Parallelism - using multiple cores
 ----------------------------------
 
 Often you will want to evaluate the function on some remote computing
 resources. ``adaptive`` works out of the box with any framework that
 implements a `PEP 3148 <https://www.python.org/dev/peps/pep-3148/>`__
 compliant executor that returns `concurrent.futures.Future` objects.
 
 `concurrent.futures`
 ~~~~~~~~~~~~~~~~~~~~
 
 On Unix-like systems by default `adaptive.Runner` creates a
 `~concurrent.futures.ProcessPoolExecutor`, but you can also pass
 one explicitly e.g. to limit the number of workers:
 
 .. code:: python
 
     from concurrent.futures import ProcessPoolExecutor
 
     executor = ProcessPoolExecutor(max_workers=4)
 
     learner = adaptive.Learner1D(f, bounds=(-1, 1))
     runner = adaptive.Runner(learner, executor=executor, goal=lambda l: l.loss() < 0.05)
     runner.live_info()
     runner.live_plot(update_interval=0.1)
 
 `ipyparallel.Client`
 ~~~~~~~~~~~~~~~~~~~~
 
 .. code:: python
 
     import ipyparallel
 
     client = ipyparallel.Client()  # You will need to start an `ipcluster` to make this work
 
     learner = adaptive.Learner1D(f, bounds=(-1, 1))
     runner = adaptive.Runner(learner, executor=client, goal=lambda l: l.loss() < 0.01)
     runner.live_info()
     runner.live_plot()
 
 `distributed.Client`
 ~~~~~~~~~~~~~~~~~~~~
 
 On Windows by default `adaptive.Runner` uses a `distributed.Client`.
 
 .. code:: python
 
     import distributed
 
     client = distributed.Client()
 
     learner = adaptive.Learner1D(f, bounds=(-1, 1))
     runner = adaptive.Runner(learner, executor=client, goal=lambda l: l.loss() < 0.01)
     runner.live_info()
     runner.live_plot(update_interval=0.1)