| 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. |
| generator | DOEGenerator | N/A | ['DOEGenerator'] | The case generator. If default, no cases are generated. |
| 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. |
| procs_per_model | 1 | N/A | ['int'] | Number of processors to give each model under MPI. |
| run_parallel | False | [True, False] | ['bool'] | Set to True to execute cases in parallel. |
class Paraboloid(om.ExplicitComponent):\n """\n Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3.\n """\n\n def setup(self):\n self.add_input('x', val=0.0)\n self.add_input('y', val=0.0)\n\n self.add_output('f_xy', val=0.0)\n\n def setup_partials(self):\n self.declare_partials('*', '*')\n\n def compute(self, inputs, outputs):\n """\n f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3\n\n Optimal solution (minimum): x = 6.6667; y = -7.3333\n """\n x = inputs['x']\n y = inputs['y']\n\n outputs['f_xy'] = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0\n\n def compute_partials(self, inputs, partials):\n """\n Jacobian for our paraboloid.\n """\n x = inputs['x']\n y = inputs['y']\n\n partials['f_xy', 'x'] = 2.0*x - 6.0 + y\n partials['f_xy', 'y'] = 2.0*y + 8.0 + x\n| driver |
|---|
| rank0:DOEDriver_Uniform|0 |
| rank0:DOEDriver_Uniform|1 |
| rank0:DOEDriver_Uniform|2 |
| rank0:DOEDriver_Uniform|3 |
| rank0:DOEDriver_Uniform|4 |
Note
This feature requires MPI, and may not be able to be run on Colab or Binder.
The following script is executed using mpiexec -n 4 mpi_script_0.py:
import openmdao.api as om\n",
"from openmdao.test_suite.components.paraboloid import Paraboloid\n",
"\n",
"prob = om.Problem()\n",
"\n",
"prob.model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])\n",
"prob.model.add_design_var('x', lower=0.0, upper=1.0)\n",
"prob.model.add_design_var('y', lower=0.0, upper=1.0)\n",
"prob.model.add_objective('f_xy')\n",
"\n",
"prob.driver = om.DOEDriver(om.FullFactorialGenerator(levels=3))\n",
"prob.driver.options['run_parallel'] = True\n",
"prob.driver.options['procs_per_model'] = 1\n",
"\n",
"prob.driver.add_recorder(om.SqliteRecorder("doe_driver.sql"))\n",
"\n",
"prob.setup()\n",
"prob.run_driver()\n",
"prob.cleanup()\n",
"\n",
"# check recorded cases from each case file\n",
"from mpi4py import MPI\n",
"rank = MPI.COMM_WORLD.rank\n",
"\n",
"filename = "doe_driver.sql_%d" % rank\n",
"\n",
"cr = om.CaseReader(prob.get_outputs_dir() / filename)\n",
"cases = cr.list_cases('driver', out_stream=None)\n",
"print(cases)\n",
"\n",
"values = []\n",
"for case in cases:\n",
" outputs = cr.get_case(case).outputs\n",
" values.append((outputs['x'].item(), outputs['y'].item(), outputs['f_xy'].item()))\n",
"\n",
"print("\\n"+"\\n".join(["x: %5.2f, y: %5.2f, f_xy: %6.2f" % xyf for xyf in values]))\n",
"\n",
"import numpy as np\n",
"\n",
"assert(len(cases) == 3 if rank == 0 else 2)\n",
"if rank == 0:\n",
" expected = [\n",
" {'x': np.array([0.]), 'y': np.array([0.]), 'f_xy': np.array([22.00])},\n",
" {'x': np.array([.5]), 'y': np.array([.5]), 'f_xy': np.array([23.75])},\n",
" {'x': np.array([1.]), 'y': np.array([1.]), 'f_xy': np.array([27.00])}]\n",
"elif rank == 1:\n",
" expected = [\n",
" {'x': np.array([.5]), 'y': np.array([0.]), 'f_xy': np.array([19.25])},\n",
" {'x': np.array([1.]), 'y': np.array([.5]), 'f_xy': np.array([21.75])}]\n",
"elif rank == 2:\n",
" expected = [\n",
" {'x': np.array([1.]), 'y': np.array([0.]), 'f_xy': np.array([17.00])},\n",
" {'x': np.array([0.]), 'y': np.array([1.]), 'f_xy': np.array([31.00])}]\n",
"else:\n",
" expected = [\n",
" {'x': np.array([0.]), 'y': np.array([.5]), 'f_xy': np.array([26.25])},\n",
" {'x': np.array([.5]), 'y': np.array([1.]), 'f_xy': np.array([28.75])}]\n",
"\n",
"\n",
"exp_values = []\n",
"for case in expected:\n",
" exp_values.append((case['x'].item(), case['y'].item(), case['f_xy'].item()))\n",
"expect_text = "\\n"+"\\n".join(["x: %5.2f, y: %5.2f, f_xy: %6.2f" % vals_i for vals_i in exp_values])\n",
"\n",
"assert("\\n"+"\\n".join(["x: %5.2f, y: %5.2f, f_xy: %6.2f" % xyf for xyf in values]) == expect_text)\n",
"Note
This feature requires MPI, and may not be able to be run on Colab or Binder.
