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 | [] | ['desvars', 'nl_cons', 'ln_cons', 'objs', 'totals'] | ['list'] | List of what type of Driver variables to print at each iteration. |
invalid_desvar_behavior | warn | ['warn', 'raise', 'ignore'] | N/A | Behavior 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 : 1.1115E-02 s
model_evals : 5
model_time : 1.0074E-03 s
deriv_evals : 4
deriv_time : 2.1029E-03 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: 0.0002909290001298359 secs
In mode: fwd.
('y', [1])
Elapsed Time: 0.0004486780001116131 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: 0.00013771699991593778 secs
In mode: fwd.
('y', [1])
Elapsed Time: 0.00023099700001694146 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: 0.00014275499984250928 secs
In mode: fwd.
('y', [1])
Elapsed Time: 0.00021714199988309701 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: 0.0001755810001213831 secs
In mode: fwd.
('y', [1])
Elapsed Time: 0.00029096299999764597 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 : 1.4910E-02 s
model_evals : 5
model_time : 7.1992E-04 s
deriv_evals : 4
deriv_time : 1.1184E-02 s
exit_status : SUCCESS