Checking if the optimizer support gradient - openmdao

Question

I want to distinguish optimizers (gradients based and free). If I use the sample optimization in the main webpage of OpenMDAO which uses SLSQP and check if the optimizer supports gradients I get "False" as in;
prob.driver.supports['gradients']
is this OpenMDAO or Scipy related issue?
Is there another way to see if the optimizer will use the gradient calculations or not before the problem is run.
Based on the answer below I added this in the beginning of my script; Thanks!
prob = om.api.Problem()
prob.driver = user_driver_object
prob.setup()
prob.final_setup()
grads.append(prob.driver.supports['gradients'])

Answer 1

In the ScipyOptimizeDriver not all optimizers support gradient optimization, hence you cannot determine the correct value until you setup your driver This is done in final_setup() of your problem (which calls _setup_driver() in your driver). This method is called in run_model() and run_driver(), but you can also call it in itself to get the correct properties of your optimizer.
In the example below I am asking the driver 3 times if it supports gradients. The first time, after the problem setup it gives a False answer (the default), because the driver was not touched yet. If I call final_setup(), this will setup the driver, and all the properties of the driver will be correct. If run_model() or run_driver() is called, of course this will also setup the driver.
So my advice is to just use final_setup() before querying anything from your driver, that can change during the setup (which are mostly optimizer specific properties).
import openmdao.api as om
# build the model
prob = om.Problem()
indeps = prob.model.add_subsystem('indeps', om.IndepVarComp())
indeps.add_output('x', 3.0)
indeps.add_output('y', -4.0)
prob.model.add_subsystem('paraboloid', om.ExecComp('f = (x-3)**2 + x*y + (y+4)**2 - 3'))
prob.model.connect('indeps.x', 'paraboloid.x')
prob.model.connect('indeps.y', 'paraboloid.y')
# setup the optimization
driver = prob.driver = om.ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.model.add_design_var('indeps.x', lower=-50, upper=50)
prob.model.add_design_var('indeps.y', lower=-50, upper=50)
prob.model.add_objective('paraboloid.f')
prob.setup()
print("\nSupports gradients (after setup)?")
print(prob.driver.supports['gradients'])
prob.final_setup()
print("\nSupports gradients (after final setup)?")
print(prob.driver.supports['gradients'])
prob.run_driver()
print("\nSupports gradients (after run)?")
print(prob.driver.supports['gradients'])
This results in the following output:
Supports gradients (after setup)?
False
Supports gradients (after final setup)?
True
Optimization terminated successfully. (Exit mode 0)
Current function value: -27.33333333333333
Iterations: 5
Function evaluations: 6
Gradient evaluations: 5
Optimization Complete
-----------------------------------
Supports gradients (after run)?
True