, but the user can specify their own directory name. The pyomo command-line --tempdir option propagates through to the TempFileManager service. One can accomplish the same through the following few lines of code in a script:

from pyutilib.services import TempfileManager
TempfileManager.tempdir = YourDirectoryNameGoesHere