The following script is executed using mpiexec -n 4 mpi_script_1.py:
import numpy as np\n",
"import openmdao.api as om\n",
"\n",
"\n",
"class FanInGrouped(om.Group):\n",
" """\n",
" Topology where two components in a Group feed a single component\n",
" outside of that Group.\n",
" """\n",
"\n",
" def __init__(self):\n",
" super().__init__()\n",
"\n",
" self.set_input_defaults('x1', 1.0)\n",
" self.set_input_defaults('x2', 1.0)\n",
"\n",
" self.sub = self.add_subsystem('sub', om.ParallelGroup(),\n",
" promotes_inputs=['x1', 'x2'])\n",
"\n",
" self.sub.add_subsystem('c1', om.ExecComp(['y=-2.0*x']),\n",
" promotes_inputs=[('x', 'x1')])\n",
" self.sub.add_subsystem('c2', om.ExecComp(['y=5.0*x']),\n",
" promotes_inputs=[('x', 'x2')])\n",
"\n",
" self.add_subsystem('c3', om.ExecComp(['y=3.0*x1+7.0*x2']))\n",
"\n",
" self.connect("sub.c1.y", "c3.x1")\n",
" self.connect("sub.c2.y", "c3.x2")\n",
"\n",
"prob = om.Problem(FanInGrouped())\n",
"\n",
"prob.model.add_design_var('x1', lower=0.0, upper=1.0)\n",
"prob.model.add_design_var('x2', lower=0.0, upper=1.0)\n",
"prob.model.add_objective('c3.y')\n",
"\n",
"prob.driver = om.DOEDriver(om.FullFactorialGenerator(levels=3))\n",
"prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))\n",
"\n",
"# the FanInGrouped model uses 2 processes, so we can run\n",
"# two instances of the model at a time, each using 2 of our 4 procs\n",
"prob.driver.options['run_parallel'] = True\n",
"prob.driver.options['procs_per_model'] = procs_per_model = 2\n",
"\n",
"prob.setup()\n",
"prob.run_driver()\n",
"prob.cleanup()\n",
"\n",
"# a separate case file will be written by rank 0 of each parallel model\n",
"# (the top two global ranks)\n",
"rank = prob.comm.rank\n",
"\n",
"num_models = prob.comm.size // procs_per_model\n",
"\n",
"if rank < num_models:\n",
" filename = "cases.sql_%d" % rank\n",
"\n",
" cr = om.CaseReader(prob.get_outputs_dir() / filename)\n",
" cases = cr.list_cases('driver', out_stream=None)\n",
"\n",
" values = []\n",
" for case in cases:\n",
" outputs = cr.get_case(case).outputs\n",
" values.append((outputs['x1'].item(), outputs['x2'].item(), outputs['c3.y'].item()))\n",
"\n",
" print("\\n"+"\\n".join(["x1: %5.2f, x2: %5.2f, c3.y: %6.2f" % (x1, x2, y) for x1, x2, y in values]))\n",
"if rank == 0:\n",
" expected = [\n",
" {'x1': np.array([0.]), 'x2': np.array([0.]), 'c3.y': np.array([0.00])},\n",
" {'x1': np.array([1.]), 'x2': np.array([0.]), 'c3.y': np.array([-6.00])},\n",
" {'x1': np.array([.5]), 'x2': np.array([.5]), 'c3.y': np.array([14.50])},\n",
" {'x1': np.array([0.]), 'x2': np.array([1.]), 'c3.y': np.array([35.00])},\n",
" {'x1': np.array([1.]), 'x2': np.array([1.]), 'c3.y': np.array([29.00])}]\n",
"elif rank == 1:\n",
" expected = [\n",
" {'x1': np.array([.5]), 'x2': np.array([0.]), 'c3.y': np.array([-3.00])},\n",
" {'x1': np.array([0.]), 'x2': np.array([.5]), 'c3.y': np.array([17.50])},\n",
" {'x1': np.array([1.]), 'x2': np.array([.5]), 'c3.y': np.array([11.50])},\n",
" {'x1': np.array([.5]), 'x2': np.array([1.]), 'c3.y': np.array([32.00])}]\n",
"\n",
"if rank < 2:\n",
" exp_values = []\n",
" for idx, case in enumerate(expected):\n",
" exp_values.append((case['x1'].item(), case['x2'].item(), case['c3.y'].item()))\n",
"\n",
" expect_text = "\\n"+"\\n".join([\n",
" "x1: %5.2f, x2: %5.2f, c3.y: %6.2f" % vals_i for vals_i in exp_values])\n",
" assert("\\n"+"\\n".join(["x1: %5.2f, x2: %5.2f, c3.y: %6.2f" % (x1, x2, y) for x1, x2, y in values]) == expect_text)\n",
"