Driver Debug Printing

Driver Debug Printing

When working with a model, it may sometimes be helpful to print out the design variables, constraints, and objectives as the Driver iterates. OpenMDAO provides options on the Driver to let you do that.

Driver Options

OptionDefaultAcceptable ValuesAcceptable TypesDescription
debug_print[]['desvars', 'nl_cons', 'ln_cons', 'objs', 'totals']['list']List of what type of Driver variables to print at each iteration.
invalid_desvar_behaviorwarn['warn', 'raise', 'ignore']N/ABehavior of driver if the initial value of a design variable exceeds its bounds. The default value may beset using the `OPENMDAO_INVALID_DESVAR_BEHAVIOR` environment variable to one of the valid options.

Usage

This example shows how to use the Driver debug printing options. The debug_print option is a list of strings. Valid strings include ‘desvars’, ‘ln_cons’, ‘nl_cons’, and ‘objs’. Note that the values for the design variables printed are unscaled, physical values.

import openmdao.api as om
from openmdao.test_suite.components.paraboloid import Paraboloid

prob = om.Problem()
model = prob.model

model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_subsystem('con', om.ExecComp('c = - x + y'), promotes=['*'])

model.set_input_defaults('x', 50.0)
model.set_input_defaults('y', 50.0)

prob.set_solver_print(level=0)

prob.driver = om.ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = False

prob.driver.options['debug_print'] = ['desvars','ln_cons','nl_cons','objs']

model.add_design_var('x', lower=-50.0, upper=50.0)
model.add_design_var('y', lower=-50.0, upper=50.0)
model.add_objective('f_xy')
model.add_constraint('c', upper=-15.0)

prob.setup()

prob.run_driver()
Driver debug print for iter coord: rank0:ScipyOptimize_SLSQP|0
--------------------------------------------------------------
Design Vars
{'x': array([50.]), 'y': array([50.])}
Nonlinear constraints
{'con.c': array([0.])}

Linear constraints
None

Objectives
{'comp.f_xy': array([7622.])}
Driver debug print for iter coord: rank0:ScipyOptimize_SLSQP|1
--------------------------------------------------------------
Design Vars
{'x': array([50.]), 'y': array([50.])}
Nonlinear constraints
{'con.c': array([0.])}

Linear constraints
None

Objectives
{'comp.f_xy': array([7622.])}
Driver debug print for iter coord: rank0:ScipyOptimize_SLSQP|2
--------------------------------------------------------------
Design Vars
{'x': array([-35.]), 'y': array([-50.])}
Nonlinear constraints
{'con.c': array([-15.])}

Linear constraints
None

Objectives
{'comp.f_xy': array([5307.])}
Driver debug print for iter coord: rank0:ScipyOptimize_SLSQP|3
--------------------------------------------------------------
Design Vars
{'x': array([7.16706813]), 'y': array([-7.83293187])}
Nonlinear constraints
{'con.c': array([-15.])}

Linear constraints
None

Objectives
{'comp.f_xy': array([-27.08333285])}
Driver debug print for iter coord: rank0:ScipyOptimize_SLSQP|4
--------------------------------------------------------------
Design Vars
{'x': array([7.16666667]), 'y': array([-7.83333333])}
Nonlinear constraints
{'con.c': array([-15.])}

Linear constraints
None

Objectives
{'comp.f_xy': array([-27.08333333])}
False

We can also use the debug printing to print some basic information about the derivative calculations so that you can see which derivative is being solved, how long it takes, and the computed values by including the ‘totals’ string in the “debug_print” list.

import openmdao.api as om
from openmdao.test_suite.components.paraboloid import Paraboloid

prob = om.Problem()
model = prob.model

model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_subsystem('con', om.ExecComp('c = - x + y'), promotes=['*'])

model.set_input_defaults('x', 50.0)
model.set_input_defaults('y', 50.0)

prob.set_solver_print(level=0)

prob.driver = om.ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = False

prob.driver.options['debug_print'] = ['totals']

model.add_design_var('x', lower=-50.0, upper=50.0)
model.add_design_var('y', lower=-50.0, upper=50.0)
model.add_objective('f_xy')
model.add_constraint('c', upper=-15.0)

prob.setup()

prob.run_driver()
Driver total derivatives for iteration: 2
-----------------------------------------

In mode: fwd.
('x', [0])
Elapsed Time: 0.00022880300002725562 secs
In mode: fwd.
('y', [1])
Elapsed Time: 0.0001855030000115221 secs
{('comp.f_xy', 'x'): array([[144.]])}
{('comp.f_xy', 'y'): array([[158.]])}
{('con.c', 'x'): array([[-1.]])}
{('con.c', 'y'): array([[1.]])}
Driver total derivatives for iteration: 3
-----------------------------------------

In mode: fwd.
('x', [0])
Elapsed Time: 0.00017500199987807719 secs
In mode: fwd.
('y', [1])
Elapsed Time: 0.00022000299986757454 secs
{('comp.f_xy', 'x'): array([[-126.]])}
{('comp.f_xy', 'y'): array([[-127.]])}
{('con.c', 'x'): array([[-1.]])}
{('con.c', 'y'): array([[1.]])}
Driver total derivatives for iteration: 4
-----------------------------------------

In mode: fwd.
('x', [0])
Elapsed Time: 0.00019210300001759606 secs
In mode: fwd.
('y', [1])
Elapsed Time: 0.00023210300014397944 secs
{('comp.f_xy', 'x'): array([[0.50120438]])}
{('comp.f_xy', 'y'): array([[-0.49879562]])}
{('con.c', 'x'): array([[-1.]])}
{('con.c', 'y'): array([[1.]])}
Driver total derivatives for iteration: 5
-----------------------------------------

In mode: fwd.
('x', [0])
Elapsed Time: 0.00014040199994269642 secs
In mode: fwd.
('y', [1])
Elapsed Time: 0.00016890199981389742 secs
{('comp.f_xy', 'x'): array([[0.5]])}
{('comp.f_xy', 'y'): array([[-0.5]])}
{('con.c', 'x'): array([[-1.]])}
{('con.c', 'y'): array([[1.]])}
False