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

Option Default Acceptable Values Acceptable Types Description
debug_print[] N/A ['list'] List of what type of Driver variables to print at each iteration. Valid items in list are 'desvars', 'ln_cons', 'nl_cons', 'objs', 'totals'

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: 0.0012514591217041016 secs
In mode: fwd.
('y', [1])
Elapsed Time: 0.0002295970916748047 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: 0.00023126602172851562 secs
In mode: fwd.
('y', [1])
Elapsed Time: 0.00021266937255859375 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: 0.0001990795135498047 secs
In mode: fwd.
('y', [1])
Elapsed Time: 0.00018525123596191406 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: 0.00025081634521484375 secs
In mode: fwd.
('y', [1])
Elapsed Time: 0.0007686614990234375 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