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.

Paraboloid class definition

class Paraboloid(om.ExplicitComponent):
    """
    Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3.
    """

    def setup(self):
        self.add_input('x', val=0.0)
        self.add_input('y', val=0.0)

        self.add_output('f_xy', val=0.0)

    def setup_partials(self):
        self.declare_partials('*', '*')

    def compute(self, inputs, outputs):
        """
        f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3

        Optimal solution (minimum): x = 6.6667; y = -7.3333
        """
        x = inputs['x']
        y = inputs['y']

        outputs['f_xy'] = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0

    def compute_partials(self, inputs, partials):
        """
        Jacobian for our paraboloid.
        """
        x = inputs['x']
        y = inputs['y']

        partials['f_xy', 'x'] = 2.0*x - 6.0 + y
        partials['f_xy', 'y'] = 2.0*y + 8.0 + x


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
{'c': array([0.])}

Linear constraints
None

Objectives
{'f_xy': array([7622.])}

Driver debug print for iter coord: rank0:ScipyOptimize_SLSQP|1
--------------------------------------------------------------
Design Vars
{'x': array([50.]), 'y': array([50.])}

Nonlinear constraints
{'c': array([0.])}

Linear constraints
None

Objectives
{'f_xy': array([7622.])}

Driver debug print for iter coord: rank0:ScipyOptimize_SLSQP|2
--------------------------------------------------------------
Design Vars
{'x': array([-35.]), 'y': array([-50.])}

Nonlinear constraints
{'c': array([-15.])}

Linear constraints
None

Objectives
{'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
{'c': array([-15.])}

Linear constraints
None

Objectives
{'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
{'c': array([-15.])}

Linear constraints
None

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

Problem: problem
Driver:  ScipyOptimizeDriver
  success     : True
  iterations  : 5
  runtime     : 4.5010E-02 s
  model_evals : 5
  model_time  : 7.8469E-04 s
  deriv_evals : 4
  deriv_time  : 2.8253E-02 s
  exit_status : SUCCESS

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: 0.00019317999999657332 secs

In mode: fwd.
('y', [1])

Elapsed Time: 0.000158603000045332 secs

{('f_xy', 'x'): array([[144.]])}
{('f_xy', 'y'): array([[158.]])}
{('c', 'x'): array([[-1.]])}
{('c', 'y'): array([[1.]])}

Driver total derivatives for iteration: 3
-----------------------------------------

In mode: fwd.
('x', [0])

Elapsed Time: 0.0001402509999479662 secs

In mode: fwd.
('y', [1])

Elapsed Time: 0.00014624200002799626 secs

{('f_xy', 'x'): array([[-126.]])}
{('f_xy', 'y'): array([[-127.]])}
{('c', 'x'): array([[-1.]])}
{('c', 'y'): array([[1.]])}

Driver total derivatives for iteration: 4
-----------------------------------------

In mode: fwd.
('x', [0])

Elapsed Time: 0.00014434700005949708 secs

In mode: fwd.
('y', [1])

Elapsed Time: 0.00014088400007494783 secs

{('f_xy', 'x'): array([[0.50120438]])}
{('f_xy', 'y'): array([[-0.49879562]])}
{('c', 'x'): array([[-1.]])}
{('c', 'y'): array([[1.]])}

Driver total derivatives for iteration: 5
-----------------------------------------

In mode: fwd.
('x', [0])

Elapsed Time: 0.00016515399988747959 secs

In mode: fwd.
('y', [1])

Elapsed Time: 0.001186598000003869 secs

{('f_xy', 'x'): array([[0.5]])}
{('f_xy', 'y'): array([[-0.5]])}
{('c', 'x'): array([[-1.]])}
{('c', 'y'): array([[1.]])}

Problem: problem2
Driver:  ScipyOptimizeDriver
  success     : True
  iterations  : 5
  runtime     : 3.2536E-02 s
  model_evals : 5
  model_time  : 5.3585E-04 s
  deriv_evals : 4
  deriv_time  : 2.5178E-02 s
  exit_status : SUCCESS
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